{"version":"https://jsonfeed.org/version/1","title":"The Python Podcast.__init__","home_page_url":"https://www.pythonpodcast.com","feed_url":"https://www.pythonpodcast.com/json","description":"The podcast about Python and the people who make it great","_fireside":{"subtitle":"The podcast about Python and the people who make it great","pubdate":"2022-12-11T21:00:00.000-05:00","explicit":false,"copyright":"2024 by Boundless Notions, LLC.","owner":"Tobias Macey","image":"https://assets.fireside.fm/file/fireside-images/podcasts/images/a/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/cover.jpg?v=1"},"items":[{"id":"podlove-2022-12-12t02:17:13+00:00-f3b1aa30d86dfd1","title":"Update Your Model's View Of The World In Real Time With Streaming Machine Learning Using River","url":"https://www.pythonpodcast.com/river-streaming-machine-learning-episode-388","content_text":"Preamble\nThis is a cross-over episode from our new show The Machine Learning Podcast, the show about going from idea to production with machine learning.\nSummary\nThe majority of machine learning projects that you read about or work on are built around batch processes. The model is trained, and then validated, and then deployed, with each step being a discrete and isolated task. Unfortunately, the real world is rarely static, leading to concept drift and model failures. River is a framework for building streaming machine learning projects that can constantly adapt to new information. In this episode Max Halford explains how the project works, why you might (or might not) want to consider streaming ML, and how to get started building with River.\nAnnouncements\n\nHello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.\nBuilding good ML models is hard, but testing them properly is even harder. At Deepchecks, they built an open-source testing framework that follows best practices, ensuring that your models behave as expected. Get started quickly using their built-in library of checks for testing and validating your model’s behavior and performance, and extend it to meet your specific needs as your model evolves. Accelerate your machine learning projects by building trust in your models and automating the testing that you used to do manually. Go to themachinelearningpodcast.com/deepchecks today to get started!\nYour host is Tobias Macey and today I’m interviewing Max Halford about River, a Python toolkit for streaming and online machine learning\n\nInterview\n\nIntroduction\nHow did you get involved in machine learning?\nCan you describe what River is and the story behind it?\nWhat is \"online\" machine learning?\n\nWhat are the practical differences with batch ML?\nWhy is batch learning so predominant?\nWhat are the cases where someone would want/need to use online or streaming ML?\n\n\nThe prevailing pattern for batch ML model lifecycles is to train, deploy, monitor, repeat. What does the ongoing maintenance for a streaming ML model look like?\n\nConcept drift is typically due to a discrepancy between the data used to train a model and the actual data being observed. How does the use of online learning affect the incidence of drift?\n\n\nCan you describe how the River framework is implemented?\n\nHow have the design and goals of the project changed since you started working on it?\n\n\nHow do the internal representations of the model differ from batch learning to allow for incremental updates to the model state?\nIn the documentation you note the use of Python dictionaries for state management and the flexibility offered by that choice. What are the benefits and potential pitfalls of that decision?\nCan you describe the process of using River to design, implement, and validate a streaming ML model?\n\nWhat are the operational requirements for deploying and serving the model once it has been developed?\n\n\nWhat are some of the challenges that users of River might run into if they are coming from a batch learning background?\nWhat are the most interesting, innovative, or unexpected ways that you have seen River used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on River?\nWhen is River the wrong choice?\nWhat do you have planned for the future of River?\n\nContact Info\n\nEmail\n@halford_max on Twitter\nMaxHalford on GitHub\n\nParting Question\n\nFrom your perspective, what is the biggest barrier to adoption of machine learning today?\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nRiver\nscikit-multiflow\nFederated Machine Learning\nHogwild! Google Paper\nChip Huyen concept drift blog post\nDan Crenshaw Berkeley Clipper MLOps\nRobustness Principle\nNY Taxi Dataset\nRiverTorch\nRiver Public Roadmap\nBeaver tool for deploying online models\nProdigy ML human in the loop labeling\n\nThe intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0\n\n\nSponsored By:Linode: Do you want to try out some of the tools and applications that you heard about on Podcast.\\_\\_init\\_\\_? Do you have a side project that you want to share with the world? With Linode's managed Kubernetes platform it's now even easier to get started with the latest in cloud technologies. With the combined power of the leading container orchestrator and the speed and reliability of Linode's object storage, node balancers, block storage, and dedicated CPU or GPU instances, you've got everything you need to scale up. Go to [pythonpodcast.com/linode](https://www.pythonpodcast.com/linode) today and get a $100 credit to launch a new cluster, run a server, upload some data, or... And don't forget to thank them for being a long time supporter of Podcast.\\_\\_init\\_\\_!","content_html":"

Preamble

\n

This is a cross-over episode from our new show The Machine Learning Podcast, the show about going from idea to production with machine learning.

\n

Summary

\n

The majority of machine learning projects that you read about or work on are built around batch processes. The model is trained, and then validated, and then deployed, with each step being a discrete and isolated task. Unfortunately, the real world is rarely static, leading to concept drift and model failures. River is a framework for building streaming machine learning projects that can constantly adapt to new information. In this episode Max Halford explains how the project works, why you might (or might not) want to consider streaming ML, and how to get started building with River.

\n

Announcements

\n\n

Interview

\n\n

Contact Info

\n\n

Parting Question

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

\n
\n\n

\"\"

Sponsored By:

","summary":"An interview with Max Halford about the benefits of streaming machine learning for systems that need to learn continuously without being taken offline and how the River library supports building those models.","date_published":"2022-12-11T21:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/faed0882-77e3-4f04-8ca4-85342ee030ac.mp3","mime_type":"audio/mpeg","size_in_bytes":45718134,"duration_in_seconds":4582}]},{"id":"podlove-2022-12-05t00:36:57+00:00-d50e203e235a018","title":"Declarative Machine Learning For High Performance Deep Learning Models With Predibase","url":"https://www.pythonpodcast.com/predibase-declarative-machine-learning-episode-387","content_text":"Preamble\nThis is a cross-over episode from our new show The Machine Learning Podcast, the show about going from idea to production with machine learning.\nSummary\nDeep learning is a revolutionary category of machine learning that accelerates our ability to build powerful inference models. Along with that power comes a great deal of complexity in determining what neural architectures are best suited to a given task, engineering features, scaling computation, etc. Predibase is building on the successes of the Ludwig framework for declarative deep learning and Horovod for horizontally distributing model training. In this episode CTO and co-founder of Predibase, Travis Addair, explains how they are reducing the burden of model development even further with their managed service for declarative and low-code ML and how they are integrating with the growing ecosystem of solutions for the full ML lifecycle.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great!\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host is Tobias Macey and today I’m interviewing Travis Addair about Predibase, a low-code platform for building ML models in a declarative format\n\nInterview\n\nIntroduction\nHow did you get involved in machine learning?\nCan you describe what Predibase is and the story behind it?\nWho is your target audience and how does that focus influence your user experience and feature development priorities?\nHow would you describe the semantic differences between your chosen terminology of \"declarative ML\" and the \"autoML\" nomenclature that many projects and products have adopted?\n\nAnother platform that launched recently with a promise of \"declarative ML\" is Continual. How would you characterize your relative strengths?\n\n\nCan you describe how the Predibase platform is implemented?\n\nHow have the design and goals of the product changed as you worked through the initial implementation and started working with early customers?\nThe operational aspects of the ML lifecycle are still fairly nascent. How have you thought about the boundaries for your product to avoid getting drawn into scope creep while providing a happy path to delivery?\n\n\nLudwig is a core element of your platform. What are the other capabilities that you are layering around and on top of it to build a differentiated product?\nIn addition to the existing interfaces for Ludwig you created a new language in the form of PQL. What was the motivation for that decision?\n\nHow did you approach the semantic and syntactic design of the dialect?\nWhat is your vision for PQL in the space of \"declarative ML\" that you are working to define?\n\n\nCan you describe the available workflows for an individual or team that is using Predibase for prototyping and validating an ML model?\n\nOnce a model has been deemed satisfactory, what is the path to production?\n\n\nHow are you approaching governance and sustainability of Ludwig and Horovod while balancing your reliance on them in Predibase?\nWhat are some of the notable investments/improvements that you have made in Ludwig during your work of building Predibase?\nWhat are the most interesting, innovative, or unexpected ways that you have seen Predibase used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Predibase?\nWhen is Predibase the wrong choice?\nWhat do you have planned for the future of Predibase?\n\nContact Info\n\nLinkedIn\ntgaddair on GitHub\n@travisaddair on Twitter\n\nParting Question\n\nFrom your perspective, what is the biggest barrier to adoption of machine learning today?\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nPredibase\nHorovod\nLudwig\n\nPodcast.__init__ Episode\n\n\nSupport Vector Machine\nHadoop\nTensorflow\nUber Michaelangelo\nAutoML\nSpark ML Lib\nDeep Learning\nPyTorch\nContinual\n\nData Engineering Podcast Episode\n\n\nOverton\nKubernetes\nRay\nNvidia Triton\nWhylogs\n\nData Engineering Podcast Episode\n\n\nWeights and Biases\nMLFlow\nComet\nConfusion Matrices\ndbt\n\nData Engineering Podcast Episode\n\n\nTorchscript\nSelf-supervised Learning\n\nThe intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0\n\n\n","content_html":"

Preamble

\n

This is a cross-over episode from our new show The Machine Learning Podcast, the show about going from idea to production with machine learning.

\n

Summary

\n

Deep learning is a revolutionary category of machine learning that accelerates our ability to build powerful inference models. Along with that power comes a great deal of complexity in determining what neural architectures are best suited to a given task, engineering features, scaling computation, etc. Predibase is building on the successes of the Ludwig framework for declarative deep learning and Horovod for horizontally distributing model training. In this episode CTO and co-founder of Predibase, Travis Addair, explains how they are reducing the burden of model development even further with their managed service for declarative and low-code ML and how they are integrating with the growing ecosystem of solutions for the full ML lifecycle.

\n

Announcements

\n\n

Interview

\n\n

Contact Info

\n\n

Parting Question

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

\n
\n\n

\"\"

","summary":"An interview with Travis Addair about how the Predibase platform lets you start building machine learning applications rapidly without an artificial ceiling on what you can create.","date_published":"2022-12-04T19:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/0953d918-5492-4eb7-bd93-383db9e02812.mp3","mime_type":"audio/mpeg","size_in_bytes":38501757,"duration_in_seconds":3562}]},{"id":"podlove-2022-11-28t01:30:17+00:00-36d12ff2ae2d116","title":"Build Better Machine Learning Models With Confidence By Adding Validation With Deepchecks","url":"https://www.pythonpodcast.com/deepchecks-machine-learning-validation-episode-386","content_text":"Preamble\nThis is a cross-over episode from our new show The Machine Learning Podcast, the show about going from idea to production with machine learning.\nSummary\nMachine learning has the potential to transform industries and revolutionize business capabilities, but only if the models are reliable and robust. Because of the fundamental probabilistic nature of machine learning techniques it can be challenging to test and validate the generated models. The team at Deepchecks understands the widespread need to easily and repeatably check and verify the outputs of machine learning models and the complexity involved in making it a reality. In this episode Shir Chorev and Philip Tannor explain how they are addressing the problem with their open source deepchecks library and how you can start using it today to build trust in your machine learning applications.\nAnnouncements\n\nHello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.\nDo you wish you could use artificial intelligence to drive your business the way Big Tech does, but don’t have a money printer? Graft is a cloud-native platform that aims to make the AI of the 1% accessible to the 99%. Wield the most advanced techniques for unlocking the value of data, including text, images, video, audio, and graphs. No machine learning skills required, no team to hire, and no infrastructure to build or maintain. For more information on Graft or to schedule a demo, visit themachinelearningpodcast.com/graft today and tell them Tobias sent you.\nPredibase is a low-code ML platform without low-code limits. Built on top of our open source foundations of Ludwig and Horovod, our platform allows you to train state-of-the-art ML and deep learning models on your datasets at scale. Our platform works on text, images, tabular, audio and multi-modal data using our novel compositional model architecture. We allow users to operationalize models on top of the modern data stack, through REST and PQL – an extension of SQL that puts predictive power in the hands of data practitioners. Go to themachinelearningpodcast.com/predibase today to learn more and try it out!\nData powers machine learning, but poor data quality is the largest impediment to effective ML today. Galileo is a collaborative data bench for data scientists building Natural Language Processing (NLP) models to programmatically inspect, fix and track their data across the ML workflow (pre-training, post-training and post-production) – no more excel sheets or ad-hoc python scripts. Get meaningful gains in your model performance fast, dramatically reduce data labeling and procurement costs, while seeing 10x faster ML iterations. Galileo is offering listeners a free 30 day trial and a 30% discount on the product there after. This offer is available until Aug 31, so go to themachinelearningpodcast.com/galileo and request a demo today!\nYour host is Tobias Macey and today I’m interviewing Shir Chorev and Philip Tannor about Deepchecks, a Python package for comprehensively validating your machine learning models and data with minimal effort.\n\nInterview\n\nIntroduction\nHow did you get involved in machine learning?\nCan you describe what Deepchecks is and the story behind it?\nWho is the target audience for the project?\n\nWhat are the biggest challenges that these users face in bringing ML models from concept to production and how does DeepChecks address those problems?\n\n\nIn the absence of DeepChecks how are practitioners solving the problems of model validation and comparison across iteratiosn?\n\nWhat are some of the other tools in this ecosystem and what are the differentiating features of DeepChecks?\n\n\nWhat are some examples of the kinds of tests that are useful for understanding the \"correctness\" of models?\n\nWhat are the methods by which ML engineers/data scientists/domain experts can define what \"correctness\" means in a given model or subject area?\n\n\nIn software engineering the categories of tests are tiered as unit -> integration -> end-to-end. What are the relevant categories of tests that need to be built for validating the behavior of machine learning models?\nHow do model monitoring utilities overlap with the kinds of tests that you are building with deepchecks?\nCan you describe how the DeepChecks package is implemented?\n\nHow have the design and goals of the project changed or evolved from when you started working on it?\nWhat are the assumptions that you have built up from your own experiences that have been challenged by your early users and design partners?\n\n\nCan you describe the workflow for an individual or team using DeepChecks as part of their model training and deployment lifecycle?\nTest engineering is a deep discipline in its own right. How have you approached the user experience and API design to reduce the overhead for ML practitioners to adopt good practices?\nWhat are the interfaces available for creating reusable tests and composing test suites together?\nWhat are the additional services/capabilities that you are providing in your commercial offering?\n\nHow are you managing the governance and sustainability of the OSS project and balancing that against the needs/priorities of the business?\n\n\nWhat are the most interesting, innovative, or unexpected ways that you have seen DeepChecks used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on DeepChecks?\nWhen is DeepChecks the wrong choice?\nWhat do you have planned for the future of DeepChecks?\n\nContact Info\n\nShir\n\nLinkedIn\nshir22 on GitHub\n\n\nPhilip\n\nLinkedIn\n@philiptannor on Twitter\n\n\n\nParting Question\n\nFrom your perspective, what is the biggest barrier to adoption of machine learning today?\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nDeepChecks\nRandom Forest\nTalpiot Program\nSHAP\n\nPodcast.__init__ Episode\n\n\nAirflow\nGreat Expectations\n\nData Engineering Podcast Episode\n\n\n\nThe intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0\n\n\n","content_html":"

Preamble

\n

This is a cross-over episode from our new show The Machine Learning Podcast, the show about going from idea to production with machine learning.

\n

Summary

\n

Machine learning has the potential to transform industries and revolutionize business capabilities, but only if the models are reliable and robust. Because of the fundamental probabilistic nature of machine learning techniques it can be challenging to test and validate the generated models. The team at Deepchecks understands the widespread need to easily and repeatably check and verify the outputs of machine learning models and the complexity involved in making it a reality. In this episode Shir Chorev and Philip Tannor explain how they are addressing the problem with their open source deepchecks library and how you can start using it today to build trust in your machine learning applications.

\n

Announcements

\n\n

Interview

\n\n

Contact Info

\n\n

Parting Question

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

\n
\n\n

\"\"

","summary":"A cross-over episode from The Machine Learning Podcast with the team from Deepchecks, exploring the challenges of testing and validating machine learning applications and their work to make it easier.","date_published":"2022-11-27T20:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/be492325-5c0c-4b6d-9854-f5057a99d857.mp3","mime_type":"audio/mpeg","size_in_bytes":31181655,"duration_in_seconds":2856}]},{"id":"podlove-2022-11-21t18:34:38+00:00-ffc9f5dc71a1147","title":"Build A Full Stack ML Powered App In An Afternoon With Baseten","url":"https://www.pythonpodcast.com/baseten-full-stack-ml-app-episode-385","content_text":"Preamble\nThis is a cross-over episode from our new show The Machine Learning Podcast, the show about going from idea to production with machine learning.\nSummary\nBuilding an ML model is getting easier than ever, but it is still a challenge to get that model in front of the people that you built it for. Baseten is a platform that helps you quickly generate a full stack application powered by your model. You can easily create a web interface and APIs powered by the model you created, or a pre-trained model from their library. In this episode Tuhin Srivastava, co-founder of Basten, explains how the platform empowers data scientists and ML engineers to get their work in production without having to negotiate for help from their application development colleagues.\nAnnouncements\n\nHello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host is Tobias Macey and today I’m interviewing Tuhin Srivastava about Baseten, an ML Application Builder for data science and machine learning teams\n\nInterview\n\nIntroduction\nHow did you get involved in machine learning?\nCan you describe what Baseten is and the story behind it?\nWho are the target users for Baseten and what problems are you solving for them?\nWhat are some of the typical technical requirements for an application that is powered by a machine learning model?\n\nIn the absence of Baseten, what are some of the common utilities/patterns that teams might rely on?\n\n\nWhat kinds of challenges do teams run into when serving a model in the context of an application?\nThere are a number of projects that aim to reduce the overhead of turning a model into a usable product (e.g. Streamlit, Hex, etc.). What is your assessment of the current ecosystem for lowering the barrier to product development for ML and data science teams?\nCan you describe how the Baseten platform is designed?\n\nHow have the design and goals of the project changed or evolved since you started working on it?\nHow do you handle sandboxing of arbitrary user-managed code to ensure security and stability of the platform?\n\n\nHow did you approach the system design to allow for mapping application development paradigms into a structure that was accessible to ML professionals?\nCan you describe the workflow for building an ML powered application?\nWhat types of models do you support? (e.g. NLP, computer vision, timeseries, deep neural nets vs. linear regression, etc.)\n\nHow do the monitoring requirements shift for these different model types?\nWhat other challenges are presented by these different model types?\n\n\nWhat are the limitations in size/complexity/operational requirements that you have to impose to ensure a stable platform?\nWhat is the process for deploying model updates?\nFor organizations that are relying on Baseten as a prototyping platform, what are the options for taking a successful application and handing it off to a product team for further customization?\nWhat are the most interesting, innovative, or unexpected ways that you have seen Baseten used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Baseten?\nWhen is Baseten the wrong choice?\nWhat do you have planned for the future of Baseten?\n\nContact Info\n\n@tuhinone on Twitter\nLinkedIn\n\nParting Question\n\nFrom your perspective, what is the biggest barrier to adoption of machine learning today?\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nBaseten\nGumroad\nscikit-learn\nTensorflow\nKeras\nStreamlit\n\nPodcast.__init__ Episode\n\n\nRetool\nHex\n\nPodcast.__init__ Episode\n\n\nKubernetes\nReact Monaco\nHuggingface\nAirtable\nDall-E 2\nGPT-3\nWeights and Biases\n\nThe intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0\n\n\n","content_html":"

Preamble

\n

This is a cross-over episode from our new show The Machine Learning Podcast, the show about going from idea to production with machine learning.

\n

Summary

\n

Building an ML model is getting easier than ever, but it is still a challenge to get that model in front of the people that you built it for. Baseten is a platform that helps you quickly generate a full stack application powered by your model. You can easily create a web interface and APIs powered by the model you created, or a pre-trained model from their library. In this episode Tuhin Srivastava, co-founder of Basten, explains how the platform empowers data scientists and ML engineers to get their work in production without having to negotiate for help from their application development colleagues.

\n

Announcements

\n\n

Interview

\n\n

Contact Info

\n\n

Parting Question

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

\n
\n\n

\"\"

","summary":"An interview with Tuhin Srivastava about how the Baseten platform allows data scientists and ML engineers to build a full stack machine learning powered application by themselves in an afternoon","date_published":"2022-11-21T13:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/de713875-ebeb-494e-b1ad-1777bb6eabe4.mp3","mime_type":"audio/mpeg","size_in_bytes":27142652,"duration_in_seconds":2722}]},{"id":"podlove-2022-11-07t00:16:07+00:00-acb49c7834aff86","title":"Skip Straight To The Fun Part Of Your Project With PyScaffold","url":"https://www.pythonpodcast.com/pyscaffold-project-template-boilerplate-episode-384","content_text":"Summary\nStarting a new project is always exciting and full of possibility, until you have to set up all of the repetitive boilerplate. Fortunately there are useful project templates that eliminate that drudgery. PyScaffold goes above and beyond simple template repositories, and gives you a toolkit for different application types that are packed with best practices to make your life easier. In this episode Florian Wilhelm shares the story behind PyScaffold, how the templates are designed to reduce friction when getting a new project off the ground, and how you can extend it to suit your needs. Stop wasting time with boring boilerplate and get straight to the fun part with PyScaffold!\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great!\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Florian Wilhelm about PyScaffold, a Python project template generator with batteries included\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what PyScaffold is and the story behind it?\n\nWhat is the main goal of the project?\n\n\nThere are a huge number of templates and starter projects available (both in Python and other languages). What are the aspects of PyScaffold that might encourage someone to adopt it?\nWhat are the different types/categories of applications that you are focused on supporting with the scaffolding?\n\nFor each category, what is your selection process for which dependencies to include?\n\n\nHow do you approach the work of keeping the various components up to date with community \"best practices\"?\nCan you describe how PyScaffold is implemented?\n\nHow have the design and goals of the project changed since you first started it?\n\n\nWhat is the user experience for someone bootstrapping a project with PyScaffold?\n\nHow can you adapt an existing project into the structure of a pyscaffold template?\nAre there any facilities for updating a project started with PyScaffold to include patches/changes in the source template?\n\n\nWhat are the most interesting, innovative, or unexpected ways that you have seen PyScaffold used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on PyScaffold?\nWhen is PyScaffold the wrong choice?\nWhat do you have planned for the future of PyScaffold?\n\nKeep In Touch\n\nWebsite\nLinkedIn\nFlorianWilhelm on GitHub\n@florianwilhelm on Twitter\n\nPicks\n\nTobias\n\nDaredevil TV series\n\n\nFlorian\n\nThe Peripheral\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nPyScaffold\nInnovex\nSAP\nCookiecutter\nPytest\n\nPodcast Episode\n\n\nSphinx\npre-commit\n\nPodcast Episode\n\n\nBlack\nFlake8\n\nPodcast Episode\n\n\nPoetry\nSetuptools\nmkdocs\nReStructured Text\nMarkdown\nSetuptools-SCM\nHatch\nFlit\nVersioneer\nGource git visualization\nMyPy Compiler\nRust Cargo\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Starting a new project is always exciting and full of possibility, until you have to set up all of the repetitive boilerplate. Fortunately there are useful project templates that eliminate that drudgery. PyScaffold goes above and beyond simple template repositories, and gives you a toolkit for different application types that are packed with best practices to make your life easier. In this episode Florian Wilhelm shares the story behind PyScaffold, how the templates are designed to reduce friction when getting a new project off the ground, and how you can extend it to suit your needs. Stop wasting time with boring boilerplate and get straight to the fun part with PyScaffold!

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview with Florian Wilhelm about PyScaffold, an extensible toolkit filled with templates that have best practices embedded so that you can skip straight to working on the part of your project that you actually care about.","date_published":"2022-11-06T19:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/a662c06c-3264-4cfe-910e-4b26e5216516.mp3","mime_type":"audio/mpeg","size_in_bytes":34677318,"duration_in_seconds":3466}]},{"id":"podlove-2022-10-30t23:16:37+00:00-eae12f1ce1dd81f","title":"Add Configuration Best Practices To Your Application In An Afternoon With Dynaconf","url":"https://www.pythonpodcast.com/dynaconf-application-configuration-management-episode-383","content_text":"Summary\nApplication configuration is a deceptively complex problem. Everyone who is building a project that gets used more than once will end up needing to add configuration to control aspects of behavior or manage connections to other systems and services. At first glance it seems simple, but can quickly become unwieldy. Bruno Rocha created Dynaconf in an effort to provide a simple interface with powerful capabilities for managing settings across environments with a set of strong opinions. In this episode he shares the story behind the project, how its design allows for adapting to various applications, and how you can start using it today for your own projects.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great!\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Bruno Rocha about Dynaconf, a powerful and flexible framework for managing your application’s configuration settings\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what Dynaconf is and the story behind it?\nWhat are your main goals for Dynaconf?\n\nWhat kinds of projects (e.g. web, devops, ML, etc.) are you focused on supporting with Dynaconf?\n\n\nSettings management is a deceptively complex and detailed aspect of software engineering, with a lot of conflicting opinions about the \"right way\". What are the design philosophies that you lean on for Dynaconf?\nMany engineers end up building their own frameworks for managing settings as their use cases and environments get increasingly complicated. What are some of the ways that those efforts can go wrong or become unmaintainable?\nCan you describe how Dynaconf is implemented?\n\nHow have the design and goals of the project evolved since you first started it?\n\n\nWhat is the workflow for getting started with Dynaconf on a new project?\n\nHow does the usage scale with the complexity of the host project?\n\n\nWhat are some strategies that you recommend for integrating Dynaconf into an existing project that already has complex requirements for settings across multiple environments?\nSecrets management is one of the most frequently under- or over-engineered aspects of application configuration. What are some of the ways that you have worked to strike a balance of making the \"right way\" easy?\nWhat are some of the more advanced or under-utilized capabilities of Dynaconf?\nWhat are the most interesting, innovative, or unexpected ways that you have seen Dynaconf used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Dynaconf?\nWhen is Dynaconf the wrong choice?\nWhat do you have planned for the future of Dynaconf?\n\nKeep In Touch\n\nrochacbruno on GitHub\n@rochacbruno on Twitter\nWebsite\nLinkedIn\n\nPicks\n\nTobias\n\nSOPS\n\n\nBruno\n\nSeverance tv series\nLearn Rust\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nDynaconf\nDynaconf GitHub Org\nAnsible\nBash\nPerl\n12 Factor Applications\nTOML\nHashicorp Vault\nPydantic\nAirflow\nHydroconf\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Application configuration is a deceptively complex problem. Everyone who is building a project that gets used more than once will end up needing to add configuration to control aspects of behavior or manage connections to other systems and services. At first glance it seems simple, but can quickly become unwieldy. Bruno Rocha created Dynaconf in an effort to provide a simple interface with powerful capabilities for managing settings across environments with a set of strong opinions. In this episode he shares the story behind the project, how its design allows for adapting to various applications, and how you can start using it today for your own projects.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Bruno Rocha about how the Dynaconf framework for configuration management in Python applications simplifies the challenge of deploying across environments with security and best practices","date_published":"2022-10-30T19:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/6e94c8f7-cff1-4503-aa97-cd7fe4bc7820.mp3","mime_type":"audio/mpeg","size_in_bytes":42423304,"duration_in_seconds":3839}]},{"id":"podlove-2022-10-21t20:29:06+00:00-32735904b71ebae","title":"Take A Tour Of The Hidden Language Of Hardware And How It Powers Your Code","url":"https://www.pythonpodcast.com/code-hidden-language-second-edition-episode-382","content_text":"Summary\nSoftware is eating the world, but that code has to have hardware to execute the instructions. Most people, and many software engineers, don’t have a proper understanding of how that hardware functions. Charles Petzold wrote the book \"Code: The Hidden Language of Computer Hardware and Software\" to make this a less opaque subject. In this episode he discusses what motivated him to revise that work in the second edition and the additional details that he packed in to explore the functioning of the CPU.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great!\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Charles Petzold about his work on the second edition of Code: The Hidden Language of Computer Hardware and Software\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing the focus and goal of \"Code\" and the story behind it?\n\nWho is the target audience for the book?\n\n\nThe sequencing of the topics parallels the curriculum of a computer engineering course of study. Why do you think that it is useful/important for a general audience to understand the electrical engineering principles that underly modern computers?\nWhat was your process for determining how to segment the information that you wanted to address in the book to balance the pacing of the reader with the density of the information?\nTechnical books are notoriously challenging to write due to the constantly changing subject matter. What are some of the ways that the first edition of \"Code\" was becoming outdated?\n\nWhat are the most notable changes in the foundational elements of computing that have happened in the time since the first edition was published?\n\n\nOne of the concepts that I have found most helpful as a software engineer is that of \"mechanical sympathy\". What are some of the ways that a better understanding of computer hardware and electrical signal processing can influence and improve the way that an engineer writes code?\nWhat are some of the insights that you gained about your own use of computers and software while working on this book?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while writing \"Code\" and revising it for the second edition?\nOnce the reader has finished with your book, what are some of the other references/resources that you recommend?\n\nKeep In Touch\n\nWebsite\n\nPicks\n\nTobias\n\nThe Imitation Game movie\n\n\nCharles\n\nThe Annotated Turing book by Charles Petzold\nConfidence Man: The Making of Donald Trump and the Breaking of America by Maggie Haberman\n\n\n\nLinks\n\nCode: The Hidden Language of Computer Hardware and Software\nFortran\nPL/I\nBASIC\nC#\nZ80\nIntel 8080\nPC Magazine\nAssembly Language\nLogic Gates\nC Language\nASCII == American Standard Code for Information Interchange\nSkiaSharp\nAlgol\nCode first edition bibliography\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Software is eating the world, but that code has to have hardware to execute the instructions. Most people, and many software engineers, don’t have a proper understanding of how that hardware functions. Charles Petzold wrote the book "Code: The Hidden Language of Computer Hardware and Software" to make this a less opaque subject. In this episode he discusses what motivated him to revise that work in the second edition and the additional details that he packed in to explore the functioning of the CPU.

\n

Announcements

\n\n

Interview

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Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview with Charles Petzold about the second edition of his book "Code: The Hidden Language Of Computer Hardware and Software" and how an understanding of how hardware works can make you a better software engineer.","date_published":"2022-10-23T19:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/030a0ef5-c398-43a3-9da8-ce33b5132e5b.mp3","mime_type":"audio/mpeg","size_in_bytes":32343535,"duration_in_seconds":2509}]},{"id":"podlove-2022-10-16t20:16:05+00:00-f6d2e40d84fd2a9","title":"Take Control Of Your Electrical Systems With The Open Source FlexMeasures Energy Management System","url":"https://www.pythonpodcast.com/flexmeasures-energy-management-system-episode-381","content_text":"Summary\nThe generation, distribution, and consumption of energy is one of the most critical pieces of infrastructure for the modern world. With the rise of renewable energy there is an accompanying need for systems that can respond in real-time to the availability and demand for electricity. FlexMeasures is an open source energy management system that is designed to integrate a variety of inputs intelligently allocate energy resources to reduce waste in your home or grid. In this episode Nicolas Höning explains how the project is implemented, how it is being used in his startup Seita, and how you can try it out for your own energy needs.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great!\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Nicolas Höning about FlexMeasures, an open source project designed to manage energy resources dynamically to improve efficiency\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what FlexMeasures is and the story behind it?\n\nWhat are the primary goals/objectives of the project?\nThe energy sector is huge. Where can FlexMeasures be used?\n\n\nEnergy systems are typically governed by a marketplace system. What are the benefits that FlexMeasures can provide for each side of that market?\n\nHow do renewable sources of energy confuse/complicate the role that the different stakeholders represent?\nWhat are the different points of interaction that producers/consumers might have with the FlexMeasures platform?\n\n\nWhat are some examples of the types of decisions/recommendations that FlexMeasures might generate and how to they manifest in the energy systems?\n\nWhat are the types of information that FlexMeasures relies on for driving those decisions?\n\n\nCan you describe how FlexMeasures is implemented?\n\nHow have the design and goals of the system changed/evolved since you started working on it?\n\n\nWhat are the interfaces that you provide for integrating with and extending the functionality of a FlexMeasures installation?\nWhat are the operating scales that FlexMeasures is designed for?\nWhat are the most interesting, innovative, or unexpected ways that you have seen FlexMeasures used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on FlexMeasures?\nWhen is FlexMeasures the wrong choice?\nWhat do you have planned for the future of FlexMeasures?\n\nKeep In Touch\n\nWebsite\n@nhoening on Twitter\nLinkedIn\n\nPicks\n\nTobias\n\nShe-Hulk\n\n\nNicholas\n\nKleo on Netflix\nAltair\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nFlexMeasures:\n\nGithub\nLinux Energy Foundation\nMailing List\nTwitter\n\n\nEyeQuant\nEnergy Management System\nOpenEMS\nICT == Information and Communications Technology\nHomeAssistant\n\nPodcast Episode\n\n\nFlexMeasures HomeAssistant Plugin\nUniversal Smart Energy Framework\nPostgreSQL\n\nData Engineering Podcast Episode\n\n\nTimescaleDB\n\nData Engineering Podcast Episode\n\n\nOpenWeatherMap\nTimely-Beliefs library\nFlask\nClick\nPyomo\nscikit-learn\nsktime\nLF Energy\nFlake8\nMyPy\n\nPodcast Episode\n\n\nBlack\nArima Model\nRandom Forest\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

The generation, distribution, and consumption of energy is one of the most critical pieces of infrastructure for the modern world. With the rise of renewable energy there is an accompanying need for systems that can respond in real-time to the availability and demand for electricity. FlexMeasures is an open source energy management system that is designed to integrate a variety of inputs intelligently allocate energy resources to reduce waste in your home or grid. In this episode Nicolas Höning explains how the project is implemented, how it is being used in his startup Seita, and how you can try it out for your own energy needs.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview with Nicolas Höning about the open source FlexMeasures project for building real-time and adaptable energy management systems","date_published":"2022-10-16T19:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/4587c88f-e10b-4308-a4a4-4335040e00e3.mp3","mime_type":"audio/mpeg","size_in_bytes":36249540,"duration_in_seconds":2956}]},{"id":"podlove-2022-10-10t01:14:39+00:00-f289d41c84069ee","title":"How And Why To Build Effective Teams As An Engineering Leader","url":"https://www.pythonpodcast.com/effective-engineering-teams-jigar-desai-episode-380","content_text":"Summary\nYour ability to build and maintain a software project is tempered by the strength of the team that you are working with. If you are in a position of leadership, then you are responsible for the growth and maintenance of that team. In this episode Jigar Desai, currently the SVP of engineering at Sisu Data, shares his experience as an engineering leader over the past several years and the useful insights he has gained into how to build effective engineering teams.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great!\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nThe biggest challenge with modern data systems is understanding what data you have, where it is located, and who is using it. Select Star’s data discovery platform solves that out of the box, with a fully automated catalog that includes lineage from where the data originated, all the way to which dashboards rely on it and who is viewing them every day. Just connect it to your dbt, Snowflake, Tableau, Looker, or whatever you’re using and Select Star will set everything up in just a few hours. Go to pythonpodcast.com/selectstar today to double the length of your free trial and get a swag package when you convert to a paid plan.\nYour host as usual is Tobias Macey and today I’m interviewing Jigar Desai about building effective engineering teams\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nWhat have you found to be the central challenges involved in building an effective engineering team?\n\nWhat are the measures that you use to determine what \"effective\" means for a given team?\n\n\nhow to establish mutual trust in an engineering team\nchallenges introduced at different levels of team size/organizational complexity\nestablishing and managing career ladders\nYou have mostly worked in heavily tech-focused companies. How do industry verticals impact the ways that you think about formation and structure of engineering teams?\n\nWhat are some of the different roles that you might focus on hiring/team compositions in industries that aren’t purely software? (e.g. fintech, logistics, etc.)\n\n\nnotable evolutions in engineering practices/paradigm shifts in the industry\n\nWhat are some of the predictions that you have about how the future of engineering will look?\nWhat impact do you think low-code/no-code solutions will have on the types of projects that code-first developers will be tasked with?\n\n\nWhat are the most interesting, innovative, or unexpected ways that you have seen organizational leaders address the work of building and scaling engineering capacity?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working in engineering leadership?\nWhat are the most informative mistakes that you would like to share?\nWhat are some resources and reference material that you recommend for anyone responsible for the success of their engineering teams?\n\nKeep In Touch\n\nLinkedIn\n\nPicks\n\nTobias\n\nBullet Train movie\n\n\nJigar\n\nTop Gun Maverick movie\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nSisu Data\nOpenStack\nJava\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Your ability to build and maintain a software project is tempered by the strength of the team that you are working with. If you are in a position of leadership, then you are responsible for the growth and maintenance of that team. In this episode Jigar Desai, currently the SVP of engineering at Sisu Data, shares his experience as an engineering leader over the past several years and the useful insights he has gained into how to build effective engineering teams.

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Announcements

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Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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\n\n

\"\"

","summary":"An interview with Jigar Desai about his extensive experience building and scaling engineering teams and useful lessons that you can apply to your own work as an engineering leader.","date_published":"2022-10-09T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/d92d46ab-6760-4912-b157-44113870f102.mp3","mime_type":"audio/mpeg","size_in_bytes":51172674,"duration_in_seconds":3890}]},{"id":"podlove-2022-10-03t02:11:04+00:00-0e80f69642bb78c","title":"Complete Your Hardware \"Weekend Projects\" In An Actual Weekend With Belay","url":"https://www.pythonpodcast.com/belay-micropython-hardware-helper-episode-379","content_text":"Summary\nWorking on hardware projects often has significant friction involved when compared to pure software. Brian Pugh enjoys tinkering with microcontrollers, but his \"weekend projects\" often took longer than a weekend to complete, so he created Belay. In this episode he explains how Belay simplifies the interactions involved in developing for MicroPython boards and how you can use it to speed up your own experimentation.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great!\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nThe biggest challenge with modern data systems is understanding what data you have, where it is located, and who is using it. Select Star’s data discovery platform solves that out of the box, with a fully automated catalog that includes lineage from where the data originated, all the way to which dashboards rely on it and who is viewing them every day. Just connect it to your dbt, Snowflake, Tableau, Looker, or whatever you’re using and Select Star will set everything up in just a few hours. Go to pythonpodcast.com/selectstar today to double the length of your free trial and get a swag package when you convert to a paid plan.\nYour host as usual is Tobias Macey and today I’m interviewing Brian Pugh about Belay, a python library that enables the rapid development of projects that interact with hardware via a micropython-compatible board.\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what Belay is and the story behind it?\nWho are the target users for Belay?\nWhat are some of the points of friction involved in developing for hardware projects?\n\nWhat are some of the features of Belay that make that a smoother process?\n\n\nWhat are some of the ways that simplifying the develop/debug cycles can improve the overall experience of developing for hardware platforms?\n\nWhat are some of the inherent limitations of constrained hardware that Belay is unable to paper over?\n\n\nCan you describe how Belay is implemented?\nWhat does the workflow look like when using Belay as compared to using MicroPython directly?\nWhat are some of the ways that you are using Belay in your own projects?\nWhat are the most interesting, innovative, or unexpected ways that you have seen Belay used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Belay?\nWhen is Belay the wrong choice?\nWhat do you have planned for the future of Belay?\n\nKeep In Touch\n\nBrianPugh on GitHub\nLinkedIn\n\nPicks\n\nTobias\n\nGunnar Computer Glasses\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nBelay\nGeomagical\nPIC Microcontroller\nAVR Microcontroller\nMatlab\nMicroPython\n\nPodcast Episode\n\n\nCircuitPython\n\nPodcast Episode\n\n\nCelery\nPotentiometer\nRaspberry Pi\nRaspberry Pi Pico\nADC Converter\nThonny\n\nPodcast Episode\n\n\nAdafruit\nPyboard\nPython Inspect Module\nPython Tokenize\nMagnetometer Project\nLidar\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Working on hardware projects often has significant friction involved when compared to pure software. Brian Pugh enjoys tinkering with microcontrollers, but his "weekend projects" often took longer than a weekend to complete, so he created Belay. In this episode he explains how Belay simplifies the interactions involved in developing for MicroPython boards and how you can use it to speed up your own experimentation.

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Announcements

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Interview

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Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

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Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Brian Pugh about the Belay project that he created to speed up his work on hardware projects built with MicroPython and the various challenges related to developing for micronctrollers","date_published":"2022-10-02T22:15:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/23d208d3-28be-4a10-8f43-501924468925.mp3","mime_type":"audio/mpeg","size_in_bytes":35363560,"duration_in_seconds":2909}]},{"id":"podlove-2022-09-19t01:06:56+00:00-ba8344310a54356","title":"Catching Up With Pyre, A Fast Type Checker For Python","url":"https://www.pythonpodcast.com/pyre-type-checker-episode-378","content_text":"Summary\nStatic typing versus dynamic typing is one of the oldest debates in software development. In recent years a number of dynamic languages have worked toward a middle ground by adding support for type hints. Python’s type annotations have given rise to an ecosystem of tools that use that type information to validate the correctness of programs and help identify potential bugs. At Instagram they created the Pyre project with a focus on speed to allow for scaling to huge Python projects. In this episode Shannon Zhu discusses how it is implemented, how to use it in your development process, and how it compares to other type checkers in the Python ecosystem.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Shannon Zhu about Pyre, a type checker for Python 3 built from the ground up to support gradual typing and deliver responsive incremental checks\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what Pyre is and the story behind it?\nThere have been a number of tools created to support various aspects of typing for Python. How would you describe the various goals that they support and how Pyre fits in that ecosystem?\nWhat are the core goals and notable features of Pyre?\nCan you describe how Pyre is implemented?\n\nHow have the design and goals of the project changed/evolved since you started working on it?\n\n\nWhat are the different ways that Pyre is used in the development workflow for a team or individual?\nWhat are some of the challenges/roadblocks that people run into when adopting type definitions in their Python projects?\nHow has the evolution of type annotations and overall support for them affected your work on Pyre?\nAs someone who is working closely with type systems, what are the strongest aspects of Python’s implementation and opportunities for improvement?\nWhat are the most interesting, innovative, or unexpected ways that you have seen Pyre used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Pyre?\nWhen is Pyre the wrong choice?\nWhat do you have planned for the future of Pyre?\n\nKeep In Touch\n\nshannonzhu on GitHub\n\nPicks\n\nTobias\n\nLord Of The Rings: The Rings of Power on Amazon Video\n\n\nShannon\n\nKing’s Dilemma board game\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nPYre\nMyPy\n\nPodcast Episode\n\n\nPyRight\nPyType\nMonkeyType\n\nPodcast Episode\n\n\nJava\nC\nPEP 484\nFlow\nHack\nContinuous Integration\nOCaml\nPEP 675 – Arbitrary literal strings\nGradual Typing\nAST == Abstract Syntax Tree\nLanguage Server Protocol\nTensor\nType Arithmetic\nPyCon: Securing Code With The Python Type System\nPyCon: Type Checked Python In The Real World\nPyCon: Łukasz Lange 2022 Keynote\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

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Static typing versus dynamic typing is one of the oldest debates in software development. In recent years a number of dynamic languages have worked toward a middle ground by adding support for type hints. Python’s type annotations have given rise to an ecosystem of tools that use that type information to validate the correctness of programs and help identify potential bugs. At Instagram they created the Pyre project with a focus on speed to allow for scaling to huge Python projects. In this episode Shannon Zhu discusses how it is implemented, how to use it in your development process, and how it compares to other type checkers in the Python ecosystem.

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Announcements

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Interview

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Keep In Touch

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Picks

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Closing Announcements

\n\n

Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Shannon Zhu about the Pyre project, and how to decide which type checker is right for you","date_published":"2022-09-18T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/a8082554-26c4-4353-9c3a-8ff4ba948a5d.mp3","mime_type":"audio/mpeg","size_in_bytes":38166030,"duration_in_seconds":3105}]},{"id":"podlove-2022-09-13t01:23:48+00:00-28ba815b4d39222","title":"Standardizing On Python For All Software Projects At Ascend.io","url":"https://www.pythonpodcast.com/ascend-python-standardization-episode-377","content_text":"Summary\nEvery software project is subject to a series of decisions and tradeoffs. One of the first decisions to make is which programming language to use. For companies where their product is software, this is a decision that can have significant impact on their overall success. In this episode Sean Knapp discusses the languages that his team at Ascend use for building a service that powers complex and business critical data workflows. He also explains his motivation to standardize on Python for all layers of their system to improve developer productivity.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Sean Knapp about his motivations and experiences standardizing on Python for development at Ascend\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what Ascend is and the story behind it?\nHow many engineers work at Ascend?\n\nWhat are their different areas of focus?\n\n\nWhat are your policies for selecting which technologies (e.g. languages, frameworks, dev tooling, deployment, etc.) are supported at Ascend?\n\nWhat does it mean for a technology to be supported?\n\n\nYou recently started standardizing on Python as the default language for development. How has Python been used up to now?\n\nWhat other languages are in common use at Ascend?\nWhat are some of the challenges/difficulties that motivated you to establish this policy?\n\n\nWhat are some of the tradeoffs that you have seen in the adoption of Python in place of your other adopted languages?\n\nHow are you managing ongoing maintenance of projects/products that are not written in Python?\n\n\nWhat are some of the potential pitfalls/risks that you are guarding against in your investment in Python?\nWhat are the most interesting, innovative, or unexpected ways that you have seen Python used where it was previously a different technology?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on aligning all of your development on a single language?\nWhen is Python the wrong choice?\nWhat do you have planned for the future of engineering practices at Ascend?\n\nKeep In Touch\n\nLinkedIn\n@seanknapp on Twitter\n\nPicks\n\nTobias\n\nDelver Lens app for scanning Magic: The Gathering cards\n\n\nSean\n\nTyper\nDuckDB\nAmp It Up book (affiliate link)\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nAscend\n\nData Engineering Podcast Episode\n\n\nPerl\nGoogle Sawzall\nTechnical Debt\nRuby\ngRPC\nGo Language\nJava\nPySpark\nApache Arrow\nThrift\nSQL\nScala\nSnowflake runtime for Python Snowpark\nTyper CLI framework\nPydantic\n\nPodcast Episode\n\n\nPulumi\n\nPodcast Episode\n\n\nPyInfra\n\nPodcast Episode\n\n\nPacker\nPlot.ly Dash\nDuckDB\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Every software project is subject to a series of decisions and tradeoffs. One of the first decisions to make is which programming language to use. For companies where their product is software, this is a decision that can have significant impact on their overall success. In this episode Sean Knapp discusses the languages that his team at Ascend use for building a service that powers complex and business critical data workflows. He also explains his motivation to standardize on Python for all layers of their system to improve developer productivity.

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Announcements

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Interview

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Keep In Touch

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Picks

\n\n

Closing Announcements

\n\n

Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Sean Knapp, CEO and co-founder of Ascend, about his experiences aligning all development to use Python to increase developer productivity.","date_published":"2022-09-12T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/b019429a-b78e-4f1b-8de8-af3d5382842b.mp3","mime_type":"audio/mpeg","size_in_bytes":34566020,"duration_in_seconds":3025}]},{"id":"podlove-2022-09-04t17:34:20+00:00-eedac54b3569dd7","title":"Exploring The Process And Practice Of Building Better Software Through Code Reviews","url":"https://www.pythonpodcast.com/code-review-for-better-software-episode-376","content_text":"Summary\nWriting code is only one piece of creating good software. Code reviews are an important step in the process of building applications that are maintainable and sustainable. In this episode On Freund shares his thoughts on the myriad purposes that code reviews serve, as well as exploring some of the patterns and anti-patterns that grow up around a seemingly simple process.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing On Freund about the intricacies and importance of code reviews\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by giving us your description of what a code review is?\n\nWhat is the purpose of the code review?\n\n\nAt face value a code review appears to be a simple task. What are some of the subtleties that become evident with time and experience?\nWhat are some of the ways that code reviews can go wrong?\nWhat are some common anti-patterns that get applied to code reviews?\nWhat are the elements of code review that are useful to automate?\n\nWhat are some of the risks/bad habits that can result from overdoing automated checks/fixes or over-reliance on those tools in code reviews?\n\n\nidentifying who can/should do a review for a piece of code\nhow to use code reviews as a teaching tool for new/junior engineers\nhow to use code reviews for avoiding siloed experience/promoting cross-training\nPR templates for capturing relevant context\nWhat are the most interesting, innovative, or unexpected ways that you have seen code reviews used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while leading and supporting engineering teams?\nWhat are some resources that you recommend for anyone who wants to learn more about code review strategies and how to use them to scale their teams?\n\nKeep In Touch\n\nLinkedIn\n@onfreund on Twitter\n\nPicks\n\nTobias\n\nThe Girl Who Drank The Moon\n\n\nOn\n\nBetter Call Saul\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nWilco\nCode Review\nHome Assistant\n\nPodcast Episode\n\n\nTrunk-based Development\nGit Flow\nPair Programming\nFeature Flags\n\nPodcast Episode\n\n\nKPI == Key Performance Indicator\nMIT Open Learning Engineering Handbook\nPEP Repository\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Writing code is only one piece of creating good software. Code reviews are an important step in the process of building applications that are maintainable and sustainable. In this episode On Freund shares his thoughts on the myriad purposes that code reviews serve, as well as exploring some of the patterns and anti-patterns that grow up around a seemingly simple process.

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Announcements

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Interview

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Keep In Touch

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Picks

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Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with On Freund about the benefits of code review for software teams and how to incorporate it into your development practice in a positive and productive manner.","date_published":"2022-09-04T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/02987284-897b-48b8-8674-a72ff96787a7.mp3","mime_type":"audio/mpeg","size_in_bytes":45079449,"duration_in_seconds":3444}]},{"id":"podlove-2022-08-28t19:53:41+00:00-a614aa6dabbe4f9","title":"Ship With Confidence By Automating Quality Assurance","url":"https://www.pythonpodcast.com/keysight-quality-assurance-automation-episode-375","content_text":"Summary\nQuality assurance in the software industry has become a shared responsibility in most organizations. Given the rapid pace of development and delivery it can be challenging to ensure that your application is still working the way it’s supposed to with each release. In this episode Jonathon Wright discusses the role of quality assurance in modern software teams and how automation can help.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Jonathon Wright about the role of automation in your testing and QA strategies\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you share your relationship with software testing/QA and automation?\nWhat are the main categories of how companies and software teams address testing and validation of their applications?\n\nWhat are some of the notable tradeoffs/challenges among those approaches?\n\n\nWith the increased adoption of agile practices and the \"shift left\" mentality of DevOps, who is responsible for software quality?\n\nWhat are some of the cases where a discrete QA role or team becomes necessary? (or is it always necessary?)\n\n\nWith testing and validation being a shared responsibility, competing with other priorities, what role does automation play?\n\nWhat are some of the ways that automation manifests in software quality and testing?\nHow is automation distinct from software tests and CI/CD?\n\n\nFor teams who are investing in automation for their applications, what are the questions they should be asking to identify what solutions to adopt? (what are the decision points in the build vs. buy equation?)\nAt what stage(s) of the software lifecycle does automation live?\nWhat is the process for identifying which capabilities and interactions to target during the initial application of automation for QA and validation?\nOne of the perennial challenges with any software testing, particularly for anything in the UI, is that it is a constantly moving target. What are some of the patterns and techniques, both from a developer and tooling perspective, that increase the robustness of automated validation?\nWhat are the most interesting, innovative, or unexpected ways that you have seen automation used for QA?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on QA and automation?\nWhen is automation the wrong choice?\nWhat are some of the resources that you recommend for anyone who wants to learn more about this topic?\n\nKeep In Touch\n\nLinkedIn\n@Jonathon_Wright on Twitter\nWebsite\n\nPicks\n\nTobias\n\nThe Sandman Netflix series and Graphic Novels by Neil Gaimain\n\n\nJonathon\n\nHouse of the Dragon HBO series\nMystic Quest TV series\nIt’s Always Sunny in Philadelphia\n\n\n\nLinks\n\nHaskell\nIdris\nEsperanto\nKlingon\nPlanguage\nLisp Language\nTDD == Test Driven Development\nBDD == Behavior Driven Development\nGherkin Format\nIntegration Testing\nChaos Engineering\nGremlin\nChaos Toolkit\n\nPodcast Episode\n\n\nRequirements Engineering\nKeysight\nQA Lead Podcast\nCognitive Learning TED Talk\nOpenTelemetry\n\nPodcast Episode\n\n\nQuality Engineering\nSelenium\nSwagger\nXPath\nRegular Expression\nTest Guild\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Quality assurance in the software industry has become a shared responsibility in most organizations. Given the rapid pace of development and delivery it can be challenging to ensure that your application is still working the way it’s supposed to with each release. In this episode Jonathon Wright discusses the role of quality assurance in modern software teams and how automation can help.

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Announcements

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Interview

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Keep In Touch

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Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Jonathon Wright about the challenges of quality assurance in modern software development and how automation can reduce the burden for everyone.","date_published":"2022-08-28T16:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/5388c1a8-17ce-4499-8823-bb4e5f6e7974.mp3","mime_type":"audio/mpeg","size_in_bytes":60857582,"duration_in_seconds":4144}]},{"id":"podlove-2022-08-14t10:30:56+00:00-c2be45765f825ba","title":"Remove Roadblocks And Let Your Developers Ship Faster With Self-Serve Infrastructure","url":"https://www.pythonpodcast.com/quali-self-serve-infrastructure-episode-374","content_text":"Summary\nThe goal of every software team is to get their code into production without breaking anything. This requires establishing a repeatable process that doesn’t introduce unnecessary roadblocks and friction. In this episode Ronak Rahman discusses the challenges that development teams encounter when trying to build and maintain velocity in their work, the role that access to infrastructure plays in that process, and how to build automation and guardrails for everyone to take part in the delivery process.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Ronak Rahman about how automating the path to production helps to build and maintain development velocity\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what Quali is and the story behind it?\nWhat are the problems that you are trying to solve for software teams?\n\nHow does Quali help to address those challenges?\n\n\nWhat are the bad habits that engineers fall into when they experience friction with getting their code into test and production environments?\n\nHow do those habits contribute to negative feedback loops?\n\n\nWhat are signs that developers and managers need to watch for that signal the need for investment in developer experience improvements on the path to production?\nCan you describe what you have built at Quali and how it is implemented?\n\nHow have the design and goals shifted/evolved from when you first started working on it?\n\n\nWhat are the positive and negative impacts that you have seen from the evolving set of options for application deployments? (e.g. K8s, containers, VMs, PaaS, FaaS, etc.)\nCan you describe how Quali fits into the workflow of software teams?\nOnce a team has established patterns for deploying their software, what are some of the disruptions to their flow that they should guard against?\nWhat are the most interesting, innovative, or unexpected ways that you have seen Quali used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Quali?\nWhen is Quali the wrong choice?\nWhat do you have planned for the future of Quali?\n\nKeep In Touch\n\n@OfRonak on Twitter\n\nPicks\n\nTobias\n\nThe Terminal List on Amazon\n\n\nRonak\n\nMidnight Gospel on Amazon\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nQuali\n\nTorque\nVisual Studio Plugin\n\n\nSubversion\nIaC == Infrastructure as Code\nDevOps\nTerraform\nPulumi\n\nPodcast Episode\n\n\nCloudformation\nFlask\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

The goal of every software team is to get their code into production without breaking anything. This requires establishing a repeatable process that doesn’t introduce unnecessary roadblocks and friction. In this episode Ronak Rahman discusses the challenges that development teams encounter when trying to build and maintain velocity in their work, the role that access to infrastructure plays in that process, and how to build automation and guardrails for everyone to take part in the delivery process.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Ronak Rahman, head of developer relations at Quali, about the benefits of providing self-serve access to infrastructure for your developers so that your teams can build and ship faster without unnecessary friction.","date_published":"2022-08-14T06:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/d22797fe-3bb1-429a-9dd1-dba093856dd1.mp3","mime_type":"audio/mpeg","size_in_bytes":46687524,"duration_in_seconds":3708}]},{"id":"podlove-2022-07-31t23:04:53+00:00-d9fafee6eaccf11","title":"The Benefits Of Python And Django For Going From Zero To MVP At Speed","url":"https://www.pythonpodcast.com/planeks-python-mvp-development-episode-373","content_text":"Summary\nEvery startup begins with an idea, but that won’t get you very far without testing the feasibility of that idea. A common practice is to build a Minimum Viable Product (MVP) that addresses the problem that you are trying to solve and working with early customers as they engage with that MVP. In this episode Tony Pavlovych shares his thoughts on Python’s strengths when building and launching that MVP and some of the potential pitfalls that businesses can run into on that path.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Tony Pavlovych about Python’s strengths for startups and the steps to building an MVP (minimum viable product)\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what PLANEKS is and the story behind it?\nOne of the services that you offer is building an MVP. What are the goals and outcomes associated with an MVP?\n\nWhat is the process for identifying the product focus and feature scope?\n\n\nWhat are some of the common misconceptions about building and launching MVPs that you have dealt with in your work with customers?\n\nWhat are the common pitfalls that companies encounter when building and validating an MVP?\n\n\nCan you describe the set of tools and frameworks (e.g. Django, Poetry, cookiecutter, etc.) that you have invested in to reduce the overhead of starting and maintaining velocity on multiple projects?\n\nWhat are the configurations that are most critical to keep constant across projects to maintain familiarity and sanity for your developers? (e.g. linting rules, build toolchains, etc.)\n\n\nWhat are the architectural patterns that you have found most useful to make MVPs flexible for adaptation and extension?\nOnce the MVP is built and launched, what are the next steps to validate the product and determine priorities?\nWhat benefits do you get from choosing Python as your language for building an MVP/launching a startup?\n\nWhat are the challenges/risks involved in that choice?\n\n\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on MVPs for your clients at PLANEKS?\nWhen is an MVP the wrong choice?\nWhat are the developments in the Python and broader software ecosystem that you are most interested in for the work you are doing for your team and clients?\n\nKeep In Touch\n\nLinkedIn\n\nPicks\n\nTobias\n\ndatamodel-code-generator\n\n\nTony\n\nScrew It, Let’s Do It by Richard Branson (affiliate link)\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nPLANEKS\nMinimum Viable Product\nDjango\nCookiecutter\nDjango Boilerplate\nOCR == Optical Character Recognition\nTesseract OCR framework\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Every startup begins with an idea, but that won’t get you very far without testing the feasibility of that idea. A common practice is to build a Minimum Viable Product (MVP) that addresses the problem that you are trying to solve and working with early customers as they engage with that MVP. In this episode Tony Pavlovych shares his thoughts on Python’s strengths when building and launching that MVP and some of the potential pitfalls that businesses can run into on that path.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview with Tony Pavlovych of PLANEKS about the lessons that he and his team have learned building Minimum Viable Products (MVPs) for startups with Python and Django and how to test your startup's ideas","date_published":"2022-07-31T19:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/5f11b8ea-2037-4274-9d7b-0200e3f6ef88.mp3","mime_type":"audio/mpeg","size_in_bytes":32561934,"duration_in_seconds":2826}]},{"id":"podlove-2022-07-24t18:49:48+00:00-466027a0ae50499","title":"Powering The Next Generation Of Application Architectures With Web Assembly And The Fermyon Platform","url":"https://www.pythonpodcast.com/fermyon-web-assembly-application-architecture-episode-372","content_text":"Summary\nApplication architectures have been in a constant state of evolution as new infrastructure capabilities are introduced. Virtualization, cloud, containers, mobile, and now web assembly have each introduced new options for how to build and deploy software. Recognizing the transformative potential of web assembly, Matt Butcher and his team at Fermyon are investing in tooling and services to improve the developer experience. In this episode he explains the opportunity that web assembly offers to all language communities, what they are building to power lightweight server-side microservices, and how Python developers can get started building and contributing to this nascent ecosystem.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nNeed to automate your Python code in the cloud? Want to avoid the hassle of setting up and maintaining infrastructure? Shipyard is the premier orchestration platform built to help you quickly launch, monitor, and share python workflows in a matter of minutes with 0 changes to your code. Shipyard provides powerful features like webhooks, error-handling, monitoring, automatic containerization, syncing with Github, and more. Plus, it comes with over 70 open-source, low-code templates to help you quickly build solutions with the tools you already use. Go to dataengineeringpodcast.com/shipyard to get started automating with a free developer plan today!\nYour host as usual is Tobias Macey and today I’m interviewing Matt Butcher about Fermyon and the impact of WebAssembly on software architecture and deployment across language boundaries\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nFor anyone who isn’t familiar with WebAssembly can you give your elevator pitch for why it matters?\nWhat is the current state of language support for Python in the WASM ecosystem?\nCan you describe what Fermyon is and the story behind it?\nWhat are your goals with Fermyon and what are the products that you are building to support those goals?\nThere has been a steady progression of technologies aimed at better ways to build, deploy, and manage software (e.g. virtualization, cloud, containers, etc.). What are the problems with the previous options and how does WASM address them?\nWhat are some examples of the types of applications/services that work well in a WASM environment?\nCan you describe how you have architected the Fermyon platform?\n\nHow did you approach the design of the interfaces and tooling to support developer ergonomics?\nHow have the design and goals of the platform changed or evolved since you started working on it?\n\n\nCan you describe what a typical workflow is for an application team that is using Spin/Fermyon to build and deploy a service?\nWhat are some of the architectural patterns that WASM/Fermyon encourage?\nWhat are some of the limitations that WASM imposes on services using it as a runtime? (e.g. system access, threading/multiprocessing, library support, C extensions, etc.)\nWhat are the new and emerging topics and capabilities in the WASM ecosystem that you are keeping track of?\nWith Spin as the core building block of your platform, how are you approaching governance and sustainability of the open source project?\n\nWhat are your guiding principles for when a capability belongs in the OSS vs. commercial offerings?\n\n\nWhat are the most interesting, innovative, or unexpected ways that you have seen Fermyon used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Fermyon?\nWhen is Fermyon the wrong choice?\nWhat do you have planned for the future of Fermyon?\n\nKeep In Touch\n\nLinkedIn\n@technosophos on Twitter\ntechnosophos on GitHub\n\nPicks\n\nTobias\n\nThor: Love & Thunder movie\n\n\nMatt\n\nRemembrance of Earth’s Past trilogy (\"Three Body Problem\" is the first) by Cixin Liu\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nFermyon\nOur Python entry for the Wasm Language Matrix\nSingleStore’s WASI-Python\nGreat notes about Wasm support in CPyton\nPyodide for Python in the Browser\nSlashDot\nWeb Assembly (WASM)\nRust\nAssemblyScript\nGrain WASM language\nSingleStore\n\nData Engineering Podcast Episode\n\n\nWASI\nPyO3\nPyOxidizer\nRustPython\nDrupal\nOpenStack\nDeis\nHelm\nRedPanda\n\nData Engineering Podcast Episode\n\n\nEnvoy Proxy\nFastly\nFunctions as a Service\nCloudEvents\nFinicky Whiskers\nFermyon Spin\nNomad\nTree Shaking\nZappa\nChalice\nOpenFaaS\nCNCF\nBytecode Alliance\nFinicky Whiskers Minecraft\nKotlin\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Application architectures have been in a constant state of evolution as new infrastructure capabilities are introduced. Virtualization, cloud, containers, mobile, and now web assembly have each introduced new options for how to build and deploy software. Recognizing the transformative potential of web assembly, Matt Butcher and his team at Fermyon are investing in tooling and services to improve the developer experience. In this episode he explains the opportunity that web assembly offers to all language communities, what they are building to power lightweight server-side microservices, and how Python developers can get started building and contributing to this nascent ecosystem.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
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\"\"

","summary":"An interview with Matt Butcher about his work at Fermyon to drive adoption of web assembly as the next deployment target for software architectures in the cloud","date_published":"2022-07-24T20:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/be9fb942-7eff-47f2-a12c-a8530c2b5a89.mp3","mime_type":"audio/mpeg","size_in_bytes":57154201,"duration_in_seconds":4239}]},{"id":"podlove-2022-07-17t23:41:19+00:00-bbf40252b9bfbae","title":"Gain A Deeper Understanding Of What Your Code Is Doing And Where It Spends Its Time With VizTracer","url":"https://www.pythonpodcast.com/viztracer-visual-python-profiling-episode-371","content_text":"Summary\nAs your code scales beyond a trivial level of complexity and sophistication it becomes difficult or impossible to know everything that it is doing. The flow of logic and data through your software and which parts are taking the most time are impossible to understand without help from your tools. VizTracer is the tool that you will turn to when you need to know all of the execution paths that are being exercised and which of those paths are the most expensive. In this episode Tian Gao explains why he created VizTracer and how you can use it to gain a deeper familiarity with the code that you are responsible for maintaining.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nNeed to automate your Python code in the cloud? Want to avoid the hassle of setting up and maintaining infrastructure? Shipyard is the premier orchestration platform built to help you quickly launch, monitor, and share python workflows in a matter of minutes with 0 changes to your code. Shipyard provides powerful features like webhooks, error-handling, monitoring, automatic containerization, syncing with Github, and more. Plus, it comes with over 70 open-source, low-code templates to help you quickly build solutions with the tools you already use. Go to dataengineeringpodcast.com/shipyard to get started automating with a free developer plan today!\nYour host as usual is Tobias Macey and today I’m interviewing Tian Gao about VizTracer, a low-overhead logging/debugging/profiling tool that can trace and visualize your python code execution\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what VizTracer is and the story behind it?\nWhat are the main goals that you are focused on with VizTracer?\nWhat are some examples of the types of bugs that profiling can help diagnose?\n\nHow does profiling work together with other debugging approaches? (e.g. logging, breakpoint debugging, etc.)\n\n\nThere are a number of profiling utilities for Python. What feature or combination of features were missing that motivated you to create VizTracer?\nCan you describe how VizTracer is implemented?\n\nHow have the design and goals changed since you started working on it?\nThere are a number of styles of profiling, what was your process for deciding which approach to use?\n\n\nWhat are the most complex engineering tasks involved in building a profiling utility?\nCan you describe the process of using VizTracer to identify and debug errors and performance issues in a project?\nWhat are the options for using VizTracer in a production environment?\nWhat are the interfaces and extension points that you have built in to allow developers to customize VizTracer?\nWhat are some of the ways that you have used VizTracer while working on VizTracer?\nWhat are the most interesting, innovative, or unexpected ways that you have seen VizTracer used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on VizTracer?\nWhen is VizTracer the wrong choice?\nWhat do you have planned for the future of VizTracer?\n\nKeep In Touch\n\ngaogaotiantian on GitHub\nLinkedIn\n\nPicks\n\nTobias\n\nTravelers show on Netflix\n\n\nTian\n\nobjprint\nLincoln Lawyer\nbilibili – Tian’s coding sessions in Chinese\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nViztracer\nPython cProfile\nSampling Profiler\nPerfetto\nCoverage.py\n\nPodcast Episode\n\n\nPython setxprofile hook\nCircular Buffer\nCatapult Trace Viewer\npy-spy\npsutil\ngdb\nFlame graph\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

As your code scales beyond a trivial level of complexity and sophistication it becomes difficult or impossible to know everything that it is doing. The flow of logic and data through your software and which parts are taking the most time are impossible to understand without help from your tools. VizTracer is the tool that you will turn to when you need to know all of the execution paths that are being exercised and which of those paths are the most expensive. In this episode Tian Gao explains why he created VizTracer and how you can use it to gain a deeper familiarity with the code that you are responsible for maintaining.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Tian Gao about the open source VizTracer project and how it grants you a deeper understanding of what your code is doing and what takes the most time through a combination of profiling and intuitive visualization","date_published":"2022-07-17T19:45:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/0b43e3f4-3467-48dd-9b4f-47b79bf3fae0.mp3","mime_type":"audio/mpeg","size_in_bytes":42459331,"duration_in_seconds":2913}]},{"id":"podlove-2022-07-10t22:53:47+00:00-c5d64eb7180224c","title":"Stream Processing In Real Time And At Scale In Pure Python With Bytewax","url":"https://www.pythonpodcast.com/bytewax-python-stream-processing-episode-370","content_text":"Summary\nAnalysis of streaming data in real time has long been the domain of big data frameworks, predominantly written in Java. In order to take advantage of those capabilities from Python requires using client libraries that suffer from impedance mis-matches that make the work harder than necessary. Bytewax is a new open source platform for writing stream processing applications in pure Python that don’t have to be translated into foreign idioms. In this episode Bytewax founder Zander Matheson explains how the system works and how to get started with it today.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nThe biggest challenge with modern data systems is understanding what data you have, where it is located, and who is using it. Select Star’s data discovery platform solves that out of the box, with a fully automated catalog that includes lineage from where the data originated, all the way to which dashboards rely on it and who is viewing them every day. Just connect it to your dbt, Snowflake, Tableau, Looker, or whatever you’re using and Select Star will set everything up in just a few hours. Go to pythonpodcast.com/selectstar today to double the length of your free trial and get a swag package when you convert to a paid plan.\nNeed to automate your Python code in the cloud? Want to avoid the hassle of setting up and maintaining infrastructure? Shipyard is the premier orchestration platform built to help you quickly launch, monitor, and share python workflows in a matter of minutes with 0 changes to your code. Shipyard provides powerful features like webhooks, error-handling, monitoring, automatic containerization, syncing with Github, and more. Plus, it comes with over 70 open-source, low-code templates to help you quickly build solutions with the tools you already use. Go to dataengineeringpodcast.com/shipyard to get started automating with a free developer plan today!\nYour host as usual is Tobias Macey and today I’m interviewing Zander Matheson about Bytewax, an open source Python framework for building highly scalable dataflows to process ANY data stream.\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what Bytewax is and the story behind it?\nWho are the target users for Bytewax?\nWhat is the problem that you are trying to solve with Bytewax?\nWhat are the alternative systems/architectures that you might replace with Bytewax?\nCan you describe how Bytewax is implemented?\n\nWhat are the benefits of Timely Dataflow as a core building block for a system like Bytewax?\nHow have the design and goals of the project changed/evolved since you first started working on it?\n\n\nWhat are the axes available for scaling Bytewax execution?\nHow have you approached the design of the Bytewax API to make it accessible to a broader audience?\nCan you describe what is involved in building a project with Bytewax?\n\nWhat are some of the stream processing concepts that engineers are likely to run up against as they are experimenting and designing their code?\n\n\nWhat is your motivation for providing the core technology of your business as an open source engine?\n\nHow are you approaching the balance of project governance and sustainability with opportunities for commercialization?\n\n\nWhat are the most interesting, innovative, or unexpected ways that you have seen Bytewax used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Bytewax?\nWhen is Bytewax the wrong choice?\nWhat do you have planned for the future of Bytewax?\n\nKeep In Touch\n\nSlack\nTwitter\nLinkedIn\n\nPicks\n\nTobias\n\nAlta Racks\n\n\nZander\n\nAtherton Bikes\n\n\n\nLinks\n\nBytewax\n\nGitHub\n\n\nFlink\n\nData Engineering Podcast Episode\n\n\nSpark Streaming\nKafka Connect\nFaust\n\nPodcast Episode\n\n\nRay\n\nPodcast Episode\n\n\nDask\n\nData Engineering Podcast Episode\n\n\nTimely Dataflow\nPyO3\nMaterialize\n\nData Engineering Podcast Episode\n\n\nHyperLogLog\nPython River Library\nShannon Entropy Calculation\nThe blog post using incremental shannon entropy\nNATS\nwaxctl\nPrometheus\nGrafana\nStreamz\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Analysis of streaming data in real time has long been the domain of big data frameworks, predominantly written in Java. In order to take advantage of those capabilities from Python requires using client libraries that suffer from impedance mis-matches that make the work harder than necessary. Bytewax is a new open source platform for writing stream processing applications in pure Python that don’t have to be translated into foreign idioms. In this episode Bytewax founder Zander Matheson explains how the system works and how to get started with it today.

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Announcements

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Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Zander Matheson about the open source Bytewax framework for scalable real-time stream processing in pure Python","date_published":"2022-07-10T19:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/cf4f524c-d728-4a4b-974a-fed6cec59d27.mp3","mime_type":"audio/mpeg","size_in_bytes":35073264,"duration_in_seconds":2552}]},{"id":"podlove-2022-07-03t20:51:37+00:00-94a85e141782600","title":"Tetra: A Full Stack Web Framework That Doesn't Make You Write Everything Twice","url":"https://www.pythonpodcast.com/tetra-full-stack-component-web-framework-episode-369","content_text":"Summary\nBuilding a fully functional web application has been growing in complexity along with the growing popularity of javascript UI frameworks such as React, Vue, Angular, etc. Users have grown to expect interactive experiences with dynamic page updates, which leads to duplicated business logic and complex API contracts between the server-side application and the Javascript front-end. To reduce the friction involved in writing and maintaining a full application Sam Willis created Tetra, a framework built on top of Django that embeds the Javascript logic into the Python context where it is used. In this episode he explains his design goals for the project, how it has helped him build applications more rapidly, and how you can start using it to build your own projects today.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nSo now your modern data stack is set up. How is everyone going to find the data they need, and understand it? Select Star is a data discovery platform that automatically analyzes & documents your data. For every table in Select Star, you can find out where the data originated, which dashboards are built on top of it, who’s using it in the company, and how they’re using it, all the way down to the SQL queries. Best of all, it’s simple to set up, and easy for both engineering and operations teams to use. With Select Star’s data catalog, a single source of truth for your data is built in minutes, even across thousands of datasets. Try it out for free and double the length of your free trial today at pythonpodcast.com/selectstar. You’ll also get a swag package when you continue on a paid plan.\nNeed to automate your Python code in the cloud? Want to avoid the hassle of setting up and maintaining infrastructure? Shipyard is the premier orchestration platform built to help you quickly launch, monitor, and share python workflows in a matter of minutes with 0 changes to your code. Shipyard provides powerful features like webhooks, error-handling, monitoring, automatic containerization, syncing with Github, and more. Plus, it comes with over 70 open-source, low-code templates to help you quickly build solutions with the tools you already use. Go to dataengineeringpodcast.com/shipyard to get started automating with a free developer plan today!\nYour host as usual is Tobias Macey and today I’m interviewing Sam Willis about Tetra, a full stack component framework for your Django applications\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what Tetra is and the story behind it?\nWhat are the problems that you are aiming to solve with this project?\n\nWhat are some of the other ways that you have addressed those problems?\nWhat are the shortcomings that you encountered with those solutions?\n\n\nWhat was missing in the existing landscape of full-stack application development patterns that prompted you to build a new meta-framework?\nWhat are some of the sources of inspiration (positive and negative) that you looked to while deciding on the component selection and implementation strategy?\nCan you describe how Tetra is implemented?\n\nWhat are the core principles that you are relying on to drive your design of APIs and developer experience?\n\n\nWhat is the process for building a full component in Tetra?\nWhat are some of the application design challenges that are introduced by Combining the javascript and Django logic and attributes? (e.g. reusing JS logic/CSS styles across components)\nA perennial challenge with combining the syntax across multiple languages in a single file is editor support. How are you thinking about that with Tetra’s implementation?\nWhat is your grand vision for Tetra and how are you working to make it sustainable?\nWhat are the most interesting, innovative, or unexpected ways that you have seen Tetra used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Tetra?\nWhen is Tetra the wrong choice?\nWhat do you have planned for the future of Tetra?\n\nKeep In Touch\n\n@samwillis on Twitter\nWebsite\nLinkedIn\nsamwillis on GitHub\n\nPicks\n\nTobias\n\nThe Machine Learning Podcast\n\n\nSam\n\nSlow Horses TV Show\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nTetra Framework\nDjango\nPHP\nASP\nAlpine.js\nHTMX\nRuby\nRuby on Rails\nFlutterbox\nVue.js\nLaravel Livewire\nPython Import Hooks\npython-inline-source\nTailwind CSS\nPostCSS\nPickle\nFernet\nesbuild\nWebpack\nRich\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Building a fully functional web application has been growing in complexity along with the growing popularity of javascript UI frameworks such as React, Vue, Angular, etc. Users have grown to expect interactive experiences with dynamic page updates, which leads to duplicated business logic and complex API contracts between the server-side application and the Javascript front-end. To reduce the friction involved in writing and maintaining a full application Sam Willis created Tetra, a framework built on top of Django that embeds the Javascript logic into the Python context where it is used. In this episode he explains his design goals for the project, how it has helped him build applications more rapidly, and how you can start using it to build your own projects today.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Sam Willis about his work on the Tetra framework for easily building full stack web apps in Django without having to wrestle with a separate Javascript project and all of the complexity that it brings.","date_published":"2022-07-03T17:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/4c922e7a-15d2-47b4-9124-5b5313cc89e7.mp3","mime_type":"audio/mpeg","size_in_bytes":37299612,"duration_in_seconds":3186}]},{"id":"podlove-2022-06-27t01:37:40+00:00-1eb561059c6860a","title":"Design Real-World Objects In Python With CadQuery","url":"https://www.pythonpodcast.com/cadquery-python-programmatic-cad-episode-368","content_text":"Summary\nVirtually everything that you interact with on a daily basis and many other things that make modern life possible were designed and modeled in software called CAD or Computer-Aided Design. These programs are advanced suites with graphical editing environments tailored to domain experts in areas such as mechanical engineering, electrical engineering, architecture, etc. While the UI-driven workflow is more accessible, it isn’t scalable which opens the door to code-driven workflows. In this episode Jeremy Wright discusses the design, uses, and benefits of the CadQuery framework for building 3D CAD models entirely in Python.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nSo now your modern data stack is set up. How is everyone going to find the data they need, and understand it? Select Star is a data discovery platform that automatically analyzes & documents your data. For every table in Select Star, you can find out where the data originated, which dashboards are built on top of it, who’s using it in the company, and how they’re using it, all the way down to the SQL queries. Best of all, it’s simple to set up, and easy for both engineering and operations teams to use. With Select Star’s data catalog, a single source of truth for your data is built in minutes, even across thousands of datasets. Try it out for free and double the length of your free trial today at pythonpodcast.com/selectstar. You’ll also get a swag package when you continue on a paid plan.\nNeed to automate your Python code in the cloud? Want to avoid the hassle of setting up and maintaining infrastructure? Shipyard is the premier orchestration platform built to help you quickly launch, monitor, and share python workflows in a matter of minutes with 0 changes to your code. Shipyard provides powerful features like webhooks, error-handling, monitoring, automatic containerization, syncing with Github, and more. Plus, it comes with over 70 open-source, low-code templates to help you quickly build solutions with the tools you already use. Go to dataengineeringpodcast.com/shipyard to get started automating with a free developer plan today!\nYour host as usual is Tobias Macey and today I’m interviewing Jeremy Wright about CadQuery, an easy-to-use Python module for building parametric 3D CAD models\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what CAD is and some of the real-world applications of it?\nCan you describe what CadQuery is and the story behind it?\n\nHow did you get involved with it and what keeps you motivated?\nWhat are the different methods that are in common use for building CAD models?\nAre there approaches that are more common for models used in different industries?\n\n\nWhat was missing in other projects for programmatically generating CAD models that motivated you to build CadQuery?\nCan you describe how the CadQuery library is implemented?\n\nHow have the design and goals of the project changed or evolved since you started working on it?\nHow would you characterize the rate of change/evolution in the CAD ecosystem, and how has that factored into your work on CadQuery?\n\n\nHow did you approach the process of API design?\n\nHow do you balance accessibility for non-professionals with domain-related nomenclature?\n\n\nCan you describe some example workflows for going from idea to finished product with CadQuery?\nHow are you using CadQuery in your own work?\nWhat are the most interesting, innovative, or unexpected ways that you have seen CadQuery used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on CadQuery?\nWhen is CadQuery the wrong choice?\nWhat do you have planned for the future of CadQuery?\n\nKeep In Touch\n\nDiscord\nTwitter\nGitHub\nGitLab\n\nPicks\n\nTobias\n\nDoctor Strange: In The Multiverse of Madness\n\n\nJeremy\n\nStar Trek: Strange New Worlds\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nCadQuery\nCAD == Computer Assisted Design\n3D Printer\nJeremy’s CNC Router\njQuery\nBlender\nFusion 360\nOpen Cascade (OCCT)\nFluent API\nFreeCAD\nKiCAD\nSemblage\ncq-editor\njupyter-cadquery\ncq-kit\nFX Bricks\nVoxels\ncq_warehouse\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Virtually everything that you interact with on a daily basis and many other things that make modern life possible were designed and modeled in software called CAD or Computer-Aided Design. These programs are advanced suites with graphical editing environments tailored to domain experts in areas such as mechanical engineering, electrical engineering, architecture, etc. While the UI-driven workflow is more accessible, it isn’t scalable which opens the door to code-driven workflows. In this episode Jeremy Wright discusses the design, uses, and benefits of the CadQuery framework for building 3D CAD models entirely in Python.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"In this episode Jeremy Wright explains the varied use cases for Computer Aided Design in building real-world objects that you use every day, and how you can use Python for modeling your own physical and virtual structures.","date_published":"2022-06-26T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/4cccbd39-5ef3-4e2f-94f7-23a52f9001b4.mp3","mime_type":"audio/mpeg","size_in_bytes":42620375,"duration_in_seconds":2704}]},{"id":"podlove-2022-06-15t02:48:16+00:00-464fdc26f6cd5f6","title":"Intelligent Dependency Resolution For Optimal Compatibility And Security With Project Thoth","url":"https://www.pythonpodcast.com/thoth-dependency-resolution-episode-367","content_text":"Summary\nBuilding any software project is going to require relying on dependencies that you and your team didn’t write or maintain, and many of those will have dependencies of their own. This has led to a wide variety of potential and actual issues ranging from developer ergonomics to application security. In order to provide a higher degree of confidence in the optimal combinations of direct and transitive dependencies a team at Red Hat started Project Thoth. In this episode Fridolín Pokorný explains how the Thoth resolver uses multiple signals to find the best combination of dependency versions to ensure compatibility and avoid known security issues.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nNeed to automate your Python code in the cloud? Want to avoid the hassle of setting up and maintaining infrastructure? Shipyard is the premier orchestration platform built to help you quickly launch, monitor, and share python workflows in a matter of minutes with 0 changes to your code. Shipyard provides powerful features like webhooks, error-handling, monitoring, automatic containerization, syncing with Github, and more. Plus, it comes with over 70 open-source, low-code templates to help you quickly build solutions with the tools you already use. Go to dataengineeringpodcast.com/shipyard to get started automating with a free developer plan today!\nYour host as usual is Tobias Macey and today I’m interviewing Fridolín Pokorný about Project Thoth, a resolver service that computes the optimal combination of versions for your dependencies\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what Project Thoth is and the story behind it?\nWhat are some examples of the types of problems that can be introduced by mismanaged dependency versions?\nThe Python ecosystem has seen a number of dependency management tools introduced recently. What are the capabilities that Thoth offers that make it stand out?\n\nHow does it compare to e.g. pip, Poetry, pip-tools, etc.?\nHow do those other tools approach resolution of dependencies?\n\n\nCan you describe how Thoth is implemented?\n\nHow have the scope and design of the project evolved since it was started?\n\n\nWhat are the sources of information that it relies on for generating the possible solution space?\n\nWhat are the algorithms that it relies on for finding an optimal combination of packages?\n\n\nCan you describe how Thoth fits into the workflow of a developer while selecting a set of dependencies and keeping them up to date over the life of a project?\nWhat are the opportunities for expanding Thoth’s application to other language ecosystems?\nWhat are the interfaces available for extending or integrating with Thoth?\nWhat are the most interesting, innovative, or unexpected ways that you have seen Thoth used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Thoth?\nWhen is Thoth the wrong choice?\nWhat do you have planned for the future of Thoth?\n\nKeep In Touch\n\nLinkedIn\nWebsite\n\nPicks\n\nTobias\n\nBrass Against\n\n\nFridolin\n\nmicropipenv\n\n\n\nLinks\n\nRedhat\n\nEmerging Technologies Group\n\n\nProject Thoth\nThamos CLI\nPyPA Advisory Database\nProject2Vec\nThoth Prescriptions\nThoth: Egyptian God\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Building any software project is going to require relying on dependencies that you and your team didn’t write or maintain, and many of those will have dependencies of their own. This has led to a wide variety of potential and actual issues ranging from developer ergonomics to application security. In order to provide a higher degree of confidence in the optimal combinations of direct and transitive dependencies a team at Red Hat started Project Thoth. In this episode Fridolín Pokorný explains how the Thoth resolver uses multiple signals to find the best combination of dependency versions to ensure compatibility and avoid known security issues.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Fridolín Pokorný about Project Thoth and how it improves on existing dependency resolution approaches for computing the best set of versions in your Python projects.","date_published":"2022-06-15T09:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/97025915-af6b-487d-a524-db3c062a44a7.mp3","mime_type":"audio/mpeg","size_in_bytes":25981128,"duration_in_seconds":1891}]},{"id":"podlove-2022-05-30t20:17:50+00:00-e907ce900110b35","title":"Take A Deep Dive On How Code Completion Works And How To Customize It","url":"https://www.pythonpodcast.com/code-completion-deep-dive-episode-366","content_text":"Summary\nMost developers have encountered code completion systems and rely on them as part of their daily work. They allow you to stay in the flow of programming, but have you ever stopped to think about how they work? In this episode Meredydd Luff takes us behind the scenes to dig into the mechanics of code completion engines and how you can customize them to fit your particular use case.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Meredydd Luff about how code completion works and what it takes to build your own\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nMost programmers are familiar with the idea of code completion, but can you just give the elevator pitch to get us all on the same page?\nYou gave a presentation recently at PyCon about how to build a code completion system. What was your approach to identifying what fundamental concepts needed to be addressed and how to fit that lesson into the available time?\nIn the presentation you mentioned that you had built a more full-featured completion engine into Anvil. Can you describe what possessed you to build your own code completion tool?\n\nWhat are the core components required to build a completion engine?\nWhat are the benefits that can be realized by customizing the completion engine for a given language or task?\n\n\nCan you describe the feature set and implementation details of the full-fledged completion engine that is available in Anvil?\nBeyond the toy example, there are a number of considerations to address if you want to make the completion engine \"production grade\". Can you talk through some of the obvious edge cases and how to solve for them? (e.g. handling parsing of incomplete code)\nWhat are the inputs that you use to build up the list of candidate tokens for completion?\nOnce you have a functioning baseline for offering completions, what are some of the signals that you hook into for ranking suggestions?\nIn your presentation you leaned on the machinery available in the Python standard library. What are some of the ways that you might think about generalizing across languages vs. coupling to a given language?\nWhat design/architectural advice do you have for compartmentalizing logic in a full-featured completion engine?\nWhat are some of the complexities that become a factor when you are trying to scale across an entire code base?\nBeyond just being able to parse and process a body of code, there is also the question of integrating with the development environment. What are some of the challenges that get introduced when trying to access the appropriate set(s) of files and code through the editor interface(s)?\nWhat are the most interesting, innovative, or unexpected ways that you have seen code completion applied to developer experience?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on code completion for Anvil?\nWhen is code completion more effort than it’s worth?\nWhat do you have planned for the future of the Anvil code completion functionality?\n\nKeep In Touch\n\nLinkedIn\nmeredydd on GitHub\n@meredydd on Twitter\n\nPicks\n\nTobias\n\n\"Weird Al\" Yankovic\n\n\nMeredydd\n\nTimescaleDB\n\nData Engineering Podcast Episode\n\n\nPromscale\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nPyCon presentation about building a completion engine\nAnvil\n\nPodcast Episode\n\n\nNano\nLanguage Server Protocol\nJedi\n\nPodcast Episode\n\n\nSkulpt\nParser\nAbstract Syntax Tree\nOpenAPI\nGitHub Copilot\nHalting Problem\nParser Generator\nPython Language Grammar Definition\nLezer Parser Generator\nTree-sitter\nPyScript\nGrafana Tempo Tracing Service\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Most developers have encountered code completion systems and rely on them as part of their daily work. They allow you to stay in the flow of programming, but have you ever stopped to think about how they work? In this episode Meredydd Luff takes us behind the scenes to dig into the mechanics of code completion engines and how you can customize them to fit your particular use case.

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Interview

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Keep In Touch

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Picks

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Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Meredydd Luff about his experience building a custom code completion engine for the Anvil editor and the mechanics of how to build your own completion engine to keep your development environment flowing","date_published":"2022-05-30T16:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/4db8eee3-4b8e-4fb9-8c06-39ebcf63605d.mp3","mime_type":"audio/mpeg","size_in_bytes":52264150,"duration_in_seconds":3611}]},{"id":"podlove-2022-05-24t02:29:40+00:00-17f3c47b0119f8d","title":"Hunting Black Swans With Bees: Catching Up With The Inimitable Russell Keith-Magee","url":"https://www.pythonpodcast.com/beeware-revisited-episode-365","content_text":"Summary\nRussell Keith-Magee is an accomplished engineer and a fixture of the Python community. His work on the Beeware suite of projects is one of the most ambitious undertakings in the ecosystem and unfailingly forward-looking. With his recent transition to working for Anaconda he is now able to dedicate his full focus to the effort. In this episode he reflects on the journey that he has taken so far, how Beeware is helping to address some of the threats to Python’s long term viability, and how he envisions its future in light of the recent release of PyScript, an in-browser runtime for Python.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Russell Keith-Magee about the latest status of the Beeware project, the state of Python’s black swans, and how the PyScript project ties into his ambitions for world domination\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nFor anyone who hasn’t been graced with the BeeWare vision, can you give the elevator pitch of what it is and why it matters?\nAt PyCon US 2019 you presented a keynote about the various potential threats to the Python language community and its future viability. With the clarity of 3 years hindsight, how has the landscape shifted?\nWhat is PyScript and how does it fit into the venn diagram of BeeWare’s objectives and the portents of black swan events (and what is your involvement with it)?\n\nHow does it differ from the dozens of other \"Python in the browser\" and \"Python transpiled to Javascript\" projects that have sprouted over the years?\n\n\nNow that you have been granted the opportunity to dedicate your full attention to BeeWare and build a team to support it, what new potential does that unlock?\nWhat are the current areas of focus/challenges that you are spending your time on for the BeeWare project?\nWhat are some of the efforts in the BeeWare suite that proved to be dead-ends?\nWhat are the most interesting, innovative, or unexpected ways that you have seen the BeeWare suite/PyScript used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on BeeWare?\nWhen is BeeWare the wrong choice?\nWhat do you have planned for the future of BeeWare/PyScript/Python/world domination?\n\nKeep In Touch\n\nLinkedIn\nWebsite\n@freakboy3742 on Twitter\n\nPicks\n\nTobias\n\nJoby Gorillapod\n\n\nRussell\n\nPyScript\nThe Great TV Show\n\n\n\nLinks\n\nBlack Swans Episode\nBeeWare Episode\nBeeWare\nDjango\nCordova\nBlack Swan\nApple II\nAltair\nBriefcase\nWeb Assembly (WASM)\nGary Bernhardt\nPyScript\nPyodide\nToga\nKotlin\nSwift\nGaffer Tape\nRepl.it\nBrython\nTranscrypt\nPython Anywhere\nBatavia\nAnaconda\nConda\nVoc\nMaestral\nEddington GUI\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Russell Keith-Magee is an accomplished engineer and a fixture of the Python community. His work on the Beeware suite of projects is one of the most ambitious undertakings in the ecosystem and unfailingly forward-looking. With his recent transition to working for Anaconda he is now able to dedicate his full focus to the effort. In this episode he reflects on the journey that he has taken so far, how Beeware is helping to address some of the threats to Python’s long term viability, and how he envisions its future in light of the recent release of PyScript, an in-browser runtime for Python.

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Announcements

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Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Russell Keith-Magee about how his newfound ability to work on Beeware full time is shaping the future of the project, and how that will help to address some of Python's current shortcomings for sustained longevity","date_published":"2022-05-23T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/5028ade6-be6e-4815-a1f6-edf7f2bec8d2.mp3","mime_type":"audio/mpeg","size_in_bytes":44319344,"duration_in_seconds":3371}]},{"id":"podlove-2022-05-15t01:24:03+00:00-9fa1aaef71e1c46","title":"Take Control Of Your Digital Photos By Running Your Own Smart Library Manager With LibrePhotos","url":"https://www.pythonpodcast.com/librephotos-digital-photo-management-episode-364","content_text":"Summary\nDigital cameras and the widespread availability of smartphones has allowed us all to generate massive libraries of personal photographs. Unfortunately, now we are all left to our own devices of how to manage them. While cloud services such as iPhotos and Google Photos are convenient, they aren’t always affordable and they put your pictures under the control of large companies with their own agendas. LibrePhotos is an open source and self-hosted alternative to these services that puts you in control of your digital memories. In this episode the maintainer of LibrePhotos, Niaz Faridani-Rad, explains how he got involved with the project, the capabilities that it offers for managing your image library, and how to get your own instance set up to take back control of your pictures.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nThis episode is sponsored by Mergify. It’s an amazing tool to make you and your team way more productive with GitHub. Mergify is all about leveling up your pull requests with useful features that eliminate busy work. Automatic merges allow you define the conditions for acceptance and Mergify will take care of merging the pull request as soon as it’s ready. Automatic updates take care of merging your pull requests serially on top of each other, so there is no way to introduce a regression. With a merge queue you can merge your urgent pull request first, organize your Prs as you wish and Mergify will merge them in that order. Mergify’s backports feature will even copy the pull request into another branch once the pull request has been merged, shipping your bug fixes on multiple branches automatically. By saving time you and your team can focus on projects that matter. Mergify is coordinated with any CI and fully integrated into GitHub. They have a Startup Program that offers a 12 months credit to leverage Mergify (up to $21,000 of value). Start saving time; visit pythonpodcast.com/mergify today to sign up for a demo and get started! Or just click the link in the show notes.\nYour host as usual is Tobias Macey and today I’m interviewing Niaz Faridani-Rad about LibrePhotos, an open source, self-hosted application for managing your personal photo collection\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what LibrePhotos is and the story behind it?\nWhat are the core objectives of the project?\n\nWhat kind of users are you focused on?\n\n\nWhat are some of the major features of LibrePhotos?\nThere are a number of open source and commercial options for different photo oriented use cases. What are the main capabilities that influence someone’s decision to use one over the other?\nMany people’s baseline expectations will be around services such as Google Photos or iPhotos. What are some of the challenges that you face in trying to provide a comparable experience?\n\nOne of the features that users rely on with these services is backup/disaster recovery of their photo library. What is the recommended approach for users of LibrePhotos?\n\n\nCan you describe how LibrePhotos is architected?\n\nHow have the design and goals evolved since you first started working on it?\n\n\nHow have recent advances in machine learning algorithms and related tooling improved the availability and quality of advanced features in LibrePhotos?\n\nHow much improvement of accuracy in face/object recognition do you see as users invest in cataloging and organizing their collections?\nIs there a minimum quantity of images/iindividual people that are necessary to start using the ML powered features?\n\n\nWhat kinds of storage locations are supported?\nWhat are the interfaces available for extending/enhancing/integrating with LibrePhotos?\nWhat are the most interesting, innovative, or unexpected ways that you have seen LibrePhotos used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on LibrePhotos?\nWhen is LibrePhotos the wrong choice?\nWhat do you have planned for the future of LibrePhotos?\n\nKeep In Touch\n\nderneuere on GitHub\n@der_neuere on Twitter\nWebsite\nLinkedIn\n\nPicks\n\nTobias\n\nUncharted movie\n\n\nNiaz\n\nSteam Deck\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nLibrePhotos\nSelf-hosted Sub-Reddit\nOwnPhotos\nGoogle Photos\nGoogle Takeout\nDigikam\nx265\nHEIC Files\nRAW Image Format\nImageMagick\nPanorama Photograph\nLytro light field cameras\nrq asynchronous task library\nTypescript\nRedux Toolkit\nMobileNet v3\nDLib\nARM Processor\nDocker Compose\nLibrePhotos Comparison Page\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Digital cameras and the widespread availability of smartphones has allowed us all to generate massive libraries of personal photographs. Unfortunately, now we are all left to our own devices of how to manage them. While cloud services such as iPhotos and Google Photos are convenient, they aren’t always affordable and they put your pictures under the control of large companies with their own agendas. LibrePhotos is an open source and self-hosted alternative to these services that puts you in control of your digital memories. In this episode the maintainer of LibrePhotos, Niaz Faridani-Rad, explains how he got involved with the project, the capabilities that it offers for managing your image library, and how to get your own instance set up to take back control of your pictures.

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Announcements

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Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Niaz Faridani-Rad about the open source LibrePhotos project and how you can use it to manage your personal photo library without having to sacrifice useful features like facial recognition and automatic album creation","date_published":"2022-05-15T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/30f7d4ac-1d9c-46ad-827d-847169c4e985.mp3","mime_type":"audio/mpeg","size_in_bytes":31563006,"duration_in_seconds":2714}]},{"id":"podlove-2022-05-10t00:44:21+00:00-6c94621522056b9","title":"Making Investment Data Easy To Access And Analyze With The OpenBB Terminal","url":"https://www.pythonpodcast.com/openbb-terminal-investment-data-framework-episode-363","content_text":"Summary\nInvesting effectively is largely a game of information access and analysis. This can involve a substantial amount of research and time spent on finding, validating, and acquiring different information sources. In order to reduce the barrier to entry and provide a powerful framework for amateur and professional investors alike Didier Rodrigues Lopes created the OpenBB Terminal. In this episode he explains how a pandemic project that started as an experiment has led to him founding a new company and dedicating his time to growing and improving the project and its community.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Didier Rodrigues Lopes about the OpenBB Terminal, a modern Python-based integrated environment for investment research\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what OpenBB is and the story behind it?\n\nWhat is the problem that you are trying to address by creating the OpenBB project and providing it as open source?\n\n\nWhat are some of the use cases where someone might need to use this project?\nThe elephant in the room for financial data research is the Bloomberg Terminal. What are the other tools or services available for that purpose?\n\nWhat are the differentiating features of the OpenBB Terminal?\n\n\nCan you describe how the OpenBB Terminal is implemented?\n\nHow have the design and goals/scope of the project changed since you started working on it?\n\n\nCan you describe a typical workflow for someone who is using the OpenBB Terminal?\n\nHow have you approached the user experience design, and what are you optimizing for?\nWhat kinds of utilities do you offer beyond raw data access?\n\n\nWhat are some examples of data sources that you rely on?\n\nWhat is involved in integrating a new data source?\n\n\nWhat are the extension points and integration capabilities for expanding the functionality of the tool?\nWhat are the most interesting, innovative, or unexpected ways that you have seen OpenBB Terminal used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on OpenBB Terminal?\nWhen is OpenBB Terminal the wrong choice?\nWhat do you have planned for the future of OpenBB Terminal?\n\nKeep In Touch\n\nDidierRLopes on GitHub\nLinkedIn\n@didier_lopes on Twitter\n\nPicks\n\nTobias\n\nVikings: Valhalla show on Netflix\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nOpenBB\nMatlab\nPapermill\nBloomberg Terminal\nRobinhood\nCoinbase\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Investing effectively is largely a game of information access and analysis. This can involve a substantial amount of research and time spent on finding, validating, and acquiring different information sources. In order to reduce the barrier to entry and provide a powerful framework for amateur and professional investors alike Didier Rodrigues Lopes created the OpenBB Terminal. In this episode he explains how a pandemic project that started as an experiment has led to him founding a new company and dedicating his time to growing and improving the project and its community.

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Announcements

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Interview

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Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview with Didier Rogrigues Lopes about his work on the open source OpenBB Terminal and how it lowers the barrier to entry for amateur and professional investors to access and analyze investment data","date_published":"2022-05-09T20:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/84c4efc1-7e06-49e8-a489-eed76adf0331.mp3","mime_type":"audio/mpeg","size_in_bytes":36713285,"duration_in_seconds":2833}]},{"id":"podlove-2022-05-02t10:08:36+00:00-4d03589972edb2b","title":"Accelerate Your Machine Learning Experimentation With Automatic Checkpoints Using FLOR","url":"https://www.pythonpodcast.com/flor-machine-learning-experiment-episode-362","content_text":"Summary\nThe experimentation phase of building a machine learning model requires a lot of trial and error. One of the limiting factors of how many experiments you can try is the length of time required to train the model which can be on the order of days or weeks. To reduce the time required to test different iterations Rolando Garcia Sanchez created FLOR which is a library that automatically checkpoints training epochs and instruments your code so that you can bypass early training cycles when you want to explore a different path in your algorithm. In this episode he explains how the tool works to speed up your experimentation phase and how to get started with it.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Rolando Garcia about FLOR, a suite of machine learning tools for hindsight logging that lets you speed up model experimentation by checkpointing training data\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what FLOR is and the story behind it?\nWhat is the core problem that you are trying to solve for with FLOR?\n\nWhat are the fundamental challenges in model training and experimentation that make it necessary?\nHow do machine learning reasearchers and engineers address this problem in the absence of something like FLOR?\n\n\nCan you describe how FLOR is implemented?\n\nWhat were the core engineering problems that you had to solve for while building it?\n\n\nWhat is the workflow for integrating FLOR into your model development process?\nWhat information are you capturing in the log structures and epoch checkpoints?\n\nHow does FLOR use that data to prime the model training to a given state when backtracking and trying a different approach?\n\n\nHow does the presence of FLOR change the costs of ML experimentation and what is the long-range impact of that shift?\n\nOnce a model has been trained and optimized, what is the long-term utility of FLOR?\n\n\nWhat are the opportunities for supporting e.g. Horovod for distributed training of large models or with large datasets?\nWhat does the maintenance process for research-oriented OSS projects look like?\nWhat are the most interesting, innovative, or unexpected ways that you have seen FLOR used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on FLOR?\nWhen is FLOR the wrong choice?\nWhat do you have planned for the future of FLOR?\n\nKeep In Touch\n\nrlnsanz on GitHub\n@rogarcia_sanz on Twitter\n\nPicks\n\nTobias\n\nThe Batman\n\n\nRolando\n\nSeverance\nGitHub Codespaces\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nFLOR\nUC Berkeley\nJoe Hellerstein\nMLOps\n\nData Engineering Podcast Episode\n\n\nRISE Lab\nAMP Lab\nClipper Model Serving\nGround Data Context Service\nContext: The Missing Piece Of The Machine Learning Lifecycle\nAirflow\nCopy on write\nASTor\nGreen Tree Snakes: Python AST Documentation\nMLFlow\nAmazon Sagemaker\nCloudpickle\nHorovod\n\nPodcast Episode\n\n\nRay Anyscale\nPyTorch\nTensorflow\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

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The experimentation phase of building a machine learning model requires a lot of trial and error. One of the limiting factors of how many experiments you can try is the length of time required to train the model which can be on the order of days or weeks. To reduce the time required to test different iterations Rolando Garcia Sanchez created FLOR which is a library that automatically checkpoints training epochs and instruments your code so that you can bypass early training cycles when you want to explore a different path in your algorithm. In this episode he explains how the tool works to speed up your experimentation phase and how to get started with it.

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Announcements

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Interview

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Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Rolando Garcia Sanchez about how the FLOR library instruments your machine learning code to give you easy checkpointing and state recovery so that you don't have to re-run the parts of your model training that haven't changed between experiments.","date_published":"2022-05-02T06:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/dca460ce-6624-48ee-b7b5-7e3f01bfe4bd.mp3","mime_type":"audio/mpeg","size_in_bytes":32389934,"duration_in_seconds":2791}]},{"id":"podlove-2022-04-25t19:16:46+00:00-6edc3f7e9092181","title":"Automatically Enforce Software Structures With Powerful Code Modifications Powered By LibCST","url":"https://www.pythonpodcast.com/libcst-automated-code-modification-episode-361","content_text":"Summary\nProgrammers love to automate tedious processes, including refactoring your code. In order to support the creation of code modifications for your Python projects Jimmy Lai created LibCST. It provides a richly typed and high level API for creating and manipulating concrete syntax trees of your source code. In this episode Jimmy Lai and Zsolt Dollenstein explain how it works, some of the linting and automatic code modification utilities that you can build with it and how to get started with using it to maintain your own Python projects.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Zsolt Dollenstein and Jimmy Lai about LibCST, a concrete syntax tree parser and serializer library for Python\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what LibCST is and the story behind it?\nHow does a concrete syntax tree differ from an abstract syntax tree?\n\nWhat are some of the situations where the preservation of the exact structure is necessary?\n\n\nThere are a few other libraries in Python for creating concrete syntax trees. What was missing in the available options that made it necessary to create LibCST?\nWhat are the use cases that LibCST is focused on supporting\nCan you describe how LibCST is implemented?\n\nHow have the design and goals of the project changed or evolved since you started working on it?\n\n\nHow might I use LibCST for something like restructuring a set of modules to move a function definition while maintaining proper imports?\n\nHow do the capabilities of LibCST for codemodding compare to the Rope framework?\n\n\nWhat are some other workflows that someone might build with LibCST?\nWhat are some of the ways that LibCST is being used in your own work?\nWhat are the most interesting, innovative, or unexpected ways that you have seen LibCST used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on LibCST?\nWhen is LibCST the wrong choice?\nWhat do you have planned for the future of LibCST?\n\nKeep In Touch\n\nZsolt\n\nzsol on GitHub\nLinkedIn\n\n\nJimmy\n\njimmylai on GitHub\nLinkedIn\n\n\n\nPicks\n\nTobias\n\nOsprey Manta Backpack\n\n\nZsolt\n\nAutotransform\nGlean\n\n\nJimmy\n\nPaying down technical debt\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nLibCST\nCarta\nlib2to3\nAbstract Syntax Tree\nConcrete Syntax Tree\nPyre\nParso\nCython\n\nPodcast Episode\n\n\nmypyc\nRope\nFlake8\n\nPodcast Episode\n\n\nPylint\nESLint\nFixit\nMonkeyType\n\nPodcast Episode\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

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Programmers love to automate tedious processes, including refactoring your code. In order to support the creation of code modifications for your Python projects Jimmy Lai created LibCST. It provides a richly typed and high level API for creating and manipulating concrete syntax trees of your source code. In this episode Jimmy Lai and Zsolt Dollenstein explain how it works, some of the linting and automatic code modification utilities that you can build with it and how to get started with using it to maintain your own Python projects.

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Jimmy Lai and Zsolt Dollenstein about how LibCST simplifies the work of building automated code modification programs for linting, refactoring, and more.","date_published":"2022-04-25T15:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/5bb3be78-dff3-4247-9824-9b49f3425cb5.mp3","mime_type":"audio/mpeg","size_in_bytes":43358561,"duration_in_seconds":3407}]},{"id":"podlove-2022-04-18t22:07:36+00:00-005c3b3b42bc67d","title":"Cloud Native Networking For Developers With The Gloo Platform","url":"https://www.pythonpodcast.com/solo-gloo-cloud-native-networking-episode-360","content_text":"Summary\nCommunication is a fundamental requirement for any program or application. As the friction involved in deploying code has gone down, the motivation for architecting your system as microservices goes up. This shifts the communication patterns in your software from function calls to network calls. In this episode Idit Levine explains how the Gloo platform that she and her team at Solo have created makes it easier for you to configure and monitor the network topologies for your microservice environments. She also discusses what developers need to know about networking in cloud native environments and how a combination of API gateways and service mesh technologies allow you to more rapidly iterate on your systems.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Idit Levine about what developers need to know about service-oriented networking and her work at Solo on the Gloo project\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what Solo is and the story behind it?\nHow much should developers need to know about the ways that their applications and services are communicating?\nWhat is the current state of networking for applications across physical, cloud, and containerized environments?\nHow do service mesh features influence the architectural decisions that software teams make while building their applications?\n\nWhat operational capabilities do they unlock?\n\n\nWhat are the aspects of application networking that are simplified or enhanced by service mesh platforms?\n\nIn what ways has service mesh introduced new complexity to operating software systems?\n\n\nHow can developers mirror the network topologies for production environments while working on new features?\nWhat are the most interesting, innovative, or unexpected ways that you have seen Gloo used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Gloo?\nWhen is Gloo the wrong choice?\nWhat do you have planned for the future of Gloo?\n\nKeep In Touch\n\nLinkedIn\n@Idit_Levine on Twitter\n\nPicks\n\nTobias\n\nShadow and Bone on Netflix\n\n\nIdit\n\nElizabeth Holmes HBO Documentary\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nSolo\nComputational Biology\nMicroservices\nKubernetes\nService Mesh\nIstio\nLinkerD\nEnvoy Proxy\nAPI Gateway\nCRD == Custom Resource Definition\nGloo Edge\nBazel Build System\nGraphQL\nmTLS\nGitOps\nDagger\nWASM == Web Assembly\nKubernetes Gateway API\nConsul Connect\neBPF\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

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Communication is a fundamental requirement for any program or application. As the friction involved in deploying code has gone down, the motivation for architecting your system as microservices goes up. This shifts the communication patterns in your software from function calls to network calls. In this episode Idit Levine explains how the Gloo platform that she and her team at Solo have created makes it easier for you to configure and monitor the network topologies for your microservice environments. She also discusses what developers need to know about networking in cloud native environments and how a combination of API gateways and service mesh technologies allow you to more rapidly iterate on your systems.

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Interview

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Keep In Touch

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Picks

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Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Idit Levine about how the open source Gloo platform simplifies the adoption of cloud-native networking for your microservice environments","date_published":"2022-04-18T20:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/16f3dfa2-73e4-4595-ae4e-62e9330cea34.mp3","mime_type":"audio/mpeg","size_in_bytes":39435077,"duration_in_seconds":3033}]},{"id":"podlove-2022-04-09t22:02:24+00:00-d65ed39321404fc","title":"Accelerate And Simplify Cloud Native Development For Kubernetes Environments With Gefyra","url":"https://www.pythonpodcast.com/gefyra-cloud-native-development-episode-359","content_text":"Summary\nCloud native architectures have been gaining prominence for the past few years due to the rising popularity of Kubernetes. This introduces new complications to development workflows due to the need to integrate with multiple services as you build new components for your production systems. In order to reduce the friction involved in developing applications for cloud native environments Michael Schilonka created Gefyra. In this episode he explains how it connects your local machine to a running Kubernetes environment so that you can rapidly iterate on your software in the context of the whole system. He also shares how the Django Hurricane plugin lets your applications work closely with the Kubernetes process model.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nSo now your modern data stack is set up. How is everyone going to find the data they need, and understand it? Select Star is a data discovery platform that automatically analyzes & documents your data. For every table in Select Star, you can find out where the data originated, which dashboards are built on top of it, who’s using it in the company, and how they’re using it, all the way down to the SQL queries. Best of all, it’s simple to set up, and easy for both engineering and operations teams to use. With Select Star’s data catalog, a single source of truth for your data is built in minutes, even across thousands of datasets. Try it out for free and double the length of your free trial today at pythonpodcast.com/selectstar. You’ll also get a swag package when you continue on a paid plan.\nYour host as usual is Tobias Macey and today I’m interviewing Michael Schilonka about Gefyra and what is involved with developing applications for Kubernetes environments\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what Gefyra is and the story behind it?\nWhat are the challenges that Kubernetes introduces to the development process?\n\nWhat are some of the strategies that developers might use for developing and testing applications that are deployed to Kubernetes environments?\n\n\nWhat are the use cases that Gefyra is focused on enabling?\n\nWhat are some of the other tools or platforms that Gefyra might replace or supplement?\n\n\nWhat are the services that need to be present in the K8s cluster to enable Gefyra’s functionality?\nCan you describe how Gefyra is implemented?\n\nHow have the design and goals of the project changed since you first started working on it?\n\n\nWhat is the process for getting Gefyra set up between a K8s cluster and a developer’s laptop?\nCan you describe what the developer’s workflow looks like when using Gefyra?\n\nHow do you avoid collisions/resource contention among a team of developers who are working on the same project?\n\n\nWhat are some of the ways that developing for Kubernetes influences the architectural and design decisions for a project?\nWhat are some of the additional practices or systems that you have found to be beneficial for accelerating development in cloud-native environments?\nWhat are the most interesting, innovative, or unexpected ways that you have seen Gefyra used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Gefyra?\nWhen is Gefyra the wrong choice?\nWhat do you have planned for the future of Gefyra?\n\nKeep In Touch\n\nLinkedIn\nSchille on GitHub\n\nPicks\n\nTobias\n\nkubernetes.el – Kubernetes interface for Emacs\n\n\nMichael\n\nIt’s fermentation friday, perfect for baking a sourdough bread or brewing beer\nTwo of my favorit YouTube channels Kurzgesagt – In a Nutshell and LockPickingLawyer\nFor entrepreneurial spirits: Reddit community research with (GummySearch)[https://gummysearch.com/]?utm_source=rss&utm_medium=rss\n\n\n\nLinks\n\nKopf framework\nPyOxidizer\nTuna\nWireguard-go\nhttps://k3d.io/?utm_source=rss&utm_medium=rss\nkind\nDjango Hurricane\nBlueshoe\nDjango\nKubernetes\nK3d\nTelepresence\nUnikube\nSidecar Pattern\nDocker-compose\nKubernetes Patterns book\n\nO’Reilly Platform\nAmazon (affiliate link)\n\n\nCodeZero\nCoreDNS\nNginx\nCookiecutter\nTornado\n\nPodcast Episode\n\n\nuWSGI\n\nPodcast Episode\n\n\n12 Factor App\nPycloak\nKeycloak\nKubernetes Operator\nKubernetes CRD (Custom Resource Definition\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n\n\n","content_html":"

Summary

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Cloud native architectures have been gaining prominence for the past few years due to the rising popularity of Kubernetes. This introduces new complications to development workflows due to the need to integrate with multiple services as you build new components for your production systems. In order to reduce the friction involved in developing applications for cloud native environments Michael Schilonka created Gefyra. In this episode he explains how it connects your local machine to a running Kubernetes environment so that you can rapidly iterate on your software in the context of the whole system. He also shares how the Django Hurricane plugin lets your applications work closely with the Kubernetes process model.

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Interview

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Keep In Touch

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Picks

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Michael Schilonka about cloud native development for Python engineers with the Gefyra and Django Hurricane open source projects","date_published":"2022-04-10T20:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/844eb1fc-2b06-4e7a-bccb-d3df9b4bc9fc.mp3","mime_type":"audio/mpeg","size_in_bytes":32114842,"duration_in_seconds":2294}]},{"id":"podlove-2022-03-27t18:32:00+00:00-7b2ecbac89b8f35","title":"Building A Community And Technology Stack For Scalable Big Data Geoscience At Pangeo","url":"https://www.pythonpodcast.com/pangeo-big-data-geoscience-episode-358","content_text":"Summary\nScience is founded on the collection and analysis of data. For disciplines that rely on data about the earth the ability to simulate and generate that data has been growing faster than the tools for analysis of that data can keep up with. In order to help scale that capacity for everyone working in geosciences the Pangeo project compiled a reference stack that combines powerful tools into an out-of-the-box solution for researchers to be productive in short order. In this episode Ryan Abernathy and Joe Hamman explain what the Pangeo project really is, how they have integrated a combination of XArray, Dask, and Jupyter to power these analytical workflows, and how it has helped to accelerate research on multidimensional geospatial datasets.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nSo now your modern data stack is set up. How is everyone going to find the data they need, and understand it? Select Star is a data discovery platform that automatically analyzes & documents your data. For every table in Select Star, you can find out where the data originated, which dashboards are built on top of it, who’s using it in the company, and how they’re using it, all the way down to the SQL queries. Best of all, it’s simple to set up, and easy for both engineering and operations teams to use. With Select Star’s data catalog, a single source of truth for your data is built in minutes, even across thousands of datasets. Try it out for free and double the length of your free trial today at pythonpodcast.com/selectstar. You’ll also get a swag package when you continue on a paid plan.\nYour host as usual is Tobias Macey and today I’m interviewing Ryan Abernathy and Joe Hamman about Pangeo, a community platform for Big Data geoscience\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what Pangeo is and the story behind it?\n\nWhat is your role in the project/community and how did you get involved?\n\n\nWhat are the goals of the project and community?\n\nWhat are the areas of effort and how are they organized?\n\n\nWhat are the scientific domains that Pangeo is focused on supporting?\n\nWhat are the primary challenges associated with data management and analysis in these scientific communities?\n\n\nWhat are the forms that these data take and how have they been evolving? (e.g. formats/sources)\nWhat are some of the challenges introduced by the widespread adoption of cloud resources and the associated architectural patterns?\nCan you describe the technical components that fall under the Pangeo umbrella?\n\nHow do they come together to form a functional workflow for geo sciences?\n\n\nHow has the scope of the Pangeo project changed or evolved since it started?\nWhat are the most interesting, innovative, or unexpected ways that you have seen Pangeo used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Pangeo?\nWhen is Pangeo the wrong choice?\nWhat do you have planned for the future of Pangeo?\n\nKeep In Touch\n\nJoe\n\n@HammanHydro on Twitter\n\n\nRyan\n\n@rabernat on Twitter\nrabernat on GitHub\nWebsite\n\n\n\nPicks\n\nTobias\n\nMountain Biking\n\n\nRyan\n\nKlara And The Sun by Kazuo Ishiguro\n\n\nJoe\n\nRange by David Epstein\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nPangeo\nPangeo Forge\nCarbonPlan\nM2LInES\nLEAP\nColumbia University\nXArray\nMIT\nMatLab\nPHP\nRuby\nJava\nNumPy\nSciPy\nMatplotlib\nC\nFortran\nPerl\nDask\n\nData Engineering Podcast Episode\n\n\nJupyter\nIDL\nHDF5\nUnidata\nNetCDF\nCF Metadata Conventions\nIntake\n\nPodcast Episode\n\n\nFSSpec\nParquet\n\nData Engineering Podcast Episode\n\n\nZarr\nData Engineering Podcast\nPangeo Forge\nAirbyte\n\nData Engineering Podcast Episode\n\n\nFivetran\n\nData Engineering Podcast Episode\n\n\nStitch\nTileDB\n\nData Engineering Podcast Episode\n\n\nPythia\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

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Science is founded on the collection and analysis of data. For disciplines that rely on data about the earth the ability to simulate and generate that data has been growing faster than the tools for analysis of that data can keep up with. In order to help scale that capacity for everyone working in geosciences the Pangeo project compiled a reference stack that combines powerful tools into an out-of-the-box solution for researchers to be productive in short order. In this episode Ryan Abernathy and Joe Hamman explain what the Pangeo project really is, how they have integrated a combination of XArray, Dask, and Jupyter to power these analytical workflows, and how it has helped to accelerate research on multidimensional geospatial datasets.

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Interview

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Keep In Touch

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Picks

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Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Ryan Abernathy and Joe Hamman about how the Pangeo project has grown a community and technology stack for empowering scientific research and analysis for big data in the geosciences.","date_published":"2022-03-27T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/43352771-b89b-4dcc-a477-30a759106089.mp3","mime_type":"audio/mpeg","size_in_bytes":42133600,"duration_in_seconds":3128}]},{"id":"podlove-2022-03-20t20:38:51+00:00-11f22fce7b08f0b","title":"Automating Application Lifecycles For Developer Happiness At Wayfair","url":"https://www.pythonpodcast.com/wayfair-application-lifecycle-automation-episode-357","content_text":"Summary\nA common piece of advice when starting anything new is to \"begin with the end in mind\". In order to help the engineers at Wayfair manage the complete lifecycle of their applications Joshua Woodward runs a team that provides tooling and assistance along every step of the journey. In this episode he shares some of the lessons and tactics that they have developed while assisting other engineering teams with starting, deploying, and sunsetting projects. This is an interesting look at the inner workings of large organizations and how they invest in the scaffolding that supports their myriad efforts.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nSo now your modern data stack is set up. How is everyone going to find the data they need, and understand it? Select Star is a data discovery platform that automatically analyzes & documents your data. For every table in Select Star, you can find out where the data originated, which dashboards are built on top of it, who’s using it in the company, and how they’re using it, all the way down to the SQL queries. Best of all, it’s simple to set up, and easy for both engineering and operations teams to use. With Select Star’s data catalog, a single source of truth for your data is built in minutes, even across thousands of datasets. Try it out for free and double the length of your free trial today at pythonpodcast.com/selectstar. You’ll also get a swag package when you continue on a paid plan.\nYour host as usual is Tobias Macey and today I’m interviewing Joshua Woodward about how the application lifecycle team at Wayfair uses Python to\n\nInterview\n\nIntroductions\n\nJosh Woodward, for the past year have been managing the application lifecycle team at Wayfair. Prior to that, IC on python platforms team. Embed with teams looking to decouple from monolith. See pain points first hand.\n\n\nHow did you get introduced to Python?\n\nHigh school physics class, TI84 Calculator, friend wrote a program to solve vector problems, I thought it was amazing.\nUsed TI-Basic to solve specific physics problems for me. (Give fixed inputs, run through equation, get outputs)\nApproaching college, thinking about student loans.\nHeard about python and decided to give it a shot.\nWrote program to simulate various payback / interest scenarios.\nWent to college for ME, switched to SE when I found out my dorm neighbors were using python to draw cool images with python + turtle\n\n\nCan you describe what the role of the application lifecycle team is and the story behind it?\n\nStory behind it:\n\nAround 2018, in a state where we had deploy congestion, challenging to iterate and ship changes. tech org invested in containerization and decoupling to directly combat this problem. Teams incentiviced to decouple.\nWhile on python platforms, the team had already been experimenting with code templating.\nStandard cookiecutter template for flask apps.\nWayfair experimenting with Kubernetes late 2017.\nSpent 1 year embedding with 4 different teams to help knowledge transfer re: k8s, containers, application setup, python best practices, testing, linting, etc – through that we got a lot of great feedback on our tooling.\nTook senior engineers weeks to get something setup.\n\nKnow who to contact, click the right buttons, file the right ticket\n\n\nApproach: Counted manual steps. Something like 60 distinct / atomic activities that had to be performed to get a \"hello world\" response from a basic flask app in production.\nFocus on reduce manual steps\nReleased product (Mamba, on theme of snakes)\nInitially, supporting one main user story.\nUser story: \"As an engineer, I would like to create a production ready application in 10 minutes so that I can have a reliable and standardized application setup that follows best practices.\"\ngrew out of python platforms, created own team with own scope, that was about 1.5 years ago.\n\n\n\n\nWhat is your team’s scope now?\n\nTeam Scope is to facilitate the creation, maintenance, and decommissioning of decoupled applications at Wayfair.\n\n\nWhat are the interfaces that your team has to the rest of the organization?\n\nPeople Interfaces:\nWe value getting feedback on our work to build strong products.\nMake assumptions, Willing to be wrong. Validate assumptions with customers.\nSoftware Interfaces:\nfor mamba, CLI at first\nBackstage (open sourced from spotify)\nLots of Github\n\n\nWhat is your method of determining what projects to work on?\n\n(See above). Known pain points. Intuition, Free day fridays. Being comfortable taking risk (using friday time). Vet solution with customers.\nHow do you measure the impact of your work on the rest of the organization?\n\nWe don’t force use of our products. Adoption of tooling.\n\nNumber of microservices being spun up.\nNumber of automated pull requests being created, merged.\n\n\nDORA metrics throughput (deployment frequency, lead time for changes) and stability (change failure rate, mean time to recovery)\n\n\n\n\nWhat is the role of Python in your work?\n\nwe use it and love it!\n\nexisting skillset from incubation phase within python platforms\n\n\nright tool for the job\n\nlightweight automation\nhitting lots of APIs\ndefine lots of user facing specifications (json, yaml)\npydantic has been great for creating descriptive, human and machine specifications.\n\n\nopen source (we rely on it, we also have some presence)\n\ncookiecutter -> columbo\ngitpython -> pygitops\n\n\n\n\nCan you tell me more about your application creation solution. Who can use it, and what does it actually do?\n\nWritten in python, though it templates out code for any language.\nRuns automation to onboard an application to production\n\ngit repo, build pipeline, calling out to various APIs to signal a new app is present\n\n\nWayfair has a variety of applications (python, java, .net, php, javascript, some go)\nTeam interested in integrating with our solution will create a github repository containing 1..* cookiecutter template(s)\nProvide a specification for what questions to ask users.\n\nLimitation with cookiecutter where the approach to ask questions isn’t dynamic. lack of validation.\nPat Lannigan -> Columbo (open sourced). Python DSL to describe the set of questions to ask users.\npython fastapi application will have a completely different set of questions than a java library for example.\n\n\n\n\nYou had mentioned that another part of your team scope is to facilitate the maintenance of applications. Can you tell me more about that?\n\nReduce engineering toil around keeping applications up to date.\nAverage engineer owns several, dozens of repos\nCreate automated pull requests:\n\nVersioned dependencies (Renovate)\nPropagating platform changes (Gator)\nEx1: python apps use \"black\" to format code and our python platform team would like to prescribe a line length. Our tooling can be used to declare desired changes. yaml specification -> pr automation at scale.\nEx2: shared library, new version released, breaking interface change. Code instructions for performing AST manipulation and resolving breaking change for people.\nShift from: \"We need you to do this\", \"I am proactively letting you know that something needs to change, and I also made the change for you!\"\n\n\n\n\nHow do you actually go about creating automated pull requests?\n\nmanual steps would involve cloning, checking out feature branch, applying code changes, staging / committing, pushing up branch, creating the PR\ngitpython is an existing and extremely powerful tool, but its api is fairly involved and (by design) doesn’t provide the type of high level abstractions that we need.\ncreated pygitops (open sourced), built completely on top of gitpython\nhigh level abstractions for the workflow I described.\ncoolest / most pythonic part about it is the \"feature branch\" context manager.\ncode changes are made in the context of a feature branch\nwhen you intentionally or accidentally leave the context of a feature branch, we want certain things to be true (default / main branch, clean workdir, no unstaged changes)\nwhen writing PR automation, don’t have to worry about this!\n\n\nCan you describe some of the more technical details about how your change propagation system (Gator) works?\n\nheavily inspired by kubernetes resource model (resources are defined via a declarative specification)\nKubernetes itself ships with resources that implement behaviors of common resources (pods, services, etc)\nGator’s execution model is broken up into two parts:\n\nwhat repos to act on (Source)\nwhat are the changes that need to be applied. (Output)\n\n\nEx: Source to proxy github search. write github search query to get back list of repos\nOutput to scan a repo for regex pattern at specified paths and replace with some fixed term. Very popular, engineers love find and replace.\n\n\nWhat are the most interesting, innovative, or unexpected ways that you have seen mamba / gator used?\n\nresource model of gator supports the idea of we don’t know, what we don’t know\nreference k8s, CRDs, resource model.\ncontainer execution\nlog4j identification and remidiation\n\nautomate some of the work for identifying vulnerabilities\njava platform team was able to use java native tooling in the environment of their choosing to identify vulnerable apps.\n\n\n\n\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on application lifecycle concerns?\nWhat do you have planned for the future of application lifecycle management/developer experience improvements at Wayfair?\n\nHope to start open sourcing interesting aspects of our change propagation tool (Gator)\nAs someone who maintains many open source projects, or even at the enterprise level, we think that some of our patterns and approaches can be shared! yaml -> code changes\n\n\n\nKeep In Touch\n\nEmail\nGithub\nLinkedin\n\nPicks\n\nTobias\n\nNocciolata hazelnut spread\n\n\nJoshua\n\nCities Skylines Game\nCities Skylines – Cities Planner Plays: Verde Beach\n\n\n\nLinks\n\npygitops\ncolumbo\nbackstage\nrenovate\nDORA metrics\nTI-84 Calculator\nTI BASIC\nWayfair Python Platforms Team Podcast Episode\nPydantic\n\nPodcast Episode\n\n\nHelm\nPyUp\nGitPython\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

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A common piece of advice when starting anything new is to "begin with the end in mind". In order to help the engineers at Wayfair manage the complete lifecycle of their applications Joshua Woodward runs a team that provides tooling and assistance along every step of the journey. In this episode he shares some of the lessons and tactics that they have developed while assisting other engineering teams with starting, deploying, and sunsetting projects. This is an interesting look at the inner workings of large organizations and how they invest in the scaffolding that supports their myriad efforts.

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Joshua Woodward about the work that he and the application lifecycle team are doing at Wayfair to accelerate the speed of application delivery and use automation to remove obstacles.","date_published":"2022-03-20T16:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/b4306afb-d678-4df0-a832-3c5c73c1c7b1.mp3","mime_type":"audio/mpeg","size_in_bytes":35990516,"duration_in_seconds":2771}]},{"id":"podlove-2022-03-14t00:58:45+00:00-d59b887c20ffc3b","title":"Run Your Applications Reliably On Kubernetes Without Losing Sleep With Robusta","url":"https://www.pythonpodcast.com/robusta-kubernetes-application-maintenance-episode-356","content_text":"Summary\nKubernetes is a framework that aims to simplify the work of running applications in production, but it forces you to adopt new patterns for debugging and resolving issues in your systems. Robusta is aimed at making that a more pleasant experience for developers and operators through pre-built automations, easy debugging, and a simple means of creating your own event-based workflows to find, fix, and alert on errors in production. In this episode Natan Yellin explains how the project got started, how it is architected and tested, and how you can start using it today to keep your Python projects running reliably.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nSo now your modern data stack is set up. How is everyone going to find the data they need, and understand it? Select Star is a data discovery platform that automatically analyzes & documents your data. For every table in Select Star, you can find out where the data originated, which dashboards are built on top of it, who’s using it in the company, and how they’re using it, all the way down to the SQL queries. Best of all, it’s simple to set up, and easy for both engineering and operations teams to use. With Select Star’s data catalog, a single source of truth for your data is built in minutes, even across thousands of datasets. Try it out for free and double the length of your free trial today at pythonpodcast.com/selectstar. You’ll also get a swag package when you continue on a paid plan.\nYour host as usual is Tobias Macey and today I’m interviewing Natan Yellin about Robusta,\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what Robusta is and the story behind it?\nWhat are some of the challenges that teams face when running their systems in Kubernetes?\n\nHow does Robusta help address those difficulties?\n\n\nHow does Robusta compare to e.g. Rookout?\nWhat are some of the ways that Robusta is able to provide specific insights for Python applications?\nCan you describe how Robusta is implemented?\n\nWhat are some of the most challenging engineering tasks that you have had to work through while building Robusta?\nHow have the capabilities and components evolved from when you started working on it?\n\n\nWhat is the workflow for integrating Robusta into a Kubernetes environment and a team’s maintenance processes?\nWhat are some examples of the kinds of questions that Robusta can help answer out of the box?\n\nWhat are some tasks that Robusta facilitates which require manual exploration?\n\n\nWhat are the interfaces available for customizing and extending the functionality of Robusta?\n\nWhat is involved in adding a new automation capability to Robusta?\n\n\nHow have you approached the design of the tool to make it ergonomic and intuitive so that it doesn’t contribute to the stresses of dealing with errors in production?\nGiven that it is a tool to help resolve problems in production infrastructure, how have you worked to ensure its reliability and resilience?\nWhat is the governance and sustainability model for Robusta?\nWhat are the most interesting, innovative, or unexpected ways that you have seen Robusta used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Robusta?\nWhen is Robusta the wrong choice?\nWhat do you have planned for the future of Robusta?\n\nKeep In Touch\n\nLinkedIn\n@aantn on Twitter\naantn on GitHub\nWebsite\n\nPicks\n\nTobias\n\nKubernetes: Up And Running (affiliate link)\n\n\nNatan\n\nKubernetes for SysAdmins Youtube video by Kelsey Hightower\nLearn to delegate\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nRobusta\nGHOP\nObjective C\nSnyk\nHeroku\nGoogle AppEngine\nOOM Killer\nBin Packing/Knapsack Problem\nPrometheus\nKubernetes Pods\nPySpy\ntracemalloc\nPyrasite\nVSCode Debugger\nPydantic\n\nPodcast Episode\n\n\nHelm – Kubernetes package manager\nWhy Profiler\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

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Kubernetes is a framework that aims to simplify the work of running applications in production, but it forces you to adopt new patterns for debugging and resolving issues in your systems. Robusta is aimed at making that a more pleasant experience for developers and operators through pre-built automations, easy debugging, and a simple means of creating your own event-based workflows to find, fix, and alert on errors in production. In this episode Natan Yellin explains how the project got started, how it is architected and tested, and how you can start using it today to keep your Python projects running reliably.

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Picks

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Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Natan Yellin about how the Robusta framework simplifies the work of debugging and maintaining applications running on Kubernetes and how you can customize it to fit your specific needs.","date_published":"2022-03-13T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/16e2078a-5261-435b-8eb1-336b55f41f1b.mp3","mime_type":"audio/mpeg","size_in_bytes":42237128,"duration_in_seconds":3223}]},{"id":"podlove-2022-03-06t21:17:33+00:00-d758541214fed33","title":"Accelerate The Development And Delivery Of Your Machine Learning Applications Using Ray And Deploy It At Anyscale","url":"https://www.pythonpodcast.com/anyscale-machine-learning-applications-episode-355","content_text":"Summary\nBuilding a machine learning application is inherently complex. Once it becomes necessary to scale the operation or training of the model, or introduce online re-training the process becomes even more challenging. In order to reduce the operational burden of AI developers Robert Nishihara helped to create the Ray framework that handles the distributed computing aspects of machine learning operations. To support the ongoing development and simplify adoption of Ray he co-founded Anyscale. In this episode he re-joins the show to share how the project, its community, and the ecosystem around it have grown and evolved over the intervening two years. He also explains how the techniques and adoption of machine learning have influenced the direction of the project.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Robert Nishihara about his work at Anyscale and the Ray distributed execution framework\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what Anyscale is and the story behind it?\nHow has the Ray project and ecosystem evolved since we last spoke? (2 years ago)\n\nHow has the landscape of AI/ML technologies and techniques shifted in that time?\n\n\nWhat are the main areas where organizations are trying to apply ML/AI?\nWhat are some of the issues that teams encounter when trying to move from prototype to production with ML/AI applications?\n\nWhat are the features of Ray that help to mitigate those challenges?\n\n\nWith the introduction of more widely available streaming/real-time technologies the viability of reinforcement learning has increased. What new challenges does that approach introduce?\nWhat are some of the operational complexities associated with managing a deployment of Ray?\n\nWhat are some of the specialized utilities that you have had to develop to maintain a large and multi-tenant platform for your customers?\n\n\nWhat is the governance model around the Ray project and how does the work at Anyscale influence the roadmap?\nWhat are the most interesting, innovative, or unexpected ways that you have seen Anyscale/Ray used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Ray and Anyscale?\nWhen is Anyscale/Ray the wrong choice?\nWhat do you have planned for the future of Anyscale/Ray?\n\nKeep In Touch\n\nrobertnishihara on GitHub\n@robertnishihara on Twitter\nWebsite\nLinkedIn\n\nPicks\n\nTobias\n\nThe Edge Chronicles: Beyond The Deepwoods\n\n\nRobert\n\nProduction RL Summit\nProject Hail Mary by Andy Weir\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nRay\n\nPodcast Episode\n\n\nAnyscale\nUC Berkeley\nMatlab\nDeep Learning\nPandas\nNumPy\nHorovod\n\nPodcast Episode\n\n\nXGBoost\nModin\n\nPodcast Episode\n\n\nDask\nRay Datasets\nReinforcement Learning\nProduction Reinforcement Learning Summit\nAlphaGo\nDatabricks\nSnowflake\n\nData Engineering Podcast Episode\n\n\nTPU == Tensor Processing Unit\nWeights and Biases\nMLFlow\nRLLib\nRay Serve\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

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Building a machine learning application is inherently complex. Once it becomes necessary to scale the operation or training of the model, or introduce online re-training the process becomes even more challenging. In order to reduce the operational burden of AI developers Robert Nishihara helped to create the Ray framework that handles the distributed computing aspects of machine learning operations. To support the ongoing development and simplify adoption of Ray he co-founded Anyscale. In this episode he re-joins the show to share how the project, its community, and the ecosystem around it have grown and evolved over the intervening two years. He also explains how the techniques and adoption of machine learning have influenced the direction of the project.

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Closing Announcements

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Robert Nishihara about how the Ray framework for distributed computing can simplify the development and delivery of your machine learning applications, and the work that he and his team at Anyscale are doing to simplify operations.","date_published":"2022-03-06T16:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/b66961b8-40af-403d-a7aa-0b12aee7e71e.mp3","mime_type":"audio/mpeg","size_in_bytes":35268641,"duration_in_seconds":2758}]},{"id":"podlove-2022-02-28t00:23:09+00:00-4ac19136f3f78f5","title":"See The Structure Of Your Software At A Glance With Call Graphs From Code2Flow","url":"https://www.pythonpodcast.com/code2flow-call-graph-episode-354","content_text":"Summary\nAs software projects grow and change it can become difficult to keep track of all of the logical flows. By visualizing the interconnections of function definitions, classes, and their invocations you can speed up the time to comprehension for newcomers to a project, or help yourself remember what you worked on last month. In this episode Scott Rogowski shares his work on Code2Flow as a way to generate a call graph of your programs. He explains how it got started, how it works, and how you can start using it to understand your Python, Ruby, and PHP projects.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nSubsurface Live is the cloud data lake conference, a virtual conference where data engineers, data scientists, data architects, and data analysts can gather and hear about cloud data lakes and the data ecosystem. Subsurface Live Winter 2022 includes keynote talks from Bill Inmon, the father of the data warehouse, Author of Deep Work Cal Newport, and several more from companies such as Dremio, AWS, dbt, and more. Subsurface will also have many breakout sessions featuring Pandas creator Wes McKinney, Apache Superset & Airflow creator Maxime Beauchemin, and engineers from Apple, Uber, Adobe, Bloomberg, and more. Meet other data professionals and learn about the data technologies and practices helping companies meet their current and future data needs. Register today at pythonpodcast.com/subsurface\nYour host as usual is Tobias Macey and today I’m interviewing Scott Rogowski about Code2Flow, a utility for generating \"pretty good\" call graphs for dynamic languages\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what Code2Flow is and the story behind it?\nWhat are some of the ways that a program’s call graph might be used?\nHow does the visual representation generated by Code2Flow help with exploring the structure of a project?\n\nWhat are some of the alternative approaches/tools that might be used to gain similar insights?\nWhat do you see as the overlap in utility between Code2Flow and e.g. SourceGraph?\n\n\nCan you describe how the Code2Flow project is implemented?\n\nHow have the design and goals of the project changed since you first began working on it?\n\n\nGiven that Code2Flow is implemented in Python, how have you managed the parsing/processing of the other languages that you support?\nVisualizing a complex program can quickly become very messy. How have you approached the layout of the output to enhance comprehension?\nWhat are some of the situations where Code2Flow will be unable to provide a full picture of a program’s call graph?\nWhat are some of the pieces of information that are unavailable due to the static analysis approach that you have taken?\nCan you describe the process of applying Code2Flow to a project?\n\nOnce the structure is on display, what are some next steps that an individual or team might take to analyze and act on the information?\n\n\nGiven the static nature of the output, how might Code2Flow be incorporated in a CI/CD system to provide insight into the evolution of a projects structure?\nWhat are the most interesting, innovative, or unexpected ways that you have seen Code2Flow used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Code2Flow?\nWhen is Code2Flow the wrong choice?\nWhat do you have planned for the future of Code2Flow?\n\nKeep In Touch\n\nWebsite\nscottrogowski on GitHub\n\nPicks\n\nTobias\n\nTaking Vacation\nUniversal Studios, Florida\n\n\nScott\n\nService work\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nCode2Flow\nColombia\nMongita\nTI-83\nRuby\nPHP\nAST == Abstract Syntax Tree\nGraphviz\nPylint\nRobert Frost\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

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As software projects grow and change it can become difficult to keep track of all of the logical flows. By visualizing the interconnections of function definitions, classes, and their invocations you can speed up the time to comprehension for newcomers to a project, or help yourself remember what you worked on last month. In this episode Scott Rogowski shares his work on Code2Flow as a way to generate a call graph of your programs. He explains how it got started, how it works, and how you can start using it to understand your Python, Ruby, and PHP projects.

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Announcements

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Interview

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Keep In Touch

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Picks

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Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Scott Rogowski about his work on the Code2Flow package that lets you quickly view the structure of your code with "pretty good" call graphs that support Python, Ruby, and PHP.","date_published":"2022-02-27T21:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/6399cf08-5d0b-4053-b558-3aa370286278.mp3","mime_type":"audio/mpeg","size_in_bytes":32030608,"duration_in_seconds":2734}]},{"id":"podlove-2022-02-21t03:04:20+00:00-bb9f392f8dca949","title":"Scaling Knowledge Management For Technical Teams With Knowledge Repo","url":"https://www.pythonpodcast.com/knowledge-repo-open-source-knowledge-management-episode-353","content_text":"Summary\nOne of the most persistent challenges faced by organizations of all sizes is the recording and distribution of institutional knowledge. In technical teams this is exacerbated by the need to incorporate technical review feedback and manage access to data before publishing. When faced with this problem as an early data scientist at AirBnB, Chetan Sharma helped create the Knowledge Repo project as a solution. In this episode he shares the story behind its creation and growth, how and why it was released as open source, and the features that make it a compelling option for your own team’s knowledge management journey.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Chetan Sharma about Knowledge Repo, an open source framework for managing documentation for technical users\n\nInterview\n\n\nIntroductions\n\n\nHow did you get introduced to Python?\n\nEE + CS/AI + Stats degrees\nAirbnb working on ML models\nKnowledge Repo itself\n\n\n\nCan you describe what Knowledge Repo is and the story behind it?\n\nWe started seeing interviewees use ipython notebooks, thought they were great\nWanted to push more people to use notebooks, but they weren’t very shareable, vettable\nExisting notebook hosting services weren’t very good, and weren’t built for people who aren’t data stakeholders. It was especially poor with images, annoying cell blocks\nMade a simple post processor to remove cell blocks, push the images to s3, and host on flask\nOnce we were pushing notebooks into a Github repo for hosting on a flask app, so many things became possible\n\nReview cycles\nShareability / collaboration features\nIndexing / searching\n\n\nConcurrently, great work was happening on developing internal R packages / python libraries to provide consistent, branded aesthetics\n\n\n\nWhat are some of the approaches that teams typically take for recording and sharing institutional knowledge?\n\nCopy and paste to google docs, slides\nFacebook was using facebook photo albums\nuntrustworthy, not discoverable, divorced from the code\n\n\n\nWhat are the unique requirements that are introduced when attempting to record and distribute learnings related to data such as A/B experiments, analytical methods, data sets, etc.?\n\nReproducibility is a big one\nMaking sure the learnings are trustworthy (good data? no bugs?)\nDistributing widely, across the org and across time\nExperimentation\n\nExperimentation is at the end of a research-design-build-measure cycle, strategic analysis is often before\nCapturing all of the context\n\n\n\n\n\nCan you describe how the Knowledge Repo project is architected?\n\nRepositories: a store of posts, most commonly a github repo\nMarkdown as original lingua franca, eventually a KR specific “KR post” concept (which is still basically markdown)\nPost processors\n\nConvert whatever upstream file to markdown / KR post (Jupyter notebook, R Markdown, markdown were the original ones)\nHandle images and other large assets, usually pushing them to cloud storage\nEvolved to handle PDFs, googledocs, keynotes\n\n\n\n\n\nWhat were the motivating factors for making it available as an open source project?\n\nIt was such a common problem. Even incredibly sophisticated data teams at Uber, Facebook, etc. were begging us to share the system.\n\n\n\nWhat is the workflow for creating, sharing, and discovering information in an installation of Knowledge Repo?\n\nCreate a github repo for hosting strategic analysis\nUse the KR script to create a stub/template for whatever format you’re working in\nDo your work in Jupyter, etc.\nInstead of using github scripts (git add) use knowledge scripts (knowledge add), which is basically the github scripts with postprocessors\nDo typical Github workflows\nSee the result in the hosted knowledge repo app\n\n\n\nWhat are some of the options available for extending or customizing an installation of Knowledge Repo?\n\nMore postprocessors! google docs, presentations, UX research, anything can be done in KR with a simple postprocessor to turn it to markdown/images/PDF\nTying the system to your internal data tools. For example, an experimentation system like Eppo or whatever you use for marketing campaigns\n\n\n\nIf you were to start over today, what are some of the ways that you might approach the solution to knowledge management differently?\n\nThink of it more holistically:\n\n\n\nWhat are the most interesting, innovative, or unexpected ways that you have seen Knowledge Repo used?\n\nUX research\nWriting up guide for acquihiring\nDemonstrating of capabilities, data framework\n\n\n\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Knowledge Repo?\n\nStrategic analysis needs to be elevated, this leads to paradigm changes\nOrganization problems are helped by tools like KR: eg. promotions\nMeeting people’s tools/workflows where they are is powerful\n\n\n\nWhen is Knowledge Repo the wrong choice?\n\n\nKeep In Touch\n\nLinkedIn\n@chesharma87\n\nPicks\n\nTobias\n\nLearning Guitar\n\n\nChetan\n\nUnderrated cooking ingredients: chickpea flour, butter fried kimchi (in grilled cheese, nachos)\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nEppo\n\nData Engineering Podcast Episode\n\n\nKnowledge Repo\nIPython\nJupyter\nFlask\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

One of the most persistent challenges faced by organizations of all sizes is the recording and distribution of institutional knowledge. In technical teams this is exacerbated by the need to incorporate technical review feedback and manage access to data before publishing. When faced with this problem as an early data scientist at AirBnB, Chetan Sharma helped create the Knowledge Repo project as a solution. In this episode he shares the story behind its creation and growth, how and why it was released as open source, and the features that make it a compelling option for your own team’s knowledge management journey.

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Announcements

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Interview

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Keep In Touch

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Picks

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Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about the open source Knowledge Repo project for collecting, sharing, and scaling your knowledge management process across your technical teams.","date_published":"2022-02-20T22:15:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/6fa710cc-c8ad-480b-97bc-06e6fc026cd8.mp3","mime_type":"audio/mpeg","size_in_bytes":25761972,"duration_in_seconds":2374}]},{"id":"podlove-2022-02-14t00:16:54+00:00-3eec7d642fdfa5e","title":"Simplify And Scale Your Software Development Cycles By Putting On Pants (Build Tool)","url":"https://www.pythonpodcast.com/pants-software-development-lifecycle-tool-episode-352","content_text":"Summary\nSoftware development is a complex undertaking due to the number of options available and choices to be made in every stage of the lifecycle. In order to make it more scaleable it is necessary to establish common practices and patterns and introduce strong opinions. One area that can have a huge impact on the productivity of the engineers engaged with a project is the tooling used for building, validating, and deploying changes introduced to the software. In this episode maintainers of the Pants build tool Eric Arellano, Stu Hood, and Andreas Stenius discuss the recent updates that add support for more languages, efforts made to simplify its adoption, and the growth of the community that uses it. They also explore how using Pants as the single entry point for all of your routine tasks allows you to spend your time on the decisions that matter.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nBuilding data integration workflows is time consuming and tedious, requiring an unpleasant amount of boilerplate code to do it right. Rivery is a managed platform for building our ELT pipelines that offers the industry’s first native integration with Python, allowing you to seamlessly load and export Pandas dataframes to and from all of your databases, services, and data warehouses with a few clicks and no extra code. Rivery is hosting a live demo of their first class Python support on February 22nd, and when you use the promo code \"Python\" during registration you will be entered to win a brand new series 7 apple watch. Go to pythonpodcast.com/rivery today to learn more and register.\nYour host as usual is Tobias Macey and today I’m interviewing Eric Arellano, Stu Hood, and Andreas Stenius about the Pants build tool and all of the work that has gone into it recently\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what Pants is and the story behind it?\n\nWhat is the scope of concerns that Pants is focused on addressing?\n\n\nWhat are some of the notable changes in the project and its ecosystem over the past 1 1/2 years?\nHow do you approach the work of defining the target scope of the Pants toolchain?\n\nWhat are some of your guiding principles to decide when a feature request belongs in the core vs as a plugin?\n\n\nWhat are some of the ergonomic improvements that you have added to simplify the work of getting started with Pants and adopting it across teams?\nWhat are some of the challenges that teams run into as they start to scale the size of their monorepos? (e.g. project design, boilerplate reduction, etc.)\nHow are you managing the work of growing and supporting the community as you move beyond early adopters/experts into newcomers to Pants and programming?\nHow are you handling support for multiple language ecosystems?\n\nWhat are some of the challenges involved with making Pants feel idiomatic for such a range of communities?\n\n\nHow does the use of Python as the plugin/extension syntax work for teams that don’t use it as their primary language?\nWhat are the architectural changes that needed to be made for you to be capable of integrating with the different execution environments?\nHow would you characterize the level of feature coverage across the different supported languages?\nNow that you have laid the foundation, how much effort is required to add new language targets?\nWhat are the most interesting, innovative, or unexpected ways that you have seen Pants used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Pants?\nWhen is Pants the wrong choice?\nWhat do you have planned for the future of Pants?\n\nKeep In Touch\n\nEric\n\nLinkedIn\nEric-Arellano on GitHub\n@earellanoaz on Twitter\n\n\nStu\n\nLinkedIn\n@stuhood on Twitter\nstuhood on GitHub\n\n\nAndreas\n\n@andreasstenius on Twitter\nkaos on GitHub\n\n\n\nPicks\n\nTobias\n\nLast Kingdom on Netflix\n\n\nEric\n\nGetting Curious\n\n\nStu\n\nChecks and Balance Podcast\n\n\nAndreas\n\nThe Pragmatic Programmer\n\n\n\nLinks\n\nPants\nMake\nEarthly\n\nPodcast Episode\n\n\nMyPy\n\nPodcast Episode\n\n\nPyRight\nPylint\nFlake8\n\nPodcast Episode\n\n\nBazel\npre-commit\n\nPodcast Episode\n\n\nUnderpants library\nPyOxidizer\n\nPodcast Episode\n\n\nEric PyCon Talk\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Software development is a complex undertaking due to the number of options available and choices to be made in every stage of the lifecycle. In order to make it more scaleable it is necessary to establish common practices and patterns and introduce strong opinions. One area that can have a huge impact on the productivity of the engineers engaged with a project is the tooling used for building, validating, and deploying changes introduced to the software. In this episode maintainers of the Pants build tool Eric Arellano, Stu Hood, and Andreas Stenius discuss the recent updates that add support for more languages, efforts made to simplify its adoption, and the growth of the community that uses it. They also explore how using Pants as the single entry point for all of your routine tasks allows you to spend your time on the decisions that matter.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with maintainers of the Pants build tool about how its consistent interface and rapid evolution let you simplify each stage of the software development lifecycle by using a single tool with a consistent interface for all of your projects.","date_published":"2022-02-13T19:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/c4b75e46-6f3c-4e32-b31b-3655022b5a4c.mp3","mime_type":"audio/mpeg","size_in_bytes":53561683,"duration_in_seconds":3494}]},{"id":"podlove-2022-02-06t22:11:28+00:00-64d71f60d5fca51","title":"Achieve Repeatable Builds Of Your Software On Any Machine With Earthly","url":"https://www.pythonpodcast.com/earthly-repeatable-build-tool-episode-351","content_text":"Summary\nIt doesn’t matter how amazing your application is if you are unable to deliver it to your users. Frustrated with the rampant complexity involved in building and deploying software Vlad A. Ionescu created the Earthly tool to reduce the toil involved in creating repeatable software builds. In this episode he explains the complexities that are inherent to building software projects and how he designed the syntax and structure of Earthly to make it easy to adopt for developers across all language environments. By adopting Earthly you can use the same techniques for building on your laptop and in your CI/CD pipelines.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Vlad A. Ionescu about Earthly, a syntax and runtime for software builds to reduce friction between development and delivery\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what Earthly is and the story behind it?\nWhat are the core principles that engineers should consider when designing their build and delivery process?\nWhat are some of the common problems that engineers run into when they are designing their build process?\n\nWhat are some of the challenges that are unique to the Python ecosystem?\n\n\nWhat is the role of Earthly in the overall software lifecycle?\n\nWhat are the other tools/systems that a team is likely to use alongside Earthly?\nWhat are the components that Earthly might replace?\n\n\nHow is Earthly implemented?\n\nWhat were the core design requirements when you first began working on it?\nHow have the design and goals of Earthly changed or evolved as you have explored the problem further?\n\n\nWhat is the workflow for a Python developer to get started with Earthly?\n\nHow can Earthly help with the challenge of managing Javascript and CSS assets for web application projects?\n\n\nWhat are some of the challenges (technical, conceptual, or organizational) that an engineer or team might encounter when adopting Earthly?\nWhat are some of the features or capabilities of Earthly that are overlooked or misunderstood that you think are worth exploring?\nWhat are the most interesting, innovative, or unexpected ways that you have seen Earthly used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Earthly?\nWhen is Earthly the wrong choice?\nWhat do you have planned for the future of Earthly?\n\nKeep In Touch\n\nLinkedIn\n@VladAIonescu on Twitter\nWebsite\n\nPicks\n\nTobias\n\nShape Up book\n\n\nVlad\n\nHigh Output Management by Andy Grove\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nEarthly\nBazel\nPants\n\nPodcast Episode\n\n\nARM\nAWS Graviton\nApple M1 CPU\nQemu\nPhoenix web framework for Elixir language\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

It doesn’t matter how amazing your application is if you are unable to deliver it to your users. Frustrated with the rampant complexity involved in building and deploying software Vlad A. Ionescu created the Earthly tool to reduce the toil involved in creating repeatable software builds. In this episode he explains the complexities that are inherent to building software projects and how he designed the syntax and structure of Earthly to make it easy to adopt for developers across all language environments. By adopting Earthly you can use the same techniques for building on your laptop and in your CI/CD pipelines.

\n

Announcements

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Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
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\"\"

","summary":"An interview about the Earthly tool for easily creating repeatable builds for all of your software on any machine and how it helps to address the complexity inherent in the software delivery process","date_published":"2022-02-06T17:15:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/02851788-16b9-413a-a6ea-f64e2efefa47.mp3","mime_type":"audio/mpeg","size_in_bytes":41792975,"duration_in_seconds":3241}]},{"id":"podlove-2022-01-31t01:58:18+00:00-410617490400042","title":"Building A Detailed View Of Your Software Delivery Process With The Eiffel Protocol","url":"https://www.pythonpodcast.com/eiffel-protocol-software-delivery-process-visibility-episode-350","content_text":"Summary\nThe process of getting software delivered to an environment where users can interact with it requires many steps along the way. In some cases the journey can require a large number of interdependent workflows that need to be orchestrated across technical and organizational boundaries, making it difficult to know what the current status is. Faced with such a complex delivery workflow the engineers at Ericsson created a message based protocol and accompanying tooling to let the various actors in the process provide information about the events that happened across the different stages. In this episode Daniel Ståhl and Magnus Bäck explain how the Eiffel protocol allows you to build a tooling agnostic visibility layer for your software delivery process, letting you answer all of your questions about what is happening between writing a line of code and your users executing it.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Daniel Ståhl and Magnus Bäck about Eiffel, an open protocol for platform agnostic communication for CI/CD systems\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what Eiffel is and the story behind it?\n\nWhat are the goals of the Eiffel protocol and ecosystem?\nWhat is the role of Python in the Eiffel ecosystem?\n\n\nWhat are some of the types of questions that someone might ask about their CI/CD workflow?\n\nHow does Eiffel help to answer those questions?\n\n\nWho are the personas that you would expect to interact with an Eiffel system?\nCan you describe the core architectural elements required to integrate Eiffel into the software lifecycle?\n\nHow have the design and goals of the Eiffel protocol/architecture changed or evolved since you first began working on it?\n\n\nWhat are some example workflows that an engineering/product team might build with Eiffel?\nWhat are some of the challenges that teams encounter when integrating Eiffel into their delivery process?\nWhat are the most interesting, innovative, or unexpected ways that you have seen Eiffel used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Eiffel?\nWhen is Eiffel the wrong choice?\nWhat do you have planned for the future of Eiffel?\n\nKeep In Touch\n\nDaniel\n\nd-stahl-ericsson on GitHub\nLinkedIn\n\n\nMagnus\n\nLinkedIn\nmagnusbaeck on GitHub\n\n\n\nPicks\n\nTobias\n\nRed Notice\n\n\nDaniel\n\nThe Witcher\n\n\nMagnus\n\nLego\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nEiffel\nEricsson\nAxis Communications\nHudson CI framework\nSpinnaker\nJenkins\nTekton\nGradle\nArtifactory\nJSON Schema\nRabbitMQ\nPrometheus\nContinuous Delivery Foundation\nCD Events\nXKCD Competing Standards\nPython Eiffel SDK\nPydantic\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

The process of getting software delivered to an environment where users can interact with it requires many steps along the way. In some cases the journey can require a large number of interdependent workflows that need to be orchestrated across technical and organizational boundaries, making it difficult to know what the current status is. Faced with such a complex delivery workflow the engineers at Ericsson created a message based protocol and accompanying tooling to let the various actors in the process provide information about the events that happened across the different stages. In this episode Daniel Ståhl and Magnus Bäck explain how the Eiffel protocol allows you to build a tooling agnostic visibility layer for your software delivery process, letting you answer all of your questions about what is happening between writing a line of code and your users executing it.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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\"\"

","summary":"An interview about the Eiffel protocol and how you can integrate it into your software delivery process and answer all of your questions about when your code gets to your end users and the steps that it takes to get there.","date_published":"2022-01-30T21:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/0987851c-5a53-46ff-86f8-d334cb6708f9.mp3","mime_type":"audio/mpeg","size_in_bytes":37765195,"duration_in_seconds":2994}]},{"id":"podlove-2022-01-23t13:10:16+00:00-4d3624930c14986","title":"Improve Your Productivity By Investing In Developer Experience Design For Your Projects","url":"https://www.pythonpodcast.com/developer-experience-episode-349","content_text":"Summary\nWhen we are creating applications we spend a significant amount of effort on optimizing the experience of our end users to ensure that they are able to complete the tasks that the system is intended for. A similar effort that we should all consider is optimizing the developer experience for ourselves and other engineers who contribute to the projects that we work on. Adam Johnson recently wrote a book on how to improve the developer experience for Django projects and in this episode he shares some of the insights that he has gained through that project and his work with clients to help you improve the experience that you and your team have when collaborating on software development.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Adam Johnson about optimizing your developer experience\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what you mean by the term \"developer experience\"?\n\nHow does it compare to the concept of user experience design?\n\n\nWhat are the main goals that you aim for through improving DX?\nWhen considering DX, what are the categories of focus for improvement? (e.g. the experience of a given software project, the developer’s physical environment, their editing environment, etc.)\nWhat are some of the most high impact optimizations that a developer can make?\nWhat are some of the areas of focus that have the most variable impact on a developer’s experience of a project?\nWhat are some of the most helpful tools or practices that you rely on in your own projects?\nHow does the size of a development team or the scale of an organization impact the decisions and benefits around DX improvements?\nOne of the perennial challenges with selecting a given tool or architectural pattern is the continually changing landscape of software. How have your choices for DX strategies changed or evolved over the years?\nWhat are the most interesting, innovative, or unexpected developer experience tweaks that you have encountered?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on your book?\nWhat are some of the potential pitfalls that individuals and teams need to guard against in their quest to improve developer experience for their projects?\nWhat are some of the new tools or practices that you are considering incorporating into your own work?\n\nKeep In Touch\n\n@AdamChainz on Twitter\nWebsite\nadamchainz on GitHub\n\nPicks\n\nTobias\n\nEternals movie\n\n\nAdam\n\nFan of Eternals, enjoyed Neil Gaiman series\nAlso general MCU fan, watched it all in lockdown\nMoon Knight trailer\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nBoost Your Django DX\nRust\nRipgrep\nFactory Boy\nMimesis\n\nPodcast Episode\n\n\nLanguage Server Protocol\nEditorConfig\nStarship Command Prompt\nPre-Commit\n\nPodcast Episode\n\n\nFlake8\n\nPodcast Episode\n\n\nDevDocs\nDash library documentation search tool\npyupgrade\nStandardJS\nCython\n\nPodcast Episode\n\n\nThe Phoenix Project\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

When we are creating applications we spend a significant amount of effort on optimizing the experience of our end users to ensure that they are able to complete the tasks that the system is intended for. A similar effort that we should all consider is optimizing the developer experience for ourselves and other engineers who contribute to the projects that we work on. Adam Johnson recently wrote a book on how to improve the developer experience for Django projects and in this episode he shares some of the insights that he has gained through that project and his work with clients to help you improve the experience that you and your team have when collaborating on software development.

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Announcements

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Interview

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Keep In Touch

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Picks

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Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Adam Johnson about the benefits of investing in developer experience improvements for your software development projects as a way to reduce friction for yourself, your team, and outside contributors.","date_published":"2022-01-24T06:30:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/4183ce0d-5abb-4d83-b56b-617d25e644c6.mp3","mime_type":"audio/mpeg","size_in_bytes":35370112,"duration_in_seconds":2573}]},{"id":"podlove-2022-01-15t19:43:08+00:00-96e5be594809a81","title":"An Exploration Of Effective Pandas Practices With Matt Harrison","url":"https://www.pythonpodcast.com/effective-pandas-book-episode-348","content_text":"Summary\nPandas has grown to be a ubiquitous tool for working with data at every stage. It has become so well known that many people learn Python solely for the purpose of using Pandas. With all of this activity and the long history of the project it can be easy to find misleading or outdated information about how to use it. In this episode Matt Harrison shares his work on the book \"Effective Pandas\" and some of the best practices and potential pitfalls that you should know for applying Pandas in your own work.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Matt Harrison about best practices for using Pandas for data exploration, manipulation, and analysis\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nWhat motivated you to write a book about Pandas?\n\nThere are a number of books available that cover some aspect of the Pandas framework or its application. What was missing from the available literature?\nWho is your target audience for this book?\n\n\nWhat are some of the most surprising things that you have learned about Pandas while working on this book?\nWhat are the sharp edges that you see newcomers to pandas run into most frequently?\nIt is easy to use Pandas in a naive manner and get things done. What are some of the bad habits that you have seen people form in their work with Pandas?\n\nHow and when do those habits become harmful?\n\n\nWhat are the most interesting, innovative, or unexpected ways that you have seen Pandas used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on this book?\nWhat are some of the projects that you are planning to work on in the near/medium term?\n\nKeep In Touch\n\nWebsite\n@__mharrison__ on Twitter\nBlog\nmattharrison on GitHub\n\nPicks\n\nTobias\n\nMSR Snowshoes\n\n\nMatt\n\nTelemark Skiing\n22 Designs\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nEffective Pandas Book (affiliate link with 20% discount code applied)\n\nDiscount code INIT\n\n\nTCL\nPerl\nPandas\n\nPodcast Episode\n\n\nPandas Extension Arrays\n\nPodcast Episode\n\n\nKoalas\nDask\n\nData Engineering Podcast Episode\n\n\nModin\n\nPodcast Episode\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Pandas has grown to be a ubiquitous tool for working with data at every stage. It has become so well known that many people learn Python solely for the purpose of using Pandas. With all of this activity and the long history of the project it can be easy to find misleading or outdated information about how to use it. In this episode Matt Harrison shares his work on the book "Effective Pandas" and some of the best practices and potential pitfalls that you should know for applying Pandas in your own work.

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Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Matt Harrison about his recent book "Effective Pandas" and some of the best practices and potential pitfalls that you should know when working with the popular data processing framework.","date_published":"2022-01-15T14:45:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/52f0d58a-b5a6-4f20-8e46-2d3bc9660ba3.mp3","mime_type":"audio/mpeg","size_in_bytes":40777408,"duration_in_seconds":2997}]},{"id":"podlove-2022-01-13t23:24:22+00:00-a63ac8830c1afba","title":"Generate Your Text Files With Python Using Cog","url":"https://www.pythonpodcast.com/cog-python-text-generation-episode-347","content_text":"Summary\nDevelopers hate wasting effort on manual processes when we can write code to do it instead. Cog is a tool to manage the work of automating the creation of text inside another file by executing arbitrary Python code. In this episode Ned Batchelder shares the story of why he created Cog in the first place, some of the interesting ways that he uses it in his daily work, and the unique challenges of maintaining a project with a small audience and a well defined scope.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Ned Batchelder about Cog, a tool for generating files or text from embedded Python logic\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what Cog is and the story behind it?\nWhat are the use cases that you initially created Cog to address?\nWhat were the shortcomings or extraneous overhead that you encountered in tools such as Jinja, Mako, Genshi, etc. that led you to create a new tool?\nWhat was your path from a quick and dirty script that suited your own purposes to turning it into a niche open source project that was general and stable enough for the broader community?\nOne of your claims to fame is your role as the maintainer for coverage.py. How has your experience managing such a widely used project translated to the relatively small and low traffic project like Cog?\nCan you describe how Cog is implemented?\n\nHow did you approach the design of the syntactic elements for embedding Python code into a host file?\n\n\nWhat is the workflow for someone using Cog to generate all or parts of a file?\n\nHow does the introduction of third party dependencies impact the viability and utility of Cog as compared to other templating systems?\n\n\nWhat are the most interesting, innovative, or unexpected ways that you have seen Cog used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Cog?\nWhen is Cog the wrong choice?\nWhat do you have planned for the future of Cog?\n\nKeep In Touch\n\nWebsite\nnedbat on GitHub\n@nedbat on Twitter\nLinkedIn\n\nPicks\n\nTobias\n\nSamson Q9U Microphone\n\n\nNed\n\nMcFly Command Line History Tool\nGo for a walk\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nCog\nBoston Python\nLotus\nLotus Notes\nZope\nCheetah Template Engine\nCoverage.py\n\nPodcast Episode\n\n\nUnix Philosophy\nHungarian Notation\nJupyter Notebooks\nGitHub Profile ReadMe\nNed’s GitHub Profile\n\nRaw Markdown\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Developers hate wasting effort on manual processes when we can write code to do it instead. Cog is a tool to manage the work of automating the creation of text inside another file by executing arbitrary Python code. In this episode Ned Batchelder shares the story of why he created Cog in the first place, some of the interesting ways that he uses it in his daily work, and the unique challenges of maintaining a project with a small audience and a well defined scope.

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Announcements

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Interview

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Keep In Touch

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Picks

\n\n

Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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\"\"

","summary":"An interview with Ned Batchelder about the interesting history of the Cog project and how you can use it to automate the work of generating text with arbitrary Python code","date_published":"2022-01-13T18:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/faac5f40-8e97-4e53-b5ee-306cebedb789.mp3","mime_type":"audio/mpeg","size_in_bytes":43273064,"duration_in_seconds":3032}]},{"id":"podlove-2022-01-02t17:36:20+00:00-2c8c74c475d9c63","title":"A Friendly Approach To Regression Models For Programmers","url":"https://www.pythonpodcast.com/regression-models-friendly-guide-episode-346","content_text":"Summary\nStatistical regression models are a staple of predictive forecasts in a wide range of applications. In this episode Matthew Rudd explains the various types of regression models, when to use them, and his work on the book \"Regression: A Friendly Guide\" to help programmers add regression techniques to their toolbox.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Matthew Rudd about the applications of statistical modeling and regression, and how to start using it for your work\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing some use cases for statistical regression?\nWhat was your motivation for writing a book to explain this family of algorithms to programmers?\n\nWhat are your goals for the book?\nWho is the target audience?\n\n\nWhat are some of the different categories of regression algorithms?\nWhat are some heuristics for identifying which regression to use?\nHow have you approached the balance of using software principles for explaining the work of building the models with the mathematical underpinnings that make them work?\nWhat are some of the concepts that are most challenging for people who are first working with regression models?\nWhat are the most interesting, innovative, or unexpected ways that you have seen statistical regression models used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on your book?\nWhat are some of the resources that you recommend for folks who want to learn more about the inner workings and applications of regression models after they finish your book?\n\nKeep In Touch\n\nLinkedIn\n@MatthewBRudd on Twitter\n\nPicks\n\nTobias\n\nThe Argument podcast from the NY Times\n\n\nMatthew\n\nPrimus\nClaypool Lennon Delirium\nSouth of Reality\n\n\n\nLinks\n\nRegression: A Friendly Guide\nSewanee University of the South\nSewanee Data Lab\nMark Lutz Python books\nElements of Statistical Learning\nLinear Regression\nLogistic Regression\nModeling Binary Data\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Statistical regression models are a staple of predictive forecasts in a wide range of applications. In this episode Matthew Rudd explains the various types of regression models, when to use them, and his work on the book "Regression: A Friendly Guide" to help programmers add regression techniques to their toolbox.

\n

Announcements

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Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

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Closing Announcements

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Matthew Rudd about his book on regression models and how to apply them in your work as a programmer.","date_published":"2022-01-02T12:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/60be1043-c6b1-456f-997e-167987f33446.mp3","mime_type":"audio/mpeg","size_in_bytes":35974052,"duration_in_seconds":2715}]},{"id":"podlove-2021-12-26t20:13:12+00:00-188b8b83d05256f","title":"Fast, Flexible, and Incremental Task Automation With doit","url":"https://www.pythonpodcast.com/doit-software-task-automation-episode-345","content_text":"Summary\nEvery software project needs a tool for managing the repetitive tasks that are involved in building, running, and deploying the code. Frustrated with the limitations of tools like Make, Scons, and others Eduardo Schettino created doit to handle task automation in his own work and released it as open source. In this episode he shares the story behind the project, how it is implemented under the hood, and how you can start using it in your own projects to save you time and effort.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Eduardo Schettino about Doit, a flexible and low overhead task automation tool\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what doit is and the story behind it?\nWhat are the main goals and use cases of doit?\nCan you describe how you approached the implementation of Doit?\n\nHow has the design changed or evolved since you first began working on it?\n\n\nThe realm of task automation tools for developers is an exceedingly crowded one, with each tool prioritizing certain use cases. How would you characterize the position of doit in the current ecosystem?\n\nHow does it compare to e.g. Click, Invoke, Typer, etc.?\n\n\nWhat is your guiding philosophy for when and how to add new features?\n\nYou have been running the project for ~13 years now. How has the evolution of the Python language and ecosystem influenced your approach to the development and maintenance of doit?\n\n\nWhat is the workflow for getting started with doit and integrating it into your development process?\nFor every project there are some tasks that are identical and some that are bespoke for that application. What are the options for maintaining a standard set of tasks across repositories and composing them with per-project activites?\nWhat are some of the useful patterns that you and the community have established for designing tasks and execution graphs?\nHow do you use doit in your own work?\nWhat are the most interesting, innovative, or unexpected ways that you have seen doit used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on doit?\nWhen is doit the wrong choice?\nWhat do you have planned for the future of doit?\n\nKeep In Touch\n\nLinkedIn\nschettino72 on GitHub\n\nPicks\n\nTobias\n\nThe Matrix series\n\n\nEduardo\n\nJohn Pilger\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\ndoit\nZope\nTwisted\nDjango\nPyflakes\nscons\nMake\nNikola\n\nPodcast Episode\n\n\nNose\nPytest\n\nPodcast Episode\n\n\nClick\nTyper\nInvoke\nPuppet\nAnsible\nChef\nSphinx\nSnakemake\nAirflow\nLuigi\npytest-incremental\nimport-deps\ndbm\nMetalK8s\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Every software project needs a tool for managing the repetitive tasks that are involved in building, running, and deploying the code. Frustrated with the limitations of tools like Make, Scons, and others Eduardo Schettino created doit to handle task automation in his own work and released it as open source. In this episode he shares the story behind the project, how it is implemented under the hood, and how you can start using it in your own projects to save you time and effort.

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Announcements

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Interview

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Keep In Touch

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Picks

\n\n

Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Eduardo Schettino about his frustration with existing build tools and how he wrote the doit framework to manage repetitive tasks in his software projects.","date_published":"2021-12-26T19:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/7372e15c-ee32-40c8-a441-186f199924f1.mp3","mime_type":"audio/mpeg","size_in_bytes":32296564,"duration_in_seconds":2367}]},{"id":"podlove-2021-12-20t01:25:37+00:00-1b440acf7d99a9e","title":"The Technological, Business, and Sales Challenges Of Building The Ethical Ads Network","url":"https://www.pythonpodcast.com/ethical-ads-network-episode-344","content_text":"Summary\nWhether we like it or not, advertising is a common and effective way to make money on the internet. In order to support the work being done at Read The Docs they decided to include advertisements on the documentation sites they were hosting, but they didn’t want to alienate their users or collect unnecessary information. In this episode David Fischer explains how they built the Ethical Ads network to solve their problem, the technical and business challenges that are involved, and the open source application that they built to power their network.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing David Fischer about the Ethical Ads marketplace and the technology that runs\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what the Ethical Ads project is and the story behind it?\nWhat are the technical and organizational requirements involved in running an ad network?\n\nHow have you approached the problem of kickstarting the flywheel for the two-sided marketplace?\n\n\nWhat are some of the challenges that you face in building an accurate profile of your audience without using detailed tracking methods?\n\nWhat are the benefits that you see in focusing exclusively on developers in your publisher relationships?\n\n\nCan you describe the design and implementation of the ad server?\n\nHow has the architecture evolved since you first began working on it?\nIf you were to start over today what might you do differently?\n\n\nHow have you approached scaling for performance and geographic distribution?\nWhat mechanisms do you use for tracking impressions/measuring ad effectiveness?\nHow can advertisers experiment with A/B testing of ad copy?\nIf someone wants to run their own advertisements with the ethical ads server, what is involved in getting it deployed and integrated into their sites?\n\nWhat are the integration and extension points available for customizing the behavior of the platform?\n\n\nWhat are some of the most notable lessons that you have learned about online advertising since you first started working on the Ethical Ads project?\nWhat are the most interesting, innovative, or unexpected ways that you have seen Ethical Ads used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on the Ethical Ads platform?\nWhat do you have planned for the future of the Ethical Ads platform?\n\nKeep In Touch\n\ndavidfischer on GitHub\n@djfische on Twitter\nLinkedIn\n\nPicks\n\nTobias\n\nShip It! Podcast\n\n\nDavid\n\nLocal Python Meetup\nClick CLI framework\nuseragents library\nTLD for parsing internet domains\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nEthical Ads Network\nEthical Ads Server\nSan Diego Python\nRead The Docs\n\nPodcast Episode\n\n\nCodeFund\nCPM == Cost Per Mille\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Whether we like it or not, advertising is a common and effective way to make money on the internet. In order to support the work being done at Read The Docs they decided to include advertisements on the documentation sites they were hosting, but they didn’t want to alienate their users or collect unnecessary information. In this episode David Fischer explains how they built the Ethical Ads network to solve their problem, the technical and business challenges that are involved, and the open source application that they built to power their network.

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Announcements

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Interview

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Keep In Touch

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Picks

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Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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\n\n

\"\"

","summary":"An interview with David Fischer about his work on building the Ethical Ads network to support the work being done at Read The Docs and the lessons that he has learned along the way","date_published":"2021-12-19T20:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/3ebf052b-ab9f-404c-b6ae-8c0572a356c7.mp3","mime_type":"audio/mpeg","size_in_bytes":50452217,"duration_in_seconds":3348}]},{"id":"podlove-2021-12-12t01:44:32+00:00-6706bbfc0f6a837","title":"Accidentally Building A Business With Python At Listen Notes","url":"https://www.pythonpodcast.com/listen-notes-python-business-podcast-api-episode-343","content_text":"Summary\nPodcasts are one of the few mediums in the internet era that are still distributed through an open ecosystem. This has a number of benefits, but it also brings the challenge of making it difficult to find the content that you are looking for. Frustrated by the inability to pick and choose single episodes across various shows for his listening Wenbin Fang started the Listen Notes project to fulfill his own needs. He ended up turning that project into his full time business which has grown into the most full featured podcast search engine on the market. In this episode he explains how he build the Listen Notes application using Python and Django, his work to turn it into a sustainable business, and the various ways that you can build other applications and experiences on top of his API.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Wenbin Fang about the technology powering the Listen Notes podcast discovery platform\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what Listen Notes is and the story behind it?\nWhat are some of the main goals that listeners have when searching for a podcast?\n\nWhat are the challenges that they commonly encounter when looking for information in a podcast?\nWhat are the different sources of information that you can use to extract useful details about a podcast?\n\n\nHow do you identify and prioritize new features or product enhancements?\nCan you describe how the Listen Notes platform is architected?\n\nHow has it changed or evolved since you first began working on it?\nHow did you approach the technology selection for the initial version of Listen Notes?\n\nIf you were to start over today, what might you do differently?\n\n\n\n\nWhat are the technical challenges that are posed by the ecosystem around podcasts?\n\nWhat are the biggest changes that have happened in the methods of production and consumption for podcasts since you first became involved in the space?\n\n\nHow do you approach the design and contracts of the Listen Notes web API given how core that is to your platform?\nWhat are the most complex or complicated engineering projects that you have done for Listen Notes?\nWhat are the pieces of the infrastructure for podcasts that you would like to see improved, changed, or replaced?\nWhat are some of the kinds of projects that developers can build with the Listen Notes API?\nWhat, if any, impact have the introduction of podcasts to closed platforms such as Spotify, Amazon Music, etc. had on your business?\nWhat are some of the most surprising things that you have learned about podcasts and their consumption while building Listen Notes?\nWhat are the most interesting, innovative, or unexpected ways that you have seen Listen Notes used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Listen Notes?\nWhat do you have planned for the future of Listen Notes?\n\nKeep In Touch\n\nWebsite\nLinkedIn\nwenbinf on GitHub\n@wenbinf on Twitter\n\nPicks\n\nTobias\n\nWheel of Time TV Series\n\n\nWenbin\n\nSuperhuman email client \n\n\n\nLinks\n\nListen Notes\nGraphviz\nNextDoor\nPostgreSQL\nElasticsearch\nRedis\nRabbitMQ\nCelery\nReactJS\nDjango\nBootstrap CSS\nDigital Ocean\nTailwind CSS\nEntity Resolution\nClickhouse\n\nData Engineering Podcast Episode\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Podcasts are one of the few mediums in the internet era that are still distributed through an open ecosystem. This has a number of benefits, but it also brings the challenge of making it difficult to find the content that you are looking for. Frustrated by the inability to pick and choose single episodes across various shows for his listening Wenbin Fang started the Listen Notes project to fulfill his own needs. He ended up turning that project into his full time business which has grown into the most full featured podcast search engine on the market. In this episode he explains how he build the Listen Notes application using Python and Django, his work to turn it into a sustainable business, and the various ways that you can build other applications and experiences on top of his API.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

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","summary":"An interview with Listen Notes founder Wenbin Fang about his experience building a one person company powered by Python and his views on the podcast ecosystem.","date_published":"2021-12-11T20:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/08743032-a0b5-4c7f-b205-0fb6c04d06ff.mp3","mime_type":"audio/mpeg","size_in_bytes":35567752,"duration_in_seconds":2608}]},{"id":"podlove-2021-11-27t20:33:58+00:00-38124b44216e373","title":"Making Orbital Mechanics More Accessible With Poliastro","url":"https://www.pythonpodcast.com/poliastro-python-orbital-mechanics-episode-342","content_text":"Summary\nOuter space holds a deep fascination for people of all ages, and the key principle in its exploration both near and far is orbital mechanics. Poliastro is a pure Python package for exploring and simulating orbit calculations. In this episode Juan Luis Cano Rodriguez shares the story behind the project, how you can use it to learn more about space travel, and some of the interesting projects that have used it for planning planetary and interplanetary missions.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Juan Luis Cano Rodriguez about Poliastro, an open source library for interactive Astrodynamics and Orbital Mechanics, with a focus on ease of use, speed, and quick visualization.\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what Poliastro is and the story behind it?\nWhat are some of the simulations that Poliastro is designed to be used for?\nHow much knowledge of orbital mechanics is necessary to get started with Poliastro?\nCan you describe how the project is implemented?\n\nHow have the goals and design of the project changed or evolved since you first started it?\n\n\nWhat are some of the design philosophies that you focus on to make the package accessible to the range of users that you support?\nCan you talk through the workflow of using Poliastro to do something like track the path of the ISS and its traversal of the debris field from the recent satellite destruction?\nWhat are some of the other libraries or frameworks that are commonly used with Poliastro?\nHow are you using Poliastro in your own work?\nWhat are some overlooked or underused aspects of the project that you would like to highlight?\nWhat are the most interesting, innovative, or unexpected ways that you have seen Poliastro used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Poliastro?\nWhen is Poliastro the wrong choice?\nWhat do you have planned for the future of Poliastro?\n\nKeep In Touch\n\nLinkedIn\nGitHub\nEmail\nTwitter\n\nPicks\n\nTobias\n\nJosh Blue (comedian)\n\n\nJuan Luis\n\nDJ Cotts\nDJ Weaver\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nPoliastro\nFortran 90 (if only this community existed back then! https://ondrejcertik.com/blog/2021/03/resurrecting-fortran/)?utm_source=rss&utm_medium=rss\nSatellogic\nRead the Docs\nWolfram Alpha\nMathematica\nSageMath\n2-Body Problem\nAstroPy\n\nPodcast Episode\n\n\nNumba\nImport Linter\nVallado \"Fundamentals of Astrodynamics\"\nInternational Space Station\nStarlink Satellites\nPlanetary Ephemeritas Data\nSatellite Data\nKerbal Space Program\nNumFOCUS\nOpen Collective\nPython SGP4\nLibre Space Foundation\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Outer space holds a deep fascination for people of all ages, and the key principle in its exploration both near and far is orbital mechanics. Poliastro is a pure Python package for exploring and simulating orbit calculations. In this episode Juan Luis Cano Rodriguez shares the story behind the project, how you can use it to learn more about space travel, and some of the interesting projects that have used it for planning planetary and interplanetary missions.

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Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview about the Poliastro package for building and visualizing simulations of orbital mechanics problems in pure Python.","date_published":"2021-11-27T15:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/d5af7312-8a9d-4610-a274-6fbcbf82f498.mp3","mime_type":"audio/mpeg","size_in_bytes":49178576,"duration_in_seconds":3539}]},{"id":"podlove-2021-11-14t14:45:23+00:00-125c86aa2431091","title":"Build Better Analytics And Models With A Focus On The Data Experience","url":"https://www.pythonpodcast.com/modern-data-experience-episode-340","content_text":"Summary\nA lot of time and energy goes into data analysis and machine learning projects to address various goals. Most of the effort is focused on the technical aspects and validating the results, but how much time do you spend on considering the experience of the people who are using the outputs of these projects? In this episode Benn Stancil explores the impact that our technical focus has on the perceived value of our work, and how taking the time to consider what the desired experience will be can lead us to approach our work more holistically and increase the satisfaction of everyone involved.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Benn Stancil about the perennial frustrations of working with data and thoughts on how to improve the experience\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by discussing your perspective on the most frustrating elements of working with data in an organization?\n\nHow might that compound when working with machine learning?\n\n\nWhat are the sources of the disconnect between our level of technical sophistication and our ability to produce meaningful insights from our data?\nThere have been a number of formulations about a \"hierarchy of needs\" pertaining to data. When the goal is to bring ML/AI methods to bear on an organization’s processes or products how can thinking about the intended experience act to improve the end result?\n\nWhat are some failure modes or suboptimal outcomes that might be expected when building from a tooling/technology/technique first mindset?\n\n\nWhat are some of the design elements that we can incorporate into our development environments/data infrastructure/data modeling that can incentivize a more experience driven process for building data products/analyses/ML models?\nHow does the design and capabilities of the Mode platform allow teams to progress along the journey from data discovery to descriptive analytics, to ML experiments?\nWhat are the most interesting, innovative, or unexpected approaches that you have seen for encouraging the creation of positive data experiences?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Mode and data analysis?\nWhen is a data experience the wrong approach?\nWhat do you have planned for the future of Mode to support this ideal?\n\nKeep In Touch\n\nLinkedIn\n@bennstancil on Twitter\n\nPicks\n\nTobias\n\nVenture Unlocked Podcast\n\n\nBenn\n\nWrap Text by Bobby Pinero\nCounting Stuff by Randy Au\nRay Data Co by Mr Ben\nModern Data Democracy By JP Monteiro\nBad Blood Podcast\nBad Blood Book\n\n\n\nLinks\n\nMode Analytics\nTidyverse\nAirflow\nFivetran\n\nData Engineering Podcast Episode\n\n\ndbt\n\nData Engineering Podcast Episode\n\n\nConway’s Law\nCinchy\n\nData Engineering Podcast Episode\n\n\nReverse ETL\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

A lot of time and energy goes into data analysis and machine learning projects to address various goals. Most of the effort is focused on the technical aspects and validating the results, but how much time do you spend on considering the experience of the people who are using the outputs of these projects? In this episode Benn Stancil explores the impact that our technical focus has on the perceived value of our work, and how taking the time to consider what the desired experience will be can lead us to approach our work more holistically and increase the satisfaction of everyone involved.

\n

Announcements

\n\n

Interview

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Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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\n\n

\"\"

","summary":"An interview with Benn Stancil about how focusing on the data experience for consumers of analytics and machine learning models leads to better outcomes for everyone.","date_published":"2021-11-21T21:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/fe28c618-7f3e-49c5-b06c-b028ebccb082.mp3","mime_type":"audio/mpeg","size_in_bytes":42020550,"duration_in_seconds":3567}]},{"id":"podlove-2021-11-22t02:32:27+00:00-15ead9e7e3a6ff4","title":"Declarative Deep Learning From Your Laptop To Production With Ludwig and Horovod","url":"https://www.pythonpodcast.com/ludwig-horovod-distributed-declarative-deep-learning-episode-341","content_text":"Summary\nDeep learning frameworks encourage you to focus on the structure of your model ahead of the data that you are working with. Ludwig is a tool that uses a data oriented approach to building and training deep learning models so that you can experiment faster based on the information that you actually have, rather than spending all of our time manipulating features to make them match your inputs. In this episode Travis Addair explains how Ludwig is designed to improve the adoption of deep learning for more companies and a wider range of users. He also explains how the Horovod framework plugs in easily to allow for scaling your training workflow from your laptop out to a massive cluster of servers and GPUs. The combination of these tools allows for a declarative workflow that starts off easy but gives you full control over the end result.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Travis Adair about building and training machine learning models with Ludwig and Horovod\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what Horovod and Ludwig are?\n\nHow do the projects work together?\n\n\nWhat was your path to being involved in those projects and what is your current role?\nThere are a number of AutoML libraries available for frameworks such as scikit-learn, etc. What are the challenges that are introduced by applying that workflow to deep learning architectures?\nWhat are the use cases that Ludwig is designed to enable?\nWho are the target users of Ludwig?\n\nHow do the workflows change/progress for the different personas?\n\n\nHow is the underlying framework architected?\n\nWhat are the available extension points to provide a progressive exposure of complexity?\nHow have the goals and design of the project changed or evolved as it has gained more widespread adoption beyond Uber?\n\nWhat was the motivation for migrating the core of Ludwig from Tensorflow to Pytorch?\n\n\n\n\nCan you describe the workflow of building a model definition with Ludwig?\n\nHow much knowledge of neural network architectures and their relevant characteristics is necessary to use Ludwig effectively?\n\n\nWhat are the motivating factors for adding Horovod to the process?\n\nWhat is involved in moving from a single machine/single process training loop to a multi-core or multi-machine distributed training process?\n\n\nThe combination of Ludwig and Horovod provide a shallower learning curve for building and scaling model training. What do you see as their potential impact on the availability and adoption of more sophisticated ML capabilities across organizations of varying scale?\n\nWhat do you see as other significant barriers to widespread use of ML functionality?\n\n\nWhat are the most interesting, innovative, or unexpected ways that you have seen Ludwig and/or Horovod used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Ludwig and Horovod?\nWhen is Ludwig and/or Horovod the wrong choice?\nWhat do you have planned for the future of both projects?\n\nKeep In Touch\n\nLinkedIn\n@TravisAddair on Twitter\ntgaddair on GitHub\n\nPicks\n\nTobias\n\nZeal and Ardor\n\n\nTravis\n\nOpeth\nAgaloch\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nLudwig\nHorovod\nPredibase\nUber\nMichelangelo\nTensorflow\nPyTorch\n\nPodcast Episode\n\n\nGradient Boosted Trees\nXGBoost\nCatBoost\nLightGBM\nPyCaret\nHyperBand\nscikit-optimize\nKeras\nVision Transformer Architecture\nHuggingFace\nJax\nDeepSpeed\nAllReduce\nNvidia Collective Communications Library (NCCL)\nTraining Epoch\nElasticDL\nRaft Consensus Algorithm\nTorchScript\nTransfer Learning\nGordon Bell Prize\nAnyscale\nRay\n\nPodcast Episode\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Deep learning frameworks encourage you to focus on the structure of your model ahead of the data that you are working with. Ludwig is a tool that uses a data oriented approach to building and training deep learning models so that you can experiment faster based on the information that you actually have, rather than spending all of our time manipulating features to make them match your inputs. In this episode Travis Addair explains how Ludwig is designed to improve the adoption of deep learning for more companies and a wider range of users. He also explains how the Horovod framework plugs in easily to allow for scaling your training workflow from your laptop out to a massive cluster of servers and GPUs. The combination of these tools allows for a declarative workflow that starts off easy but gives you full control over the end result.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview with Travis Addair about the open source Ludwig and Horovod projects and how they simplify the work of going from idea to production with declarative deep learning and distributed training.","date_published":"2021-11-21T21:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/a5028862-cb0c-4ddf-8027-4b32dbc80579.mp3","mime_type":"audio/mpeg","size_in_bytes":53564999,"duration_in_seconds":3888}]},{"id":"podlove-2021-11-06t21:32:29+00:00-490dbdde9b060c1","title":"Building Conversational AI to Augment Sales Teams at Structurely","url":"https://www.pythonpodcast.com/structurely-conversational-ai-sales-episode-339","content_text":"Summary\nThe true power of artificial intelligence is its ability to work collaboratively with humans. Nate Joens co-founded Structurely to create a conversational AI platform that augments human sales teams to help guide potential customers through the initial steps of the funnel. In this episode he discusses the technical and social considerations that need to be combined for a seamless conversational experience and how he and his team are tackling the problem.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Nate Joens about his work at Structurely to build conversational AI utilities that augment human sales interactions\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what Structurely is and the story behind it?\nWhat are the elements that comprise a \"conversational AI\"?\n\nHow is it distinct from the wave of chatbots that were popular in recent years?\nWhat lessons from that approach can we take forward into AI enabled conversational platforms?\n\n\nHow are you applying AI to the sales process?\n\nHow much domain expertise is necessary to make an effective and engaging conversational AI? (e.g. knowledge of sales techniques vs. knowledge of real estate, etc.)\n\n\nCan you describe how you have designed the Structurely platform?\n\nWhat are the biggest engineering challenges that you have had to work through?\n\nWhat challenges or complexities have been most persistent?\n\n\n\n\nWhat are the design complexities that you have to work through to make the AI accessible for end users?\nWhat are some of the advancements in AI/NLP/transfer learning that have been most beneficial for teams building conversational AI?\nWhat are the signals that you emphasize when monitoring the performance of your models?\n\nWhat is your approach for feeding real-world customer interactions back into your model development and training loop?\n\n\nWhat are the most active areas of research in conversational AI applications and techniques?\nWhat are the most interesting, innovative, or unexpected ways that you have seen Structurely and/or conversational AI used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on conversational AI at Structurely?\nWhen is conversational AI the wrong choice?\nWhat do you have planned for the future of Structurely?\n\nKeep In Touch\n\n@whonatejoens on Twitter\nLinkedIn\n\nPicks\n\nTobias\n\nVantage AWS Cost Management\n\n\nNate\n\nVideoForm\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nStucturely\nGIS\nGenerative AI\nGPT-3\nSanky Diagram\nPyTorch\n\nPodcast Episode\n\n\nAllen Institute for AI\nF Score\nSnorkel\n\nPodcast Episode\n\n\nFew-Shot Learning\nZero Shot Learning\nVoxable\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

The true power of artificial intelligence is its ability to work collaboratively with humans. Nate Joens co-founded Structurely to create a conversational AI platform that augments human sales teams to help guide potential customers through the initial steps of the funnel. In this episode he discusses the technical and social considerations that need to be combined for a seamless conversational experience and how he and his team are tackling the problem.

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Announcements

\n\n

Interview

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Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview with Nate Joens about the technical and social complexities involved in building a platform for improving the efficiency of sales teams through conversational AI","date_published":"2021-11-06T17:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/43108a61-9967-453e-97d1-2ac9ec630b1e.mp3","mime_type":"audio/mpeg","size_in_bytes":42700045,"duration_in_seconds":3059}]},{"id":"podlove-2021-10-31t00:51:15+00:00-864030ba4aa26b1","title":"Build Composable And Reusable Feature Engineering Pipelines with Feature-Engine","url":"https://www.pythonpodcast.com/feature-engine-feature-engineering-pipelines-episode-338","content_text":"Summary\nEvery machine learning model has to start with feature engineering. This is the process of combining input variables into a more meaningful signal for the problem that you are trying to solve. Many times this process can lead to duplicating code from previous projects, or introducing technical debt in the form of poorly maintained feature pipelines. In order to make the practice more manageable Soledad Galli created the feature-engine library. In this episode she explains how it has helped her and others build reusable transformations that can be applied in a composable manner with your scikit-learn projects. She also discusses the importance of understanding the data that you are working with and the domain in which your model will be used to ensure that you are selecting the right features.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Soledad Galli about feature-engine, a Python library to engineer features for use in machine learning models\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what feature-engine is and the story behind it?\nWhat are the complexities that are inherent to feature engineering?\n\nWhat are the problems that are introduced due to incidental complexity and technical debt?\n\n\nWhat was missing in the available set of libraries/frameworks/toolkits for feature engineering that you are solving for with feature-engine?\nWhat are some examples of the types of domain knowledge that are needed to effectively build features for an ML model?\nGiven the fact that features are constructed through methods such as normalizing data distributions, imputing missing values, combining attributes, etc. what are some of the potential risks that are introduced by incorrectly applied transformations or invalid assumptions about the impact of these manipulations?\nCan you describe how feature-engine is implemented?\n\nHow have the design and goals of the project changed or evolved since you started working on it?\n\n\nWhat (if any) difference exists in the feature engineering process for frameworks like scikit-learn as compared to deep learning approaches using PyTorch, Tensorflow, etc.?\nCan you describe the workflow of identifying and generating useful features during model development?\n\nWhat are the tools that are available for testing and debugging of the feature pipelines?\n\n\nWhat do you see as the potential benefits or drawbacks of integrating feature-engine with a feature store such as Feast or Tecton?\nWhat are the most interesting, innovative, or unexpected ways that you have seen feature-engine used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on feature-engine?\nWhen is feature-engine the wrong choice?\nWhat do you have planned for the future of feature-engine?\n\nKeep In Touch\n\nLinkedIn\n@Soledad_Galli on Twitter\nsolegalli on GitHub\n\nPicks\n\nTobias\n\nDune Movie\nDune Series\n\n\nSoledad\n\nThe Social Dilemma\nDon’t Be Evil by Rana Foroohar\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nfeature-engine\nFeature Engineering\nPython Feature Engineering Cookbook\nscikit-learn\nFeature Stores\n\nPodcast Episode\n\n\nPandas\n\nPodcast Episode\n\n\nPyTorch\n\nPodcast Episode\n\n\nTensorflow\nFeast\nTecton\n\nData Engineering Podcast Episode\n\n\nKaggle\nDask\n\nData Engineering Podcast Episode\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Every machine learning model has to start with feature engineering. This is the process of combining input variables into a more meaningful signal for the problem that you are trying to solve. Many times this process can lead to duplicating code from previous projects, or introducing technical debt in the form of poorly maintained feature pipelines. In order to make the practice more manageable Soledad Galli created the feature-engine library. In this episode she explains how it has helped her and others build reusable transformations that can be applied in a composable manner with your scikit-learn projects. She also discusses the importance of understanding the data that you are working with and the domain in which your model will be used to ensure that you are selecting the right features.

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Announcements

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Interview

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Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Soledad Galli about the feature-engine library and how you can use it to build cleaner and more maintainable feature engineering pipelines for your scikit-learn models.","date_published":"2021-10-30T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/360bc422-03fa-4423-bad9-a4d5c6feb446.mp3","mime_type":"audio/mpeg","size_in_bytes":41057221,"duration_in_seconds":3209}]},{"id":"podlove-2021-10-25t21:57:14+00:00-5d6a1eee3af1299","title":"Speed Up Your Python Data Applications By Parallelizing Them With Bodo","url":"https://www.pythonpodcast.com/bodo-parallel-python-hpc-episode-337","content_text":"Summary\nThe speed of Python is a subject of constant debate, but there is no denying that for compute heavy work it is not the optimal tool. Rather than rewriting your data oriented applications, or having to rearchitect them, the team at Bodo wrote a compiler that will do the optimization for you. In this episode Ehsan Totoni explains how they are able to translate pure Python into massively parallel processes that are optimized for high performance compute systems.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Ehsan Totoni about Bodo, an inferential compiler for Python that automatically parallelizes your data oriented projects\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what Bodo is and the story behind it?\nWhat are some of the use cases that it is being applied to?\nWhat are the motivating factors for something like Dask or Ray as compared to Bodo?\nWhat are the software patterns that contribute to slowdowns in data processing code?\n\nWhat are some of the ways that the compiler is able to optimize those operations?\n\n\nCan you describe how Bodo is implemented?\nHow does Bodo process the Python code for compiling to the optimized form?\n\nWhat are the compilation techniques for understanding the semantics of the code being processed?\nHow do you manage packages that rely on C extensions?\nWhat do you use as an intermediate representation for translating into the optimized output?\n\n\nWhat is the workflow for applying Bodo to a Python project?\n\nWhat debugging utilities does it provide for identifying any errors that occur due to the added parallelism?\n\n\nWhat kind of support does Bodo have for optimizing a machine learning project with Bodo? (e.g. using PyTorch/Tensorflow/MxNet/etc.)\nWhen working with a workflow orchestrator such as Dagster for Airflow, what would the integration process look like for being able to take advantage of the optimized Bodo output?\nWhat are the most interesting, innovative, or unexpected ways that you have seen Bodo used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Bodo?\nWhen is Bodo the wrong choice?\nWhat do you have planned for the future of Bodo?\n\nKeep In Touch\n\nLinkedIn\n@EhsanTn on Twitter\nehsantn on GitHub\n\nPicks\n\nTobias\n\nParacord Crafts\n\n\nEhsan\n\n[\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\n\nLinks\n\nBodo\n\nData Engineering Podcast Episode\n\n\nUniversity of Illinois Urbana-Champaign\nHPC\nMPI\nElastic Fabric Adapter\nAll-to-All Communication\nDask\n\nData Engineering Podcast Episode\n\n\nRay\n\nPodcast Episode\n\n\nPandas Extension Arrays\n\nPodcast Episode\n\n\nGeoPandas\nNumba\nLLVM\nscikit-learn\nHorovod\nDagster\n\nPodcast.__init__ Episode\nData Engineering Podcast Episode\n\n\nAirflow\n\nPodcast Episode\n\n\nIPython Parallel\nParquet\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

The speed of Python is a subject of constant debate, but there is no denying that for compute heavy work it is not the optimal tool. Rather than rewriting your data oriented applications, or having to rearchitect them, the team at Bodo wrote a compiler that will do the optimization for you. In this episode Ehsan Totoni explains how they are able to translate pure Python into massively parallel processes that are optimized for high performance compute systems.

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Announcements

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Interview

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Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Bodo's CTO Ehsan Totoni about how they have built a compiler and execution platform that will automatically parallelize your data heavy Python applications","date_published":"2021-10-25T18:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/9e765988-2569-4cb2-ac1f-8629fd810981.mp3","mime_type":"audio/mpeg","size_in_bytes":47992445,"duration_in_seconds":3486}]},{"id":"podlove-2021-10-16t01:12:53+00:00-6053e1cd5f3f9ba","title":"An Exploration Of Financial Exchange Risk Management Strategies","url":"https://www.pythonpodcast.com/fx-risk-management-strategies-episode-33","content_text":"Summary\nThe world of finance has driven the development of many sophisticated techniques for data analysis. In this episode Paul Stafford shares his experiences working in the realm of risk management for financial exchanges. He discusses the types of risk that are involved, the statistical methods that he has found most useful for identifying strategies to mitigate that risk, and the software libraries that have helped him most in his work.\nAnnouncements\n\nHello and welcome to the Data Engineering Podcast, the show about modern data management\nWhen you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Paul Stafford about building risk models to guard against financial exchange rate volatility\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nWhat are the principles involved in risk management, and how are statistical methods used?\nHow did you get involved in financial markets?\n\nIn what ways did your background in science and engineering prepare you for work in finance and risk management?\n\n\nWhat are the tools that you have found most useful in your career in finance?\nHow have recent trends such as the widespread adoption of deep learning impacted the capabilities and risks present in foreign exchange strategies?\nWhat are the challenges that you face in obtaining and validating the input data that you are relying on for building financial and statistical models?\n\nHow has the volatility of the pandemic impacted the robustness and resilience of your predictive capabilities?\n\n\nWhat are the areas where the available tools are typically insufficient?\nWhat are the most interesting, innovative, or unexpected strategies or techniques that you have seen applied to risk management?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working in risk management?\nWhat are the economic and industry trends that you are keeping a close eye on for your work at Deaglo and your own personal projects?\n\nKeep In Touch\n\nLinkedIn\n\nPicks\n\nTobias\n\nThe Vault (movie)\n\n\nPaul\n\nMotorcycle Trip of the Grand Canyon\n\n\n\nLinks\n\nDeaglo Partners, LLC.\nValue At Risk (VaR)\nBlack-Scholes Equation\nLinear Algebra\nPrincipal Component Analysis\nEigenvectors and Eigenvalues\nMarkov Chain Monte Carlo\nViolin Plot\nKurtosis\nPyMC3\n\nPodcast Episode\n\n\nBayesian Regression\nConstrained Optimization\nEthereum\nSmart Contracts\nBehavioral Finance\nBlack Swan by Nassim Nicholas Taleb (affiliate link)\nSciPy Convention\nRealPython\n3Blue1Brown\nSentiment Analysis\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

The world of finance has driven the development of many sophisticated techniques for data analysis. In this episode Paul Stafford shares his experiences working in the realm of risk management for financial exchanges. He discusses the types of risk that are involved, the statistical methods that he has found most useful for identifying strategies to mitigate that risk, and the software libraries that have helped him most in his work.

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Announcements

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Interview

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Keep In Touch

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Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Paul Stafford about his experiences using computational methods to build risk management strategies for financial exchange markets.","date_published":"2021-10-15T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/bca45845-5092-47d7-a582-fc13aa15cf66.mp3","mime_type":"audio/mpeg","size_in_bytes":22833633,"duration_in_seconds":2070}]},{"id":"podlove-2021-10-09t01:16:36+00:00-c0184771dc136c4","title":"Build Better Machine Learning Models By Understanding Their Decisions With SHAP","url":"https://www.pythonpodcast.com/shap-explainable-machine-learning-episode-335","content_text":"Summary\nMachine learning and deep learning techniques are powerful tools for a large and growing number of applications. Unfortunately, it is difficult or impossible to understand the reasons for the answers that they give to the questions they are asked. In order to help shine some light on what information is being used to provide the outputs to your machine learning models Scott Lundberg created the SHAP project. In this episode he explains how it can be used to provide insight into which features are most impactful when generating an output, and how that insight can be applied to make more useful and informed design choices. This is a fascinating and important subject and this episode is an excellent exploration of how to start addressing the challenge of explainability.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Scott Lundberg about SHAP, a library that implements a game theoretic approach to explain the output of any machine learning model\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what SHAP is and the story behind it?\nWhat are some of the contexts that create the need to explain the reasoning behind the outputs of an ML model?\nHow do different types of models (deep learning, CNN/RNN, bayesian vs. frequentist, etc.) and different categories of ML (e.g. NLP, computer vision) influence the challenge of understanding the meaningful signals in their reasoning?\nTaking a step back, how do you define \"explainability\" when discussing inferences produced by ML models?\n\nWhat are the degrees of specificity/accuracy when seeking to understand the decision processes involved?\n\n\nCan you describe how SHAP is implemented?\n\nWhat are the signals that you are tracking to understand what features are being used to determine a given output?\nWhat are the assumptions that you had as you started this project that have been challenged or updated as you explored the problem in greater depth?\n\n\nCan you describe the workflow for someone using SHAP?\n\nWhat are the challenges faced by practitioners in interpreting the visualizations generated from SHAP?\n\n\nHow much domain knowledge and context is necessary to use SHAP effectively?\nWhat are the ongoing areas of research around tracking of ML decision processes?\nHow are you using SHAP in your own work?\nWhat are the most interesting, innovative, or unexpected ways that you have seen SHAP used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on SHAP?\nWhen is SHAP the wrong choice?\nWhat do you have planned for the future of SHAP?\n\nKeep In Touch\n\nslundberg on GitHub\nWebsite\nLinkedIn\n\nPicks\n\nTobias\n\nReminiscence\n\n\nScott\n\nAugustine’s Confessions\n\n\n\nLinks\n\nSHAP\nMicrosoft Research\nMatlab\nGame Theory\nComputational Biology\nLIME\nShapley Values\nJulia Language\nResNet\nCNN == Convolutional Neural Network\nRNN == Recurrent Neural Network\nA* Algorithm\nCFPB == Consumer Financial Protection Bureau\nNP Hard\nHuggingface\nRight for the Right Reasons: Training Differentiable Models by Constraining their Explanations\nNumba\nLog Odds\nInterpretML\nPolyjuice\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Machine learning and deep learning techniques are powerful tools for a large and growing number of applications. Unfortunately, it is difficult or impossible to understand the reasons for the answers that they give to the questions they are asked. In order to help shine some light on what information is being used to provide the outputs to your machine learning models Scott Lundberg created the SHAP project. In this episode he explains how it can be used to provide insight into which features are most impactful when generating an output, and how that insight can be applied to make more useful and informed design choices. This is a fascinating and important subject and this episode is an excellent exploration of how to start addressing the challenge of explainability.

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Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Scott Lundberg about his work on SHAP and how it can be used to understand the reasoning behind your machine learning model's decisions.","date_published":"2021-10-08T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/36b78298-f0b8-45cf-b024-96d96dbb3be7.mp3","mime_type":"audio/mpeg","size_in_bytes":51113438,"duration_in_seconds":3894}]},{"id":"podlove-2021-09-30t01:12:36+00:00-423ae66f4bad378","title":"Accelerating Drug Discovery Using Machine Learning With TorchDrug","url":"https://www.pythonpodcast.com/torchdrug-drug-discovery-machine-learning-episode-334","content_text":"Summary\nFinding new and effective treatments for disease is a complex and time consuming endeavor, requiring a high degree of domain knowledge and specialized equipment. Combining his expertise in machine learning and graph algorithms with is interest in drug discovery Jian Tang created the TorchDrug project to help reduce the amount of time needed to find new candidate molecules for testing. In this episode he explains how the project is being used by machine learning researchers and biochemists to collaborate on finding effective treatments for real-world diseases.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Jian Tang about TorchDrug\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what TorchDrug is and the story behind it?\nWhat are the goals of the TorchDrug project?\n\nWho are the target users of the project?\nWhat are the main ways that it is being used?\n\n\nWhat are the challenges faced by biologists and chemists working on development and discovery of pharmaceuticals?\n\nWhat are some of the other tools/techniques that they would use (in isolation or combination with TorchDrug)?\n\n\nCan you describe how TorchDrug is implemented?\n\nHow have you approached the design of the project and its APIs to make it accessible to engineers that don’t possess domain expertise in drug discovery research?\n\n\nHow do graph structures help when modeling and experimenting with chemical structures for drug discovery?\nWhat are the formats and sources of data that you are working with?\n\nWhat are some of the complexities/challenges that you have had to deal with to integrate with up or downstream systems to fit into the overall research process?\n\n\nCan you talk through the workflow of using TorchDrug to build and validate a model?\n\nWhat is involved in determining and codifying a goal state for the model to optimize for?\n\n\nWhat are the biggest open questions in the area of drug discovery and research?\n\nHow is TorchDrug being used to assist in the exploration of those problems?\n\n\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on TorchDrug?\nWhen is TorchDrug the wrong choice?\nWhat do you have planned for the future of TorchDrug?\n\nKeep In Touch\n\ntangjianpku on GitHub\n@tangjianpku on Twitter\nWebsite\nLinkedIn\n\nPicks\n\nTobias\n\nRope refactoring library\n\n\nJian\n\nAttending conferences once the pandemic is over\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nTorchDrug\nMila\nYoshua Bengio\nAlphafold\nFew-shot learning\nMetalearning\nPyTorch Geometric\nDeepGraph Library\nNetworKit\n\nPodcast Episode\n\n\ngraph-tool\n\nPodcast Episode\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Finding new and effective treatments for disease is a complex and time consuming endeavor, requiring a high degree of domain knowledge and specialized equipment. Combining his expertise in machine learning and graph algorithms with is interest in drug discovery Jian Tang created the TorchDrug project to help reduce the amount of time needed to find new candidate molecules for testing. In this episode he explains how the project is being used by machine learning researchers and biochemists to collaborate on finding effective treatments for real-world diseases.

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Announcements

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Interview

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Keep In Touch

\n\n

Picks

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Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Jian Tang about his work on TorchDrug to accelerate drug discovery for treatment of disease with the power of machine learning.","date_published":"2021-09-29T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/868dbbd5-608c-4598-bd72-5661600bcd9b.mp3","mime_type":"audio/mpeg","size_in_bytes":46454828,"duration_in_seconds":2670}]},{"id":"podlove-2021-09-26t00:52:23+00:00-a45690c65bf8fd4","title":"An Exploration Of Automated Speech Recognition","url":"https://www.pythonpodcast.com/automated-speech-recognition-episode-333","content_text":"Summary\nThe overwhelming growth of smartphones, smart speakers, and spoken word content has corresponded with increasingly sophisticated machine learning models for recognizing speech content in audio data. Dylan Fox founded Assembly to provide access to the most advanced automated speech recognition models for developers to incorporate into their own products. In this episode he gives an overview of the current state of the art for automated speech recognition, the varying requirements for accuracy and speed of models depending on the context in which they are used, and what is required to build a special purpose model for your own ASR applications.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Dylan Fox about the challenges of training and deploying large models for automated speech recognition\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nWhat is involved in building an ASR model?\n\nHow does the complexity/difficulty compare to models for other data formats? (e.g. computer vision, NLP, NER, etc.)\n\n\nHow have ASR models changed over the last 5, 10, 15 years?\nWhat are some other categories of ML applications that work with audio data?\n\nHow does the level of complexity compare to ASR applications?\n\n\nWhat is the typical size of an ASR model that you are deploying at Assembly?\n\nWhat are the factors that contribute to the overall size of a given model?\n\n\nHow does accuracy compare with model size?\nHow does the size of a model contribute to the overall challenge of deploying/monitoring/scaling it in a production environment?\nHow can startups effectively manage the time/cost that comes with training large models?\nWhat are some techniques that you use/attributes that you focus on for feature definitions in the source audio data?\nCan you describe the lifecycle stages of an ASR model at Assembly?\nWhat are the aspects of ASR which are still intractable or impractical to productionize?\nWhat are the most interesting, innovative, or unexpected ways that you have seen ASR technology used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on ASR?\nWhat are the trends in research or industry that you are keeping an eye on?\n\nKeep In Touch\n\nLinkedIn\n@YouveGotFox on Twitter\n\nPicks\n\nTobias\n\nThe Hitman’s Wife’s Bodyguard\n\n\nDylan\n\nInspiration 4 Documentary\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nLearn Python The Hard Way\nDeepSpeech\nWav2Letter\nBERT\nGPT-3\nConvolutional Neural Network (CNN)\nRecurrent Neural Network (RNN)\nMycroft\n\nPodcast Episode\n\n\nCMU Sphinx\nPocket Sphinx\nGaussian Mixture Model (GMM)\nHidden Markov Model (HMM)\nDeepSpeech Paper\nTransformer Architecture\nAudio Analytic Sound Recognition Podcast Episode\nHorovod distributed training library\nKnowledge Distillation\nLibre Speech Data Set\nLambda Labs\nWav2Vec\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

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The overwhelming growth of smartphones, smart speakers, and spoken word content has corresponded with increasingly sophisticated machine learning models for recognizing speech content in audio data. Dylan Fox founded Assembly to provide access to the most advanced automated speech recognition models for developers to incorporate into their own products. In this episode he gives an overview of the current state of the art for automated speech recognition, the varying requirements for accuracy and speed of models depending on the context in which they are used, and what is required to build a special purpose model for your own ASR applications.

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Announcements

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Interview

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Keep In Touch

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Picks

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Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"A conversation about the current state of the art for automated speech recognition, the challenges involved in providing fast and accurate processing of speech audio data, and how to start working on building your own models.","date_published":"2021-09-25T20:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/7160908d-9882-4a56-9468-600a71275297.mp3","mime_type":"audio/mpeg","size_in_bytes":48274194,"duration_in_seconds":3241}]},{"id":"podlove-2021-09-19t20:45:33+00:00-687b01f44e4a8d6","title":"Experimenting With Reinforcement Learning Using MushroomRL","url":"https://www.pythonpodcast.com/mushroomrl-reinforcement-learning-library-episode-332","content_text":"Summary\nReinforcement learning is a branch of machine learning and AI that has a lot of promise for applications that need to evolve with changes to their inputs. To support the research happening in the field, including applications for robotics, Carlo D’Eramo and Davide Tateo created MushroomRL. In this episode they share how they have designed the project to be easy to work with, so that students can use it in their study, as well as extensible so that it can be used by businesses and industry professionals. They also discuss the strengths of reinforcement learning, how to design problems that can leverage its capabilities, and how to get started with MushroomRL for your own work.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Davide Tateo and Carlo D’Eramo about MushroomRL, a library for building reinforcement learning experiments\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what reinforcement learning is and how it differs from other approaches for machine learning?\nWhat are some example use cases where reinforcement learning might be necessary?\nCan you describe what MushroomRL is and the story behind it?\n\nWho are the target users of the project?\nWhat are its main goals?\n\n\nWhat are your suggestions to other developers for implementing a\nsuccesful library?\nWhat are some of the core concepts that researchers and/or engineers need to understand to be able to effectively use reinforcement learning techniques?\nCan you describe how MushroomRL is architected?\n\nHow have the goals and design of the project changed or evolved since you began working on it?\n\n\nWhat is the workflow for building and executing an experiment with MushroomRL?\n\nHow do you track the states and outcomes of experiments?\n\n\nWhat are some of the considerations involved in designing an environment and reward functions for an agent to interact with?\nWhat are some of the open questions that are being explored in reinforcement learning?\nHow are you using MushroomRL in your own research?\nWhat are the most interesting, innovative, or unexpected ways that you have seen MushroomRL used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on MushroomRL?\nWhen is MushroomRL the wrong choice?\nWhat do you have planned for the future of MushroomRL?\nHow can the open-source community contribute to MushroomRL?\nWhat kind of support you are willing to provide to users?\n\nKeep In Touch\n\nDavide\n\nboris-il-forte on GitHub\nWebsite\n\n\nCarlo\n\ncarloderamo on GitHub\nWebsite\n\n\n\nPicks\n\nTobias\n\nBritannia TV Series\n\n\nDavide\n\n1984 by George Orwell\n\n\nCarlo\n\nTwin Peaks TV Series\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nMushroomRL\nTU Darmstadt\nMuJoCo\nPyBullet\niGibson\nHabitat\nOpenAI Gym\nPyTorch\n\nPodcast Episode\n\n\nRLLib\nRay\n\nPodcast Episode\n\n\nOpenAI Baselines\nStable Baselines\nROS\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

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Reinforcement learning is a branch of machine learning and AI that has a lot of promise for applications that need to evolve with changes to their inputs. To support the research happening in the field, including applications for robotics, Carlo D’Eramo and Davide Tateo created MushroomRL. In this episode they share how they have designed the project to be easy to work with, so that students can use it in their study, as well as extensible so that it can be used by businesses and industry professionals. They also discuss the strengths of reinforcement learning, how to design problems that can leverage its capabilities, and how to get started with MushroomRL for your own work.

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Announcements

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Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about the MushroomRL library for experimenting with reinforcement learning techniques and its use for researching robotics applications.","date_published":"2021-09-19T16:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/fe20a2a0-b31a-4c22-a8ae-e28273cccef2.mp3","mime_type":"audio/mpeg","size_in_bytes":41463083,"duration_in_seconds":3258}]},{"id":"podlove-2021-09-10t02:00:33+00:00-954d51bbe70a8d4","title":"Doing Dask Powered Data Science In The Saturn Cloud","url":"https://www.pythonpodcast.com/saturn-cloud-scaling-open-source-python-data-science-episode-331","content_text":"Summary\nA perennial problem of doing data science is that it works great on your laptop, until it doesn’t. Another problem is being able to recreate your environment to collaborate on a problem with colleagues. Saturn Cloud aims to help with both of those problems by providing an easy to use platform for creating reproducible environments that you can use to build data science workflows and scale them easily with a managed Dask service. In this episode Julia Signall, head of open source at Saturn Cloud, explains how she is working with the product team and PyData community to reduce the points of friction that data scientists encounter as they are getting their work done.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Julia Signell about building distributed processing workflows in Python through the power of Dask\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what you are building at Saturn Cloud?\n\nWho are your target users and how does that inform the features and priorities that you build into your platform?\n\n\nWhat are the road blocks that data scientists typically encounter when working on their laptop/workstation?\nHow does open source factor into the Saturn product?\n\nWhat are some of the projects that you are collaborating with/contributing to as part of your work at Saturn?\nHow has your experience at Anaconda informed your work at Saturn?\n\n\nCan you describe how the Saturn Cloud platform is architected?\n\nHow has it changed or evolved since it was first launched?\n\n\nCan you describe the learning curve that data scientists go through when adopting Dask?\nWhat are some examples of projects or workflows that Dask enables which are not possible/practical to do locally?\nHow would you characterize the overall awareness/adoption of Dask in the Python data science community?\nWhat are the most interesting, innovative, or unexpected ways that you have seen Saturn Cloud used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Saturn Cloud?\nWhen is Saturn Cloud the wrong choice?\nWhat do you have planned for the future of Saturn Cloud?\n\nKeep In Touch\n\n@jsignell on Twitter\njsignell on GitHub\n\nPicks\n\nTobias\n\nPeter Rabbit 2\n\n\nJulia\n\nPawPaw Fruit\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nSaturn Cloud\nDask\n\nPodcast Episode\n\n\nPangeo\nXArray\nConda\nMamba\nHoloviz\nDash\nAnaconda\n\nPodcast Episode\n\n\nKubernetes\nTornado\n\nPodcast Episode\n\n\nPrefect\n\nPodcast Episode\n\n\nDagster\n\nPodcast Episode\n\n\nAirflow\nRay\n\nPodcast Episode\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

A perennial problem of doing data science is that it works great on your laptop, until it doesn’t. Another problem is being able to recreate your environment to collaborate on a problem with colleagues. Saturn Cloud aims to help with both of those problems by providing an easy to use platform for creating reproducible environments that you can use to build data science workflows and scale them easily with a managed Dask service. In this episode Julia Signall, head of open source at Saturn Cloud, explains how she is working with the product team and PyData community to reduce the points of friction that data scientists encounter as they are getting their work done.

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Announcements

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Interview

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Keep In Touch

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Picks

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Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about how the Saturn Cloud platform is working to make scaling and collaborating on projects built with the open source Python data science ecosystem easier to manage.","date_published":"2021-09-09T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/1218367c-e0c9-4269-9731-436e1b434320.mp3","mime_type":"audio/mpeg","size_in_bytes":29369247,"duration_in_seconds":2280}]},{"id":"podlove-2021-09-03t00:55:24+00:00-c5b7cfb9b3d555e","title":"Monitor The Health Of Your Machine Learning Products In Production With Evidently","url":"https://www.pythonpodcast.com/evidently-machine-learning-monitoring-episode-330","content_text":"Summary\nYou’ve got a machine learning model trained and running in production, but that’s only half of the battle. Are you certain that it is still serving the predictions that you tested? Are the inputs within the range of tolerance that you designed? Monitoring machine learning products is an essential step of the story so that you know when it needs to be retrained against new data, or parameters need to be adjusted. In this episode Emeli Dral shares the work that she and her team at Evidently are doing to build an open source system for tracking and alerting on the health of your ML products in production. She discusses the ways that model drift can occur, the types of metrics that you need to track, and what to do when the health of your system is suffering. This is an important and complex aspect of the machine learning lifecycle, so give it a listen and then try out Evidently for your own projects.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Emeli Dral about monitoring machine learning models in production with Evidently\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what Evidently is and the story behind it?\nWhat are the metrics that are useful for determining the performance and health of a machine learning model?\n\nWhat are the questions that you are trying to answer with those metrics?\n\n\nHow does monitoring of machine learning models compare to monitoring of infrastructure or \"traditional\" software projects?\nWhat are the failure modes for a model?\nCan you describe the design and implementation of Evidently?\n\nHow has the architecture changed or evolved since you started working on it?\n\n\nWhat categories of model is Evidently designed to work with?\n\nWhat are some strategies for making models conducive to monitoring?\n\n\nWhat is involved in monitoring a model on a continuous basis?\nWhat are some considerations when establishing useful thresholds for metrics to alert on?\n\nOnce an alert has been triggered what is the process for resolving it?\nIf the training process takes a long time, how can you mitigate the impact of a model failure until the new/updated version is deployed?\n\n\nWhat are the most interesting, innovative, or unexpected ways that you have seen Evidently used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Evidently?\nWhen is Evidently the wrong choice?\nWhat do you have planned for the future of Evidently?\n\nKeep In Touch\n\nLinkedIn\n@EmeliDral on Twitter\nemeli-dral on GitHub\n\nPicks\n\nTobias\n\nThe Suicide Squad\n\n\nEmeli\n\nAirflow\n\n\n\nLinks\n\nEvidently AI\n\nOpen Source\n\n\nYandex\nGrafana\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

You’ve got a machine learning model trained and running in production, but that’s only half of the battle. Are you certain that it is still serving the predictions that you tested? Are the inputs within the range of tolerance that you designed? Monitoring machine learning products is an essential step of the story so that you know when it needs to be retrained against new data, or parameters need to be adjusted. In this episode Emeli Dral shares the work that she and her team at Evidently are doing to build an open source system for tracking and alerting on the health of your ML products in production. She discusses the ways that model drift can occur, the types of metrics that you need to track, and what to do when the health of your system is suffering. This is an important and complex aspect of the machine learning lifecycle, so give it a listen and then try out Evidently for your own projects.

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Announcements

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Interview

\n\n

Keep In Touch

\n\n

Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Emeli Dral about the importance of monitoring your machine learning projects in production, how to use Evidently to gain visibility into your model performance, and what to do when its health begins to suffer.","date_published":"2021-09-02T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/0a3e222c-aeb9-49bf-b2cc-6a8c3d5ad2e2.mp3","mime_type":"audio/mpeg","size_in_bytes":36096883,"duration_in_seconds":3059}]},{"id":"podlove-2021-08-25t10:50:54+00:00-07b05609ccfc7df","title":"Making Automated Machine Learning More Accessible With EvalML","url":"https://www.pythonpodcast.com/evalml-automated-machine-learning-episode-329","content_text":"Summary\nBuilding a machine learning model is a process that requires a lot of iteration and trial and error. For certain classes of problem a large portion of the searching and tuning can be automated. This allows data scientists to focus their time on more complex or valuable projects, as well as opening the door for non-specialists to experiment with machine learning. Frustrated with some of the awkward or difficult to use tools for AutoML, Angela Lin and Jeremy Shih helped to create the EvalML framework. In this episode they share the use cases for automated machine learning, how they have designed the EvalML project to be approachable, and how you can use it for building and training your own models.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Angela Lin, Jeremy Shih about EvalML, an AutoML library which builds, optimizes, and evaluates machine learning pipelines\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what EvalML is and the story behind it?\nWhat do we mean by the term AutoML?\nWhat are the kinds of problems that are best suited to applications of automated ML?\nWhat does the landscape for AutoML tools look like?\n\nWhat was missing in the available offerings that motivated you and your team to create EvalML?\n\n\nWho is the target audience for EvalML?\nHow is the EvalML project implemented?\n\nHow has the project changed or evolved since you first began working on it?\n\n\nWhat is the workflow for building a model with EvalML?\n\nCan you describe the preprocessing steps that are necessary and the input formats that it is expecting?\n\n\nWhat are the supported algorithms/model architectures?\nHow does EvalML explore the search space for an optimal model?\n\nWhat decision functions does it employ to determine an appropriate stopping point?\n\n\nWhat is involved in operationalizing an AutoML pipeline?\nWhat are some challenges or edge cases that you see users of EvalML run into?\nWhat are the most interesting, innovative, or unexpected ways that you have seen EvalML used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on EvalML?\nWhen is EvalML the wrong choice?\nWhen is auto ML the wrong approach?\nWhat do you have planned for the future of EvalML?\n\nKeep In Touch\n\nAngela\n\nangela97lin on GitHub\nLinkedIn\n\n\nJeremy\n\njeremyliweishih on GitHub\nLinkedIn\n\n\n\nPicks\n\nTobias\n\nGloryhammer\n\n\nAngela\n\nSarma mediterranean restaurant\n\n\nJeremy\n\nCrucial Conversations by Stephen Covey (affiliate link)\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nEvalML\nFeatureLabs\nAlteryx\nScheme\nNetLogo\nFlask\nAutoML\nWoodwork\nFeatureTools\nCompose\nRandom Forest\nXGBoost\nProphet\nGreyKite\nShap\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

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Building a machine learning model is a process that requires a lot of iteration and trial and error. For certain classes of problem a large portion of the searching and tuning can be automated. This allows data scientists to focus their time on more complex or valuable projects, as well as opening the door for non-specialists to experiment with machine learning. Frustrated with some of the awkward or difficult to use tools for AutoML, Angela Lin and Jeremy Shih helped to create the EvalML framework. In this episode they share the use cases for automated machine learning, how they have designed the EvalML project to be approachable, and how you can use it for building and training your own models.

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Announcements

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Interview

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Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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\n\n

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","summary":"An interview with Angela Lin and Jeremy Shih about the open source EvalML framework for building automated machine learning workflows.","date_published":"2021-08-25T06:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/492f4432-0738-4c01-88c0-5895970a34f5.mp3","mime_type":"audio/mpeg","size_in_bytes":36920076,"duration_in_seconds":2753}]},{"id":"podlove-2021-08-19t11:10:08+00:00-fa349fd6e5697a4","title":"Growing And Supporting The Data Science Community At Anaconda","url":"https://www.pythonpodcast.com/anaconda-python-data-science-episode-328","content_text":"Summary\nData scientists are tasked with answering challenging questions using data that is often messy and incomplete. Anaconda is on a mission to make the lives of data professionals more manageable through creation and maintenance of high quality libraries and frameworks, the distribution of an easy to use Python distribution and package ecosystem, and high quality training material. In this episode Kevin Goldsmith, CTO of Anaconda, discusses the technical and social challenges faced by data scientists, the ways that the Python ecosystem has evolved to help address those difficulties, and how Anaconda is engaging with the community to provide high quality tools and education for this constantly changing practice.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Kevin Goldsmith about Anaconda’s contributions to the Python ecosystem for data science\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what Anaconda focuses on solving for?\n\nWhat was your path into the CTO position?\n\n\nFrom your perspective as the CTO of Anaconda, what are the biggest challenges facing data scientists today?\n\nWhat is the breakdown between technical and organizational sources for those difficulties?\n\n\nHow is the Anaconda product suite architected to help address some of those problems?\nWhere are you spending your focus to allow Anaconda to address the current and future needs of data scientists?\nPython has been a dominant force in the data and analytics ecosystem for several years now. What do you see as the future of the space? (e.g. monoglot vs. polyglot workflows)\nWhat are the most interesting, innovative, or unexpected ways that you have seen the Anaconda platform used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Anaconda and data science tooling?\n\nKeep In Touch\n\nLinkedIn\n@KevinGoldsmith on Twitter\nWebsite\n\nPicks\n\nTobias\n\nPerdido Street Station\nThe Scar\nIron Council\n\n\nKevin\n\nLego Typewriter\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nAnaconda\nSpotify\nLisp\nScheme\nC#\nAnaconda Nucleus\nPyData\nAnacondaCon\nGrid Computing\nPyTorch\n\nPodcast Episode\n\n\nTensorflow\nPyston\n\nPodcast Episode\n\n\nDask\n\nPodcast Episode\n\n\nNumba\nPanel dashboard framework\nDatashader\nJupyter\nR\nJulia\nAstroPy\n\nPodcast Episode\n\n\nArrow\nData Teams by Jesse Anderson\n\nPodcast Episode\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Data scientists are tasked with answering challenging questions using data that is often messy and incomplete. Anaconda is on a mission to make the lives of data professionals more manageable through creation and maintenance of high quality libraries and frameworks, the distribution of an easy to use Python distribution and package ecosystem, and high quality training material. In this episode Kevin Goldsmith, CTO of Anaconda, discusses the technical and social challenges faced by data scientists, the ways that the Python ecosystem has evolved to help address those difficulties, and how Anaconda is engaging with the community to provide high quality tools and education for this constantly changing practice.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
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","summary":"An interview with Kevin Goldsmith, CTO of Anaconda, about the challenges that data scientists are faced with, how the role is continuing to evolve, and the tools and educational resources that they are building to support the community","date_published":"2021-08-19T07:15:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/972be8af-bdb1-4af9-b5c7-2d15d0abf208.mp3","mime_type":"audio/mpeg","size_in_bytes":48043209,"duration_in_seconds":3348}]},{"id":"podlove-2021-08-15t02:25:16+00:00-bfddf538686ee77","title":"Network Analysis At The Speed Of C With The Power Of Python Using NetworKit","url":"https://www.pythonpodcast.com/networkit-efficient-network-analysis-episode-327","content_text":"Summary\nAnalysing networks is a growing area of research in academia and industry. In order to be able to answer questions about large or complex relationships it is necessary to have fast and efficient algorithms that can process the data quickly. In this episode Eugenio Angriman discusses his contributions to the NetworKit library to provide an accessible interface for these algorithms. He shares how he is using NetworKit for his own research, the challenges of working with large and complex networks, and the kinds of questions that can be answered with data that fits on your laptop.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Eugenio Angriman about NetworKit, an open-source toolkit for large-scale network analysis\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what NetworKit is and the story behind it?\nA core focus of the project is for use with graphs containing millions to billions of nodes. What are some of the situations where you might encounter networks of that scale?\nThere are a number of network analysis libraries in Python. How would you characterize NetworKit’s position in the ecosystem?\nWhat are the algorithmic challenges that graph structures pose when aiming for scalability and performance?\n\nHow do you approach building efficient algorithms for complex network analysis?\n\n\nCan you describe how NetworKit is architected?\n\nWhat are the design principles that you focus on for the library?\nHow have the design and goals of the project changed or evolved since you have been working on it?\n\n\nNetworKit’s code base has now a discrete size and several developers contributed to it. Are there any minimum quality requirements that new code needs to fulfill before it can be merged into NetworKit? How do you ensure that such requirements are met?\nWhat are some of the active areas of research for networked data analysis?\nHow are you using NetworKit for your own work?\nWhat are kind of background knowledge in graph analysis is necessary for users of NetworKit?\nWhat are some of the underutilized or overlooked aspects of NetworKit that you think should be highlighted?\nWhat are the most interesting, innovative, or unexpected ways that you have seen NetworKit used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on NetworKit?\nWhen is NetworKit the wrong choice?\nWhat do you have planned for the future of NetworKit?\n\nKeep In Touch\n\nangriman on GitHub\nLinkedIn\n\nPicks\n\nTobias\n\nEdgar Allen Poe\n\n\nNetworKit\n\nThe Spinoza Problem\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nNetworKit\nHumboldt University Berlin\ngraph-tool\n\nPodcast Episode\n\n\nNetworkX\nAdjacency List\nCython\n\nPodcast Episode\n\n\nNode Embeddings\nCentrality Score\nNetworKit In The Cloud\nGunrock\nHornet\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Analysing networks is a growing area of research in academia and industry. In order to be able to answer questions about large or complex relationships it is necessary to have fast and efficient algorithms that can process the data quickly. In this episode Eugenio Angriman discusses his contributions to the NetworKit library to provide an accessible interface for these algorithms. He shares how he is using NetworKit for his own research, the challenges of working with large and complex networks, and the kinds of questions that can be answered with data that fits on your laptop.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview with Eugenio Angriman about the NetworKit library and how you can use it to gain insights into large volumes of networked data","date_published":"2021-08-14T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/8a7c9806-61ed-444f-8356-ccf33cdf8d81.mp3","mime_type":"audio/mpeg","size_in_bytes":28112060,"duration_in_seconds":2227}]},{"id":"podlove-2021-08-04t00:36:19+00:00-e0988a2eccb41a4","title":"Delivering Deep Learning Powered Speech Recognition As A Service For Developers At AssemblyAI","url":"https://www.pythonpodcast.com/assemblyai-deep-learning-speech-recognition-episode-326","content_text":"Summary\nBuilding a software-as-a-service (SaaS) business is a fairly well understood pattern at this point. When the core of the service is a set of machine learning products it introduces a whole new set of challenges. In this episode Dylan Fox shares his experience building Assembly AI as a reliable and affordable option for automatic speech recognition that caters to a developer audience. He discusses the machine learning development and deployment processes that his team relies on, the scalability and performance considerations that deep learning models introduce, and the user experience design that goes into building for a developer audience. This is a fascinating conversation about a unique cross-section of considerations and how Dylan and his team are building an impressive and useful service.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Dylan Fox about AssemblyAI, a powerful and easy to use speech recognition API designed for developers\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what Assembly AI is and the story behind it?\nSpeech recognition is a service that is being added to every cloud platform, video service, and podcast product. What do you see as the motivating factors for the current growth in this industry?\n\nHow would you characterize your overall position in the market?\n\n\nWhat are the core goals that you are focused on with AssemblyAI?\nCan you describe the different ways that you are using Python across the company?\nHow is the AssemblyAI platform architected?\n\nWhat are the complexities that you have to work around to maintain high uptime for an API powered by a deep learning model?\nWhat are the scaling challenges that crop up, whether on the training or serving?\n\n\nWhat are the axes for improvement for a speech recognition model?\n\nHow do you balance tradeoffs of speed and accuracy as you iterate on the model?\n\n\nWhat is your process for managing the deep learning workflow?\nHow do you manage CI/CD for your deep learning models?\nWhat are the open areas of research in speech recognition?\nWhat are the most interesting, innovative, or unexpected ways that you have seen AssemblyAI used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on AssemblyAI?\nWhen is AssemblyAI the wrong choice?\nWhat do you have planned for the future of AssemblyAI?\n\nKeep In Touch\n\nLinkedIn\n@YouveGotFox on Twitter\n\nPicks\n\nTobias\n\nH.P. Lovecraft\n\n\nDylan\n\nProject Hail Mary by Andy Weir\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nAssemblyAI\nTwo Scoops of Django\nNuance\nDragon Natural Speaking\nPyTorch\n\nPodcast Episode\n\n\nTensorflow\nFastAPI\nFlask\nTornado\n\nPodcast Episode\n\n\nNeural Magic\n\nPodcast Episode\n\n\nThe Martian\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Building a software-as-a-service (SaaS) business is a fairly well understood pattern at this point. When the core of the service is a set of machine learning products it introduces a whole new set of challenges. In this episode Dylan Fox shares his experience building Assembly AI as a reliable and affordable option for automatic speech recognition that caters to a developer audience. He discusses the machine learning development and deployment processes that his team relies on, the scalability and performance considerations that deep learning models introduce, and the user experience design that goes into building for a developer audience. This is a fascinating conversation about a unique cross-section of considerations and how Dylan and his team are building an impressive and useful service.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview with Dylan Fox about the technological and business challenges of delivering a high quality experience for developers that is powered by deep learning models for automated speech recognition","date_published":"2021-08-03T20:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/bbacaff5-3805-4784-87b8-829b057ee250.mp3","mime_type":"audio/mpeg","size_in_bytes":40704170,"duration_in_seconds":3140}]},{"id":"podlove-2021-07-28t02:48:55+00:00-6895044f2133136","title":"Taking Aim At The Legacy Of SQL With The Preql Relational Language","url":"https://www.pythonpodcast.com/preql-relational-algebra-sql-replacement-episode-325","content_text":"Summary\nSQL has gone through many cycles of popularity and disfavor. Despite its longevity it is objectively challenging to work with in a collaborative and composable manner. In order to address these shortcomings and build a new interface for your database oriented workloads Erez Shinan created Preql. It is based on the same relational algebra that inspired SQL, but brings in more robust computer science principles to make it more manageable as you scale in complexity. In this episode he shares his motivation for creating the Preql project, how he has used Python to develop a new language for interacting with database engines, and the challenges of taking on the legacy of SQL as an individual.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Erez Shinan about Preql, an interpreted, relational programming language, that specializes in database queries\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what Preql is and the story behind it?\n\nWhat are goals and target use cases for the project?\n\n\nThere have been numerous projects that aim to make SQL more maintainable and composable. What is it about the language and syntax that makes it so challenging?\n\nHow does Preql approach this problem that is different from other efforts? (e.g. ORMs, dbt-style Jinja, PyPika)\n\n\nHow did you approach the design of the syntax to make it familiar to people who know SQL?\nCan you describe how Preql is implemented?\n\nHow has the design and architecture changed or evolved since you began working on it?\n\n\nWhat is a typical workflow for someone using Preql to build a library of analytical queries?\nBeyond strict compilation to SQL, what are some of the other features that you have incorporated into Preql?\n\nHow does a Preql program get executed against a target database, particularly when using capabilities that can’t be directly translated to SQL?\n\n\n** What are the main difficulties / challenges of compiling to SQL ?\nWhat are some of the features or use cases that are not immediately obvious or prone to be overlooked that you think are worth mentioning?\nWhat are the most interesting, innovative, or unexpected ways that you have seen Preql used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Preql?\nWhen is Preql the wrong choice?\nWhat do you have planned for the future of Preql?\n\nKeep In Touch\n\nerezsh on GitHub\nerezsh on Twitter\n\nPicks\n\nTobias\n\nCounterpart\n\n\nErez\n\nBansko, Bulgaria\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nPreql\nLark\nPostgres\n\nData Engineering Podcast Episode\n\n\nMySQL\nRelational Algebra\nPandas\n\nPodcast Episode\n\n\nORM == Object Relational Mapper\ndbt\n\nData Engineering Podcast Episode\n\n\nPyPika\nGraphQL\nJulia\nruntype\nRich terminal UI library\nprompt-toolkit\nDuckDB\nAskgit\nBigQuery\nSnowflake\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

SQL has gone through many cycles of popularity and disfavor. Despite its longevity it is objectively challenging to work with in a collaborative and composable manner. In order to address these shortcomings and build a new interface for your database oriented workloads Erez Shinan created Preql. It is based on the same relational algebra that inspired SQL, but brings in more robust computer science principles to make it more manageable as you scale in complexity. In this episode he shares his motivation for creating the Preql project, how he has used Python to develop a new language for interacting with database engines, and the challenges of taking on the legacy of SQL as an individual.

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Announcements

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Interview

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Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview with Erez Shinan about his work on the ambitious Preql project to replace SQL as the default interface for relational database engines.","date_published":"2021-07-27T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/875e0e76-899b-4b07-b4a3-e2292b8b146f.mp3","mime_type":"audio/mpeg","size_in_bytes":30433530,"duration_in_seconds":2198}]},{"id":"podlove-2021-07-17t12:07:39+00:00-8984c0027cd81fa","title":"Unleash The Power Of Dataframes At Any Scale With Modin","url":"https://www.pythonpodcast.com/modin-parallel-dataframe-episode-324","content_text":"Summary\nWhen you start working on a data project there are always a variety of unknown factors that you have to explore. One of those is the volume of total data that you will eventually need to handle, and the speed and scale at which it will need to be processed. If you optimize for scale too early then it adds a high barrier to entry due to the complexities of distributed systems, but if you invest in a lot of engineering up front then it can be challenging to refactor for scale. Modin is a project that aims to remove that decision by letting you seamlessly replace your existing Pandas code and scale across CPU cores or across a cluster of machines. In this episode Devin Petersohn explains why he started working on solving this problem, how Modin is architected to allow for a smooth escalation from small to large volumes of data and compute, and how you can start using it today to accelerate your Pandas workflows.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Devin Petersohn about Modin, a Pandas compatible dataframe library for datasets from 1MB to 1TB+\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what Modin is and the story behind it?\n\nWhy study dataframes?\n\n\nHow do dataframes compare to databases?\n\nWhat can you do in a dataframe that you couldn’t in a database?\n\n\nWhat are your overall goals for the Modin project?\nWho are the target users of Modin and how does that influence your prioritization of features?\nWhat are some of the API inconsistencies that you have had to abstract and work around between Pandas, Ray, and Dask to give users a seamless experience?\nWhat are some of the considerations in terms of capabilities or user experience that will influence whether to use Ray or Dask as the execution engine?\nCan you describe how Modin is implemented?\n\nHow has the constraint of replicating the Pandas API influenced your architectural choices?\nWhat are the most complex or challenging Pandas APIs to replicate in Modin?\n\n\nIn addition to the core Pandas API you have also added experimental features such as SQL support and a spreadsheet interface. How have those capabilities affected the range of potential use cases and end users?\nWhat are some of the complexities that come from acting as a middleware between the Pandas API and the Ray and Dask frameworks?\nWhat are some of the initial ideas or assumptions that you had about the design or utility of Modin that have been challenged as you worked through building and releasing it?\nWhat are the most interesting, innovative, or unexpected ways that you have seen Modin used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Modin?\nWhen is Modin the wrong choice?\nWhat do you have planned for the future of Modin?\n\nKeep In Touch\n\ndevin-petersohn on GitHub\nLinkedIn\n\nPicks\n\nTobias\n\nxxh\n\n\nDevin\n\nLux\n\nPodcast Episode\n\n\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nModin\nUC Berkeley\nRISELAB\nXArray\nPandas\n\nPodcast Episode\n\n\nDask\n\nPodcast Episode\n\n\nRay\n\nPodcast Episode\n\n\nSpark\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

When you start working on a data project there are always a variety of unknown factors that you have to explore. One of those is the volume of total data that you will eventually need to handle, and the speed and scale at which it will need to be processed. If you optimize for scale too early then it adds a high barrier to entry due to the complexities of distributed systems, but if you invest in a lot of engineering up front then it can be challenging to refactor for scale. Modin is a project that aims to remove that decision by letting you seamlessly replace your existing Pandas code and scale across CPU cores or across a cluster of machines. In this episode Devin Petersohn explains why he started working on solving this problem, how Modin is architected to allow for a smooth escalation from small to large volumes of data and compute, and how you can start using it today to accelerate your Pandas workflows.

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Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview with Devin Petersohn about his work on Modin to make scaling Pandas workflows effortless from a single laptop to an entire datacenter.","date_published":"2021-07-21T20:45:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/230919bd-3870-4db8-bed1-c734a9a29ece.mp3","mime_type":"audio/mpeg","size_in_bytes":31902401,"duration_in_seconds":2333}]},{"id":"podlove-2021-07-14t01:17:39+00:00-4fc89a02a0fd215","title":"Exploring The SpeechBrain Toolkit For Speech Processing","url":"https://www.pythonpodcast.com/speechbrain-deep-learning-speech-toolkit-episode-323","content_text":"Summary\nWith the rising availability of computation in everyday devices, there has been a corresponding increase in the appetite for voice as the primary interface. To accomodate this desire it is necessary for us to have high quality libraries for being able to process and generate audio data that can make sense of human speech. To facilitate research and industry applications for speech data Mirco Ravanelli and Peter Plantinga are building SpeechBrain. In this episode they explain how it works under the hood, the projects that they are using it for, and how you can get started with it today.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Mirco Ravanelli and Peter Plantinga about SpeechBrain, an open-source and all-in-one speech toolkit powered by PyTorch\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what SpeechBrain is and the story behind it?\nWhat are the goals and target use cases of the SpeechBrain project?\nWhat are some of the ways that processing audio with a focus on speech differs from more general audio processing?\nWhat are some of the other libraries/frameworks/services that are available to work with speech data and what are the unique capabilities that SpeechBrain offers?\nHow is SpeechBrain implemented?\n\nWhat was your decision process for determining which framework to build on top of?\nWhat are some of the original ideas and assumptions that you had for SpeechBrain which have been changed or invalidated as you worked through implementing it?\n\n\nCan you talk through the workflow of using SpeechBrain?\n\nWhat would be involved in developing a system to automate transcription with speaker recognition and diarization?\n\n\nIn the documentation it mentions that SpeechBrain is built to be used for research purposes. What are some of the kinds of research that it is being used for?\nWhat are some of the features or capabilities of SpeechBrain which might be non-obvious that you would like to highlight?\nWhat are the most interesting, innovative, or unexpected ways that you have seen SpeechBrain used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on SpeechBrain?\nWhen is SpeechBrain the wrong choice?\nWhat do you have planned for the future of SpeechBrain?\n\nKeep In Touch\n\nMirco\n\nmravanelli on GitHub\nLinkedIn\n@mirco_ravanelli on Twitter\n\n\nPeter\n\npplantinga on GitHub\n@ComPeterScience on Twitter\nWebsite\nLinkedIn\n\n\n\nPicks\n\nTobias\n\nx.ai\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nSpeechBrain\nMila\nSpeech Processing\nSpeech Enhancement\nNumPy\nSciPy\nTheano\nPyTorch\n\nPodcast Episode\n\n\nSpeech Recognition\nNeMo\nESPNet\nSequence to Sequence (Seq2Seq)\nHyperParameters\nTorchAudio\nPyTorch Lightning\nKeras\nHuggingFace\nGenerative Adversarial Network\nSnorkel\n\nData Engineering Podcast Episode\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

With the rising availability of computation in everyday devices, there has been a corresponding increase in the appetite for voice as the primary interface. To accomodate this desire it is necessary for us to have high quality libraries for being able to process and generate audio data that can make sense of human speech. To facilitate research and industry applications for speech data Mirco Ravanelli and Peter Plantinga are building SpeechBrain. In this episode they explain how it works under the hood, the projects that they are using it for, and how you can get started with it today.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview about the open source SpeechBrain library for research on deep learning applications of speech audio data","date_published":"2021-07-13T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/61e76282-fd57-4c77-95dc-8b726e5d3fba.mp3","mime_type":"audio/mpeg","size_in_bytes":30699809,"duration_in_seconds":2246}]},{"id":"podlove-2021-07-07t02:29:48+00:00-9cd2e4603540034","title":"Fast And Educational Exploration And Analysis Of Graph Data Structures With graph-tool","url":"https://www.pythonpodcast.com/graph-tool-graph-data-analysis-episode-322","content_text":"Summary\nIf you are interested in a library for working with graph structures that will also help you learn more about the research and theory behind the algorithms then look no further than graph-tool. In this episode Tiago Peixoto shares his work on graph algorithms and networked data and how he has built graph-tool to help in that research. He explains how it is implemented, how it evolved from a simple command line tool to a full-fledged library, and the benefits that he has found from building a personal project in the open.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Tiago Peixoto about graph-tool, an efficient Python module for manipulation and statistical analysis of graphs\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what graph-tool is and the story behind it?\nWhat are some scenarious where someone might encounter a graph oriented data set?\n\nIn what ways are those graphs typically represented?\nIn your experience, what is the overlap of people who are working with networked data, and the use of graph-native databases? (e.g. Neo4J, DGraph, etc.)\n\n\nWhat kinds of analysis or manipulation might someone need to perform on a graph structure?\nThere are a few different tools in Python for working with networked data. How would you characterize the current ecosystem and why someone might choose graph-tool?\nCan you describe how graph-tool is implemented?\n\nHow have the goals and design of the package changed or evolved since you first began working on it?\n\n\nWho are your target users and what are the guiding principles that you use to inform the API design for the package?\n\nHow much knowledge of graph theory or algorithms are required to make effective use of graph-tool?\n\n\nCan you talk through an example workflow of using graph-tool to load, process, and analyze a graph?\nWhat are some of the overlooked or underutilized aspects of graph-tool that you think more people should know about?\nWhat are some systems/applications that you have seen which would be simplified by adopting a graph model for their data?\n\nWhat is your impression of the overall awareness of the benefits of graphs for simplifying aspects of data processing and analysis?\n\n\nWhat are some cases where a graph structure adds unnecessary complexity?\nWhat are the most interesting, innovative, or unexpected ways that you have seen graph-tool used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on graph-tool?\nWhen is graph-tool the wrong choice?\nWhat do you have planned for the future of graph-tool?\n\nKeep In Touch\n\nWebsite\ngraph-tool\n\nPicks\n\nTobias\n\n97 Things Every Data Engineer Should Know\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nCentral European University\nNetworkX\nGML\nGraphML\nNeo4J\nDGraph\n\nData Engineering Podcast Episode\n\n\nNetworKit\nigraph\nMatplotlib\nC++ Templates\nBoost Graph Library\nOpenMP\nMaximum Matching\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

If you are interested in a library for working with graph structures that will also help you learn more about the research and theory behind the algorithms then look no further than graph-tool. In this episode Tiago Peixoto shares his work on graph algorithms and networked data and how he has built graph-tool to help in that research. He explains how it is implemented, how it evolved from a simple command line tool to a full-fledged library, and the benefits that he has found from building a personal project in the open.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview with Tiago Peixoto about how his research and his open source efforts have come together in graph-tool to support fast exploration and analysis of graph data structures","date_published":"2021-07-06T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/2c39d5af-8bbf-4408-b25b-8bcce2c5c7bf.mp3","mime_type":"audio/mpeg","size_in_bytes":33376566,"duration_in_seconds":2519}]},{"id":"podlove-2021-06-30t01:13:41+00:00-fc562b64cef5f3d","title":"Lightening The Load For Deep Learning With Sparse Networks Using Neural Magic","url":"https://www.pythonpodcast.com/neural-magic-deep-learning-sparse-networks-episode-321","content_text":"Summary\nDeep learning has largely taken over the research and applications of artificial intelligence, with some truly impressive results. The challenge that it presents is that for reasonable speed and performance it requires specialized hardware, generally in the form of a dedicated GPU (Graphics Processing Unit). This raises the cost of the infrastructure, adds deployment complexity, and drastically increases the energy requirements for training and serving of models. To address these challenges Nir Shavit combined his experiences in multi-core computing and brain science to co-found Neural Magic where he is leading the efforts to build a set of tools that prune dense neural networks to allow them to execute on commodity CPU hardware. In this episode he explains how sparsification of deep learning models works, the potential that it unlocks for making machine learning and specialized AI more accessible, and how you can start using it today.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Nir Shavit about Neural Magic and the benefits of using sparsification techniques for deep learning models\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what Neural Magic is and the story behind it?\nWhat are the attributes of deep learning architectures that influence the bias toward GPU hardware for training them?\n\nWhat are the mathematical aspects of neural networks that have biased the current generation of software tools toward that architectural style?\n\n\nHow does sparsifying a network architecture allow for improved performance on commodity CPU architectures?\nWhat is involved in converting a dense neural network into a sparse network?\nCan you describe the components of the Neural Magic architecture and how they are used together to reduce the footprint of deep learning architectures and accelerate their performance on CPUs?\n\nWhat are some of the goals or design approaches that have changed or evolved since you first began working on the Neural Magic platform?\n\n\nFor someone who has an existing model defined, what is the process to convert it to run with the DeepSparse engine?\nWhat are some of the options for applications of deep learning that are unlocked by enabling the models to train and run without GPU or other specialized hardware?\nThe current set of components for Neural Magic is either open source or free to use. What is your long-term business model, and how are you approaching governance of the open source projects?\nWhat are the most interesting, innovative, or unexpected ways that you have seen Neural Magic and model sparsification used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Neural Magic?\nWhen is Neural Magic or sparse networks the wrong choice?\nWhat do you have planned for the future of Neural Magic?\n\nKeep In Touch\n\nResearch Overview\nLinkedIn\n\nPicks\n\nTobias\n\nThe Tick TV show\n\n\nNir\n\nBauhaus documentary\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nNeural Magic\nMIT\nComputational Neurobiology\n6.006 MIT Course\nFLOPS == FLoating point OPerations per Second\nPerceptron\nConvolutional Neural Network\nLisp\nQuantization of ML\nYOLO ML Model\nFederated Learning\n\nPodcast Episode\n\n\nReinforcement Learning\nGPT-3\nOpenAI\nTransfer Learning\n\nPodcast Episode about Transfer Learning for NLP\n\n\nTensor Columns\nNeural Magic DeepSparse Engine\nONNX\nCUDA\nSparse Zoo\nTab9\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Deep learning has largely taken over the research and applications of artificial intelligence, with some truly impressive results. The challenge that it presents is that for reasonable speed and performance it requires specialized hardware, generally in the form of a dedicated GPU (Graphics Processing Unit). This raises the cost of the infrastructure, adds deployment complexity, and drastically increases the energy requirements for training and serving of models. To address these challenges Nir Shavit combined his experiences in multi-core computing and brain science to co-found Neural Magic where he is leading the efforts to build a set of tools that prune dense neural networks to allow them to execute on commodity CPU hardware. In this episode he explains how sparsification of deep learning models works, the potential that it unlocks for making machine learning and specialized AI more accessible, and how you can start using it today.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview with Nir Shavit about his work with Neural Magic to simplify the work of pruning deep learning networks and running them at full speed on commodity hardware","date_published":"2021-06-29T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/3a04f180-31d4-42d2-a444-c81440bc4b94.mp3","mime_type":"audio/mpeg","size_in_bytes":36936515,"duration_in_seconds":2912}]},{"id":"podlove-2021-06-23t02:33:28+00:00-79a8fc39f9ff110","title":"Finding The Core Of Python For A Bright Future With Brett Cannon","url":"https://www.pythonpodcast.com/modern-python-brett-cannon-episode-320","content_text":"Summary\nBrett Cannon has been a long-time contributor to the Python language and community in many ways. In this episode he shares some of his work and thoughts on modernizing the ecosystem around the language. This includes standards for packaging, discovering the true core of the language, and how to make it possible to target mobile and web platforms.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nAre you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at pythonpodcast.com/hightouch.\nYour host as usual is Tobias Macey and today I’m interviewing Brett Cannon about improvements in the packaging ecosystem, the promise of WebAssembly, and his recent explorations of CPython’s interpreter\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nAs a core contributor to CPython, a member of the steering Council, and the team lead for VSCode’s Python extension, what are your current areas of focus for the language?\nOne of the PEPs that you were involved with recently introduced the pyproject.toml file for simplifying the work of building Python packages. Can you share some of the background behind that work and the goals that you had for it?\n\nSince its introduction a lot of people have co-opted that file for other project configuration. What was your reaction to that, and if you had foreseen that usage what might you have changed or added in the PEP to account for it?\n\n\nWhat are the long term impacts on the packaging ecosystem that you anticipate with the standardization efforts that are happening?\nAnother area where there is a lot of attention right now is being able to target additional deployment environments such as the browser, with web assembly, and mobile devices, with projects like BriefCase and Kivy. You had a recent post where you posed some questions about the true nature of Python and the possibility of removing pieces of it to simplify building for these other runtimes. What is your personal sense of the minimal set of features that we need for something to still be Python?\n\nHow have projects such as MicroPython and PyOdide influenced your thinking on the matter?\n\n\nYou have also recently been writing a series of articles about the implementation details of different syntactic elements of Python. What was your inspiration for that?\n\nWhat are some of the interesting or surprising details that you encountered while unwrapping the way that the interpreter handles those syntactic elements?\nHow have those explorations helped you in your efforts to identify the core of Python?\n\n\nRecent releases of Python have brought in some substantial changes to the interpreter and new language features (e.g. PEG parser, pattern matching). What are some of the other large initiatives that you are keeping track of?\nWhat are your personal goals for the near to medium term future of Python?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on the Python language and related tooling?\nIf you were to redesign Python today, what are some of the things that you would do differently?\n\nKeep In Touch\n\nbrettcannon on GitHub\n@brettsky on Twitter\nBlog\n\nPicks\n\nTobias\n\nCold Brew Iced Tea\nLoki on Disney+\n\n\nBrett\n\nRich\nTextual\nThe physics facts included in all of the Python 3.10 release announcements, e.g. you will never see a green star\n\n\n\nLinks\n\nBrett’s Blog\nPython VSCode Extension\nPython Steering Council\nPython Package Authority\nUC Berkeley\nVancouver, BC\nSquamish, Musquiam, Tsleil-waututh First Nations\nPascal\nPython\nC\nO’Reilly\nPyCon US 2021 Steering Council Keynote\nPython Developer-In-Residence\nPSF Visionary Sponsorship\nSetuptools\nPip\nPython Wheels\nPyPI\nPEP 518\nPEP 517\nPEP 621\npyproject.toml\nFlit\nEnscons\nPyPA Build\nPyOxidizer\nPex\nShiv\ncx_Freeze\ncibuildwheel\nThomas Kluyver\nPoetry\nVaults of Parnassus\nMicroPython\n\nPodcast Episode\n\n\nCircuitPython\n\nPodcast Episode\n\n\nDesugaring Python Blog Series\nJupyterHub\nPyOdide\nJupyterLite\nANSI C99\nPyPy\nJython\nIPython\nncurses\nKivy\nBriefcase\nToga\nPEP 401\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Brett Cannon has been a long-time contributor to the Python language and community in many ways. In this episode he shares some of his work and thoughts on modernizing the ecosystem around the language. This includes standards for packaging, discovering the true core of the language, and how to make it possible to target mobile and web platforms.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"A meandering exploration of Brett Cannon's work to standardize the Python language and packaging ecosystem, and how that will keep it relevant long into the future.","date_published":"2021-06-22T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/5500adc2-afaf-4e6b-b30c-4dd9a074b18a.mp3","mime_type":"audio/mpeg","size_in_bytes":53075325,"duration_in_seconds":3798}]},{"id":"podlove-2021-06-16t01:58:39+00:00-40bb2831026829d","title":"Traversing The Challenges And Promise Of Graph Machine Learning","url":"https://www.pythonpodcast.com/graph-machine-learning-episode-319","content_text":"Summary\nThe foundation of every ML model is the data that it is trained on. In many cases you will be working with tabular or unstructured information, but there is a growing trend toward networked, or graph data sets. Benedek Rozemberczki has focused his research and career around graph machine learning applications. In this episode he discusses the common sources of networked data, the challenges of working with graph data in machine learning projects, and describes the libraries that he has created to help him in his work. If you are dealing with connected data then this interview will provide a wealth of context and resources to improve your projects.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nAre you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at pythonpodcast.com/hightouch.\nYour host as usual is Tobias Macey and today I’m interviewing Benedek Rozemberczki about his work on machine learning for graph data, including a variety of libraries to support his efforts\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by giving an overview of when you might want to do machine learning on networked/graph data?\nHow do networked data sets change the way that you approach machine learning tasks?\nCan you describe the current state of the ecosystem for machine learning on graphs?\nYou have created a number of libraries to address different aspects of machine learning on graphs. Can you list them and share some of the stories behind their creation?\n\nHow do the different tools relate to each other?\n\n\nCan you talk through some of the structural and user experience design principles that you lean on when building these libraries?\nWhen you are working with networked data sets, what is your current workflow from idea to completion?\nWhat are the most difficult aspects of working with networked data sets for machine learning applications?\nWhat are the most interesting, innovative, or unexpected ways that you have seen graph ML used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on graph ML problems?\nWhat are some examples of when you would choose not to use some or all of your own libraries?\nWhat do you have planned for the future of your libraries/what new libraries do you anticipate needing to build?\n\nKeep In Touch\n\nbenedekrozemberczki on GitHub\n@benrozemberczki on Twitter\nLinkedIn\n\nPicks\n\nTobias\n\nWrath of Man\n\n\nBenedek\n\nHunt for the Wilderpeople\nGeometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nKarate Club\nPyTorch Geometric Temporal\nAstraZeneca\nBudapest\nUniversity of Edinburgh\nMatlab\nR\nBipartite Graph\nNode Classification\nGraph Classification\nPyTorch\n\nPodcast Episode\n\n\nPyTorch Geometric\nDGL (Deep Graph Library)\nParametric Machine Learning\ngraph-tool\nJax\nNetworkX\nLittle Ball of Fur\nGCN == Graph Convolutional Network\nNetworKit\nGensim\n\nPodcast Episode\n\n\nNvidia cuGraph\nRandom Walk\nscikit-learn\nMalNet\nGraph Representation Learning by William Hamilton\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

The foundation of every ML model is the data that it is trained on. In many cases you will be working with tabular or unstructured information, but there is a growing trend toward networked, or graph data sets. Benedek Rozemberczki has focused his research and career around graph machine learning applications. In this episode he discusses the common sources of networked data, the challenges of working with graph data in machine learning projects, and describes the libraries that he has created to help him in his work. If you are dealing with connected data then this interview will provide a wealth of context and resources to improve your projects.

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Announcements

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Interview

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Keep In Touch

\n\n

Picks

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Closing Announcements

\n\n

Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Benedek Rozemberczki about his open source contributions and research in graph machine learning problems.","date_published":"2021-06-15T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/1c956f05-f092-4937-8e0e-8400519c2a8f.mp3","mime_type":"audio/mpeg","size_in_bytes":36080467,"duration_in_seconds":2867}]},{"id":"podlove-2021-06-09t01:44:14+00:00-154c855b3c6d372","title":"Keep Your Analytics Lint Free With SQLFluff","url":"https://www.pythonpodcast.com/sqlfluff-sql-linter-episode-318","content_text":"Summary\nThe growth of analytics has accelerated the use of SQL as a first class language. It has also grown the amount of collaboration involved in writing and maintaining SQL queries. With collaboration comes the inevitable variation in how queries are written, both structurally and stylistically which can lead to a significant amount of wasted time and energy during code review and employee onboarding. Alan Cruickshank was feeling the pain of this wasted effort first-hand which led him down the path of creating SQLFluff as a linter and formatter to enforce consistency and find bugs in the SQL code that he and his team were working with. In this episode he shares the story of how SQLFluff evolved from a simple hackathon project to an open source linter that is used across a range of companies and fosters a growing community of users and contributors. He explains how it has grown to support multiple dialects of SQL, as well as integrating with projects like DBT to handle templated queries. This is a great conversation about the long detours that are sometimes necessary to reach your original destination and the powerful impact that good tooling can have on team productivity.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nWe’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to pythonpodcast.com/census today to get a free 14-day trial.\nYour host as usual is Tobias Macey and today I’m interviewing Alan Cruickshank about SQLFluff, a dialect-flexible and configurable SQL linter\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what SQLFluff is and the story behind it?\nSQL is one of the oldest programming languages that is still in regular use. Why do you think that there are so few linters for it?\nWho are the target users of SQLFluff and how do those personas influence the design and user experience of the project?\nWhat are some of the characteristics of SQL and how it is used that contribute to readability/comprehension challenges?\n\nWhat are some of the additional difficulties that are introduced by templating in the queries?\n\n\nHow is SQLFluff implemented?\n\nHow have the goals and design of the project changed since you first began working on it?\n\n\nHow do you handle support of varying SQL dialects without undue maintenance burdens?\nWhat are some of the stylistic elements and strategies for making SQL code more maintainable?\nWhat are some strategies for making queries self-documenting?\n\nWhat are some signs that you should document it anyway?\n\n\nWhat are some of the kinds of bugs that you are able to identify with SQLFluff?\nWhat are some of the resources/references that you relied on for identifying useful linting rules?\nWhat are some methods for measuring code quality in SQL?\nWhat are the most interesting, innovative, or unexpected ways that you have seen SQLFluff used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on SQLFluff?\nWhen is SQLFluff the wrong choice?\nWhat do you have planned for the future of SQLFluff?\n\nKeep In Touch\n\nalanmcruickshank on GitHub\nWebsite\nLinkedIn\n\nPicks\n\nTobias\n\nThe Nevers\n\n\nAlan\n\nLost Connections: Uncovering the Real Causes of Depression – and the Unexpected Solutions by Johann Hari (affiliate link)\nThe Wim Hof Method by Wim Hof\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nSQLFluff\nTails.com\nHypothesis\n\nPodcast Episode\n\n\nProject Euler\nFlake8\n\nPodcast Episode\n\n\nBlack\ndbt\n\nData Engineering Podcast Episode\n\n\nSnowflake\n\nData Engineering Podcast Episode\n\n\nBigQuery\nSQL Window Functions\nANSI SQL\nPostgreSQL\nMS SQL Server\nOracle DB\nAirflow\nSQL Subquery\nCommon Table Expression (CTE)\nThe Rise Of The Data Engineer blog post\nThe Downfall Of The Data Engineer blog post\nObject-Relational Mapper (ORM)\nTableau\nFishtown Analytics SQL Styleguide\nMozilla SQL Styleguide\nThe Zen of Python\ndbt Packages\nyapf\nSet Theory\nFlake8 SQL Plugin\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

The growth of analytics has accelerated the use of SQL as a first class language. It has also grown the amount of collaboration involved in writing and maintaining SQL queries. With collaboration comes the inevitable variation in how queries are written, both structurally and stylistically which can lead to a significant amount of wasted time and energy during code review and employee onboarding. Alan Cruickshank was feeling the pain of this wasted effort first-hand which led him down the path of creating SQLFluff as a linter and formatter to enforce consistency and find bugs in the SQL code that he and his team were working with. In this episode he shares the story of how SQLFluff evolved from a simple hackathon project to an open source linter that is used across a range of companies and fosters a growing community of users and contributors. He explains how it has grown to support multiple dialects of SQL, as well as integrating with projects like DBT to handle templated queries. This is a great conversation about the long detours that are sometimes necessary to reach your original destination and the powerful impact that good tooling can have on team productivity.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about how the SQLFluff project lints and formats SQL code, why it took so long for something like this to exist, and how you can use it to keep your SQL analytics neat and tidy","date_published":"2021-06-08T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/7d009ee0-c689-45e8-8ce0-0e8855381304.mp3","mime_type":"audio/mpeg","size_in_bytes":50752550,"duration_in_seconds":4393}]},{"id":"podlove-2021-06-02t00:48:47+00:00-778742a06069f3e","title":"Exploring The Patterns And Practices For Deep Learning With Andrew Ferlitsch","url":"https://www.pythonpodcast.com/deep-learning-patterns-and-practices-episode-317","content_text":"Summary\nDeep learning is gaining an immense amount of popularity due to the incredible results that it is able to offer with comparatively little effort. Because of this there are a number of engineers who are trying their hand at building machine learning models with the wealth of frameworks that are available. Andrew Ferlitsch wrote a book to capture the useful patterns and best practices for building models with deep learning to make it more approachable for newcomers ot the field. In this episode he shares his deep expertise and extensive experience in building and teaching machine learning across many companies and industries. This is an entertaining and educational conversation about how to build maintainable models across a variety of applications.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nWe’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to pythonpodcast.com/census today to get a free 14-day trial.\nScaling your data infrastructure is hard. Maintaining data quality standards as you scale is harder. Databand solves this. Their Unified Data Observability platform gives data engineers visibility over their stack without changing existing pipeline code. Get end-to-end visibility on your pipelines, and identify the root cause of issues before bad data is delivered. Seamlessly integrate with over 20 tools like Apache Airflow, Spark, Snowflake, and more. Use customizable dashboards to see where pipelines are broken and how that impacts delivery downstream. Get alerts on leading indicators of pipeline failure. Open up your pipeline and see exactly which code strings are broken – so you can fix the issue immediately. Create more reliable data products. Go to pythonpodcast.com/databand today to start your free trial!\nYour host as usual is Tobias Macey and today I’m interviewing Andrew Ferlitsch about the patterns and practices for deep learning applications\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing the major elements of a model architecture?\nWhat is the relationship between the specific learning task being addressed and the architecture of the learning network?\nIn your experience, what is the level of awareness of a typical ML engineer or data scientist with respect to the most current design patterns in deep learning?\nYour currently working on a book about deep learning patterns and practices. What was your motivation for starting that project?\n\nWhat are your goals for the book?\n\n\nHow have advancements in the operability of machine learning influenced the ways that the models are designed and trained?\n\nHow do recent approaches such as transfer learning impact the needs of the supporting tools and infrastructure?\n\n\nCan you describe the different design patterns that you cover in your book and the selection process for when and how to apply them?\nWhat are the aspects of bringing deep learning to production that continue to be a challenge?\n\nWhat are some of the emerging practices that you are optimistic about?\n\n\nWhat are some of the industry trends or areas of current research that you are most excited about?\nWhat are the most interesting, innovative, or unexpected patterns that you have encountered?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on the book?\nWhat are some of the other resources that you recommend for listeners to learn more about how to build production ready models?\n\nKeep In Touch\n\nLinkedIn\n@AndrewFerlitsch on Twitter\nandrewferlitsch on GitHub\n\nPicks\n\nTobias\n\nDesigning Data Intensive Applications (affiliate link)\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nGoogle Cloud AI\nSharp Corporation\nDeep Learning Patterns and Practices (affiliate link) use the code podinit21 at checkout for 35% off all books at Manning!\nCID Bioscience\nLatent Space\nAI Winter\nNumerical Stability\nSurrogate Model\nGAN == Generative Adversarial Network\nGradient Descent\nThe Gang of 4 – Design Patterns: Elements of Reusable Object-Oriented Software (affiliate link)\nThe Lottery Hypothesis\nManning Publications (affiliate link)\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Deep learning is gaining an immense amount of popularity due to the incredible results that it is able to offer with comparatively little effort. Because of this there are a number of engineers who are trying their hand at building machine learning models with the wealth of frameworks that are available. Andrew Ferlitsch wrote a book to capture the useful patterns and best practices for building models with deep learning to make it more approachable for newcomers ot the field. In this episode he shares his deep expertise and extensive experience in building and teaching machine learning across many companies and industries. This is an entertaining and educational conversation about how to build maintainable models across a variety of applications.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Andrew Ferlitsch about his experiences building and teaching deep learning models and his work on a book to capture those lessons for everyone to learn from.","date_published":"2021-06-01T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/262a2e07-f1e3-4d34-98a6-8d4f51f75fce.mp3","mime_type":"audio/mpeg","size_in_bytes":33723573,"duration_in_seconds":2659}]},{"id":"podlove-2021-05-25t00:05:55+00:00-2d6280adb73b77d","title":"Automatically Generate Your Unit Tests From Scratch With Pynguin","url":"https://www.pythonpodcast.com/pynguin-automatic-python-unit-tests-episode-316","content_text":"Summary\nUnit tests are an important tool to ensure the proper functioning of your application, but writing them can be a chore. Stephan Lukasczyk wants to reduce the monotony of the process for Python developers. As part of his PhD research he created the Pynguin project to automate the creation of unit tests. In this episode he explains the complexity involved in generating useful tests for a dynamic language, how he has designed Pynguin to address the challenges, and how you can start using it today for your own work.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nWe’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to pythonpodcast.com/census today to get a free 14-day trial.\nAre you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at pythonpodcast.com/hightouch.\nYour host as usual is Tobias Macey and today I’m interviewing Stephan Lukasczyk about Pynguin, the PYthoN General UnIt test geNerator\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what Pynguin is and the story behind it?\nWhat are the problems that Pynguin is designed to solve?\nWhat other projects are you drawing inspiration from?\nWhat are some of the use cases for automatic test generation?\nHow is Pynguin implemented?\n\nWhat are the challenges that the dynamic nature of Python introduces?\nWhat are some of the packages and libraries that have been most helpful while building Pynguin?\n\n\nCan you talk through the workflow of using Pynguin to generate tests for a project?\n\nWhat are some of the limitations on what kinds of projects Pynguin can be used for?\nWhat are some design or implementation strategies in the code that you are generating tests for that will help make Pynguin’s job easier?\n\n\nOnce a test suite has been created, what are the next steps?\nWhat are some of the initial assumptions or goals of the project that have been revised or challenged once you began implementing it?\nWhat are the most interesting, innovative, or unexpected ways that you have seen Pynguin used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Pynguin?\nWhen is Pynguin the wrong choice?\nWhat do you have planned for the future of Pynguin?\n\nKeep In Touch\n\nRelated to Pynguin: best via GitHub\nFind me on Twitter\n\nPicks\n\nTobias\n\nConcourse CI\n\n\nStephan\n\nCycling\nTake care of your health\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nPynguin\nUniversity of Passau\nPassau, Germany\nEvosuite\nHypothesis\n\nPodcast Episode\n\n\nAstor\nWalrus Operator\nMyPy\n\nPodcast Episode\n\n\nPytest\n\nPodcast Episode\n\n\nUnitTest\nBytecode library\nPytype\nMonkeytype\n\nPodcast Episode\n\n\nAtheris from Google – coverage-guided fuzzing\nBlog series about “Python behind the scenes”: Ten thousand meters by Victor Skvortsov\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Unit tests are an important tool to ensure the proper functioning of your application, but writing them can be a chore. Stephan Lukasczyk wants to reduce the monotony of the process for Python developers. As part of his PhD research he created the Pynguin project to automate the creation of unit tests. In this episode he explains the complexity involved in generating useful tests for a dynamic language, how he has designed Pynguin to address the challenges, and how you can start using it today for your own work.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about the Pynguin project and how it is able to automatically generate unit tests for Python applications.","date_published":"2021-05-24T20:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/fb714c6b-ec0d-4b03-948b-c2913cc8a15e.mp3","mime_type":"audio/mpeg","size_in_bytes":43387183,"duration_in_seconds":3460}]},{"id":"podlove-2021-05-18t00:44:50+00:00-78bf0a7fbcef57c","title":"Leveling Up Natural Language Processing with Transfer Learning","url":"https://www.pythonpodcast.com/paul-azunre-transfer-learning-for-natural-language-processing-episode-315","content_text":"Summary\nNatural language processing is a powerful tool for extracting insights from large volumes of text. With the growth of the internet and social platforms, and the increasing number of people and communities conducting their professional and personal activities online, the opportunities for NLP to create amazing insights and experiences are endless. In order to work with such a large and growing corpus it has become necessary to move beyond purely statistical methods and embrace the capabilities of deep learning, and transfer learning in particular. In this episode Paul Azunre shares his journey into the application and implementation of transfer learning for natural language processing. This is a fascinating look at the possibilities of emerging machine learning techniques for transforming the ways that we interact with technology.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nWe’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to pythonpodcast.com/census today to get a free 14-day trial.\nAre you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at pythonpodcast.com/hightouch.\nYour host as usual is Tobias Macey and today I’m interviewing Paul Azunre about using transfer learning for natural language processing\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what transfer learning is?\nHow is transfer learning being applied to natural language processing?\nWhat motivated you to write a book about the application of transfer learning to NLP?\nWhat are some of the applications of NLP that are impractical on intractable without transfer learning?\nAt a high level, what are the steps for building a new language model via transfer learning?\nThere have been a number of base models created recently, such as BERT and ERNIE, ELMo, GPT-3, etc. What are the factors that need to be considered when selecting which model to build from?\n\nIf there are multiple models that contain the seeds for different aspects of the end goal that you are trying to obtain, what is the feasibility of extracting the relevant capabilities from each of them and combining them in the final model?\n\n\nWhat are some of the tools or frameworks that you have found most useful while working with NLP and transfer learning?\nHow would you characterize the current state of the ecosystem for transfer learning and deep learning techniques applied to NLP problems?\nWhat are the most interesting, innovative, or unexpected applications of transfer learning with NLP that you have seen?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on the book?\nWhen is transfer learning the wrong choice for an NLP project?\nWhat are the trends or techniques that you are most excited for?\n\nKeep In Touch\n\nLinkedIn\nWebsite\n@pazunre on Twitter\n\nPicks\n\nTobias\n\nInfected Mushroom\n\n\nPaul\n\nTenet\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nTransfer Learning for Natural Language Processing by Paul Azunre (affiliate link)\nUse the code podinit21 at checkout for 35% off all books at Manning!\nLow Resource Languages\nFortran\nC++\nMatLab\nMIT 6.003\nTransfer Learning\nComputer Vision\nDeep Neural Network\nConvolutional Neural Network (CNN)\nRecurrent Neural Network (RNN)\nGLUE == General Lanuage Understanding Evaluation\nNLP SuperGLUE\nNLP Encoder\nNamed Entity Recognition\nImageNet\nMathematical Optimization\nGradient Descent\nYonder AI\nELMo language model from Allen NLP\nGhana\nArXiv\nBERT language model\nTF-IDF == Term Frequency – Inverse Document Frequency\nWord2Vec\nGPT-3\nGhana NLP\nAutomatic Speech Recognition\nULM Fit\nKeras\nTensorflow\nHuggingface Transformers\nMulti-Task Learning\nFast.ai\nOpenAI\nAWS SageMaker\nKaggle Kernels\nColab Notebooks\nAzure ML Studio\nBLEU Score\nKhaya application\n\nAndroid\niOS\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

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Natural language processing is a powerful tool for extracting insights from large volumes of text. With the growth of the internet and social platforms, and the increasing number of people and communities conducting their professional and personal activities online, the opportunities for NLP to create amazing insights and experiences are endless. In order to work with such a large and growing corpus it has become necessary to move beyond purely statistical methods and embrace the capabilities of deep learning, and transfer learning in particular. In this episode Paul Azunre shares his journey into the application and implementation of transfer learning for natural language processing. This is a fascinating look at the possibilities of emerging machine learning techniques for transforming the ways that we interact with technology.

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Paul Azunre about how you can use transfer learning techniques to build more flexible natural language processing systems and reduce the requirements for labelled data.","date_published":"2021-05-17T20:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/b69fb1c9-42b1-42cc-a85c-f63519ac345a.mp3","mime_type":"audio/mpeg","size_in_bytes":35160416,"duration_in_seconds":2794}]},{"id":"podlove-2021-05-11t02:49:30+00:00-0ce13a9a488e7ce","title":"Federated Learning For All With Flower","url":"https://www.pythonpodcast.com/flower-federated-learning-episode-314","content_text":"Summary\nMachine learning is a tool that has typically been performed on large volumes of data in one place. As more computing happens at the edge on mobile and low power devices, the learning is being federated which brings a new set of challenges. Daniel Beutel co-created the Flower framework to make federated learning more manageable. In this episode he shares his motivations for starting the project, how you can use it for your own work, and the unique challenges and benefits that this emerging model offers. This is a great exploration of the federated learning space and a framework that makes it more approachable.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nWe’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to pythonpodcast.com/census today to get a free 14-day trial.\nAre you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at pythonpodcast.com/hightouch.\nYour host as usual is Tobias Macey and today I’m interviewing Daniel Beutel about Flower, a framework for building federated learning systems\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what federated learning is?\nWhat is Flower and what’s the story behind it?\nWhat are the trade-offs between federated and centralized models of machine learning?\nWhat are some of the types of use cases or workloads that federated learning is used for?\nFederated learning appears to be a growing area of interest. How would you characterize the current state of the ecosystem?\nWhat are the most complex or challenging aspects of federating model training?\n\nHow does Flower simplify the process of distributing the model training process?\n\n\nCan you describe how Flower is implemented?\n\nHow have the goals and/or design of Flower changed or evolved since you first began working on it?\n\n\nOne of the design principles that you list is \"understandability\". What are some of the ways that that manifests in the project?\nIt also mentions extensibility. What are the interfaces that Flower exposes for integration or extending its capabilities?\nFor someone who has an existing project that runs in a centralized manner, what are some indicators that a federated approach would be beneficial?\n\nWhat is involved in translating the existing project to run in a federated fashion using Flower?\n\n\nWhat is involved in building a production ready system with Flower?\nHow does your work at Adap inform the design and product direction for Flower?\nWhat are some of the most interesting, innovative, or unexpected ways that you have seen Flower used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned from your work on and with Flower?\nWhen is Flower the wrong choice?\nWhat do you have planned for the future of the project?\n\nKeep In Touch\n\nLinkedIn\ndanieljanes on GitHub\n@daniel_janes on Twitter\n\nPicks\n\nTobias\n\nRummy Card Game\n\n\nDaniel\n\nStand Up Paddling\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nFlower\nAdap\nHyperparameter Optimization\nFederated Learning\nUniversity of Oxford\nUniversity of Cambridge\nNvidia Jetson\nPyTorch\n\nPodcast Episode\n\n\nTensorflow Lite\nTensorflow Federated\nPySyft\nFlower Summit\nJax\nCNN == Convolutional Neural Network\nKeras\ngRPC\nMQTT\nNumPy NDArray\nAWS Device Farm\nRay Framework\n\nPodcast Episode\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Machine learning is a tool that has typically been performed on large volumes of data in one place. As more computing happens at the edge on mobile and low power devices, the learning is being federated which brings a new set of challenges. Daniel Beutel co-created the Flower framework to make federated learning more manageable. In this episode he shares his motivations for starting the project, how you can use it for your own work, and the unique challenges and benefits that this emerging model offers. This is a great exploration of the federated learning space and a framework that makes it more approachable.

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Announcements

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Interview

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Keep In Touch

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Picks

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Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about the Flower framework for federated learning and how you can use it to push your machine learning to the edge.","date_published":"2021-05-10T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/24abcf7f-80f5-4e69-b64d-2bb2b8a9474b.mp3","mime_type":"audio/mpeg","size_in_bytes":45316268,"duration_in_seconds":3688}]},{"id":"podlove-2021-05-04t00:50:28+00:00-542716be3ff4474","title":"Data Exploration and Visualization Made Effortless with Lux","url":"https://www.pythonpodcast.com/lux-data-exploration-episode-313","content_text":"Summary\nData exploration is an important step in any analysis or machine learning project. Visualizing the data that you are working with makes that exploration faster and more effective, but having to remember and write all of the code to build a scatter plot or histogram is tedious and time consuming. In order to eliminate that friction Doris Lee helped create the Lux project, which wraps your Pandas data frame and automatically generates a set of visualizations without you having to lift a finger. In this episode she explains how Lux works under the hood, what inspired her to create it in the first place, and how it can help you create a better end result. The Lux project is a valuable addition to the toolbox of anyone who is doing data wrangling with Pandas.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nWe’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to pythonpodcast.com/census today to get a free 14-day trial.\nAre you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at pythonpodcast.com/hightouch.\nYour host as usual is Tobias Macey and today I’m interviewing Doris Lee about Lux, a Python library that facilitates fast and easy data exploration by automating the visualization and data analysis process\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what Lux is and how the project got started?\nWhat is the role of visualization in a data science workflow?\n\nWhat are the challenges that data scientists face in the exploratory phase of their analysis?\n\n\nThere are a wide variety of data visualization tools in the Python ecosystem with differing areas of focus. What is the role of Lux in that ecosystem?\n\nHow does Lux compare to tools such as scikit-yb?\n\n\nWhat is the workflow for someone using Lux in their analysis and what problems does it solve for them?\nCan you talk through how Lux is architected?\n\nHow have the goals and design of Lux changed or evolved since you first began working on it?\n\n\nData visualization is a broad field. How do you determine which kinds of charts or plots are best suited to a particular data set or exploration?\nWhat are some of the capabilities of Lux that are often overlooked or underutilized?\nHow has Lux impacted your own work in data analysis/data science?\nWhat are some of the other gaps that you see in the available tooling for data science?\nWhat are some of the most interesting, innovative, or unexpected ways that you have seen Lux used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on and with Lux?\nWhen is Lux the wrong choice?\nWhat do you have planned for the future of the project?\n\nKeep In Touch\n\ndorisjlee on GitHub\nWebsite\nLinkedIn\n\nPicks\n\nTobias\n\nPirates of the Carribean movies\n\n\nDoris\n\nSnake Wrangling for Kids\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nLux\nUC Berkeley\nRISE Lab\nSchool of Information\nPandas\n\nPodcast Episode\n\n\nBokeh\n\nPodcast Episode\n\n\nSeaborn\nAltair\n\nPodcast Episode\n\n\nMatplotlib\nGrammar of Graphics\nPlotly\nScikit YellowBrick\n\nPodcast Episode\n\n\nD3.js\nVega\nNumpy\nxarray\nTensorflow\nJupyter Widget\nChloropleth Map\nG10 Countries\nRay\n\nPodcast Episode\n\n\nModin\nDask\n\nData Engineering Podcast Episode\nPodcast Interview About Coiled\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Data exploration is an important step in any analysis or machine learning project. Visualizing the data that you are working with makes that exploration faster and more effective, but having to remember and write all of the code to build a scatter plot or histogram is tedious and time consuming. In order to eliminate that friction Doris Lee helped create the Lux project, which wraps your Pandas data frame and automatically generates a set of visualizations without you having to lift a finger. In this episode she explains how Lux works under the hood, what inspired her to create it in the first place, and how it can help you create a better end result. The Lux project is a valuable addition to the toolbox of anyone who is doing data wrangling with Pandas.

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Announcements

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Interview

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Keep In Touch

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Picks

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Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Doris Lee about the Lux project for data exploration and automatic visualization and how it can speed up your analysis workflow.","date_published":"2021-05-03T20:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/345692ce-4e16-4973-bced-279d656349e5.mp3","mime_type":"audio/mpeg","size_in_bytes":36942123,"duration_in_seconds":3065}]},{"id":"podlove-2021-04-26t01:08:59+00:00-b745a544d951554","title":"Extensible Open Source Authorization For Your Applications With Oso","url":"https://www.pythonpodcast.com/oso-open-source-authorization-episode-312","content_text":"Summary\nAny project that is used by more than one person will eventually need to handle permissions for each of those users. It is certainly possible to write that logic yourself, but you’ll almost certainly do it wrong at least once. Rather than waste your time fighting with bugs in your authorization code it makes sense to use a well-maintained library that has already made and fixed all of the mistakes so that you don’t have to. In this episode Sam Scott shares the Oso framework to give you a clean separation between your authorization policies and your application code. He explains how you can call a simple function to ask if something is allowed, and then manage the complex rules that match your particular needs as a separate concern. He describes the motivation for building a domain specific language based on logic programming for policy definitions, how it integrates with the host language (such as Python), and how you can start using it in your own applications today. This is a must listen even if you never use the project because it is a great exploration of all of the incidental complexity that is involved in permissions management.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nWe’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to pythonpodcast.com/census today to get a free 14-day trial.\nAre you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at pythonpodcast.com/hightouch.\nYour host as usual is Tobias Macey and today I’m interviewing Sam Scott about Oso, an open source library for managing authorization in your applications\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what Oso is and the story behind it?\nWhat was missing from the ecosystem of authorization libraries/frameworks that motivated you to create a new one?\nWhat are some of the most common mistakes that you see developers make when implementing authorization logic?\nAt a high level, what is the process of using Oso to add access control policies to a piece of software?\nWhat is the motivation for using a DSL for defining policies as opposed to writing those definitions in pure Python?\n\nHow have you approached the design of the policy language, particularly deciding what constraints to impose?\nWhat other policy frameworks or dialects have you drawn inspiration from?\n\n\nHow is the Oso framework implemented?\n\nHow have the goals and design of Oso changed or evolved since you first began working on it?\n\n\nWhat are some useful design patterns for integrating Oso into an application?\n\nHow does the type of application (e.g. web app vs. system daemon, etc.) affect the ways that Oso is used?\n\n\nGiven that Oso supports multiple language runtimes, what is involved in defining and enforcing policies that span multiple processes? (e.g. Python backend and Javascript frontend, Python microservice communicating with Go microservice, etc.)\nWhat are some of the common mistakes or areas of confusion for users who are getting started with Oso and Polar?\nWhat are some of the capabilities of Oso that are often overlooked or misunderstood?\nI noticed that you’re backed by some venture firms. What is your current product vision and how does that relate to your current open source goals?\nWhat are some of the most interesting, innovative, or unexpected ways that you have seen Oso used?\nWhat are some of the most interesting, unexpected, or challenging lessons that you have learned while working on and with oso?\nWhen is Oso the wrong choice?\nWhat do you have planned for the future of the project?\n\nKeep In Touch\n\nLinkedIn\nsamscott89 on GitHub\n@samososos on Twitter\n\nPicks\n\nTobias\n\nChaos Walking\n\n\nSam\n\nHades video game\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nOso\nOso Authorization Academy\nNumber Theory\nSage Math\nRBAC == Role-Based Access Control\nABAC == Attribute-Based Access Control\nPolar Policy Language\nProlog\nLogic Programming\nOpen Policy Agent\nAWS IAM\nXACML\nGoogle Zanzibar\nRust\nWeb Assembly (Wasm)\nOAuth Scopes\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Any project that is used by more than one person will eventually need to handle permissions for each of those users. It is certainly possible to write that logic yourself, but you’ll almost certainly do it wrong at least once. Rather than waste your time fighting with bugs in your authorization code it makes sense to use a well-maintained library that has already made and fixed all of the mistakes so that you don’t have to. In this episode Sam Scott shares the Oso framework to give you a clean separation between your authorization policies and your application code. He explains how you can call a simple function to ask if something is allowed, and then manage the complex rules that match your particular needs as a separate concern. He describes the motivation for building a domain specific language based on logic programming for policy definitions, how it integrates with the host language (such as Python), and how you can start using it in your own applications today. This is a must listen even if you never use the project because it is a great exploration of all of the incidental complexity that is involved in permissions management.

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Announcements

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Interview

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Keep In Touch

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Picks

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Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about how you can use the open source Oso framework to make authorization easy to implement and maintainable for your application.","date_published":"2021-04-26T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/7f4b34e3-b613-4583-ae80-7c87d6f63f93.mp3","mime_type":"audio/mpeg","size_in_bytes":39334440,"duration_in_seconds":3109}]},{"id":"podlove-2021-04-20t00:59:00+00:00-6614b260766c400","title":"Teaching Geeks The Value And Skills Of Public Speaking","url":"https://www.pythonpodcast.com/neil-thompson-public-speaking-episode-311","content_text":"Summary\nBeing able to present your ideas is one of the most valuable and powerful skills to have as a professional, regardless of your industry. For software engineers it is especially important to be able to communicate clearly and effectively because of the detail-oriented nature of the work. Unfortunately, many people who work in software are more comfortable in front of the keyboard than a crowd. In this episode Neil Thompson shares his story of being an accidental public speaker and how he is helping other engineers start down the road of being effective presenters. He discusses the benefits for your career, how to build the skills, and how to find opportunities to practice them. Even if you never want to speak at a conference, it’s still worth your while to listen to Neil’s advice and find ways to level up your presentation and speaking skills.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nWe’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to pythonpodcast.com/census today to get a free 14-day trial.\nAre you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at pythonpodcast.com/hightouch.\nYour host as usual is Tobias Macey and today I’m interviewing Neil Thompson about the value of public speaking skills as a developer and how to gain them\n\nInterview\n\nIntroductions\nHow did you get into engineering?\nCan you start by discussing the different types of public speaking that we are talking about and some of the different venues where it might take place?\nHow did you get into public speaking?\nWhat are some of the ways that our speaking abilities can impact the value that we provide and the trajectory of our career as engineers?\nWhat were some of the methods and resources that you used to improve your own public speaking skills?\nWhat are the common mistakes that people make when speaking to a group?\nWhat are some of the non-obvious ways that speaking skills can be useful as an engineer?\nWhat was your approach to learning how to be an effective speaker?\n\nWhat are some of the mis-steps or dead ends that you encountered?\n\n\nWhat are the different skills or capabilities that are necessary for being an effective presenter?\nWhat are some ways that engineers can practice their presentation skills?\nHow do different audiences/venues influence the approach that you take to how to prepare for a presentation?\nHow has your experience in public speaking factored into the work you do for your podcast?\nWhat are some of the most interesting, innovative, or unexpected presentations or speaking techniques that you have seen or used/created?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned from speaking and teaching others to speak in a professional context?\nWhat resources do you recommend for engineers who want to improve their speaking and presenting skills?\n\nKeep In Touch\n\nLinkedIn\n@neil_i_thompson on Twitter\n\nPicks\n\nTobias\n\nFalcon and the Winter Soldier\n\n\nNeil\n\nTeach The Geek To Speak\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nMaterials Science\nToastmasters\nTeach The Geek To Speak\nTeach The Geek Podcast\nDeveloper Advocate\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Being able to present your ideas is one of the most valuable and powerful skills to have as a professional, regardless of your industry. For software engineers it is especially important to be able to communicate clearly and effectively because of the detail-oriented nature of the work. Unfortunately, many people who work in software are more comfortable in front of the keyboard than a crowd. In this episode Neil Thompson shares his story of being an accidental public speaker and how he is helping other engineers start down the road of being effective presenters. He discusses the benefits for your career, how to build the skills, and how to find opportunities to practice them. Even if you never want to speak at a conference, it’s still worth your while to listen to Neil’s advice and find ways to level up your presentation and speaking skills.

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Announcements

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Interview

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Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Neil Thompson about the benefits of public speaking as an engineer and how to gain the skills to be effective at it.","date_published":"2021-04-19T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/08ee1200-5b1a-4743-922c-d7d74a27eece.mp3","mime_type":"audio/mpeg","size_in_bytes":32790538,"duration_in_seconds":2574}]},{"id":"podlove-2021-04-11t11:37:06+00:00-6e78d2017c4167e","title":"Let The Robots Do The Work Using Robotic Process Automation with Robocorp","url":"https://www.pythonpodcast.com/robocorp-robotic-process-automation-episode-310","content_text":"Summary\nOne of the great promises of computers is that they will make our work faster and easier, so why do we all spend so much time manually copying data from websites, or entering information into web forms, or any of the other tedious tasks that take up our time? As developers our first inclination is to \"just write a script\" to automate things, but how do you share that with your non-technical co-workers? In this episode Antti Karjalainen, CEO and co-founder of Robocorp, explains how Robotic Process Automation (RPA) can help us all cut down on time-wasting tasks and let the computers do what they’re supposed to. He shares how he got involved in the RPA industry, his work with Robot Framework and RPA framework, how to build and distribute bots, and how to decide if a task is worth automating. If you’re sick of spending your time on mind-numbing copy and paste then give this episode a listen and then let the robots do the work for you.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nWe’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to pythonpodcast.com/census today to get a free 14-day trial.\nSoftware is read more than it is written, so complex and poorly organized logic slows down everyone who has to work with it. Sourcery makes those problems a thing of the past, giving you automatic refactoring recommendations in your IDE or text editor while you write (I even have it working in Emacs). It isn’t just another linting tool that nags you about issues. It’s like pair programming with a senior engineer, finding and applying structural improvements to your functions so that you can write cleaner code faster. Best of all, listeners of Podcast.__init__ get 6 months of their Pro tier for free if you go to pythonpodcast.com/sourcery today and use the promo code INIT when you sign up.\nYour host as usual is Tobias Macey and today I’m interviewing Antti Karjalainen about the RPA Framework for automating your daily tasks and his work at Robocorp to manage your robots in production\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by giving an overview of what Robotic Process Automation is?\nWhat are some of the ways that RPA might be used?\n\nWhat are the advantages over writing a custom library or script in Python to automate a given task?\nHow does the functionality of RPA compare to automation services like Zapier, IFTTT, etc.?\n\n\nWhat are you building at Robocorp and what was your motivation for starting the business?\n\nWho is your target customer and how does that inform the products that you are building?\n\n\nCan you give an overview of the state of the ecosystem for RPA tools and products and how Robocorp and RPA framework fit within it?\n\nHow does the RPA Framework relate to Robot Framework?\n\n\nWhat are some of the challenges that developers and end users often run into when trying to build, use, or implement an RPA system?\nHow is the RPA framework itself implemented?\n\nHow has the design of the project evolved since you first began working on it?\n\n\nCan you talk through an example workflow for building a robot?\nOnce you have built a robot, what are some of the considerations for local execution or deploying it to a production environment?\nHow can you chain together multiple robots?\nWhat is involved in extending the set of operations available in the framework?\nWhat are the available integration points for plugging a robot written with RPA Framework into another Python project?\nWhat are the dividing lines between RPA Framework and Robocorp?\n\nHow are you handling the governance of the open source project?\n\n\nWhat are some of the most interesting, innovative, or unexpected ways that you have seen RPA Framework and the Robocorp platform used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while building and growing RPA Framework and the Robocorp business?\nWhen is RPA and RPA Framework the wrong choice for automation?\nWhat do you have planned for the future of the framework and business?\n\nKeep In Touch\n\naikarjal on GitHub\n@aikarjal on Twitter\nLinkedIn\n\nPicks\n\nTobias\n\nWandaVision\n\n\nAntti\n\nTenet\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nRobocorp\nRPA Framework\nRCC\nRobotic Process Automation\nZapier\nIFTTT (If This Then That)\nRobot Framework\nSelenium\nPlaywright\nConda\nMicro Mamba\nPyOxidizer\n\nPodcast Episode\n\n\nXKCD \"Is It Worth The Time?\"\nXKCD Automation Curve\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

One of the great promises of computers is that they will make our work faster and easier, so why do we all spend so much time manually copying data from websites, or entering information into web forms, or any of the other tedious tasks that take up our time? As developers our first inclination is to "just write a script" to automate things, but how do you share that with your non-technical co-workers? In this episode Antti Karjalainen, CEO and co-founder of Robocorp, explains how Robotic Process Automation (RPA) can help us all cut down on time-wasting tasks and let the computers do what they’re supposed to. He shares how he got involved in the RPA industry, his work with Robot Framework and RPA framework, how to build and distribute bots, and how to decide if a task is worth automating. If you’re sick of spending your time on mind-numbing copy and paste then give this episode a listen and then let the robots do the work for you.

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Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about how Robocorp is building and supporting tools for robotic process automation in Python to make it easier to cut down on menial tasks on the computer and let the robots do the work.","date_published":"2021-04-12T21:45:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/e18e8949-86e5-4004-b21b-5a60e9b78931.mp3","mime_type":"audio/mpeg","size_in_bytes":38181819,"duration_in_seconds":2733}]},{"id":"podlove-2021-04-05t11:15:56+00:00-57e51bf53cfca0e","title":"Keep Your Code Clean And Maintainable Using Static Analysis With Flake8","url":"https://www.pythonpodcast.com/flake8-static-analysis-episode-309","content_text":"Summary\nWhen you are writing code it is all to easy to introduce subtle bugs or leave behind unused code. Unused variables, unused imports, overly complex logic, etc. If you are careful and diligent you can find these problems yourself, but isn’t that what computers are supposed to help you with? Thankfully Python has a wealth of tools that will work with you to keep your code clean and maintainable. In this episode Anthony Sottile explores Flake8, one of the most popular options for identifying those problematic lines of code. He shares how he became involved in the project and took over as maintainer and explains the different categories of code quality tooling and how Flake8 compares to other static analyzers. He also discusses the ecosystem of plugins that have grown up around it, including some detailed examples of how you can write your own (and why you might want to).\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nWe’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to pythonpodcast.com/census today to get a free 14-day trial.\nYour host as usual is Tobias Macey and today I’m interviewing Anthony Sottile about Flake8\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by giving an overview of what Flake8 is and how you got involved with the project?\nThere are a variety of tools available for checking or enforcing code quality. How would you characterize Flake8 in comparison to the other options?\nWhat do you see as the motivating factors for individuals or teams to integrate static analysis/linting in their toolchain and workflow?\n\nWhat are some of the challenges that might prevent someone from adopting something like Flake8?\nHow can developers add Flake8 to an existing project without spending hours or days fixing all of the violations?\n\n\nCan you describe the overall design and implementation of Flake8?\n\nHow has the design and goals of the project changed or evolved?\n\n\nThere are a wide array of plugins for Flake8. What is involved in adding new functionality or linting rules?\n\nWhat capabilities does Flake8 provide that make it a viable platform for building plugins?\nWhat are some of the limitations of Flake8 as a platform?\n\n\nWhat do you see as the factors that have contributed to the widespread usage of Flake8 and the large number of available plugins?\n\nWhat challenges does that pose as a maintainer of Flake8?\n\n\nWhat are some of the other tools that you see developers use alongside Flake8 to help manage code quality and style enforcement?\nWhat are some of the most interesting, innovative, or unexpected ways that you have seen Flake8 and its plugin ecosystem used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on Flake8?\nWhen is Flake8 the wrong choice?\nWhat do you have planned for the future of Flake8?\n\nKeep In Touch\n\n@codewithanthony on Twitter\nasottile on GitHub\nLinkedIn\n\nPicks\n\nTobias\n\nSEVENEVES by Neal Stephenson\n\n\nAnthony\n\npre-commit CI\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nFlake8\nPyFlakes\nPyCodestyle\nMcCabe\npre-commit\n\nPodcast Episode\n\n\nPEP 484\nMyPy\nPylance\nPyright\nPylint\nBlack\nyapf\nautopep8\npyupgrade\nisort\nreorder-python-imports\nStatic Analysis\npydocstyle\nautoflake\npyproject.toml\nAbstract Syntax Tree\nConcrete Syntax Tree\nDagster\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

When you are writing code it is all to easy to introduce subtle bugs or leave behind unused code. Unused variables, unused imports, overly complex logic, etc. If you are careful and diligent you can find these problems yourself, but isn’t that what computers are supposed to help you with? Thankfully Python has a wealth of tools that will work with you to keep your code clean and maintainable. In this episode Anthony Sottile explores Flake8, one of the most popular options for identifying those problematic lines of code. He shares how he became involved in the project and took over as maintainer and explains the different categories of code quality tooling and how Flake8 compares to other static analyzers. He also discusses the ecosystem of plugins that have grown up around it, including some detailed examples of how you can write your own (and why you might want to).

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview about the Flake8 static analysis framework for Python and how you can use it to keep your code clean and maintainable","date_published":"2021-04-05T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/ee95889a-7c44-4133-ad8b-2b4aaec1cc31.mp3","mime_type":"audio/mpeg","size_in_bytes":40560759,"duration_in_seconds":2971}]},{"id":"podlove-2021-03-30t02:39:45+00:00-05cf8ee52d7a278","title":"Make Your Code More Readable With The Magic Of Refactoring Using Sourcery","url":"https://www.pythonpodcast.com/sourcery-automated-python-refactoring-episode-308","content_text":"Summary\nWriting code that is easy to read and understand will have a lasting impact on you and your teammates over the life of a project. Sometimes it can be difficult to identify opportunities for simplifying a block of code, especially if you are early in your journey as a developer. If you work with senior engineers they can help by pointing out ways to refactor your code to be more readable, but they aren’t always available. Brendan Maginnis and Nick Thapen created Sourcery to act as a full time pair programmer sitting in your editor of choice, offering suggestions and automatically refactoring your Python code. In this episode they share their journey of building a tool to automatically find opportunities for refactoring in your code, including how it works under the hood, the types of refactoring that it supports currently, and how you can start using it in your own work today. It always pays to keep your tool box organized and your tools sharp and Sourcery is definitely worth adding to your repertoire.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nWe’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to pythonpodcast.com/census today to get a free 14-day trial.\nYour host as usual is Tobias Macey and today I’m interviewing Nick Thapen and Brendan Maginnis about Sourcery, an advanced refactoring engine that cleans up your code as you work\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by giving an overview of what Sourcery is?\nWhat was your motivation for building a system for performing automated refactoring?\n\nWhat are your goals and priorities with Sourcery?\n\n\nThere are a number of services that aim to automate portions of the developer workflow, such as code completions, quality checks, refactoring, etc. What was lacking in the existing tooling that made Sourcery a necessary project?\n\nHow does Sourcery compare with some of the other services that offer AI or ML powered assistance? (e.g. Kite, Tab9, Codata(?))\n\n\nWhat was your reasoning for focusing solely on Python for your refactoring, rather than trying to support multiple language targets?\nCan you give some examples of the types of refactoring that you are able to automate?\nCan you describe how Sourcery is implemented?\n\nWhat are some of the ways that the system has changed or evolved in design and/or scope?\n\n\nWhat are some examples of the types of refactorings that Sourcery is ill-suited for and which still require manual intervention?\nWhat is involved in adding support for a new editor?\n\nHow much variation is there in terms of implementation or available functionality across editors?\nHow has the introduction of the Language Server Protocol influenced your approach to editor integration?\n\n\nWhat are some of the most interesting, unexpected, or challenging lessons that you have learned while working on Sourcery?\nWhen is Sourcery the wrong choice?\nWhat do you have planned for the future of Sourcery\n\nKeep In Touch\n\nNick\n\nLinkedIn\n@nthapen on Twitter\n\n\nBrendan\n\nLinkedIn\n@brendan_m6s on Twitter\nbrendanator on GitHub\n\n\n\nPicks\n\nTobias\n\nThe Croods: New Age\n\n\nNick\n\nThe Magicians TV Series\n\n\nBrendan\n\nDavid Copperfield by Charles Dickens\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nSourcery\nIBM RPG\nDelphi\nJava\nScala\nPyTorch\n\nPodcast Episode\n\n\nNLP == Natural Language Processing\nTensorflow\nLanguage Server Protocol\nKent Beck\nMartin Fowler\nMyPy\nClojure\nLisp\nAbstract Syntax Tree\nASTroid\n\nPodcast Episode\n\n\nRope\nSans I/O\npre-commit framework\n\nPodcast Episode\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Writing code that is easy to read and understand will have a lasting impact on you and your teammates over the life of a project. Sometimes it can be difficult to identify opportunities for simplifying a block of code, especially if you are early in your journey as a developer. If you work with senior engineers they can help by pointing out ways to refactor your code to be more readable, but they aren’t always available. Brendan Maginnis and Nick Thapen created Sourcery to act as a full time pair programmer sitting in your editor of choice, offering suggestions and automatically refactoring your Python code. In this episode they share their journey of building a tool to automatically find opportunities for refactoring in your code, including how it works under the hood, the types of refactoring that it supports currently, and how you can start using it in your own work today. It always pays to keep your tool box organized and your tools sharp and Sourcery is definitely worth adding to your repertoire.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
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\"\"

","summary":"An interview about the benefits of refactoring your code for clarity and ease of understanding and how Sourcery can help make it a habit.","date_published":"2021-03-29T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/17a3d37a-f291-4e74-89bb-9393640e2342.mp3","mime_type":"audio/mpeg","size_in_bytes":48046026,"duration_in_seconds":3658}]},{"id":"podlove-2021-03-22t11:25:39+00:00-d2749fd17969036","title":"Be Data Driven At Any Scale With Superset","url":"https://www.pythonpodcast.com/superset-data-driven-episode-207","content_text":"Summary\nBecoming data driven is the stated goal of a large and growing number of organizations. In order to achieve that mission they need a reliable and scalable method of accessing and analyzing the data that they have. While business intelligence solutions have been around for ages, they don’t all work well with the systems that we rely on today and a majority of them are not open source. Superset is a Python powered platform for exploring your data and building rich interactive dashboards that gets the information that your organization needs in front of the people that need it. In this episode Maxime Beauchemin, the creator of Superset, shares how the project got started and why it has become such a widely used and popular option for exploring and sharing data at companies of all sizes. He also explains how it functions, how you can customize it to fit your specific needs, and how to get it up and running in your own environment.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nWe’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to pythonpodcast.com/census today to get a free 14-day trial.\nYour host as usual is Tobias Macey and today I’m interviewing Max Beauchemin about Superset, an open source platform for data exploration and visualization\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by giving an overview of what Superset is and what it might be used for?\n\nWhat problem were you trying to solve when you created it?\nWhat tools or platforms did you consider before deciding to build something new?\n\n\nThere are a few different ways that someone might categorize Superset, such as business intelligence, data exploration, dashboarding, data visualization. How would you characterize it and how it fits in the current state of the industry and ecosystem?\nWhat are some of the lessons that you have learned from your work on Airflow that you applied to Superset?\nCan you give an overview of how Superset is implemented?\n\nHow have the goals, design and architecture evolved since you first began working on it?\n\n\nGiven its origin as a hackathon project the choice of Python seems natural. What are some of the challenges that choice has posed over the life of the project?\n\nIf you were to start the whole project over today what might you do differently?\n\n\nCan you describe what’s involved in getting started with a new setup of Superset?\n\nWhat are the available interfaces and integration points for someone who wants to extend it or add new functionality?\n\n\nWhat are some of the most often overlooked, misunderstood, or underused capabilities of Superset?\nOne of the perennial challenges with a tool that allows users to build data visualizations is the potential to build dashboards or charts that are visually appealing but ultimately meaningless or wrong. How much guidance does Superset provide in helping to select a useful representation of the data?\nIn addition to being the original author and a project maintainer you have also started a company to offer Superset as a service. What are your goals with that business and what is the opportunity that it provides?\nWhat are some of the most interesting, innovative, or unexpected ways that you have seen Superset used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while building and growing the Superset project and community?\nWhen is Superset the wrong choice?\nWhat do you have planned for the future of Superset and Preset?\n\nKeep In Touch\n\nLinkedIn\n@mistercrunch on Twitter\nmistercrunch on GitHub\n\nPicks\n\nTobias\n\nSOPS\n\n\nMax\n\nFrank Zappa Documentary\nAccelerate: The Science of Lean Software and DevOps\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nSuperset\nPreset\n\nBlog\n\n\nAirflow\n\nPodcast Episode\n\n\nAirBnB\nLyft\nDjango\nFlask\nCRUD == Create, Read, Update, Delete\nBusiness Intelligence\nApache Druid\nPresto\nTrino (formerly known as Presto SQL)\nRedash\n\nPodcast Episode\n\n\nLooker\n\nData Engineering Podcast Episode\n\n\nMetabase\n\nData Engineering Podcast Episode\n\n\nFlask App Builder\nReact Redux\nTypescript\nGraphQL\nCelery\nRedis\nRabbitMQ\nS3\nAirBnB Superset Blog Post\nD3\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Becoming data driven is the stated goal of a large and growing number of organizations. In order to achieve that mission they need a reliable and scalable method of accessing and analyzing the data that they have. While business intelligence solutions have been around for ages, they don’t all work well with the systems that we rely on today and a majority of them are not open source. Superset is a Python powered platform for exploring your data and building rich interactive dashboards that gets the information that your organization needs in front of the people that need it. In this episode Maxime Beauchemin, the creator of Superset, shares how the project got started and why it has become such a widely used and popular option for exploring and sharing data at companies of all sizes. He also explains how it functions, how you can customize it to fit your specific needs, and how to get it up and running in your own environment.

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Announcements

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Interview

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Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about the Superset platform for data exploration and how it supports organizations in being data driven","date_published":"2021-03-22T19:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/ab3d65b3-bf70-4719-9323-f0896a8130e1.mp3","mime_type":"audio/mpeg","size_in_bytes":39707391,"duration_in_seconds":2853}]},{"id":"podlove-2021-03-15t23:34:15+00:00-8758563c3501bbc","title":"Practical Advice On Using Python To Power A Business","url":"https://www.pythonpodcast.com/practical-business-python-episode-306","content_text":"Summary\nPython is a language that is used in almost every imaginable context and by people from an amazing range of backgrounds. A lot of the people who use it wouldn’t even call themselves programmers, because that is not the primary focus of their job. In this episode Chris Moffitt shares his experience writing Python as a business user. In order to share his insights and help others who have run up against the limits of Excel he maintains the site Practical Business Python where he publishes articles that help introduce newcomers to Python and explain how to perform tasks such as building reports, automating Excel files, and doing data analysis. This is a great conversation that illustrates how useful it is to learn Python even if you never intend to write software professionally.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nWe’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to pythonpodcast.com/census today to get a free 14-day trial.\nYour host as usual is Tobias Macey and today I’m interviewing Chris Moffitt about how Python is used to help manage business needs and processes and his work to share advice on this topic at Practical Business Python\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by giving an overview of your mission at Practical Business Python?\n\nWhat was your inspiration for starting the site and what keeps you motivated?\n\n\nWhat are some of the kinds of problems that a business user is looking to solve for themselves?\nWhy is Python a viable tool for a business user to become familiar with?\nHow would you characterize the difference between the ways that a software engineer and a business user approach Python?\nWhat do you see as the tipping point of complexity or time investment past which a business user will pass a given project on to a software engineer?\nHow much familiarity with adjacent concerns such as version control, software design, etc. do you consider useful for a business user?\nWhat are some of the ways that you use Python in your day-to-day?\nWhat are some of the onramps for integrating Python into a user’s workflow?\nWhat are some common stumbling blocks that business users run into when getting started with Python?\nWhat are some of the most interesting, innovative, or impressive ways that you have seen Python employed by business users?\nWhat are some of the most interesting, unexpected, or challenging lessons that you have learned while working on the Practical Business Python site?\nWhat are some cases where you would advocate for a tool other than Python for a business use case?\nWhat do you have planned for the future of the site?\n\nKeep In Touch\n\nLinkedIn\nchris1610 on GitHub\n@chris1610 on Twitter\n\nPicks\n\nTobias\n\nThe Data Science Roundup Newsletter\nThis Week In Data Newsletter\n\n\nChris Moffitt\n\nLine Of Duty BBC Series\nOut Of The Dark by David Weber\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nPractical Business Python blog\nElectrical Engineering\nUnix\nPerl\nData Science\nDjango\nRaspberry Pi\nPandas\nExcel\nVBA == Visual Basic for Applications\nVSCode\nExcel PowerFX\nPathlib\nConda\nPython Wheels\nPEP 582\nSAP\nSalesforce\nTableau\nProphet library for timeseries forecasting\nTalk Python Course Moving From Excel To Python\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Python is a language that is used in almost every imaginable context and by people from an amazing range of backgrounds. A lot of the people who use it wouldn’t even call themselves programmers, because that is not the primary focus of their job. In this episode Chris Moffitt shares his experience writing Python as a business user. In order to share his insights and help others who have run up against the limits of Excel he maintains the site Practical Business Python where he publishes articles that help introduce newcomers to Python and explain how to perform tasks such as building reports, automating Excel files, and doing data analysis. This is a great conversation that illustrates how useful it is to learn Python even if you never intend to write software professionally.

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Announcements

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Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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\"\"

","summary":"An interview with Chris Moffitt about his work on the Practical Business Python site and his experiences using and teaching Python for automating business processes.","date_published":"2021-03-15T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/ac17ae6d-8c90-4f01-a2ab-f05c65348ecc.mp3","mime_type":"audio/mpeg","size_in_bytes":46452197,"duration_in_seconds":2970}]},{"id":"podlove-2021-03-09t00:36:43+00:00-b3b6ca03b3f1750","title":"Analyzing The Ecosystem of Python Data Companies With Tony Liu","url":"https://www.pythonpodcast.com/tony-liu-python-venture-investing-episode-305","content_text":"Summary\nThere are a large and growing number of businesses built by and for data science and machine learning teams that rely on Python. Tony Liu is a venture investor who is following that market closely and betting on its continued success. In this episode he shares his own journey into the role of an investor and discusses what he is most excited about in the industry. He also explains what he looks at when investing in a business and gives advice on what potential founders and early employees of startups should be thinking about when starting on that journey.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Tony Liu about his perspectives on the landscape of Python in the data ecosystem from his role as an investor\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by sharing your background in the data ecosystem?\nWhat led you to your current role as a venture investor?\n\nWhat is your current area of focus in your investments?\n\n\nWhat do you see as the major strengths of Python in the current landscape for data and analytics?\n\nWhat are the areas where the ecosystem is still lacking?\nWhere are you seeing growth in the space and what do you see as the motivating factors?\n\n\nAs an investor, what are the qualities that you look for in a startup that is trying to compete in the data ecosystem?\n\nWhat is your process for learning about and identifying companies that demonstrate the potential to succeed?\nDo you focus on a particular problem domain and research a grouping of companies that are focused on that problem, or do you start from a given company to determine where to place your bets?\nHow has COVID changed the competitive landscape?\n\n\nCan you share some of the companies that you have invested in?\n\nWhat was noteable about their respective businesses that provided you with the confidence that they were worth investing in?\n\n\nWhat are some of the most interesting, unexpected, or challenging lessons that you have learned from your experience as a venture investor?\nWhat are some of the companies that you are keeping a close eye on, whether as potential investments or as competitors to your existing portfolio?\nWhat are some of the problem spaces that you would like to see companies try to tackle?\nWhat advice do you have for engineers who might be considering building a new business?\n\nDo you have any advice for engineers who are working at a startup as to how best to compete in the current market?\n\n\n\nKeep In Touch\n\nLinkedIn\n\nPicks\n\nTobias\n\nThe Sleepover movie\nWhat do ya do with a Bernie Sanders? music video\n\n\nTony\n\nUncut Gems\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nCostanoa Ventures\nSports Analytics\nTuro\nDatabricks\nKoalas\nDataRobot\nFaust\n\nPodcast Episode\n\n\nOozie\nAzkaban\nAirflow\n\nPodcast Episode\n\n\nPrefect\n\nData Engineering Podcast Episode\n\n\nDagster\n\nPodcast Episode\nData Engineering Podcast Episode\n\n\nKubeflow\nMLFlow\nMetaflow\n\nPodcast Episode\n\n\nPandas\n\nPodcast Episode\n\n\nSpark\n\nData Engineering Podcast Episode\n\n\nDBT\n\nData Engineering Podcast Episode\n\n\nSnowflakeDB\n\nData Engineering Podcast Episode\n\n\nCoiled\n\nPodcast Episode\n\n\nNoteable\nDask\n\nData Engineering Podcast Episode\n\n\nData Engineering Podcast Episode About Notebooks at Netflix\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

There are a large and growing number of businesses built by and for data science and machine learning teams that rely on Python. Tony Liu is a venture investor who is following that market closely and betting on its continued success. In this episode he shares his own journey into the role of an investor and discusses what he is most excited about in the industry. He also explains what he looks at when investing in a business and gives advice on what potential founders and early employees of startups should be thinking about when starting on that journey.

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Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Tony Liu of Costanoa Ventures about his perspective on the growth of Python in the landscape of data science companies.","date_published":"2021-03-08T19:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/d3cba4fd-06b3-4569-b552-b9f2cbca77da.mp3","mime_type":"audio/mpeg","size_in_bytes":28467462,"duration_in_seconds":2370}]},{"id":"podlove-2021-03-02t01:41:21+00:00-9de9cd112c0c704","title":"Go From Notebook To Pipeline For Your Data Science Projects With Orchest","url":"https://www.pythonpodcast.com/orchest-data-science-ide-episode-304","content_text":"Summary\nJupyter notebooks are a dominant tool for data scientists, but they lack a number of conveniences for building reusable and maintainable systems. For machine learning projects in particular there is a need for being able to pivot from exploring a particular dataset or problem to integrating that solution into a larger workflow. Rick Lamers and Yannick Perrenet were tired of struggling with one-off solutions when they created the Orchest platform. In this episode they explain how Orchest allows you to turn your notebooks into executable components that are integrated into a graph of execution for running end-to-end machine learning workflows.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Rick Lamers and Yannick Perrenet about Orchest, a development environment designed for building data science pipelines from notebooks and scripts.\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by giving an overview of what Orchest is and the story behind it?\nWho are the users that you are building Orchest for and what are their biggest challenges?\n\nWhat are some examples of the types of tools or workflows that they are using now?\n\n\nWhat are some of the other tools or strategies in the data science ecosystem that Orchest might replace? (e.g. MLFlow, Metaflow, etc.)\nWhat problems does Orchest solve?\nCan you describe how Orchest is implemented?\n\nHow have the design and goals of the project changed since you first started working on it?\n\n\nWhat is the workflow for someone who is using Orchest?\nWhat are some of the sharp edges that they might run into?\nWhat is the deployable unit once a pipeline has been created?\n\nHow do you handle verification and promotion of pipelines across staging and production environments?\n\n\nWhat are the interfaces available for integrating with or extending Orchest?\n\nHow might an organization incorporate a pipeline defined in Orchest with the rest of their data orchestration workflows?\n\n\nHow are you approaching governance and sustainability of the Orchest project?\nWhat are the most interesting, innovative, or unexpected ways that you have seen Orchest used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while building Orchest?\nWhen is Orchest the wrong choice?\nWhat do you have planned for the future of the project and company?\n\nKeep In Touch\n\nRick\n\nricklamers on GitHub\nLinkedIn\n@RickLamers on Twitter\n\n\nYannick\n\nyannickperrenet on GitHub\nLinkedIn\n\n\n\nPicks\n\nTobias\n\nFresh Bagels\n\n\nRick\n\nVaex\n\n\nYannick\n\nCookiecutter\nPyenv\n\n\n\nLinks\n\nOrchest\nGeoffrey Hinton\nYann LeCun\nCoffeeScript\nVim\nGAN == Generative Adversarial Network\nGit\nSQL\nBigQuery\nSoftware Carpentry\n\nPodcast Episode\n\n\nGoogle Colab\nAirflow\n\nPodcast Episode\n\n\nKedro\n\nData Engineering Podcast Episode\n\n\nnbdev\n\nPodcast Episode\n\n\nPapermill\n\nData Engineering Podcast Episode\n\n\nMLFlow\nMetaflow\n\nPodcast Episode\n\n\nDVC\n\nPodcast Episode\n\n\nAndrew Ng\nKubeflow\nLua\nCaddy\nTraefik\nDAG == Directed Acyclic Graph\nJupyter Enterprise Gateway\nStreamlit\nKubernetes\nDagster\n\nPodcast.__init__ Episode\nData Engineering Podcast Episode\n\n\nDBT\n\nData Engineering Podcast Episode\n\n\nGitLab\nSpark\nETL\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Jupyter notebooks are a dominant tool for data scientists, but they lack a number of conveniences for building reusable and maintainable systems. For machine learning projects in particular there is a need for being able to pivot from exploring a particular dataset or problem to integrating that solution into a larger workflow. Rick Lamers and Yannick Perrenet were tired of struggling with one-off solutions when they created the Orchest platform. In this episode they explain how Orchest allows you to turn your notebooks into executable components that are integrated into a graph of execution for running end-to-end machine learning workflows.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about the Orchest IDE built for data science and how it enables you to combine your notebooks into a data pipeline","date_published":"2021-03-01T21:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/47dc9971-b08e-422a-9861-3f5a9c75d72c.mp3","mime_type":"audio/mpeg","size_in_bytes":29777943,"duration_in_seconds":2664}]},{"id":"podlove-2021-02-21t02:33:08+00:00-92e6d3cdc63f677","title":"Write Your Python Scripts In A Flow Based Visual Editor With Ryven","url":"https://www.pythonpodcast.com/ryven-flow-based-visual-scripting-episode-303","content_text":"Summary\nWhen you are writing a script it can become unwieldy to understand how the logic and data are flowing through the program. To make this easier to follow you can use a flow-based approach to building your programs. Leonn Thomm created the Ryven project as an environment for visually constructing a flow-based program. In this episode he shares his inspiration for creating the Ryven project, how it changes the way you think about program design, how Ryven is implemented, and how to get started with it for your own programs.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Leon Thomm about Ryven, a flow-based visual scripting environment for Python\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by giving an overview of what Ryven is and what inspired you to create it?\nWhat is flow-based visual scripting?\nWhat are other popular flow-based visual scripting systems out there and have they been inspiring to the project?\n\nWhat problem(s) do these try to solve?\n\n\nWhat are some of the places where you are drawing inspiration for Ryven?\nWhat are the kinds of projects that someone might build with Ryven?\nHow are you using Ryven in your personal projects?\nHow does structuring a project as a set of nodes in a flow graph influence the way that you think about how to design the solution to a problem?\nCan you describe how Ryven is implemented?\n\nHow has the design or goals of the project changed or evolved since you first began working on it?\n\n\nFor someone who wants to use Ryven to build a project can you describe their workflow?\nHow do you handle things like code quality and tests for a Ryven project?\nHow do you manage collaboration for a Ryven project? (e.g. version control)\nWhat are some of the most interesting, innovative, or unexpected ways that you have seen Ryven used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while building Ryven?\nWhen is Ryven the wrong choice?\nWhat do you have planned for the future of the project?\n\nKeep In Touch\n\nleon-thomm on GitHub\n\nPicks\n\nTobias\n\nPyInfra\n\n\nLeon\n\nA Universe from Nothing! by Lawrence M. Krauss\n\n\n\nLinks\n\nRyven\nSwitzerland\nQt C++ framework\nFlow-based Scripting\nUnreal Engine\nNode-RED\nIFTTT == IF This Then That\nDAG == Directed Acyclic Graph\nMind Map\nLiterate Programming\nnbdev\n\nPodcast Episode\n\n\nOrg Mode\nOpenCV\nscikit-learn\nUnreal Python\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

When you are writing a script it can become unwieldy to understand how the logic and data are flowing through the program. To make this easier to follow you can use a flow-based approach to building your programs. Leonn Thomm created the Ryven project as an environment for visually constructing a flow-based program. In this episode he shares his inspiration for creating the Ryven project, how it changes the way you think about program design, how Ryven is implemented, and how to get started with it for your own programs.

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Announcements

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Interview

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Keep In Touch

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Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about the Ryven project for flow-based visual scripting in Python and how it can be used to change the way that you think about your program design.","date_published":"2021-02-22T21:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/650166fb-47dc-498e-a7e4-a06e3e79dcd0.mp3","mime_type":"audio/mpeg","size_in_bytes":28853794,"duration_in_seconds":2841}]},{"id":"podlove-2021-02-15t02:02:18+00:00-401818f4b67a1df","title":"CrossHair: Your Automatic Pair Programmer","url":"https://www.pythonpodcast.com/crosshair-automated-bug-finder-episode-302","content_text":"Summary\nOne of the perennial challenges in software engineering is to reduce the opportunity for bugs to creep into the system. Some of the tools in our arsenal that help in this endeavor include rich type systems, static analysis, writing tests, well defined interfaces, and linting. Phillip Schanely created the CrossHair project in order to add another ally in the fight against broken code. It sits somewhere between type systems, automated test generation, and static analysis. In this episode he explains his motivation for creating it, how he uses it for his own projects, and how to start incorporating it into yours. He also discusses the utility of writing contracts for your functions, and the differences between property based testing and SMT solvers. This is an interesting and informative conversation about some of the more nuanced aspects of how to write well-behaved programs.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Phillip Schanely about CrossHair, an analysis tool for Python that blurs the line between testing and type systems.\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by giving an overview of what the CrossHair project is and how it got started?\nWhat are some examples of the types of tools that CrossHair might augment or replace? (e.g. Pydantic, Doctest, etc.)\nWhat are the categories of bugs or problems in your code that CrossHair can help to identify or discover?\nCan you explain the benefits of implementing contracts in your software?\nWhat are the limitations of contract implementations?\nWhat are the available interfaces for creating and validating contracts?\nHow does the use of contracts in your software influence the overall design of the system?\nHow does CrossHair compare to type systems in terms of use cases or capabilities?\nCan you describe how CrossHair is implemented?\n\nHow has the design or goal of CrossHair changed or evolved since you first began working on it?\nWhat are some of the other projects that you have gained inspiration or ideas from while working on CrossHair? (inside or outside of the Python ecosystem)\n\n\nFor someone who wants to get started with CrossHair, can you talk through the developer workflow?\nI noticed that you recently added support for validating the functional equivalency of different method implementations. What was the inspiration for that capability?\n\nWhat kinds of use cases does that enable?\n\n\nHow much of CrossHair are you able to dogfood while developing CrossHair?\nWhat are some of the most interesting, innovative, or unexpected ways that you have seen CrossHair used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on CrossHair?\nWhen is CrossHair the wrong choice?\nWhat do you have planned for the future of the project?\n\nKeep In Touch\n\npschanely on GitHub\n@pschanely on Twitter\nLinkedIn\n\nPicks\n\nTobias\n\nThe War With Grandpa\n\n\nPhillip\n\nHammock chairs! (affiliate link)\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nCrossHair\nNLTK == Natural Language ToolKit\nACL2\nLiquid Haskell\nSMT Solver\nDoctest\nProperty Based Testing\nHypothesis\n\nPodcast Episode\n\n\nHalting Problem\nPydantic\nPEP 316\nicontract\nEiffel programming language\nDesign By Contract\nMetamorphic Testing\nHigher Order Types\nFuzz Testing\nThe Fuzzing Book\nPython Audit Hooks\nGitHub Scientist\n\nLaboratory Python implementation of GitHub Scientist\n\nPodcast Episode\n\n\n\n\nTaint Analysis\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

One of the perennial challenges in software engineering is to reduce the opportunity for bugs to creep into the system. Some of the tools in our arsenal that help in this endeavor include rich type systems, static analysis, writing tests, well defined interfaces, and linting. Phillip Schanely created the CrossHair project in order to add another ally in the fight against broken code. It sits somewhere between type systems, automated test generation, and static analysis. In this episode he explains his motivation for creating it, how he uses it for his own projects, and how to start incorporating it into yours. He also discusses the utility of writing contracts for your functions, and the differences between property based testing and SMT solvers. This is an interesting and informative conversation about some of the more nuanced aspects of how to write well-behaved programs.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about how the CrossHair tool helps you to define constraints for your programs and then find examples of when they are invalid.","date_published":"2021-02-15T21:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/7e4d21af-83da-4a5f-9eb6-c117599308d4.mp3","mime_type":"audio/mpeg","size_in_bytes":31825311,"duration_in_seconds":2573}]},{"id":"podlove-2021-02-09t01:57:32+00:00-69de702c74dd979","title":"Giving Your Data Science Projects And Teams A Home At DagsHub","url":"https://www.pythonpodcast.com/dagshub-data-science-collaboration-episode-301","content_text":"Summary\nCollaborating on software projects is largely a solved problem, with a variety of hosted or self-managed platforms to choose from. For data science projects, collaboration is still an open question. There are a number of projects that aim to bring collaboration to data science, but they are all solving a different aspect of the problem. Dean Pleban and Guy Smoilovsky created DagsHub to give individuals and teams a place to store and version their code, data, and models. In this episode they explain how DagsHub is designed to make it easier to create and track machine learning experiments, and serve as a way to promote collaboration on open source data science projects.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Dean Pleban and Guy Smoilovsky about DagsHub, a platform to track experiments, and version data, models & pipelines for your data science and machine learning projects.\n\nInterview\n\nIntroduction\nHow did you first get introduced to Python?\nCan you start by describing what the DagsHub platform is and why you built it?\nThere are a number of projects and platforms that aim to support collaboration among data scientists. What are the distinguishing features of DagsHub and how does it compare to the other options in the ecosystem?\n\nWhat are the biggest opportunities for improvement that you still see in the space of collaboration on data projects?\n\n\nWhat do you see as the biggest points of friction for building experiments and managing source data collaboratively?\nCan you describe how the DagsHub platform is implemented?\n\nHow have the design and goals of the system changed or evolved since you first began working on it?\nHow has your own understanding and practices of working on data science/ML projects changed changed?\n\n\nGitHub has a number of convenience features beyond just storing a git repository. What are the capabilities that you are focusing on to add value to the data science workflow within DagsHub?\nHow are you approaching the bootstrapping problem of building a critical mass of users to be able to generate a beneficial network effect?\nAre there any conventions that make it easier or more familiar for newcomers to a given project? (e.g. code layout, data labeling/tagging formats, etc.)\nWhat are your recommendations for managing onwership/licensing of data assets in public projects?\nWhat are some of the most interesting, innovative, or unexpected ways that you have seen DagsHub used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while building DagsHub?\nWhen is DagsHub the wrong choice?\nWhat do you have planned for the future of the platform and business?\n\nKeep In Touch\nFollow us on Twitter or LinkedIn, join our Discord, sign up to DAGsHub\n\n@DeanPlbn\n@Guy_T_Sky\n@TheRealDAGsHub\nDagsHub Discord\n\nPicks\n\nTobias\n\nThe Remarkable Journey of Prince Jen by Lloyd Alexander\n\n\nDean\n\nQuantum Computing Since Democritus by Scott Aaronson\nThe Expanse TV Series\n\n\nGuy\n\nTry to consume only the very best of available content, not the things that are coming out right now.\nApplies to textbooks, TV shows, movies\nLess Wrong blog\nSlate Star Codex \\ Astral Codex Ten\nAvatar: The Last Airbender\n3 Blue 1 Brown YouTube Channel\nHaskell\nClojure\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nDagsHub\nDVC\n\nPodcast Episode\n\n\nData Science Cookiecutter\nJupyter Notebooks\nPapers With Code\nConnected Papers\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Collaborating on software projects is largely a solved problem, with a variety of hosted or self-managed platforms to choose from. For data science projects, collaboration is still an open question. There are a number of projects that aim to bring collaboration to data science, but they are all solving a different aspect of the problem. Dean Pleban and Guy Smoilovsky created DagsHub to give individuals and teams a place to store and version their code, data, and models. In this episode they explain how DagsHub is designed to make it easier to create and track machine learning experiments, and serve as a way to promote collaboration on open source data science projects.

\n

Announcements

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Interview

\n\n

Keep In Touch

\n

Follow us on Twitter or LinkedIn, join our Discord, sign up to DAGsHub

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Picks

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Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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\"\"

","summary":"An interview about the DagsHub platform and how it simplifies the work of collaborating on the full lifecycle of data science and machine learning projects.","date_published":"2021-02-08T21:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/7d7c6a78-8fc9-4e9f-8073-075d26b4169c.mp3","mime_type":"audio/mpeg","size_in_bytes":44827526,"duration_in_seconds":3560}]},{"id":"podlove-2021-02-02t02:36:51+00:00-7ad11860df08715","title":"Exploring Literate Programming For Python Projects With nbdev","url":"https://www.pythonpodcast.com/nbdev-literate-programming-episode-300","content_text":"Summary\nCreating well designed software is largely a problem of context and understanding. The majority of programming environments rely on documentation, tests, and code being logically separated despite being contextually linked. In order to weave all of these concerns together there have been many efforts to create a literate programming environment. In this episode Jeremy Howard of fast.ai fame and Hamel Husain of GitHub share the work they have done on nbdev. The explain how it allows you to weave together documentation, code, and tests in the same context so that it is more natural to explore and build understanding when working on a project. It is built on top of the Jupyter environment, allowing you to take advantage of the other great elements of that ecosystem, and it provides a number of excellent out of the box features to reduce the friction in adopting good project hygiene, including continuous integration and well designed documentation sites. Regardless of whether you have been programming for 5 days, 5 years, or 5 decades you should take a look at nbdev to experience a different way of looking at your code.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Jeremy Howard and Hamel Husain about nbdev, a library for turning Jupyter notebooks into Python libraries.\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what nbdev is and the goals of the project?\n\nWhat is the story behind how and why it got started?\n\n\nWho is the target audience for the nbdev project?\n\nHow does that focus influence the features and design of nbdev?\n\n\nWhat do you see as the primary challenges of building and collaborating on projects written in notebooks?\nWhat are some of the other projects that are working to simplify or improve the experience of using notebooks?\n\nHow does nbdev compare to or complement those other tools?\n\n\nCan you describe how nbdev is implemented?\n\nHow has the design and goals of the project evolved since it was first started?\n\n\nWhat is the workflow of someone who is using nbdev?\n\nAt what point in the lifecycle of a notebook oriented project should someone start integrating nbdev?\n\n\nHow does nbdev scale when working on a project that spans multiple notebooks/modules?\nHow does working in a notebook environment change your approach to software development and project design?\nWhat are the most interesting, innovative, or unexpected ways that you have seen nbdev used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned from working on nbdev?\nWhen is nbdev the wrong choice?\nWhat do you have planned for the future of the project?\n\nKeep In Touch\n\nJeremy\n\nLinkedIn\n@jeremyphoward on Twitter\njph00 on GitHub\n\n\nHamel\n\nhamelsmu on GitHub\nWebsite\n@HamelHusain on Twitter\nLinkedIn\n\n\n\nPicks\n\nTobias\n\nRivals! Frenemies Who Changed The World\n\n\nJeremy\n\nChess\n\n\nHamel\n\nMoonwalking With Einstein by Joshua Foer (affiliate link)\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nnbdev\nfast.ai\nGitHub\nPerl\nFastmail\nR Studio\nR Markdown\nLiterate Programming\nfastcore\nJupyterLab\nnteract\nJupyter Voilà\nGitHub Actions\nSphinx\nGoogle Colab\nWorking In Public by Nadia Eghbal (affiliate link)\nJekyll\nHugo\nCython\n\nPodcast Episode\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Creating well designed software is largely a problem of context and understanding. The majority of programming environments rely on documentation, tests, and code being logically separated despite being contextually linked. In order to weave all of these concerns together there have been many efforts to create a literate programming environment. In this episode Jeremy Howard of fast.ai fame and Hamel Husain of GitHub share the work they have done on nbdev. The explain how it allows you to weave together documentation, code, and tests in the same context so that it is more natural to explore and build understanding when working on a project. It is built on top of the Jupyter environment, allowing you to take advantage of the other great elements of that ecosystem, and it provides a number of excellent out of the box features to reduce the friction in adopting good project hygiene, including continuous integration and well designed documentation sites. Regardless of whether you have been programming for 5 days, 5 years, or 5 decades you should take a look at nbdev to experience a different way of looking at your code.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview about the nbdev framework for building Python projects in a literate programming environment powered by Jupyter notebooks.","date_published":"2021-02-01T21:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/bc58c98d-f22f-45ea-ac08-0f8b5374912a.mp3","mime_type":"audio/mpeg","size_in_bytes":32551605,"duration_in_seconds":3098}]},{"id":"podlove-2021-01-26t00:18:28+00:00-06b0bbedbd701dc","title":"Making The Sans I/O Ideal A Reality For The Websockets Library","url":"https://www.pythonpodcast.com/websockets-sans-io-episode-299","content_text":"Summary\nWorking with network protocols is a common need for software projects, particularly in the current age of the internet. As a result, there are a multitude of libraries that provide interfaces to the various protocols. The problem is that implementing a network protocol properly and handling all of the edge cases is hard, and most of the available libraries are bound to a particular I/O paradigm which prevents them from being widely reused. To address this shortcoming there has been a movement towards \"sans I/O\" implementations that provide the business logic for a given protocol while remaining agnostic to whether you are using async I/O, Twisted, threads, etc. In this episode Aymeric Augustin shares his experience of refactoring his popular websockets library to be I/O agnostic, including the challenges involved in how to design the interfaces, the benefits it provides in simplifying the tests, and the work needed to add back support for async I/O and other runtimes. This is a great conversation about what is involved in making an ideal a reality.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Aymeric Augustin about his work on the websockets library and the work involved in making it sans I/O\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by giving an overview of your work on the websockets library and how the project got started?\nWhat does \"sans I/O\" mean and what are the goals associated with it?\nCan you share the history of your work on the websockets project?\n\nWhat was your motivation for starting down the path of rearchitecting a project that is already production ready?\n\n\nCan you talk through how the websockets library is architected currently?\n\nHow has the design of the project evolved since you first began working on it?\nAt a high level, what were the changes required to make it functionally sans i/o?\n\n\nWhat do you see as the primary challenges associated with making network related libraries sans i/o?\nIn your experience of porting websockets to be purely protocol oriented, what are the technical and design challenges that you faced?\nOne of the goals of the Sans I/O approach is to support reusability and composability of network protocol implementations. What has your experience been as to the viability of those goals in practice?\nWhat is your current perspective on the cost/benefit of the sans i/o conversion?\nWho are the primary consumers of the websockets library?\n\nHow do you foresee the target audience changing once you have completed extracting the protocol logic?\n\n\nWhat are some of the most interesting, innovative, or unexpected ways that you have seen the websockets project used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on the websockets project and sans i/o conversion?\nWhat do you have planned for the future of the project?\n\nKeep In Touch\n\nLinkedIn\n@aymericaugustin on Twitter\nWebsite\n\nPicks\n\nTobias\n\nJigsaw Puzzles\n\n\nAymeric\n\nInside Qonto interview\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nSans I/O: When The Rubber Meets The Road\nWebsockets library\nWebsockets Protocol\nQonto\nTulip\nAsyncio\nCERN Particle Accelerator\nSans I/O\nCory Benfield\nHTTP/2\nTwisted\nCurio\nTrio\nInversion of Control\nohneio helper library for implementing sans I/O network protocols\nSOCKS Proxy\nSanic\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Working with network protocols is a common need for software projects, particularly in the current age of the internet. As a result, there are a multitude of libraries that provide interfaces to the various protocols. The problem is that implementing a network protocol properly and handling all of the edge cases is hard, and most of the available libraries are bound to a particular I/O paradigm which prevents them from being widely reused. To address this shortcoming there has been a movement towards "sans I/O" implementations that provide the business logic for a given protocol while remaining agnostic to whether you are using async I/O, Twisted, threads, etc. In this episode Aymeric Augustin shares his experience of refactoring his popular websockets library to be I/O agnostic, including the challenges involved in how to design the interfaces, the benefits it provides in simplifying the tests, and the work needed to add back support for async I/O and other runtimes. This is a great conversation about what is involved in making an ideal a reality.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview with Aymeric Augustin about his experience refactoring the websockets library to remove I/O dependencies and make it more reusable","date_published":"2021-01-25T19:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/cde97ff7-ce78-42a0-905d-87b8021ce580.mp3","mime_type":"audio/mpeg","size_in_bytes":26666084,"duration_in_seconds":2284}]},{"id":"podlove-2021-01-19t01:47:20+00:00-5648adb4bea3b38","title":"Driving Toward A Faster Python Interpreter With Pyston","url":"https://www.pythonpodcast.com/pyston-fast-python-interpreter-episode-298","content_text":"Summary\nOne of the common complaints about Python is that it is slow. There are languages and runtimes that can execute code faster, but they are not as easy to be productive with, so many people are willing to make that tradeoff. There are some use cases, however, that truly need the benefit of faster execution. To address this problem Kevin Modzelewski helped to create the Pyston intepreter that is focused on speeding up unmodified Python code. In this episode he shares the history of the project, discusses his current efforts to optimize a fork of the CPython interpreter, and his goals for building a business to support the ongoing work to make Python faster for everyone. This is an interesting look at the opportunities that exist in the Python ecosystem and the work being done to address some of them.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Kevin Modzelewski about his work on Pyston, an interpreter for Python focused on compatibility and speed.\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what Pyston is and how it got started?\nCan you share some of the history of the project and the recent changes?\n\nWhat is your motivation for focusing on Pyston and Python optimization?\n\n\nWhat are the use cases that you are primarily focused on with Pyston?\nWhy do you think Python needs another performance project?\nCan you describe the technical implementation of Pyston?\n\nHow has the project evolved since you first began working on it?\n\n\nWhat are the biggest challenges that you face in maintaining compatibility with CPython?\nHow does the approach to Pyston compare to projects like PyPy and Pyjion?\nHow are you approaching sustainability and governance of the project?\nWhat are some of the most interesting, innovative, or unexpected uses for Pyston that you have seen?\nWhat have you found to be the most interesting, unexpected, or challenging lessons that you have learned while working on Pyston?\nWhen is Pyston the wrong choice?\nWhat do you have planned for the future of the project?\n\nKeep In Touch\n\nkmod on GitHub\nBlog\nLinkedIn\n\nPicks\n\nTobias\n\nLast Week In AWS Newsletter\n\n\nKevin\n\nMeditation\nCalm App\nHeadspace\n\n\n\nLinks\n\nPyston\n\nDiscord Chat\n\n\nDropbox\nCPython\nPyPy\nPyjion\n\nPodcast Episode\n\n\nJython\nhpy\n\nPodcast Episode\n\n\nJIT Compiler\nPython Software Foundation\n\nPodcast Episode\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n\n\n","content_html":"

Summary

\n

One of the common complaints about Python is that it is slow. There are languages and runtimes that can execute code faster, but they are not as easy to be productive with, so many people are willing to make that tradeoff. There are some use cases, however, that truly need the benefit of faster execution. To address this problem Kevin Modzelewski helped to create the Pyston intepreter that is focused on speeding up unmodified Python code. In this episode he shares the history of the project, discusses his current efforts to optimize a fork of the CPython interpreter, and his goals for building a business to support the ongoing work to make Python faster for everyone. This is an interesting look at the opportunities that exist in the Python ecosystem and the work being done to address some of them.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An episode about the work being done on the making a faster Python interpreter by the team behind the Pyston project.","date_published":"2021-01-18T21:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/f38cccec-6b4a-4b8c-9329-47f98c9be8f2.mp3","mime_type":"audio/mpeg","size_in_bytes":26856961,"duration_in_seconds":2646}]},{"id":"podlove-2021-01-12t00:04:47+00:00-119f21201c9604d","title":"Project Scaffolding That Evolves With Your Software Using Copier","url":"https://www.pythonpodcast.com/copier-project-scaffolding-episode-297","content_text":"Summary\nEvery software project has a certain amount of boilerplate to handle things like linting rules, test configuration, and packaging. Rather than recreate everything manually every time you start a new project you can use a utility to generate all of the necessary scaffolding from a template. This allows you to extract best practices and team standards into a reusable project that will save you time. The Copier project is one such utility that goes above and beyond the bare minimum by supporting project evolution, letting you bring in the changes to the source template after you already have a project that you have dedicated significant work on. In this episode Jairo Llopis explains how the Copier project works under the hood and the advanced capabilities that it provides, including managing the full lifecycle of a project, composing together multiple project templates, and how you can start using it for your own work today.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Jairo Llopis about Copier, a library for managing project templates\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what the Copier project is?\n\nHow did you get involved in the project?\nCan you share some of the history of the project?\n\n\nWhat do you see as the most common uses for a project templating tool?\nThere are a variety of different tools for scaffolding projects across a wide range of languages. What are the distinguishing features of Copier that might lead someone to choose it over the alternatives?\nCan you describe how the Copier project is implemented?\n\nHow has the design and feature set evolved over time?\n\n\nWhat is the workflow for someone building a template with Copier?\n\nWhat are some of the edge cases or complexities that they might run into?\n\n\nWhat are the options for extensibility or integration with Copier?\nWhat are some of the capabilities or use cases for Copier that are often overlooked?\nWhat are some of the most interesting, innovative, or unexpected ways that you have seen Copier used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working on and with Copier?\nWhen is Copier the wrong choice?\nWhat do you have planned for the future of the project?\n\nKeep In Touch\n\nYajo on GitHub\n__yajo on Twitter\nWebsite\n\nPicks\n\nTobias\n\nPlaying Cards\n\n\nJairo\n\nMozilla Hubs\n\n\n\nLinks\n\nCopier\nTecnativa\nOdoo Open Source ERP\nCookiecutter\nYeoman\nJinja\nCookiecutter, Yeoman, and Copier Blog Post\ndoodba-copier-template\nCopier Templates\nA Story of Duplicate Code\nTraefik\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Every software project has a certain amount of boilerplate to handle things like linting rules, test configuration, and packaging. Rather than recreate everything manually every time you start a new project you can use a utility to generate all of the necessary scaffolding from a template. This allows you to extract best practices and team standards into a reusable project that will save you time. The Copier project is one such utility that goes above and beyond the bare minimum by supporting project evolution, letting you bring in the changes to the source template after you already have a project that you have dedicated significant work on. In this episode Jairo Llopis explains how the Copier project works under the hood and the advanced capabilities that it provides, including managing the full lifecycle of a project, composing together multiple project templates, and how you can start using it for your own work today.

\n

Announcements

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Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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\"\"

","summary":"An interview about how the Copier project provides an advanced project scaffolding toolchain that allows you to evolve your software along with your templates.","date_published":"2021-01-11T19:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/e7a08931-7419-403d-bda9-5d3581eb1887.mp3","mime_type":"audio/mpeg","size_in_bytes":40558887,"duration_in_seconds":3476}]},{"id":"podlove-2021-01-03t21:17:43+00:00-b57d042bb421ca0","title":"How Python's Evolution Impacts Your Fluency With Luciano Ramalho","url":"https://www.pythonpodcast.com/luciano-ramalho-python-evolution-episode-296","content_text":"Summary\nOn its surface Python is a simple language which is what has contributed to its rise in popularity. As you move to intermediate and advanced usage you will find a number of interesting and elegant design elements that will let you build scalable and maintainable systems and design friendly interfaces. Luciano Ramalho is best known as the author of Fluent Python which has quickly become a leading resource for Python developers to increase their facility with the language. In this episode he shares his journey with Python and his perspective on how the recent changes to the interpreter and ecosystem are influencing who is adopting it and how it is being used. Luciano has an interesting perspective on how the feedback loop between the community and the language is driving the curent and future priorities of the features that are added.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Luciano Ramalho about the recent and upcoming changes in the Python language\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by giving an overview of the role that Python has played in your career?\nWhat other languages do you work with on a regular basis?\n\nHow has that experience influenced the ways that you use Python?\n\n\nWhat do you see as the biggest changes that have been added to Python in recent years?\nHow have the changes in Python changed the way that you approach program design?\nHow has your work on Fluent Python influenced your perspective on the language and its utility?\nWhat do you find to be the most confusing aspects of Python, whether for newcomers or experienced developers?\nHow would you characterize the types of features that have been added to Python in recent years?\n\nWhat, if any, trends have you observed in the types of features that are proposed and included in Python and what do you see as the motivating factors for them?\n\n\nWhat changes to the language are you tracking?\n\nWhich are you personally invested in?\n\n\nWhat new features or capabilities would you like to see included in Python?\n\nKeep In Touch\n\n@ramalhoorg on Twitter\nramalho on GitHub\nLinkedIn\n\nPicks\n\nTobias\n\nMagic: The Gathering: Arena\n\n\nLuciano\n\nThe Queen’s Gambit\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nFluent Python\nLibrary and Information Sciences\nThoughtworks\nSão Paulo, Brazil\nPerl\nPHP\nObject Oriented Programming\nDunder Methods\nPython Essential Reference\nPython In A Nutshell\nPython Typing Module\nPytype\nPyre\nMyPy\nAsyncIO\nTyping Protocols\nDuck Typing\nStatic Typing Where Possible, Dynamic Typing Where Needed\nTypeScript\nRuby 3 Type Annotations\nC#\nGo Language\nKotlinJS\nMatrix Multiplication Operator\nWalrus Operator == Assignment Expressions\nCPython PEG Parser\n\nPodcast Episode\n\n\nPEP 3099: Things that will Not Change in Python 3000\nElixir\nPattern Matching\nErlang\nProlog\nPython Pattern Matching PEP\nSWIG\nSymbolic Computation\nPython Descriptors\nBeeware\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

On its surface Python is a simple language which is what has contributed to its rise in popularity. As you move to intermediate and advanced usage you will find a number of interesting and elegant design elements that will let you build scalable and maintainable systems and design friendly interfaces. Luciano Ramalho is best known as the author of Fluent Python which has quickly become a leading resource for Python developers to increase their facility with the language. In this episode he shares his journey with Python and his perspective on how the recent changes to the interpreter and ecosystem are influencing who is adopting it and how it is being used. Luciano has an interesting perspective on how the feedback loop between the community and the language is driving the curent and future priorities of the features that are added.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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\n\n

\"\"

","summary":"A conversation with Luciano Ramalho about the recent changes to the Python language and ecosystem and how they impact your path to becoming fluent.","date_published":"2021-01-04T19:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/cee4af0d-791d-4d05-921e-ab8225c7fb5f.mp3","mime_type":"audio/mpeg","size_in_bytes":44563202,"duration_in_seconds":3613}]},{"id":"podlove-2020-12-28t15:14:59+00:00-5490a5eef2c2c97","title":"Making Content Management A Smooth Experience With A Headless CMS","url":"https://www.pythonpodcast.com/buttercms-headless-cms-episode-295","content_text":"Summary\nBuilding a web application requires integrating a number of separate concerns into a single experience. One of the common requirements is a content management system to allow product owners and marketers to make the changes needed for them to do their jobs. Rather than spend the time and focus of your developers to build the end to end system a growing trend is to use a headless CMS. In this episode Jake Lumetta shares why he decided to spend his time and energy on building a headless CMS as a service, when and why you might want to use one, and how to integrate it into your applications so that you can focus on the rest of your application.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nPython has become the default language for working with data, whether as a data scientist, data engineer, data analyst, or machine learning engineer. Springboard has launched their School of Data to help you get a career in the field through a comprehensive set of programs that are 100% online and tailored to fit your busy schedule. With a network of expert mentors who are available to coach you during weekly 1:1 video calls, a tuition-back guarantee that means you don’t pay until you get a job, resume preparation, and interview assistance there’s no reason to wait. Springboard is offering up to 20 scholarships of $500 towards the tuition cost, exclusively to listeners of this show. Go to pythonpodcast.com/springboard today to learn more and give your career a boost to the next level.\nYour host as usual is Tobias Macey and today I’m interviewing Jake Lumetta about Butter CMS and the role of a headless CMS in the modern web ecosystem.\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what a headless CMS is?\n\nHow does the use case and user experience differ from working with a traditional CMS (e.g. WordPress, etc.)?\nHow does a headless CMS compare to using a framework such as Django CMS or Wagtail?\n\n\nCan you describe what you have built at ButterCMS?\n\nWhat was your motivation for starting a business to provide a CMS as a service?\n\n\nHow would you characterize the current state of the CMS ecosystem?\n\nHow does ButterCMS compare to the available open source and commercial options?\n\n\nWhat are the trends in the web ecosystem that have made a headless CMS necessary or useful?\nWhat types of information are people managing in a CMS?\nHow are people integrating headless CMS systems into their Python applications?\nCan you describe the architecture for Butter?\n\nHow has the system changed or evolved since you first began working on it?\nWhat was your decision process for determining what language(s) and technology stack to use for building the platform?\n\n\nWhat are the aspects of building and maintaining a CMS that are most complex?\nWhat are some of the most interesting, innovative, or unexpected ways that you have seen ButterCMS used?\nWhat have you found to be the most interesting, unexpected, or challenging lessons that you have learned while building ButterCMS?\nWhen is ButterCMS the wrong choice?\nWhat do you have planned for the future of ButterCMS?\n\nKeep In Touch\n\nLinkedIn\n@jakelumetta on Twitter\n\nPicks\n\nTobias\n\nThe Arrow TV Show\n\n\nJake\n\nGhost In The Wires by Kevin Mitnick\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nButterCMS\n\nHiring: Dir of Engineering\n\n\nPHP\nDjango\nMVC == Model, View, Controller\nHeadless CMS\nWordPress\nDjango CMS\nWagtail\n\nPodcast Episode\n\n\nSEO == Search Engine Optimization\nJAM (Javascript, APIs, and Markup) Stack\nNetlify\nVercel\nCloudflare Pages\nVue.js\nReact.js\nDjango Rest Framework\nFastly\nCDN == Content Delivery Network\nAWS Cloudfront\nIonic\nReact Native\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Building a web application requires integrating a number of separate concerns into a single experience. One of the common requirements is a content management system to allow product owners and marketers to make the changes needed for them to do their jobs. Rather than spend the time and focus of your developers to build the end to end system a growing trend is to use a headless CMS. In this episode Jake Lumetta shares why he decided to spend his time and energy on building a headless CMS as a service, when and why you might want to use one, and how to integrate it into your applications so that you can focus on the rest of your application.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview with ButterCMS CEO Jake Lumetta about how using a headless CMS makes it easier to add content management to your applications.","date_published":"2020-12-28T17:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/7b16498d-43b6-42d0-9fee-68c60fbe5ee5.mp3","mime_type":"audio/mpeg","size_in_bytes":33774704,"duration_in_seconds":2930}]},{"id":"podlove-2020-12-21t22:52:48+00:00-a07ae9d4d46b29d","title":"Turning Notebooks Into Collaborative And Dynamic Data Applications With Hex","url":"https://www.pythonpodcast.com/hex-collaborative-notebooks-episode-294","content_text":"Summary\nNotebooks have been a useful tool for analytics, exploratory programming, and shareable data science for years, and their popularity is continuing to grow. Despite their widespread use, there are still a number of challenges that inhibit collaboration and use by non-technical stakeholders. Barry McCardel and his team at Hex have built a platform to make collaboration on Jupyter notebooks a first class experience, as well as allowing notebooks to be parameterized and exposing the logic through interactive web applications. In this episode Barry shares his perspective on the state of the notebook ecosystem, why it is such as powerful tool for computing and analytics, and how he has built a successful business around improving the end to end experience of working with notebooks. This was a great conversation about an important piece of the toolkit for every analyst and data scientist.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nDo you want to get better at Python? Now is an excellent time to take an online course. Whether you’re just learning Python or you’re looking for deep dives on topics like APIs, memory mangement, async and await, and more, our friends at Talk Python Training have a top-notch course for you. If you’re just getting started, be sure to check out the Python for Absolute Beginners course. It’s like the first year of computer science that you never took compressed into 10 fun hours of Python coding and problem solving. Go to pythonpodcast.com/talkpython today and get 10% off the course that will help you find your next level. That’s pythonpodcast.com/talkpython, and don’t forget to thank them for supporting the show.\nPython has become the default language for working with data, whether as a data scientist, data engineer, data analyst, or machine learning engineer. Springboard has launched their School of Data to help you get a career in the field through a comprehensive set of programs that are 100% online and tailored to fit your busy schedule. With a network of expert mentors who are available to coach you during weekly 1:1 video calls, a tuition-back guarantee that means you don’t pay until you get a job, resume preparation, and interview assistance there’s no reason to wait. Springboard is offering up to 20 scholarships of $500 towards the tuition cost, exclusively to listeners of this show. Go to pythonpodcast.com/springboard today to learn more and give your career a boost to the next level.\nYour host as usual is Tobias Macey and today I’m interviewing Barry McCardel about Hex, a managed platform to turn your notebooks into collaborative, interactive data apps and stories\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what you have built at Hex and your motivation for starting the business?\nWho are the primary users of the Hex platform?\n\nHow has that focus influenced your product direction and the features that you prioritize?\n\n\nWhat are the biggest roadblocks that you see data analysts and data consumers running into?\n\nHow have those roadblocks shifted in recent years?\n\n\nWhat is it about the concept of a notebook that has caused them to see such a massive rise in usage and popularity?\nWhat are the barriers to productivity and accessibility that still exist in the notebook ecosystem?\nWhat are the pieces for working in and with notebooks that are still missing?\n\nWhat does Hex add to the experience of working with notebooks?\n\n\nCan you describe how the Hex platform implemented?\n\nHow has the design of the platform changed or evolved since you first began working on it?\n\n\nWhere does Hex sit in the lifecycle of notebook creation and usage?\nHow does it compare to other services built to support users of notebooks such as Zepl, Saturn Cloud, Noteable, etc.?\nYou focus on the Jupyter platform, but there are a number of other notebook frameworks that have sprung up in recent years. What do you see as being the relative strengths of the available options?\nWhat are the trends in the tooling, capabilities, and use cases for notebooks that you are keeping an eye on?\nWhat are the most interesting, innovative, or unexpected ways that you have seen the Hex platform used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while building Hex?\nWhen is Hex the wrong choice?\nWhat do you have planned for the future of the Hex business and product?\n\nKeep In Touch\n\nLinkedIn\n@TheRealBarryM on Twitter\n\nPicks\n\nTobias\n\nFlakehell\nDC Extended Universe Movies\n\n\nBarry\n\nWingspan\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nHex\nPalantir\nIPython\n\nPodcast Episode\n\n\nJupyter\nMathematica\nIDE == Integrated Development Environment\nnbconvert\nObservable Javascript Notebooks\nReact\nBlueprintJS\nPapermill\nStreamlit\n\nPodcast Episode\n\n\nShiny\nRedshift\nSnowflake\n\nData Engineering Podcast Episode\n\n\nBigQuery\nPostgreSQL\n\nData Engineering Podcast Episode\n\n\nNoteable\nSaturn Cloud\nZepl\nZeplin Notebooks\nJupyterHub\nBinder\nKubeflow\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

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Notebooks have been a useful tool for analytics, exploratory programming, and shareable data science for years, and their popularity is continuing to grow. Despite their widespread use, there are still a number of challenges that inhibit collaboration and use by non-technical stakeholders. Barry McCardel and his team at Hex have built a platform to make collaboration on Jupyter notebooks a first class experience, as well as allowing notebooks to be parameterized and exposing the logic through interactive web applications. In this episode Barry shares his perspective on the state of the notebook ecosystem, why it is such as powerful tool for computing and analytics, and how he has built a successful business around improving the end to end experience of working with notebooks. This was a great conversation about an important piece of the toolkit for every analyst and data scientist.

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about how the Hex platform simplifies the workflow for Jupyter notebooks and lets you turn them into dynamic web applications.","date_published":"2020-12-21T18:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/ae9f6b37-e386-4d8b-a49b-1e498752b861.mp3","mime_type":"audio/mpeg","size_in_bytes":30211539,"duration_in_seconds":2559}]},{"id":"podlove-2020-12-15t01:20:40+00:00-5b4d3473d0159ef","title":"Add Anomaly Detection To Your Time Series Data With Luminaire","url":"https://www.pythonpodcast.com/luminaire-anomaly-detection-episode-293","content_text":"Summary\nWhen working with data it’s important to understand when it is correct. If there is a time dimension, then it can be difficult to know when variation is normal. Anomaly detection is a useful tool to address these challenges, but a difficult one to do well. In this episode Smit Shah and Sayan Chakraborty share the work they have done on Luminaire to make anomaly detection easier to work with. They explain the complexities inherent to working with time series data, the strategies that they have incorporated into Luminaire, and how they are using it in their data pipelines to identify errors early. If you are working with any kind of time series then it’s worth giving Luminaure a look.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nPython has become the default language for working with data, whether as a data scientist, data engineer, data analyst, or machine learning engineer. Springboard has launched their School of Data to help you get a career in the field through a comprehensive set of programs that are 100% online and tailored to fit your busy schedule. With a network of expert mentors who are available to coach you during weekly 1:1 video calls, a tuition-back guarantee that means you don’t pay until you get a job, resume preparation, and interview assistance there’s no reason to wait. Springboard is offering up to 20 scholarships of $500 towards the tuition cost, exclusively to listeners of this show. Go to pythonpodcast.com/springboard today to learn more and give your career a boost to the next level.\nYour host as usual is Tobias Macey and today I’m interviewing Smit Shah and Sayan Chakraborty about Luminaire, a machine learning based package for anomaly detection on timeseries data\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what Luminaire is and how the project got started?\n\nWhere does the name come from?\n\n\nHow does Luminaire compare to other frameworks for working with timeseries data such as Prophet?\nWhat are the main use cases that Luminaire is powering at Zillow?\nWhat are some of the complexities inherent to anomaly detection that are non-obvious at first glance?\n\nHow are you addressing those challenges in Luminaire?\n\n\nCan you describe how Luminaire is implemented?\n\nHow has the design of the project evolved since it was first started?\n\n\nWhat was the motivation for releasing Luminaire as open source?\nFor someone who is using Luminaire, what is the process for training and deploying a model with it?\n\nWhat are some common ways that it is used within a larger system?\n\n\nHow do sustained anomalies such as the current pandemic affect the work of identifying other sources of meaningful outliers?\nWhat are some of the most interesting, innovative, or unexpected ways that you have seen Luminaire being used?\nWhat are some of the most interesting, unexpected, or challening lessons that you have learned while building and using Luminaire?\nWhen is Luminaire the wrong choice?\nWhat do you have planned for the future of the project?\n\nKeep In Touch\n\nSmit\n\nLinkedIn\nshahsmit14 on GitHub\n\n\nSayan\n\nLinkedIn\nWebsite\n@tweettosayan on Twitter\n\n\n\nPicks\n\nTobias\n\nFlakehell\n\n\nSmit\n\nApache Ranger\n\n\nSayan\n\nPrediction Machines: The Simple Economics Of Artificial Intelligence\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nLuminaire\nZillow\nAnomaly Detection\nFacebook Prophet\nIEEE Big Data Conference\nUnsupervised Learning\nARIMA (Autoregressive Integrated Moving Average) Model\nAirflow\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

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When working with data it’s important to understand when it is correct. If there is a time dimension, then it can be difficult to know when variation is normal. Anomaly detection is a useful tool to address these challenges, but a difficult one to do well. In this episode Smit Shah and Sayan Chakraborty share the work they have done on Luminaire to make anomaly detection easier to work with. They explain the complexities inherent to working with time series data, the strategies that they have incorporated into Luminaire, and how they are using it in their data pipelines to identify errors early. If you are working with any kind of time series then it’s worth giving Luminaure a look.

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Announcements

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Interview

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Keep In Touch

\n\n

Picks

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Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about the Luminaire project and how it simplifies the work of building anomaly detection projects for time series data.","date_published":"2020-12-14T20:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/a9ab8409-0d82-427b-b0bb-40e84867007d.mp3","mime_type":"audio/mpeg","size_in_bytes":36130523,"duration_in_seconds":3263}]},{"id":"podlove-2020-12-05t21:34:02+00:00-8a4cbc7994db6ae","title":"Building Big Data Pipelines For Audio With Klio","url":"https://www.pythonpodcast.com/klio-big-data-audio-pipelines-episode-292","content_text":"Summary\nTechnologies for building data pipelines have been around for decades, with many mature options for a variety of workloads. However, most of those tools are focused on processing of text based data, both structured and unstructured. For projects that need to manage large numbers of binary and audio files the list of options is much shorter. In this episode Lynn Root shares the work that she and her team at Spotify have done on the Klio project to make that list a bit longer. She discusses the problems that are specific to working with binary data, how the Klio project is architected to allow for scalable and efficient processing of massive numbers of audio files, why it was released as open source, and how you can start using it today for your own projects. If you are struggling with ad-hoc infrastructure and a medley of tools that have been cobbled together for analyzing large or numerous binary assets then this is definitely a tool worth testing out.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nDo you want to get better at Python? Now is an excellent time to take an online course. Whether you’re just learning Python or you’re looking for deep dives on topics like APIs, memory mangement, async and await, and more, our friends at Talk Python Training have a top-notch course for you. If you’re just getting started, be sure to check out the Python for Absolute Beginners course. It’s like the first year of computer science that you never took compressed into 10 fun hours of Python coding and problem solving. Go to pythonpodcast.com/talkpython today and get 10% off the course that will help you find your next level. That’s pythonpodcast.com/talkpython, and don’t forget to thank them for supporting the show.\nPython has become the default language for working with data, whether as a data scientist, data engineer, data analyst, or machine learning engineer. Springboard has launched their School of Data to help you get a career in the field through a comprehensive set of programs that are 100% online and tailored to fit your busy schedule. With a network of expert mentors who are available to coach you during weekly 1:1 video calls, a tuition-back guarantee that means you don’t pay until you get a job, resume preparation, and interview assistance there’s no reason to wait. Springboard is offering up to 20 scholarships of $500 towards the tuition cost, exclusively to listeners of this show. Go to pythonpodcast.com/springboard today to learn more and give your career a boost to the next level.\nYour host as usual is Tobias Macey and today I’m interviewing Lynn Root about Klio, an open source pipeline for processing audio and binary data\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what Klio is and how it got started?\nWhat are some of the challenges that are unique to processing audio data as compared to text?\nWhat use cases does Klio enable?\nWhat are some of the alternative options available for working with binary data?\n\nWhat capabilities were lacking in other solutions that made it worthwhile to build a new system from scratch?\n\n\nCan you describe the design and architecture of Klio?\n\nWhat was the motivation for implementing Klio as a Python framework, rather than building on top of the Scio project?\n\n\nHow much of a challenge has it been to interface to the Beam framework from Python? (Java <-> Python impedance mismatch)\nOne of the interesting optimizations in Klio is the option for bottom up execution of a job to avoid processing a given file unless absolutely necessary. What are some of the other useful or interesting capabilities that are built into Klio?\nWhat was the motivation and process for releasing Klio as open source?\nFor someone who is building a pipeline with Klio, can you talk through the workflow?\n\nWhat are the extension and integration points that are exposed?\nHow does Klio handle third party dependencies for a given job?\n\n\nWhat are some of the challenges, misunderstandings, or edge cases that users of Klio should be aware of?\nWhat are some of the most interesting, unexpected, or challenging lessons that you have learned while building and growing the Klio project?\nWhat are some of the most interesting, innovative, or unexpected ways that you have seen Klio used?\nWhat do you have planned for the future of the project?\n\nKeep In Touch\n\nGitHub\nTwitter\nLinkedIn\n\nPicks\n\nTobias\n\nPSF Fundraiser\n\n\nLynn\n\nRoam note-taking tool\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nKlio\n\nAnnouncement Blog Post\nDocs\nGitHub\n\n\nSpotify\nPyLadies SF\nLuigi\nRAML\nramlfications\nInterrogate\nApache Beam\nLibrosa\nPyAudio\nPillow\n\nPodcast Episode\n\n\nFFMPeg\nImageMagick\nMusic Information Retrieval\nMachine Hearing\n\nData Engineering Podcast Episode\n\n\nScio\nMicrosoft Azure\nGoogle Cloud Platform\nGoogle Cloud Dataflow\nProtocol Buffers\nApache Spark\nPySpark\nDAG == Directed Acyclic Graph\nISMIR Conference\nDigital Signal Processing (DSP)\nPython Pickle\nResearch paper on separating vocals from instrumentals of a song\nNew York Times: Why songs of the summer sound the same\nMicrosoft’s Rocket Platform for video analytics\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

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Technologies for building data pipelines have been around for decades, with many mature options for a variety of workloads. However, most of those tools are focused on processing of text based data, both structured and unstructured. For projects that need to manage large numbers of binary and audio files the list of options is much shorter. In this episode Lynn Root shares the work that she and her team at Spotify have done on the Klio project to make that list a bit longer. She discusses the problems that are specific to working with binary data, how the Klio project is architected to allow for scalable and efficient processing of massive numbers of audio files, why it was released as open source, and how you can start using it today for your own projects. If you are struggling with ad-hoc infrastructure and a medley of tools that have been cobbled together for analyzing large or numerous binary assets then this is definitely a tool worth testing out.

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Announcements

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Interview

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Keep In Touch

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Picks

\n\n

Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Lynn Root about building the Klio project to power Spotify's big data pipelines to scale and simplify their capacity for working with audio files and other binary data.","date_published":"2020-12-07T18:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/8a3e5d5f-6713-4af3-9bfa-f0e579c55d13.mp3","mime_type":"audio/mpeg","size_in_bytes":39310161,"duration_in_seconds":3216}]},{"id":"podlove-2020-12-01t00:17:14+00:00-d1e57dd56bdb9d4","title":"Open Sourcing The Anvil Full Stack Python Web App Platform","url":"https://www.pythonpodcast.com/anvil-open-source-web-app-server-episode-291","content_text":"Summary\nBuilding a complete web application requires expertise in a wide range of disciplines. As a result it is often the work of a whole team of engineers to get a new project from idea to production. Meredydd Luff and his co-founder built the Anvil platform to make it possible to build full stack applications entirely in Python. In this episode he explains why they released the application server as open source, how you can use it to run your own projects for free, and why developer tooling is the sweet spot for an open source business model. He also shares his vision for how the end-to-end experience of building for the web should look, and some of the innovative projects and companies that were made possible by the reduced friction that the Anvil platform provides. Give it a listen today to gain some perspective on what it could be like to build a web app.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nDo you want to get better at Python? Now is an excellent time to take an online course. Whether you’re just learning Python or you’re looking for deep dives on topics like APIs, memory mangement, async and await, and more, our friends at Talk Python Training have a top-notch course for you. If you’re just getting started, be sure to check out the Python for Absolute Beginners course. It’s like the first year of computer science that you never took compressed into 10 fun hours of Python coding and problem solving. Go to pythonpodcast.com/talkpython today and get 10% off the course that will help you find your next level. That’s pythonpodcast.com/talkpython, and don’t forget to thank them for supporting the show.\nPython has become the default language for working with data, whether as a data scientist, data engineer, data analyst, or machine learning engineer. Springboard has launched their School of Data to help you get a career in the field through a comprehensive set of programs that are 100% online and tailored to fit your busy schedule. With a network of expert mentors who are available to coach you during weekly 1:1 video calls, a tuition-back guarantee that means you don’t pay until you get a job, resume preparation, and interview assistance there’s no reason to wait. Springboard is offering up to 20 scholarships of $500 towards the tuition cost, exclusively to listeners of this show. Go to pythonpodcast.com/springboard today to learn more and give your career a boost to the next level.\nYour host as usual is Tobias Macey and today I’m interviewing Meredydd Luff about the process and motivations for releasing the Anvil platform as open source\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by giving an overview of what Anvil is and some of the story behind it?\n\nWhat is new or different in Anvil since we last spoke in June of 2019?\n\n\nWhat are the most common or most impressive use cases for Anvil that you have seen?\n\nOn your website you mention Anvil being used for deploying models and productionizing notebooks. How does Anvil help in those use cases?\n\n\nHow much of the adoption of Anvil do you attribute to the use of Skulpt and providing a way to write Python for the browser?\n\nWhat are some of the complications that users might run into when trying to integrate with the broader Javascript ecosystem?\n\n\nHow does the release of the Anvil App Server affect your business model?\n\nHow does the workflow for users of the Anvil platform change if they decide to run their own instance?\nWhat is involved in getting it deployed to production?\n\n\nWhat other tools or companies did you look to for positive and negative examples of how to run a successful business based on open source?\nWhat was your motivation for open sourcing the core runtime of Anvil?\n\nWhat was involved in getting the code cleaned up and ready for a public release?\n\n\nWhat are the other ways that your business relies on or contributes to the open source ecosystem?\nWhat do you see as the primary threats to open source business models?\nWhat are some of the most interesting, unexpected, or challenging lessons that you have learned while building and growing Anvil?\nWhat do you have planned for the future of the platform and business?\n\nKeep In Touch\n\nLinkedIn\n@meredydd on Twitter\nmeredydd on GitHub\n\nPicks\n\nTobias\n\nMagic: The Gathering\n\n\nMeredydd\n\nAnvil Advent Calendar\nAnvil Podcast\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nAnvil\n\nPodcast Episode\n\n\nVisual Basic\nSkulpt\nStreamlit\n\nPodcast Episode\n\n\nPlot.ly Dash\nAnvil Uplink\nDOM == Document Object Model\nSQLAlchemy\nBrython\nTranscrypt\n\nPodcast Episode\n\n\nComparison of Python in the browser implementations\nBlog post about Anvil object serializer\nCreate React App\nWebpack\nJetbrains\nTraefik\nLet’s Encrypt\nCorey Quinn\nWebAssembly\nPyOdide\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Building a complete web application requires expertise in a wide range of disciplines. As a result it is often the work of a whole team of engineers to get a new project from idea to production. Meredydd Luff and his co-founder built the Anvil platform to make it possible to build full stack applications entirely in Python. In this episode he explains why they released the application server as open source, how you can use it to run your own projects for free, and why developer tooling is the sweet spot for an open source business model. He also shares his vision for how the end-to-end experience of building for the web should look, and some of the innovative projects and companies that were made possible by the reduced friction that the Anvil platform provides. Give it a listen today to gain some perspective on what it could be like to build a web app.

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Announcements

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Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about open sourcing the Anvil web application server and the economics of building a business around developer tooling.","date_published":"2020-11-30T19:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/812a2cc9-4a8c-48d1-9306-721068af7b0e.mp3","mime_type":"audio/mpeg","size_in_bytes":41181890,"duration_in_seconds":3083}]},{"id":"podlove-2020-11-23t23:16:29+00:00-808d6fb218993a5","title":"Pants Has Got Your Python Monorepo Covered","url":"https://www.pythonpodcast.com/pants-monorepo-build-tool-episode-290","content_text":"Summary\nIn a software project writing code is just one step of the overall lifecycle. There are many repetitive steps such as linting, running tests, and packaging that need to be run for each project that you maintain. In order to reduce the overhead of these repeat tasks, and to simplify the process of integrating code across multiple systems the use of monorepos has been growing in popularity. The Pants build tool is purpose built for addressing all of the drudgery and for working with monorepos of all sizes. In this episode core maintainers Eric Arellano and Stu Hood explain how the Pants project works, the benefits of automatic dependency inference, and how you can start using it in your own projects today. They also share useful tips for how to organize your projects, and how the plugin oriented architecture adds flexibility for you to customize Pants to your specific needs.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nPython has become the default language for working with data, whether as a data scientist, data engineer, data analyst, or machine learning engineer. Springboard has launched their School of Data to help you get a career in the field through a comprehensive set of programs that are 100% online and tailored to fit your busy schedule. With a network of expert mentors who are available to coach you during weekly 1:1 video calls, a tuition-back guarantee that means you don’t pay until you get a job, resume preparation, and interview assistance there’s no reason to wait. Springboard is offering up to 20 scholarships of $500 towards the tuition cost, exclusively to listeners of this show. Go to pythonpodcast.com/springboard today to learn more and give your career a boost to the next level.\nFeature flagging is a simple concept that enables you to ship faster, test in production, and do easy rollbacks without redeploying code. Teams using feature flags release new software with less risk, and release more often. ConfigCat is a feature flag service that lets you easily add flags to your Python code, and 9 other platforms. By adopting ConfigCat you and your manager can track and toggle your feature flags from their visual dashboard without redeploying any code or configuration, including granular targeting rules. You can roll out new features to a subset or your users for beta testing or canary deployments. With their simple API, clear documentation, and pricing that is independent of your team size you can get your first feature flags added in minutes without breaking the bank. Go to pythonpodcast.com/configcat today to get 35% off any paid plan with code PYTHONPODCAST or try out their free forever plan.\nYour host as usual is Tobias Macey and today I’m interviewing Eric Arellano and Stu Hood about Pants, a flexible build system that works well with monorepos.\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what Pants is and how it got started?\n\nWhat’s the story behind the name?\n\n\nWhat is a monorepo and why might I want one?\n\nWhat are the challenges caused by working with a monorepo?\nWhy are monorepos so uncommon in Python projects?\n\n\nWhat is the workflow for a developer or team who is managing a project with Pants?\nHow does Pants integrate with the broader ecosystem of Python tools for dependency management and packaging (e.g. Poetry, Pip, pip-tools, Flit, Twine, Pex, Shiv, etc.)?\nWhat is involved in setting up Pants for working with a new Python project?\n\nWhat complications might developers encounter when trying to implement Pants in an existing project?\n\n\nHow is Pants itself implemented?\n\nHow have the design, goals, or architecture evolved since Pants was first created?\nWhat are the major changes in the v2 release?\n\nWhat was the motivation for the major overhaul of the project?\n\n\n\n\nHow do you recommend developers lay out their projects to work well with Python?\nHow can I handle code shared between different modules or packages, and reducing the third party dependencies that are built into the respective packages?\nWhat are some of the most interesting, unexpected, or innovative ways that you have seen Pants used?\nWhat have you found to be the most interesting, unexpected, or challenging aspects of working on Pants?\nWhat are the cases where Pants is the wrong choice?\nWhat do you have planned for the future of the pants project?\n\nKeep In Touch\n\nEric\n\nLinkedIn\nEric-Arellano on GitHub\n@EArellanoAZ on Twitter\n\n\nStu\n\nstuhood on GitHub\n@stuhood on Twitter\nLinkedIn\n\n\n\nPicks\n\nTobias\n\nCursed TV show\n\n\nEric\n\nTurtle Graphics\n\n\nStu\n\nFaster Than Lime blog\n\n\n\nLinks\n\nPants\nFoursquare\nTwitter\nToolchain\nBazel build tool\nAnt build tool\nMonorepo\nisort\nTox\nPoetry\ndistutils\nsetuptools\nmypy\nBandit\nFlake8\nSample Python Pants Project\ngRPC\nProtocol Buffers\nRust\nGIL == Global Interpreter Lock\nPEP 420\nBlog post about using Pants to migrate from Python 2 to 3\nPex\nShiv\nPyOxidizer\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

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In a software project writing code is just one step of the overall lifecycle. There are many repetitive steps such as linting, running tests, and packaging that need to be run for each project that you maintain. In order to reduce the overhead of these repeat tasks, and to simplify the process of integrating code across multiple systems the use of monorepos has been growing in popularity. The Pants build tool is purpose built for addressing all of the drudgery and for working with monorepos of all sizes. In this episode core maintainers Eric Arellano and Stu Hood explain how the Pants project works, the benefits of automatic dependency inference, and how you can start using it in your own projects today. They also share useful tips for how to organize your projects, and how the plugin oriented architecture adds flexibility for you to customize Pants to your specific needs.

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about the Pants build tool, how it supports Python monorepos out of the box, and how its plugin architecture lets you customize it to fit your needs.","date_published":"2020-11-23T18:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/b0983f13-43f5-4490-b3aa-e19210c02f1a.mp3","mime_type":"audio/mpeg","size_in_bytes":38585352,"duration_in_seconds":3098}]},{"id":"podlove-2020-11-17t01:40:15+00:00-946fb6be42dad64","title":"Scale Your Data Science Teams With Machine Learning Operations Principles","url":"https://www.pythonpodcast.com/machine-learning-operations-episode-289","content_text":"Summary\nBuilding a machine learning model is a process that requires well curated and cleaned data and a lot of experimentation. Doing it repeatably and at scale with a team requires a way to share your discoveries with your teammates. This has led to a new set of operational ML platforms. In this episode Michael Del Balso shares the lessons that he learned from building the platform at Uber for putting machine learning into production. He also explains how the feature store is becoming the core abstraction for data teams to collaborate on building machine learning models. If you are struggling to get your models into production, or scale your data science throughput, then this interview is worth a listen.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nDo you want to get better at Python? Now is an excellent time to take an online course. Whether you’re just learning Python or you’re looking for deep dives on topics like APIs, memory mangement, async and await, and more, our friends at Talk Python Training have a top-notch course for you. If you’re just getting started, be sure to check out the Python for Absolute Beginners course. It’s like the first year of computer science that you never took compressed into 10 fun hours of Python coding and problem solving. Go to pythonpodcast.com/talkpython today and get 10% off the course that will help you find your next level. That’s pythonpodcast.com/talkpython, and don’t forget to thank them for supporting the show.\nPython has become the default language for working with data, whether as a data scientist, data engineer, data analyst, or machine learning engineer. Springboard has launched their School of Data to help you get a career in the field through a comprehensive set of programs that are 100% online and tailored to fit your busy schedule. With a network of expert mentors who are available to coach you during weekly 1:1 video calls, a tuition-back guarantee that means you don’t pay until you get a job, resume preparation, and interview assistance there’s no reason to wait. Springboard is offering up to 20 scholarships of $500 towards the tuition cost, exclusively to listeners of this show. Go to pythonpodcast.com/springboard today to learn more and give your career a boost to the next level.\nYour host as usual is Tobias Macey and today I’m interviewing Mike Del Balso about what is involved in operationalizing machine learning, and his work at Tecton to provide that platform as a service\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what is encompassed by the term \"Operational ML\"?\n\nWhat other approaches are there to building and managing machine learning projects?\nHow do these approaches differ from operational ML in terms of the use cases that they enable or the scenarios where they can be employed?\n\n\nHow would you characterize the current level of maturity for the average organization or enterprise in terms of their capacity for delivering ML projects?\nWhat are the necessary components for an operational ML platform?\nYou helped to build the Michelangelo platform at Uber. How did you determine what capabilities were necessary to provide a unified approach for building and deploying models?\nHow did your work on Michelangelo inform your work on Tecton?\nHow does the use of a feature store influence the structure and workflow of a data team?\nIn addition to the feature store, what are the other necessary components of a full pipeline for identifying, training, and deploying machine learning models?\nOnce a model is in production, what signals or metrics do you track to feed into the next iteration of model development?\nOne of the common challenges in data science and machine learning is managing collaboration. How do tools such as feature stores or the Michelangelo platform address that problem?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while building operational ML platforms?\nWhat advice or recommendations do you have for teams who are trying to work with machine learning?\nWhat do you have planned for the future of Tecton?\n\nKeep In Touch\n\nLinkedIn\n\nPicks\n\nTobias\n\nSandman graphic novel series by Neil Gaiman\n\n\nMike\n\nAt Home: A Short History of Private Life by Bill Bryson\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nTecton\nMichelangelo\nsklearn\nPandas\nData Engineering Podcast Episode About StreamSQL\nFeature Store\nMaster Data Management\nAmundsen\n\nData Engineering Podcast Episode\n\n\nJupyter\nAlgorithmia\nUnix philosophy\nFeast feature store\nKubeflow\nAndreesen Horowitz Post On Emerging Data Architectures\nWhat is a feature store? post on the Tecton blog\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

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Building a machine learning model is a process that requires well curated and cleaned data and a lot of experimentation. Doing it repeatably and at scale with a team requires a way to share your discoveries with your teammates. This has led to a new set of operational ML platforms. In this episode Michael Del Balso shares the lessons that he learned from building the platform at Uber for putting machine learning into production. He also explains how the feature store is becoming the core abstraction for data teams to collaborate on building machine learning models. If you are struggling to get your models into production, or scale your data science throughput, then this interview is worth a listen.

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Announcements

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Interview

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Keep In Touch

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Picks

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Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Tecton founder Michael Del Balso about his experiences building machine learning operations platforms and the useful lessons that he learned.","date_published":"2020-11-16T20:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/005871e8-2641-4625-bf17-2e64d72a868c.mp3","mime_type":"audio/mpeg","size_in_bytes":43870086,"duration_in_seconds":3118}]},{"id":"podlove-2020-11-10t02:58:49+00:00-9b6953e61419a0b","title":"Making The Case For A (Semi) Formal Specification Of CPython","url":"https://www.pythonpodcast.com/cpython-formal-specification-episode-288","content_text":"Summary\nThe CPython implementation has grown and evolved significantly over the past ~25 years. In that time there have been many other projects to create compatible runtimes for your Python code. One of the challenges for these other projects is the lack of a fully documented specification of how and why everything works the way that it does. In the most recent Python language summit Mark Shannon proposed implementing a formal specification for CPython, and in this episode he shares his reasoning for why that would be helpful and what is involved in making it a reality.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nDo you want to get better at Python? Now is an excellent time to take an online course. Whether you’re just learning Python or you’re looking for deep dives on topics like APIs, memory mangement, async and await, and more, our friends at Talk Python Training have a top-notch course for you. If you’re just getting started, be sure to check out the Python for Absolute Beginners course. It’s like the first year of computer science that you never took compressed into 10 fun hours of Python coding and problem solving. Go to pythonpodcast.com/talkpython today and get 10% off the course that will help you find your next level. That’s pythonpodcast.com/talkpython, and don’t forget to thank them for supporting the show.\nYour host as usual is Tobias Macey and today I’m interviewing Mark Shannon about his efforts to create a formal specification for the CPython interpreter\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing the current state of how the Python language and the CPython runtime are defined?\nWhat is your motivation in advocating for a specification?\n\nAfter ~25 years of the language, why is now the time to pursue this effort?\nHow does the history of the language and the scope of the ecosystem and community impact the effort required to make this a reality?\n\n\nWhat is involved in creating the specification and where would it be located once complete?\n\nWhat are some examples of languages that are formally specified?\n\n\nWhat are the possible benefits of creating a specification for the CPython virtual machine?\n\nWhat is the distinction between a specification for the VM as opposed to a specification for the language?\n\n\nWhat are some potential downsides to having a (semi-)formal specification become part of the definition of the interpreter?\nCan you describe the process of doing the work to create the specification?\nHow are you approaching the actual definition of the specification (e.g. prose vs programmatic)?\n\nWhat are the tradeoffs of prose vs. an executable specification (e.g. TLA+, Alloy)?\n\n\nHow does this work tie into your goals of improving the speed of the CPython interpreter?\nWhat are some of the most interesting, unexpected, or challenging aspects of your efforts to bring this specification to CPython?\nHow can the community contribute to this effort?\n\nKeep In Touch\n\nmarkshannon on GitHub\nWebsite\n\nPicks\n\nTobias\n\nAmerican Gods book and TV series\n\n\nMark\n\nRoadside Picnic\nIn Death (VR game)\n–On Steam\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nCPython\nPyPy\nPEP 380 yield from\nLanguage Summit\nRustPython\nJython\nC++\nML programming language\nJava\nPython Formal Semantics git repository\nCPython PEG Parser Episode with Pablo Galindo and Lysandros Nikolaou\nIETF RFCs\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

The CPython implementation has grown and evolved significantly over the past ~25 years. In that time there have been many other projects to create compatible runtimes for your Python code. One of the challenges for these other projects is the lack of a fully documented specification of how and why everything works the way that it does. In the most recent Python language summit Mark Shannon proposed implementing a formal specification for CPython, and in this episode he shares his reasoning for why that would be helpful and what is involved in making it a reality.

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Interview

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Closing Announcements

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Mark Shannon about his work to help create a formal specification of CPython and how it will help other implementations of Python.","date_published":"2020-11-09T22:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/3415876f-a6db-4559-883d-d58da6fd8066.mp3","mime_type":"audio/mpeg","size_in_bytes":29022695,"duration_in_seconds":2201}]},{"id":"podlove-2020-11-02t23:41:18+00:00-6b988598a52f213","title":"Bringing Artificial Intelligence Projects From Idea To Production","url":"https://www.pythonpodcast.com/henrik-landgren-artificial-intelligence-episode-287","content_text":"Summary\nArtificial intelligence applications can provide dramatic benefits to a business, but only if you can bring them from idea to production. Henrik Landgren was behind the original efforts at Spotify to leverage data for new product features, and in his current role he works on an AI system to evaluate new businesses to invest in. In this episode he shares advice on how to identify opportunities for leveraging AI to improve your business, the capabilities necessary to enable aa successful project, and some of the pitfalls to watch out for. If you are curious about how to get started with AI, or what to consider as you build a project, then this is definitely worth a listen.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nDo you want to get better at Python? Now is an excellent time to take an online course. Whether you’re just learning Python or you’re looking for deep dives on topics like APIs, memory mangement, async and await, and more, our friends at Talk Python Training have a top-notch course for you. If you’re just getting started, be sure to check out the Python for Absolute Beginners course. It’s like the first year of computer science that you never took compressed into 10 fun hours of Python coding and problem solving. Go to pythonpodcast.com/talkpython today and get 10% off the course that will help you find your next level. That’s pythonpodcast.com/talkpython, and don’t forget to thank them for supporting the show.\nEqualum’s end to end data ingestion platform is relied upon by enterprises across industries to seamlessly stream data to operational, real-time analytics and machine learning environments. Equalum combines streaming Change Data Capture, replication, complex transformations, batch processing and full data management using a no-code UI. Equalum also leverages open source data frameworks by orchestrating Apache Spark, Kafka and others under the hood. Tool consolidation and linear scalability without the legacy platform price tag. Go to pythonpodcast.com/equalum today to start a free 2 week test run of their platform, and don’t forget to tell them that we sent you.\nYour host as usual is Tobias Macey and today I’m interviewing Henrik Landgren about his experiences building AI platforms to transform business capabilities.\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by sharing your thoughts on when, where, and how AI/ML are useful tools for a business?\nWhat has been your experience in building AI platforms?\nFor organizations who are considering investing in AI capabilities, what are some alternative strategies that they might consider first?\nWhat are the cases where AI is likely to be a wasted effort, or will fail to create a return on investment?\nIn order to be succesful in bringing AI products to production, what are the foundational capabilities that are necessary?\n\nWhat have you found to be a useful composition of roles and skills for building AI products?\n\n\nThere are various statistics that all point to a remarkably low success rate for bringing AI into production. What are some of the pitfalls that organizations and engineers should be aware of when undertaking such a project?\nWhat is your strategy for identifying opportunities for a successful AI product?\n\nOnce you have determined the possible utility for such a project, how do you approach the work of making it a reality?\n\n\nWhat are the common factors in what you built at Spotify and EQT ventures?\n\nWhere do the two efforts diverge?\n\n\nYour work on Motherbrain is interesting because of the fact that it is dealing in what seems to be intangible or unpredictable forces. What kinds of input are you relying on to generate useful predictions?\nWhat are some of the most interesting, innovative, or unexpected uses of AI that you have seen?\nWhat are some of the biggest failures of AI that you are aware of?\nIn your work at Spotify and EQT ventures, what are the most interesting, unexpected, or challenging lessons that you have learned?\nWhat advice or recommendations do you have for anyone who wants to learn more about the potential for AI and the work involved in bringing it to production?\n\nKeep In Touch\n\nLinkedIn\n@hlandgren on Twitter\n\nPicks\n\nTobias\n\nWhale\nbat\n\n\nHenrik\n\nObservable\nDataform\n\nData Engineering Podcast Episode\n\n\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nEQT Ventures\nStockholm Sweden\nMotherbrain\nAccenture\nSpotify\nBasic\nC#\nASP.NET\nJavascript\nHadoop\nMcKinsey\nDeep Learning\nData Engineer\nData Scientist\nMachine Learning Engineer\nDiscover Weekly Spotify Playlist\nGPT-3\nDeep Fakes\nDBT\n\nData Engineering Podcast Episode\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

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Artificial intelligence applications can provide dramatic benefits to a business, but only if you can bring them from idea to production. Henrik Landgren was behind the original efforts at Spotify to leverage data for new product features, and in his current role he works on an AI system to evaluate new businesses to invest in. In this episode he shares advice on how to identify opportunities for leveraging AI to improve your business, the capabilities necessary to enable aa successful project, and some of the pitfalls to watch out for. If you are curious about how to get started with AI, or what to consider as you build a project, then this is definitely worth a listen.

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Interview

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Keep In Touch

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Picks

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Closing Announcements

\n\n

Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Henrik Landgren about his experience and lessons learned putting artificial intelligence projects into production at Spotify and EQT Ventures.","date_published":"2020-11-02T19:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/e761639f-f9db-4bfc-b51c-e73ee86c25b4.mp3","mime_type":"audio/mpeg","size_in_bytes":37043540,"duration_in_seconds":2869}]},{"id":"podlove-2020-10-26t22:29:27+00:00-30e8a9d3d30caf5","title":"Power Up Your Java Using Python With JPype","url":"https://www.pythonpodcast.com/jpype-java-python-bridge-episode-286","content_text":"Summary\nPython and Java are two of the most popular programming languages in the world, and have both been around for over 20 years. In that time there have been numerous attempts to provide interoperability between them, with varying methods and levels of success. One such project is JPype, which allows you to use Java classes in your Python code. In this episode the current lead developer, Karl Nelson, explains why he chose it as his preferred tool for combining these ecosystems, how he and his team are using it, and when and how you might want to use it for your own projects. He also discusses the work he has done to enable use of JPype on Android, and what is in store for the future of the project. If you have ever wanted to use a library or module from Java, but the rest of your project is already in Python, then this episode is definitely worth a listen.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!\nYour host as usual is Tobias Macey and today I’m interviewing Karl Nelson about JPype, a language bridge that lets you use Java classes in your Python programs\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by giving an overview of what JPype is?\n\nWhat was your motivation for becoming such a regular contributor to the project?\n\n\nWhy might someone want to be able to call into the Java ecosystem from a Python program?\nThere have been a number of other projects aiming to combine the capabilities of Java and Python, such as Jython and PyJNIus. What are the relative tradeoffs between the different options?\n\nMany of those other projects have stalled or stopped altogether. What about JPype has allowed it to survive for so long?\n\n\nCan you explain how JPype is implemented?\n\nHow has the design and implementation of the project evolved since it was first implemented?\nHow do the relative language versions influence the compatibility of programs on either side of the bridge?\n\n\nWhat is involved in creating a project that uses JPype?\n\nHow are dependencies, packaging, distribution, etc. handled across the Java and Python portions of the code?\n\n\nWhat are some of the ways that JPype can be used for Android applications?\nWhat are some of the sharp edges or pitfalls that users of JPype should be aware of?\nWhat are some of the most interesting, innovative, or unexpected ways that you have seen JPype used?\nWhat have you found to be the most interesting or challenging aspects of building JPype?\nWhen is JPype the wrong choice?\nWhat is in store for the future of the project?\n\nKeep In Touch\n\nThrameos on GitHub\nLinkedIn\n\nPicks\n\nTobias\n\nHiking\nAll Trails\nThe Hiking Project\n\n\nKarl\n\nSummoner’s Rift\n\n\n\nLinks\n\nJPype\nJava\nOverview of Python to Java bridges\nLawrence Livermore National Lab\nGTK–\nGnome\nPerl\nC++\nMatlab\nJava Native Interface (JNI)\nSciPy\nNumPy\nMatplotlib\nJython\nPyJNIus\nPy4J\nJep\nRuby\nReflection\nIvy\nMaven\nJDBC\nKivy\nAndroid\nPython Slots\nPyPy\nJava ASM\nArrow Columnar Memory Format\nProtocol Buffers\nGraalVM\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

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Python and Java are two of the most popular programming languages in the world, and have both been around for over 20 years. In that time there have been numerous attempts to provide interoperability between them, with varying methods and levels of success. One such project is JPype, which allows you to use Java classes in your Python code. In this episode the current lead developer, Karl Nelson, explains why he chose it as his preferred tool for combining these ecosystems, how he and his team are using it, and when and how you might want to use it for your own projects. He also discusses the work he has done to enable use of JPype on Android, and what is in store for the future of the project. If you have ever wanted to use a library or module from Java, but the rest of your project is already in Python, then this episode is definitely worth a listen.

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Announcements

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Interview

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Keep In Touch

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Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Karl Nelson about using the JPype library for bridging the Java and Python ecosystems for scientific computing","date_published":"2020-10-26T18:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/a09ab866-d994-46eb-93de-adccda729412.mp3","mime_type":"audio/mpeg","size_in_bytes":39499303,"duration_in_seconds":2919}]},{"id":"podlove-2020-10-19t23:14:22+00:00-e3e734d2d09af6e","title":"The Journey To Replace Python's Parser And What It Means For The Future","url":"https://www.pythonpodcast.com/cpython-parser-replacement-episode-285","content_text":"Summary\nThe release of Python 3.9 introduced a new parser that paves the way for brand new features. Every programming language has its own specific syntax for representing the logic that you are trying to express. The way that the rules of the language are defined and validated is with a grammar definition, which in turn is processed by a parser. The parser that the Python language has relied on for the past 25 years has begun to show its age through mounting technical debt and a lack of flexibility in defining new syntax. In this episode Pablo Galindo and Lysandros Nikolaou explain how, together with Python’s creator Guido van Rossum, they replaced the original parser implementation with one that is more flexible and maintainable, why now was the time to make the change, and how it will influence the future evolution of the language.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!\nYour host as usual is Tobias Macey and today I’m interviewing Pablo Galindo and Lysandros Nikolaou about their work on replacing the parser in CPython and what that means for the language\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by discussing the role of the parser in the lifecycle of a Python program?\nWhat were the limitations of the previous parser, and how did that contribute to complexity and technical debt in the CPython runtime?\nWhat are the options for styles of parsers, and what are the benefits of using a PEG style grammar?\nHow does the new parser impact the approachability of the CPython code for new contributors?\nWhat was the process for reimplementing the parser and guarding against regressions in the syntax?\nAs developers switch to the 3.9 release, what potential edge cases/bugs might they see from introducing the new parser?\nWhat new syntax options does this parser provide for the Python language?\n\nAre there any specific features that are planned for implementation in the 3.10 release that are enabled by the new parser grammar?\n\n\nAs the language evolves due to new capabilities offered by the updated parser, how will that impact other implementations such as PyPy?\nWhat were the most interesting, unexpected, or challenging aspects of this project?\nWhat other aspects of the CPython code do you think should be reconsidered or reimplemented in light of the changes in computing and the usage of the language?\n\nKeep In Touch\n\nPablo\n\npablogsal on GitHub\n@pyblogsal on Twitter\nLinkedIn\n\n\nLysandros\n\nLinkedIn\nlysnikolaou on GitHub\n@lysnikolaou on Twitter\n\n\n\nPicks\n\nTobias\n\nAnnual Python Developer Survey\nJessica Jones TV show\n\n\nPablo\n\nRaised By Wolves TV Series\n\n\nLysandros\n\nAfterlife TV show\n\n\n\nLinks\n\nPEP 617 – New PEG Parser for CPython\nPodcast Episode About Parsers\nCPython\nBloomberg\nPEG Parsers\nSeafair\nLL(1) Parsers\nŁukasz Langa\nParser Generator\nConcrete Syntax Tree\nAbstract Syntax Tree\nPyPy\nRustPython\n\nPodcast Episode\n\n\nIronPython\nStructural Pattern Matching – PEP 622\nPylint\nASTroid\n\nPodcast Episode\n\n\nHy\n\nPodcast Episode\n\n\nWalrus Operator/Assignment Expressions\nC99\nReference Counting\nCycle Hunting/Generational Garbage Collection\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

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The release of Python 3.9 introduced a new parser that paves the way for brand new features. Every programming language has its own specific syntax for representing the logic that you are trying to express. The way that the rules of the language are defined and validated is with a grammar definition, which in turn is processed by a parser. The parser that the Python language has relied on for the past 25 years has begun to show its age through mounting technical debt and a lack of flexibility in defining new syntax. In this episode Pablo Galindo and Lysandros Nikolaou explain how, together with Python’s creator Guido van Rossum, they replaced the original parser implementation with one that is more flexible and maintainable, why now was the time to make the change, and how it will influence the future evolution of the language.

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Interview

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Keep In Touch

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Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Pablo Galindo and Lysandros Nikolaou about their work to replace CPython's parser and the benefits that it provides for the future evolution of the language.","date_published":"2020-10-19T19:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/273904e3-fe32-446b-9a0b-33d138c3b978.mp3","mime_type":"audio/mpeg","size_in_bytes":54928743,"duration_in_seconds":3948}]},{"id":"podlove-2020-10-12t22:56:29+00:00-472dee61daf7ef0","title":"Cloud Native Application Delivery Using GitOps","url":"https://www.pythonpodcast.com/gitops-cloud-native-operations-episode-284","content_text":"Summary\nThe way that applications are being built and delivered has changed dramatically in recent years with the growing trend toward cloud native software. As part of this movement toward the infrastructure and orchestration that powers your project being defined in software, a new approach to operations is gaining prominence. Commonly called GitOps, the main principle is that all of your automation code lives in version control and is executed automatically as changes are merged. In this episode Victor Farcic shares details on how that workflow brings together developers and operations engineers, the challenges that it poses, and how it influences the architecture of your software. This was an interesting look at an emerging pattern in the development and release cycle of modern applications.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nTree Schema is a data catalog that is making metadata management accessible to everyone. With Tree Schema you can create your data catalog and have it fully populated in under five minutes when using one of the many automated adapters that can connect directly to your data stores. Tree Schema includes essential cataloging features such as first class support for both tabular and unstructured data, data lineage, rich text documentation, asset tagging and more. Built from the ground up with a focus on the intersection of people and data, your entire team will find it easier to foster collaboration around your data. With the most transparent pricing in the industry – $99/mo for your entire company – and a money-back guarantee for excellent service, you’ll love Tree Schema as much as you love your data. Go to pythonpodcast.com/treeschema today to get your first month free, and mention this podcast to get %50 off your first three months after the trial.\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!\nYour host as usual is Tobias Macey and today I’m interviewing Victor Farcic about using GitOps practices to manage your application and your infrastructure in the same workflow\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by giving an overview of what GitOps is?\nWhat are the architectural or design elements that developers need to incorporate to make their applications work well in a GitOps workflow?\nWhat are some of the tools that facilitate a GitOps approach to managing applications and their target environments?\nWhat are some useful strategies for managing local developer environments to maintain parity with how production deployments are architected?\nAs developers acquire more resonsibility for building the automation to provision the production environment for their applications, what are some of the operations principles that they need to understand?\nWhat are some of the development principles that operators and systems administrators need to acquire to be effective in contributing to an environment that is managed by GitOps?\nWhat are the areas for collaboration and dividing lines of responsibility between developers and platform engineers in a GitOps environment?\nBeyond the application development and deployment, what are some of the additional concerns that need to be built into an application in order for it to be manageable and maintainable once it is in production?\nWhat are some of the organizational principles that contribute to a successful implementation of GitOps?\nWhat are some of the most interesting, innovative, or unexpected ways that you have seen GitOps employed?\nWhat have you found to be the most challenging aspects of creating a scalable and maintainable GitOps practice?\nWhen is GitOps the wrong choice, and what are the alternatives?\nWhat resources do you recommend for anyone who wants to dig deeper into this subject?\n\nKeep In Touch\n\nLinkedIn\nBlog\n@vfarcic on Twitter\n\nPicks\n\nTobias\n\nPulumi\n\nPodcast Episode\n\n\n\n\nVictor\n\nLoki\n\n\n\nLinks\n\nGitOps\nCodeFresh\nKubernetes\nDevOps Paradox Podcast\nPerl\nCloud Native\nArgoCD\nFlux\nObservability\nPrometheus\nHelm\nKNative\nMiniKube\nViktor’s Udemy Books and Courses\nViktor’s YouTube channel\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

The way that applications are being built and delivered has changed dramatically in recent years with the growing trend toward cloud native software. As part of this movement toward the infrastructure and orchestration that powers your project being defined in software, a new approach to operations is gaining prominence. Commonly called GitOps, the main principle is that all of your automation code lives in version control and is executed automatically as changes are merged. In this episode Victor Farcic shares details on how that workflow brings together developers and operations engineers, the challenges that it poses, and how it influences the architecture of your software. This was an interesting look at an emerging pattern in the development and release cycle of modern applications.

\n

Announcements

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Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about the challenges and opportunities of using GitOps to develop and deliver your applications to production with cloud native technologies.","date_published":"2020-10-12T19:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/2b56bbeb-54a7-4905-a520-14d06fb7d286.mp3","mime_type":"audio/mpeg","size_in_bytes":42201633,"duration_in_seconds":3223}]},{"id":"podlove-2020-10-06t00:00:32+00:00-b5846cf8727ae15","title":"Threading The Needle Of Interesting And Informative While You Learn To Code","url":"https://www.pythonpodcast.com/steven-lott-learn-to-code-episode-283","content_text":"Summary\nLearning to code is a neverending journey, which is why it’s important to find a way to stay motivated. A common refrain is to just find a project that you’re interested in building and use that goal to keep you on track. The problem with that advice is that as a new programmer, you don’t have the knowledge required to know which projects are reasonable, which are difficult, and which are effectively impossible. Steven Lott has been sharing his programming expertise as a consultant, author, and trainer for years. In this episode he shares his insights on how to help readers, students, and colleagues interested enough to learn the fundamentals without losing sight of the long term gains. He also uses his own difficulties in learning to maintain, repair, and captain his sailboat as relatable examples of the learning process and how the lessons he has learned can be translated to the process of learning a new technology or skill. This was a great conversation about the various aspects of how to learn, how to stay motivated, and how to help newcomers bridge the gap between what they want to create and what is within their grasp.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nThis portion of Python Podcast is brought to you by Datadog. Do you have an app in production that is slower than you like? Is its performance all over the place (sometimes fast, sometimes slow)? Do you know why? With Datadog, you will. You can troubleshoot your app’s performance with Datadog’s end-to-end tracing and in one click correlate those Python traces with related logs and metrics. Use their detailed flame graphs to identify bottlenecks and latency in that app of yours. Start tracking the performance of your apps with a free trial at pythonpodcast.com/datadog. If you sign up for a trial and install the agent, Datadog will send you a free t-shirt.\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!\nYour host as usual is Tobias Macey and today I’m interviewing Steven F. Lott about finding a project that you care about to aid in learning to program\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by outlining your experiences working with and teaching Python?\nDoes your day-to-day experience at work suggest ways to help newcomers learn about Python?\nHow have your experiences as an author influenced your perspective on how to help newcomers become motivated to learn programming?\nOne of the common pieces of advice that I and others have given to people learning Python or other languages is to find a project that they want to build, but that’s not necessarily a practical approach. What are some of the difficulties that might come of that approach?\n\nWhat are some strategies that you have tried for helping learners identify what kinds of project are possible and practical?\n\n\nBeyond the difficulty of understanding what is possible and what is going to require a dedicated team of engineers to even attempt, there is the question of remaining motivated for long enough to follow through on a project in the face of syntax errors and design challenges. What can language developers and ecosystems do to improve the newcomer experience in exploring possibilities?\n\nHow can we make syntax errors educational and recoverable, rather than needing accrued knowledge, or hours of web searches?\n\n\nAs an author, there are complementary goals that may lead to conflict in the form of wanting to provide structured guidance and progression while allowing for creativity and experimentation. How have you approached those objectives in your books?\nWhat are some of the projects that have motivated you to learn new skills?\nWhat advice do you have for anyone who is working on or considering writing a book to teach a technical skill?\nWhat advice do you have for anyone who is trying to learn programming or acquire a skill in a new language, platform, or framework?\nWhy are both of you movie picks black and white? Are you a film noir fan?\n\nKeep In Touch\n\nWebsite\nBlog\nLinkedIn\nslott56 on GitHub\n@s_lott on Twitter\n\nPicks\n\nTobias\n\nThe Hobbit Trilogy: Extended Edition (affiliate link)\nThe Lord Of The Rings Trilogy: Extended Edition (affiliate link)\n\n\nSteven\n\nDr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb\nThe General\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nModern Python Cookbook\nPackt Publishing\nEiffel\nModula 3\nCOBOL\nStack Overflow\nCapital One\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Learning to code is a neverending journey, which is why it’s important to find a way to stay motivated. A common refrain is to just find a project that you’re interested in building and use that goal to keep you on track. The problem with that advice is that as a new programmer, you don’t have the knowledge required to know which projects are reasonable, which are difficult, and which are effectively impossible. Steven Lott has been sharing his programming expertise as a consultant, author, and trainer for years. In this episode he shares his insights on how to help readers, students, and colleagues interested enough to learn the fundamentals without losing sight of the long term gains. He also uses his own difficulties in learning to maintain, repair, and captain his sailboat as relatable examples of the learning process and how the lessons he has learned can be translated to the process of learning a new technology or skill. This was a great conversation about the various aspects of how to learn, how to stay motivated, and how to help newcomers bridge the gap between what they want to create and what is within their grasp.

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Announcements

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Interview

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Keep In Touch

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Picks

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Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with author and Python guru Steven Lott about the challenge of keeping learners interested and engaged while they learn to code and helping them understand which projects are possible and which require a significant level of expertise.","date_published":"2020-10-05T20:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/cdb7e583-dc03-4ed2-85a5-2bf4ef4c357e.mp3","mime_type":"audio/mpeg","size_in_bytes":45469411,"duration_in_seconds":3389}]},{"id":"podlove-2020-09-29t01:28:53+00:00-4d2cfafbeb2817a","title":"Solving Python Package Creation For End User Applications With PyOxidizer","url":"https://www.pythonpodcast.com/pyoxidizer-python-package-creation-episode-282","content_text":"Summary\nPython is a powerful and expressive programming language with a vast ecosystem of incredible applications. Unfortunately, it has always been challenging to share those applications with non-technical end users. Gregory Szorc set out to solve the problem of how to put your code on someone else’s computer and have it run without having to rely on extra systems such as virtualenvs or Docker. In this episode he shares his work on PyOxidizer and how it allows you to build a self-contained Python runtime along with statically linked dependencies and the software that you want to run. He also digs into some of the edge cases in the Python language and its ecosystem that make this a challenging problem to solve, and some of the lessons that he has learned in the process. PyOxidizer is an exciting step forward in the evolution of packaging and distribution for the Python language and community.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nThis portion of Python Podcast is brought to you by Datadog. Do you have an app in production that is slower than you like? Is its performance all over the place (sometimes fast, sometimes slow)? Do you know why? With Datadog, you will. You can troubleshoot your app’s performance with Datadog’s end-to-end tracing and in one click correlate those Python traces with related logs and metrics. Use their detailed flame graphs to identify bottlenecks and latency in that app of yours. Start tracking the performance of your apps with a free trial at pythonpodcast.com/datadog. If you sign up for a trial and install the agent, Datadog will send you a free t-shirt.\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!\nYour host as usual is Tobias Macey and today I’m interviewing Gregory Szorc about his work on PyOxidizer, a revolutionary new approach to building and distributing self-contained Python applications\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by giving an overview on the shortcomings of the current state of the art for distributing Python projects, both for deployment and end-user consumption?\nWhat is PyOxidizer and what motivated you to create it?\nHow does PyOxidizer differ from projects such as CxFreeze, Py2Exe, or Shiv?\nWhat are the characteristics of CPython and the packaging ecosystem that make it so challenging to easily distribute self-contained applications?\nFor someone using PyOxidizer, what is their workflow for building an executable that they can share with end users?\n\nWhat are some of the edge cases or special considerations that they need to be aware of?\n\n\nHow is PyOxidizer implemented?\n\nHow has the design or direction evolved since you first began working on it?\n\n\nFrom your experience in working on PyOxidizer, what changes would you like to see in the Python language or the CPython reference implementation?\nWhat are some of the most interesting, unexpected, or challenging lessons that you have learned while working on PyOxidizer?\nWhat do you have planned for the future of PyOxidizer?\nWhat are the ways that listeners can contribute to PyOxidizer?\n\nKeep In Touch\n\nWebsite\nindygreg on GitHub\n\nPicks\n\nTobias\n\nCarlos Santana\n\n\nGregory\n\nHome Air Quality Monitor\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nPyOxidizer\nMercurial\n\nPodcast Episode\n\n\nMozilla\nVirtualenv\nPip\nDocker\nPy2Exe\nCXFreeze\nBeeware\nShiv\nFPM\nPython Build Standalone\nImportlib\nRust\nRussell Keith-Magee Black Swans Keynote\n\nFollowup Podcast Episode\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n\n\n","content_html":"

Summary

\n

Python is a powerful and expressive programming language with a vast ecosystem of incredible applications. Unfortunately, it has always been challenging to share those applications with non-technical end users. Gregory Szorc set out to solve the problem of how to put your code on someone else’s computer and have it run without having to rely on extra systems such as virtualenvs or Docker. In this episode he shares his work on PyOxidizer and how it allows you to build a self-contained Python runtime along with statically linked dependencies and the software that you want to run. He also digs into some of the edge cases in the Python language and its ecosystem that make this a challenging problem to solve, and some of the lessons that he has learned in the process. PyOxidizer is an exciting step forward in the evolution of packaging and distribution for the Python language and community.

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Announcements

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Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Gregory Szorc about his work on the PyOxidizer project for simplifying Python package creation to simplify sharing applications with end users.","date_published":"2020-09-28T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/3a706211-4b2d-4b40-9136-08331ab15f02.mp3","mime_type":"audio/mpeg","size_in_bytes":33586697,"duration_in_seconds":2979}]},{"id":"podlove-2020-09-21t11:40:07+00:00-2426e7174848c66","title":"Flexible Network Security Detection And Response With Grapl","url":"https://www.pythonpodcast.com/grapl-network-security-episode-282","content_text":"Summary\nServers and services that have any exposure to the public internet are under a constant barrage of attacks. Network security engineers are tasked with discovering and addressing any potential breaches to their systems, which is a never-ending task as attackers continually evolve their tactics. In order to gain better visibility into complex exploits Colin O’Brien built the Grapl platform, using graph database technology to more easily discover relationships between activities within and across servers. In this episode he shares his motivations for creating a new system to discover potential security breaches, how its design simplifies the work of identifying complex attacks without relying on brittle rules, and how you can start using it to monitor your own systems today.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nThis portion of Python Podcast is brought to you by Datadog. Do you have an app in production that is slower than you like? Is its performance all over the place (sometimes fast, sometimes slow)? Do you know why? With Datadog, you will. You can troubleshoot your app’s performance with Datadog’s end-to-end tracing and in one click correlate those Python traces with related logs and metrics. Use their detailed flame graphs to identify bottlenecks and latency in that app of yours. Start tracking the performance of your apps with a free trial at pythonpodcast.com/datadog. If you sign up for a trial and install the agent, Datadog will send you a free t-shirt.\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!\nYour host as usual is Tobias Macey and today I’m interviewing Colin O’Brien about Grapl, an open source platform for detection and response of system security incidents\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what Grapl is and the problem that you are trying to solve with it?\n\nWhat was your original motivation to create it?\n\n\nWhat were the existing options for security detection and response, and how is Grapl differentiated from them?\nWho is the target audience for the Grapl project?\nHow is the Grapl system architected?\n\nHow has the design of the system evolved since you first began working on it?\nHow much effort would it be to separate the Grapl architecture from AWS to migrate it to other environments?\n\n\nWhat have you found to be the benefits of splitting the implementation of the system between Rust for the system and Python for the exploration?\n\nWhat challenges have you faced as a result of working across those languages?\n\n\nWhat data sources does Grapl use to build its graph of events within a system?\nCan you talk through the overall workflow for someone using Grapl?\nWhat are some examples of the types of exploits that you can identify with Grapl?\nWhat are some of the most interesting, unexpected, or innovative ways that you have seen Grapl used?\nWhat are some of the most interesting, unexpected, or challenging lessons that you have learned while building it?\nWhen is Grapl the wrong choice?\nWhat do you have planned for the future of Grapl?\n\nKeep In Touch\n\ninsanitybit on GitHub\nLinkedIn\n@InsanityBit on Twitter\n\nPicks\n\nTobias\n\nArtemis Fowl book series by Eoin Colfer\nArtemis Fowl Movie\n\n\nColin\n\nPyO3\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nGrapl\nGrapl Security\nSIEM == Security Information and Event Management\nRapid7\nMetasploit\nInsight IDR\nErlang\nDGraph\nSplunk\nElasticsearch\nAWS Lambda\nSysdig\nSysmon\nAWS CloudTrail\nGuard Duty\nOpenFaaS\nAWS SQS\nDynamoDB\nPyO3\nDropper Malware\nSSH Session Hijacking\nVagrant\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n\n\n","content_html":"

Summary

\n

Servers and services that have any exposure to the public internet are under a constant barrage of attacks. Network security engineers are tasked with discovering and addressing any potential breaches to their systems, which is a never-ending task as attackers continually evolve their tactics. In order to gain better visibility into complex exploits Colin O’Brien built the Grapl platform, using graph database technology to more easily discover relationships between activities within and across servers. In this episode he shares his motivations for creating a new system to discover potential security breaches, how its design simplifies the work of identifying complex attacks without relying on brittle rules, and how you can start using it to monitor your own systems today.

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Interview

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Closing Announcements

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Colin O'Brien about his work building the Grapl platform for graph based detection and response of network security events.","date_published":"2020-09-21T20:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/fc2b19ff-6d44-4408-a23f-f1bd213977a9.mp3","mime_type":"audio/mpeg","size_in_bytes":48847515,"duration_in_seconds":3212}]},{"id":"podlove-2020-09-15t00:54:55+00:00-251f652fdf8ef13","title":"Simplified Data Extraction And Analysis For Current Events With Newspaper","url":"https://www.pythonpodcast.com/newspaper-data-extraction-episode-280","content_text":"Summary\nNews media is an important source of information for understanding the context of the world. To make it easier to access and process the contents of news sites Lucas Ou-Yang built the Newspaper library that aids in automatic retrieval of articles and prepare it for analysis. In this episode he shares how the project got started, how it is implemented, and how you can get started with it today. He also discusses how recent improvements in the utility and ease of use of deep learning libraries open new possibilities for future iterations of the project.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nThis portion of Python Podcast is brought to you by Datadog. Do you have an app in production that is slower than you like? Is its performance all over the place (sometimes fast, sometimes slow)? Do you know why? With Datadog, you will. You can troubleshoot your app’s performance with Datadog’s end-to-end tracing and in one click correlate those Python traces with related logs and metrics. Use their detailed flame graphs to identify bottlenecks and latency in that app of yours. Start tracking the performance of your apps with a free trial at pythonpodcast.com/datadog. If you sign up for a trial and install the agent, Datadog will send you a free t-shirt.\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!\nYour host as usual is Tobias Macey and today I’m interviewing Lucas Ou-Yang about Newspaper, a framework for easily extracting and processing online articles.\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what the Newspaper project is and your motivations for creating it?\nWhat are the main use cases that Newspaper is built for?\n\nWhat are some libraries or tools that Newspaper might replace?\n\n\nWhat are the common structures in news sites that allow you to abstract across them for content extraction?\n\nWhat are some ways of determining whether a site will be a good candidate for using with Newspaper?\n\n\nCan you talk through the developer workflow of someone using Newspaper?\n\nWhat are some of the other libraries or tools that are commonly used alongside Newspaper?\n\n\nHow is Newspaper implemented?\n\nHow has the design of he project evolved since you first began working on it?\nWhat are some of the most complex or challenging aspects of building an automated article extraction tool?\n\n\nWhat are some of the most interesting, unexpected, or innovative projects that you have seen built with Newspaper?\nWhat keeps you interested in the ongoing support and maintenance of the project?\nWhat do you have planned for the future of Newspaper?\n\nKeep In Touch\n\nLinkedIn\n@LucasOuYang on Twitter\nWebsite\ncodelucas on GitHub\n\nPicks\n\nTobias\n\nMillion Bazillion Podcast\n\n\nLucas\n\nHackers and Painters: Big Ideas from the Computer Age by Paul Graham\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nNewspaper\nLos Angeles\nReddit\nDjango\nNLP == Natural Language Processing\nWeb Scraping\n\nPodcast Episode\n\n\nRequests\nWintria\nPython Goose\nDiffbot\nHeuristics\nStop Words\nRSS\nSpaCy\n\nPodcast Episode\n\n\nGensim\n\nPodcast Episode\n\n\nPyTorch\n\nPodcast Episode\n\n\nNLTK\nLXML\nBeautiful Soup\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

News media is an important source of information for understanding the context of the world. To make it easier to access and process the contents of news sites Lucas Ou-Yang built the Newspaper library that aids in automatic retrieval of articles and prepare it for analysis. In this episode he shares how the project got started, how it is implemented, and how you can get started with it today. He also discusses how recent improvements in the utility and ease of use of deep learning libraries open new possibilities for future iterations of the project.

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Announcements

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Interview

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Keep In Touch

\n\n

Picks

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Closing Announcements

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about how the Newspaper library simplifies data extraction for journalistic web sites and integrating with natural language processing frameworks.","date_published":"2020-09-14T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/7897d2af-d144-4d1c-93dd-7aa382419452.mp3","mime_type":"audio/mpeg","size_in_bytes":32708070,"duration_in_seconds":2607}]},{"id":"podlove-2020-09-07t22:02:52+00:00-3614fc44ee3f277","title":"Digging Into Dagster: An Opinionated Open Source Framework For Data Orchestration","url":"https://www.pythonpodcast.com/dagster-data-orchestration-episode-279","content_text":"Summary\nData applications are complex and continually evolving, often requiring collaboration across multiple teams. In order to keep everyone on the same page a high level abstraction is needed to facilitate a cross-cutting view of the data orchestration across integration, transformation, analytics, and machine learning. Dagster is an innovative new framework that leans on the power and flexibility of Python to provide an extensible interface to the complete lifecycle of data projects. In this episode Nick Schrock explains how he designed the Dagster project to allow for integration with the entire data ecosystem while providing an opinionated structure for connecting the different stages of computation. He also discusses how he is working to grow an open ecosystem around the Dagster project, and his thoughts on building a sustainable business on top of it without compromising the integrity of the community. This was a great conversation about playing the long game when building a business while providing a valuable utility to a complex problem domain.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nThis portion of Python Podcast is brought to you by Datadog. Do you have an app in production that is slower than you like? Is its performance all over the place (sometimes fast, sometimes slow)? Do you know why? With Datadog, you will. You can troubleshoot your app’s performance with Datadog’s end-to-end tracing and in one click correlate those Python traces with related logs and metrics. Use their detailed flame graphs to identify bottlenecks and latency in that app of yours. Start tracking the performance of your apps with a free trial at pythonpodcast.com/datadog. If you sign up for a trial and install the agent, Datadog will send you a free t-shirt.\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!\nYour host as usual is Tobias Macey and today I’m interviewing Nick Schrock about Dagster, an open source data orchestrator for powering data engineering, analytics, and machine learning\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what Dagster is and how it got started?\nWhat are the most common difficulties that organizations face when working with data projects?\n\nHow does Dagster help in addressing those challenges?\n\n\nThere are a number of workflow orchestration platforms, spanning a few generations of tooling. What do you see as the defining characteristics of the various options, and how does Dagster fit in that ecosystem?\nWhat are the assumptions that you made at the start of building Dagster and how have they been challenged, updated, or invalidated over the past year of working with end users?\nHow are the internals of Dagster implemented?\n\nHow has the design changed or evolved since you first began working on it?\n\n\nFor someone who is building on top of Dagster, what is their workflow from first steps through to production?\nWhat are your guiding principles for desigining the user facing API?\nWhat are the available extension points for Dagster?\nWhat was your reason for implementing Dagster as a Python framework?\n\nWith the benefit of hindsight, would you make the same decision today?\n\n\nWhat are some of the most interesting, innovative, or unexpected ways that you have seen Dagster used?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while building Dagster and working to grow its ecosystem?\nWhen is Dagster the wrong choice?\nAs you continue to build Dagster, what is your vision for it and its ecosystem?\n\nWhat are the next steps that you are taking to achieve that vision?\n\n\n\nKeep In Touch\n\n@schrockn on Twitter\nschrockn on GitHub\nLinkedIn\n\nPicks\n\nTobias\n\nCaddy web server\n\n\nNick\n\nBlack code formatter\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nDagster\nElementl\nIronPython\nFluent Python\nGraphQL\nMaslow’s Hierarchy of Needs\nHierarchy of Data Needs\nDAG == Directed Acyclic Graph\nInformatica\nAirflow\nLuigi\nDagster Config Schema\nDask\n\nData Engineering Podcast Episode\nCoiled Episode\n\n\ngRPC\nMyPy\n\nPodcast Episode\n\n\nData Lineage\nPandas\n\nPodcast Episode\n\n\nAmundsen\n\nPodcast Episode\n\n\nDataHub\n\nPodcast Episode\n\n\nGatsby.js\nPanama Papers\nMode Analytics\n\nPodcast Episode\n\n\nPapermill\n\nPodcast Episode\n\n\nDBT\n\nPodcast Episode\n\n\nDatabricks\nTobias’ Dagster Repository\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Data applications are complex and continually evolving, often requiring collaboration across multiple teams. In order to keep everyone on the same page a high level abstraction is needed to facilitate a cross-cutting view of the data orchestration across integration, transformation, analytics, and machine learning. Dagster is an innovative new framework that leans on the power and flexibility of Python to provide an extensible interface to the complete lifecycle of data projects. In this episode Nick Schrock explains how he designed the Dagster project to allow for integration with the entire data ecosystem while providing an opinionated structure for connecting the different stages of computation. He also discusses how he is working to grow an open ecosystem around the Dagster project, and his thoughts on building a sustainable business on top of it without compromising the integrity of the community. This was a great conversation about playing the long game when building a business while providing a valuable utility to a complex problem domain.

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Announcements

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Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Nick Schrock about the building and using the open source Dagster framework for flexible and well-structured data orchestration","date_published":"2020-09-07T18:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/a310f4be-58a5-4773-82c7-6b3873004418.mp3","mime_type":"audio/mpeg","size_in_bytes":45175207,"duration_in_seconds":3568}]},{"id":"podlove-2020-08-31t11:45:23+00:00-007205773ff1fa1","title":"When, Why, and How To Use Web Scraping In A Nutshell","url":"https://www.pythonpodcast.com/web-scraping-essentials-episode-278","content_text":"Summary\nThe internet is a rich source of information, but a majority of it isn’t accessible programmatically through APIs or databases. To address that shortcoming there are a variety of web scraping frameworks that aid in extracting structured data from web pages. In this episode Attila Tóth shares the challenges of web data extraction, the ways that you can use it, and how Scrapy and ScrapingHub can help you with your projects.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nThis portion of Python Podcast is brought to you by Datadog. Do you have an app in production that is slower than you like? Is its performance all over the place (sometimes fast, sometimes slow)? Do you know why? With Datadog, you will. You can troubleshoot your app’s performance with Datadog’s end-to-end tracing and in one click correlate those Python traces with related logs and metrics. Use their detailed flame graphs to identify bottlenecks and latency in that app of yours. Start tracking the performance of your apps with a free trial at datadog.com/pythonpodcast. If you sign up for a trial and install the agent, Datadog will send you a free t-shirt.\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!\nYour host as usual is Tobias Macey and today I’m interviewing Attila Tóth about doing data extraction with web scraping.\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what web scraping is and when you might want to use it?\n\nHow did you first get started with web scraping?\n\n\nThere are a number of options for web scraping tools in Python, as well as other languages. What are the characteristics of the Scrapy project and community that have made it stand out and retain such widespread popularity?\nOne of the perpetual questions with web scraping is that of copyright and content ownership. What should we all be aware of when scraping a given website?\nWhat are some of the most challenging aspects of crawling and scraping the web?\n\nWhat are some of the features of Scrapy that aid in those challenges?\n\n\nOnce you have retrieved the content from a site, what are some of the considerations for storing and processing the data that we should be thinking about?\nHow can we guard against a scraper breaking due to changes in the layout of a site, or simple updates that weren’t accounted for in the initial implementation?\nWhat are some of the most complicated aspects of scaling web scrapers?\nFor someone who is interested in using Scrapy, what are some of the common pitfalls that they should be aware of?\nWhat are some of the most interesting, innovative, or unexpected projects that are built with Scrapy and ScrapingHub?\nWhat are the most interesting, unexpected, or challenging lessons that you have learned while working with web scrapers and ScrapingHub?\nWhat resources would you recommend to anyone who is looking to learn more about web scraping?\n\nKeep In Touch\n\nLinkedIn\n\nPicks\n\nTobias\n\nGov’t Mule\n\n\nAttila\n\nAwesome Web Scraping\nAwesome Scrapy\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nWeb Scraping\nScrapingHub\nJava\nAndroid\nScrapy\nJSoup\nHTMLUnit\nSelenium\nPandas\nrobots.txt\nPuppeteer\nSplash\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

The internet is a rich source of information, but a majority of it isn’t accessible programmatically through APIs or databases. To address that shortcoming there are a variety of web scraping frameworks that aid in extracting structured data from web pages. In this episode Attila Tóth shares the challenges of web data extraction, the ways that you can use it, and how Scrapy and ScrapingHub can help you with your projects.

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Announcements

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Interview

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Keep In Touch

\n\n

Picks

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Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about what web scraping is, when it is useful, and some of the pitfalls and complexities to know before you get started.","date_published":"2020-08-31T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/ef7ce285-114d-4957-9e46-b9d9abc76b3f.mp3","mime_type":"audio/mpeg","size_in_bytes":35957303,"duration_in_seconds":2511}]},{"id":"podlove-2020-08-23t13:00:49+00:00-48167c5b5d44e1d","title":"Working In The Code Mines: Mining Software Repositories With PyDriller","url":"https://www.pythonpodcast.com/pydriller-mining-software-repositories-episode-277","content_text":"Summary\nA large portion of the software industry has standardized on Git as the version control sytem of choice. But have you thought about all of the information that you are generating with your branches, commits, and code changes? Davide Spadini created the PyDriller framework to simplify the work of mining software repositories to perform research on the technical and social aspects of software engineering. In this episode he shares some of the insights that you can gain by exploring the history of your code, the complexities of building a framework to interact with Git, and some of the interesting ways that PyDriller can be used to inform your own development practices.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!\nYour host as usual is Tobias Macey and today I’m interviewing Davide Spadini about PyDriller, a framework for mining software repositories\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what PyDriller is and how the project got started?\n\nHow is Pydriller different from other Git frameworks?\n\n\nWhat kinds of information can you discover by mining a software repository?\n\nWhere and how might the collected information be used?\n\n\nWhat are the limitations of the capabilities offered by Git for investigating the repository?\nWhat are the additional metrics that you are able to extract using PyDriller?\nCan you describe how PyDriller itself is implemented?\n\nHow has the project evolved since you first began working on it?\n\n\nI noticed that for testing PyDriller you crafted a set of repositories to serve as test cases. What has been the most complex or challenging aspect of writing meaningful tests to ensure a reasonable coverage of this problem domain?\nWhat would be required to add support for other version control systems?\nHow have you used PyDriller in your own research?\nWhat are some of the most interesting, unexpected, or innovative ways that you have seen PyDriller used?\nWhat are some of the most interesting, unexpected, or challenging lessons that you have learned while working on and with PyDriller?\nWhat do you have planned for the future of PyDriller?\n\nKeep In Touch\n\nWebsite\nishepard on GitHub\n@DavideSpadini on Twitter\n\nPicks\n\nTobias\n\npre-commit\n\n\nDavide\n\nFall guys\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nPyDriller\nDelft\nGit\nGitPython\nPyGit2\nRepoDriller\nMining Software Repositories Conference\nLizard\nHadoop\nMercurial\n\nPodcast Episode\n\n\nSubversion\nCVS\nNeo4J\nGraphRepo\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

A large portion of the software industry has standardized on Git as the version control sytem of choice. But have you thought about all of the information that you are generating with your branches, commits, and code changes? Davide Spadini created the PyDriller framework to simplify the work of mining software repositories to perform research on the technical and social aspects of software engineering. In this episode he shares some of the insights that you can gain by exploring the history of your code, the complexities of building a framework to interact with Git, and some of the interesting ways that PyDriller can be used to inform your own development practices.

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Announcements

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Interview

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Keep In Touch

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Picks

\n\n

Closing Announcements

\n\n

Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about the PyDriller framework for git mining software repositories and the interesting insights that you can uncover with it.","date_published":"2020-08-24T20:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/4d80094a-3680-4ba0-8cda-f6a500f9940d.mp3","mime_type":"audio/mpeg","size_in_bytes":24745565,"duration_in_seconds":2403}]},{"id":"podlove-2020-08-17t02:41:20+00:00-af911d765da63d2","title":"Building The Open Data Ecosystem For Music And More At Metabrainz","url":"https://www.pythonpodcast.com/metabrainz-open-data-episode-276","content_text":"Summary\nThe Musicbrainz project was an early entry in the movement to build an open data ecosystem. In recent years, the Metabrainz Foundation has fostered a growing ecosystem of projects to support the contribution of, and access to, metadata, listening habits, and review of music. The majority of those projects are written in Python, and in this episode Param Singh explains how they are built, how they fit together, and how they support the goals of the Metabrains Foundation. This was an interesting exporation of the work involved in building an ecosystem of open data, the challenges of making it sustainable, and the benefits of building for the long term rather than trying to achieve a quick win.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nBefore you put your code into production you need to make sure that it passes all of the tests, that it has been packaged with all of the dependencies, and that you haven’t introduced any security issues. Instead of running all of that on your laptop, let Codefresh handle it automatically with their continuous integration and continuous delivery platform. Built for the modern era of cloud-native computing, they make publishing to Kubernetes, serverless platforms, and virtual machines fast and seamless. With a growing library of pre-made steps, a flexible pipeline definition, and unlimited scale Codefresh lets you ship faster and safer than ever. Go to pythonpodcast.com/codefresh today to get unlimited builds on your free account.\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!\nYour host as usual is Tobias Macey and today I’m interviewing Param Singh about the ways that Python is being used across the various Metabrainz projects\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by giving an overview of what the Metabrainz organization is and the various projects that it encompasses?\n\nWhat are the motivations for creating those projects and some of the origin story for Metabrainz?\n\n\nThe Musicbrainz server is the longest running project and is written in Perl. What was the reason for switching to Python for all of the other *brainz projects?\nHow does the MetaBrainz Foundation sustain itself? Where do the funds come from?\n\nHow do you determine where and how to allocate the funding that you receive?\n\n\nWhich of the *brainz projects is the most complex or challenging to build, whether due to technical or sociological reasons?\nHow do you source and manage the information that powers all of the Metabrainz projects?\nHow is development of the various projects organized?\n\nHow does that influence the amount of code sharing that is possible between them?\n\n\nOf the projects that you have been involved in, how are they architected?\n\nWhat are the main ways that the projects differ in how they are implemented?\n\n\nWhat are some of the ways that you are using Python in support of the various projects that you work on?\nWhat are some of the most interesting, innovative, or unexpected ways that you have seen the projects or data built by Metabrainz being used?\nWhat are some of the most interesting, unexpected, or challenging lessons that you have learned while working as a contributor and maintainer of the Metabrainz projects?\nWhat is in store for the future of the existing Metabrainz projects?\nWhat are the next domains that are being considered for building a Metabrainz platform for?\n\nKeep In Touch\n\nLinkedIn\nparamsingh on GitHub\nWebsite\n\nPicks\n\nTobias\n\nBeets music library organizer\n\nPodcast Episode\n\n\n\n\nParam\n\nPrateek Kuhad\n\n\n\nLinks\n\nMetabrainz\n\nMusicbrainz\nListenbrainz\nAcousticbrainz\nBookbrainz\nCritiquebrainz\nPicard\n\n\nStripe\nThe Himalayas\nDublin Ireland\nXKCD Import Antigravity\n\nAntigravity Python Module\n\n\nLast.fm\nGoogle Summer of Code\nCDDB\nPerl\nFlask\nSQLAlchemy\n3rd anniversary cake\nRedis\nPostgreSQL\nRabbitMQ\nSpark\nMusic Technology Group\nSplunk\nArtist Origins Map on ListenBrainz\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

The Musicbrainz project was an early entry in the movement to build an open data ecosystem. In recent years, the Metabrainz Foundation has fostered a growing ecosystem of projects to support the contribution of, and access to, metadata, listening habits, and review of music. The majority of those projects are written in Python, and in this episode Param Singh explains how they are built, how they fit together, and how they support the goals of the Metabrains Foundation. This was an interesting exporation of the work involved in building an ecosystem of open data, the challenges of making it sustainable, and the benefits of building for the long term rather than trying to achieve a quick win.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about the platforms being built by the Metabrainz Foundation to support collection and sharing of open data for music.","date_published":"2020-08-17T17:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/ae83fd0d-5c04-4d53-b6e6-e122803085b1.mp3","mime_type":"audio/mpeg","size_in_bytes":30215441,"duration_in_seconds":2886}]},{"id":"podlove-2020-08-10t21:31:46+00:00-cd789b863b8e7cc","title":"Growing Dask To Make Scaling Python Data Science Easier At Coiled","url":"https://www.pythonpodcast.com/coiled-dask-python-data-science-episode-275","content_text":"Summary\nPython is a leading choice for data science due to the immense number of libraries and frameworks readily available to support it, but it is still difficult to scale. Dask is a framework designed to transparently run your data analysis across multiple CPU cores and multiple servers. Using Dask lifts a limitation for scaling your analytical workloads, but brings with it the complexity of server administration, deployment, and security. In this episode Matthew Rocklin and Hugo Bowne-Anderson discuss their recently formed company Coiled and how they are working to make use and maintenance of Dask in production. The share the goals for the business, their approach to building a profitable company based on open source, and the difficulties they face while growing a new team during a global pandemic.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nThis portion of Python Podcast is brought to you by Datadog. Do you have an app in production that is slower than you like? Is its performance all over the place (sometimes fast, sometimes slow)? Do you know why? With Datadog, you will. You can troubleshoot your app’s performance with Datadog’s end-to-end tracing and in one click correlate those Python traces with related logs and metrics. Use their detailed flame graphs to identify bottlenecks and latency in that app of yours. Start tracking the performance of your apps with a free trial at datadog.com/pythonpodcast. If you sign up for a trial and install the agent, Datadog will send you a free t-shirt.\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!\nYour host as usual is Tobias Macey and today I’m interviewing Matthew Rocklin and Hugo Bowne-Anderson about their work building a business around the Dask ecosystem at Coiled\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you give a quick overview of what Dask is and your motivations for creating it?\n\nHow has Dask changed or evolved in the past 3 1/2 years since we last talked about it?\n\n\nHow has the rest of the ecosystem changed in that time?\nAfter working on Dask for the past few years, what led you to the decision to build a business around it?\nWhat are the sharp edges of programming for Dask that users are looking for help on solving?\nWhat are the difficulties that users face in deploying and maintaining a production installation of Dask?\nWhat are the limitations of Dask when scaling both up and down?\nWhat are you building at Coiled to improve the user experience for users of Python and Dask?\n\nWhat are your thoughts on the pros and cons of orienting your messaging around the scalability of Python, as opposed to focusing on a specific industry or problem domain?\n\n\nWhat are the challenges that you are facing in managing the tensions between the open source and proprietary work that you are doing?\nHow are you handling the ongoing governance of the Dask project?\nWhat are some of the most interesting, unexpected, or challenging lessons that you have learned while building and launching a company based on an open source project?\nWhat do you have planned for the future of both Coiled and Dask?\n\nKeep In Touch\n\nMatt\n\nWebsite\n@mrocklin on Twitter\nmrocklin on GitHub\n\n\nHugo\n\nLinkedIn\n@hugobowne on Twitter\nWebsite\n\n\n\nPicks\n\nTobias\n\nThe Hobbit\n\nAudiobook\nAudible Free Trial (affiliate link)\n\n\n\n\nMatt\n\nPrefect\n\n\nHugo\n\nRace After Technology by Ruha Benjamin\nRuha Benjamin on deep learning: Computational depth without sociological depth is ‘superficial learning’\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nSign up for the Coiled Beta!\nCoiled\nDask\nData Engineering Podcast Interview About Dask\nPyData\nNumPy\nSciPy\nCell Biology\nDatacamp\nDataframed\nMatthew Rocklin on Podcast.__init__ about functional programming with Toolz\nIPython Notebook\nPyTorch\n\nPodcast Episode\n\n\nAirflow\nPrefect\nXGBoost\nTornado\nCoiled Blog Post About The Goals of Dask\nSpark\nAsyncIO\nConcurrent.futures\nPangeo\nXarray\nRAPIDS\nNvidia\nCuda\nPrefect\n\nData Engineering Podcast Episode\n\n\nCelery\nLife Sciences\nTensorflow\nSnorkel\n\nData Engineering Podcast Episode\n\n\nDagster\n\nData Engineering Podcast Episode\n\n\nDevOps\nDocker\nKubernetes\nMetaflow\n\nPodcast Episode\n\n\nRay\n\nPodcast Episode\n\n\nAnyscale\nYarn\nGartner Hype Cycle\nTravis Oliphant\nPostgres\nAmazon ECS\nDjango\nDjango Allauth\nQuansight\nWes McKinney\n\nPodcast Interview\n\n\nUrsa Labs\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Python is a leading choice for data science due to the immense number of libraries and frameworks readily available to support it, but it is still difficult to scale. Dask is a framework designed to transparently run your data analysis across multiple CPU cores and multiple servers. Using Dask lifts a limitation for scaling your analytical workloads, but brings with it the complexity of server administration, deployment, and security. In this episode Matthew Rocklin and Hugo Bowne-Anderson discuss their recently formed company Coiled and how they are working to make use and maintenance of Dask in production. The share the goals for the business, their approach to building a profitable company based on open source, and the difficulties they face while growing a new team during a global pandemic.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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\"\"

","summary":"An interview with the co-founders of Coiled about their work to make Dask easier to use for scalable Python data science.","date_published":"2020-08-10T17:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/11a7f329-e7d7-4715-b105-0da891ff7981.mp3","mime_type":"audio/mpeg","size_in_bytes":44271178,"duration_in_seconds":3127}]},{"id":"podlove-2020-08-04t03:08:58+00:00-f6765ce0b2ff36f","title":"Supporting The Full Lifecycle Of Machine Learning Projects With Metaflow","url":"https://www.pythonpodcast.com/metaflow-machine-learning-operations-episode-274","content_text":"Summary\nNetflix uses machine learning to power every aspect of their business. To do this effectively they have had to build extensive expertise and tooling to support their engineers. In this episode Savin Goyal discusses the work that he and his team are doing on the open source machine learning operations platform Metaflow. He shares the inspiration for building an opinionated framework for the full lifecycle of machine learning projects, how it is implemented, and how they have designed it to be extensible to allow for easy adoption by users inside and outside of Netflix. This was a great conversation about the challenges of building machine learning projects and the work being done to make it more achievable.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nThis portion of Python Podcast is brought to you by Datadog. Do you have an app in production that is slower than you like? Is its performance all over the place (sometimes fast, sometimes slow)? Do you know why? With Datadog, you will. You can troubleshoot your app’s performance with Datadog’s end-to-end tracing and in one click correlate those Python traces with related logs and metrics. Use their detailed flame graphs to identify bottlenecks and latency in that app of yours. Start tracking the performance of your apps with a free trial at datadog.com/pythonpodcast. If you sign up for a trial and install the agent, Datadog will send you a free t-shirt.\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!\nYour host as usual is Tobias Macey and today I’m interviewing Savin Goyal about Netflix’s infrastructure for machine learning\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing the work you are doing at Netflix to support their machine learning workloads?\nHow are you addressing the impedance mismatch of machine learning/data science work between local experimentation and production deployment?\nWhat was the motivation for building Metaflow?\n\nHow does Metaflow compare to other tools in the ecosystem such as MLFlow?\nWhat was missing in the other available tools that made Metaflow necessary?\n\n\nworkflow for someone using Metaflow\nHow do you approach the design of the developer interface to make it approachable to machine learning engineers?\nlevel of coupling with overall Netflix data stack\nHow is Metaflow implemented?\n\nHow has the architecture and design of the system evolved since you first began working on it?\n\n\nsupporting infrastructure/integration points\nmotivation/benefits of releasing it as open source\nWhat are some of the most interesting, unexpected, or challenging lessons that you have learned while building infrastructure and tooling for machine learning?\nWhen is Metaflow the wrong choice?\nWhat do you have planned for the future of Metaflow and\n\nKeep In Touch\n\nLinkedIn\n@savingoyal on Twitter\nsavingoyal on GitHub\n\nPicks\n\nTobias\n\nvdist\n\n\nSavin\n\nReparing Vintage Watches\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nMetaflow\nOCaml\nEC2\nS3\nData Lake\nPyTorch\nTensorflow\nNetflix Data Stack\nSpinnaker\nChaos Engineering\n\nChaos Toolkit Podcast Episode\n\n\nChaos Monkey\nNetflix Simian Army\nNetflix Titus\nAWS Batch\nNetflix Meson\nDataflow Programming\nDAG == Directed Acyclic Graph\nMLFlow\nDVC (Data Version Control)\n\nPodcast Episode\n\n\nCML (Continuous Machine Learning)\nAWS Step Functions\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Netflix uses machine learning to power every aspect of their business. To do this effectively they have had to build extensive expertise and tooling to support their engineers. In this episode Savin Goyal discusses the work that he and his team are doing on the open source machine learning operations platform Metaflow. He shares the inspiration for building an opinionated framework for the full lifecycle of machine learning projects, how it is implemented, and how they have designed it to be extensible to allow for easy adoption by users inside and outside of Netflix. This was a great conversation about the challenges of building machine learning projects and the work being done to make it more achievable.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview with Savin Goyal about the work that he and his team are doing to support machine learning workloads at Netflix with Metaflow","date_published":"2020-08-03T23:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/80f8c217-bb5c-423f-bd55-f8c4af39405c.mp3","mime_type":"audio/mpeg","size_in_bytes":34497783,"duration_in_seconds":2685}]},{"id":"podlove-2020-07-28t02:36:22+00:00-52b4a6074844ac4","title":"Learning To Program By Building Tiny Python Projects","url":"https://www.pythonpodcast.com/tiny-python-projects-book-episode-273","content_text":"Summary\nOne of the best methods for learning programming is to just build a project and see how things work first-hand. With that in mind, Ken Youens-Clark wrote a whole book of Tiny Python Projects that you can use to get started on your journey. In this episode he shares his inspiration for the book, his thoughts on the benefits of teaching testing principles and the use of linting and formatting tools, as well as the benefits of trying variations on a working program to see how it behaves. This was a great conversation about useful strategies for supporting new programmers in their efforts to learn a valuable skill.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nThis portion of Python Podcast is brought to you by Datadog. Do you have an app in production that is slower than you like? Is its performance all over the place (sometimes fast, sometimes slow)? Do you know why? With Datadog, you will. You can troubleshoot your app’s performance with Datadog’s end-to-end tracing and in one click correlate those Python traces with related logs and metrics. Use their detailed flame graphs to identify bottlenecks and latency in that app of yours. Start tracking the performance of your apps with a free trial at datadog.com/pythonpodcast. If you sign up for a trial and install the agent, Datadog will send you a free t-shirt.\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!\nYour host as usual is Tobias Macey and today I’m interviewing Ken Youens-Clark about his book Tiny Python Projects\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nWhat is your goal with your book of Tiny Python Projects?\n\nWhat motivated you to start writing it?\n\n\nWho is the target audience that you wrote the book for?\nOne of the notable aspects of the book is the fact that you introduce linting and testing in the first chapter. Why is that a useful subject for the first steps of someone getting started in Python?\n\nWhat are some of the problems that users experience if they are introduced to these tools after they have already established a set of habits?\n\n\nHow did you approach the structure of the book to be approachable by newcomers to Python?\nWhat was your process for deciding on the scope of the information to include in the book?\nWhat are some of the challenges that you faced in identifying self-contained projects that could fit into a single chapter?\nAs a book that is intended to serve as a learning resource, what was your process for soliciting feedback to determine if your tone and structure is effective in teaching the reader?\nWhat elements of the Python language and ecosystem did you consciously leave out to avoid overwhelming the readers?\nWhat are some of the most interesting, unexpected, or challenging lessons that you learned while working on the book?\nWhat are your thoughts on useful resources and next steps for readers who are interested in progressing in their use of Python?\n\nKeep In Touch\n\nkyclark on GitHub\nWebsite\n@kycl4rk on Twitter\n\nPicks\n\nTobias\n\nMarvel Cinematic Universe\n\n\nKen\n\nParks & Recreation TV Show\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nTiny Python Projects\nUniversity of Arizona\nBioInformatics\nPerl\nBioPython\n\nPodcast Episode\n\n\nSeq\n\nPodcast Episode\n\n\nPytest\n\nPodcast Episode\n\n\nWindows Subsystem for Linux\nPylint\n\nPodcast Episode\n\n\nYAPF\nBlack Python Formatter\nMad Libs\nBoolean Algebra\nObject Oriented Programming\nDelphi\nOmniGraffle\nKent Beck\nTest Driven Development\nClojure\nRegular Expression\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

One of the best methods for learning programming is to just build a project and see how things work first-hand. With that in mind, Ken Youens-Clark wrote a whole book of Tiny Python Projects that you can use to get started on your journey. In this episode he shares his inspiration for the book, his thoughts on the benefits of teaching testing principles and the use of linting and formatting tools, as well as the benefits of trying variations on a working program to see how it behaves. This was a great conversation about useful strategies for supporting new programmers in their efforts to learn a valuable skill.

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Interview

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Picks

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Closing Announcements

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Ken Youens-Clark about his experiences writing Tiny Python Projects and the benefits of testing as an entry level skill","date_published":"2020-07-27T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/dabea11e-3e86-41a0-acc9-e6400ad78a6c.mp3","mime_type":"audio/mpeg","size_in_bytes":40738039,"duration_in_seconds":3299}]},{"id":"podlove-2020-07-20t12:49:31+00:00-1894136670dcf7b","title":"Idiomatic Functional Programming With DRY Python","url":"https://www.pythonpodcast.com/dry-python-functional-programming-episode-272","content_text":"Summary\nPython is an intuitive and flexible language, but that versatility can also lead to problematic designs if you’re not careful. Nikita Sobolev is the CTO of Wemake Services where he works on open source projects that encourage clean coding practices and maintainable architectures. In this episode he discusses his work on the DRY Python set of libraries and how they provide an accessible interface to functional programming patterns while maintaining an idiomatic Python interface. He also shares the story behind the wemake Python styleguide plugin for Flake8 and the benefits of strict linting rules to engender good development habits. This was a great conversation about useful practices to build software that will be easy and fun to work on.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nThis portion of Python Podcast is brought to you by Datadog. Do you have an app in production that is slower than you like? Is its performance all over the place (sometimes fast, sometimes slow)? Do you know why? With Datadog, you will. You can troubleshoot your app’s performance with Datadog’s end-to-end tracing and in one click correlate those Python traces with related logs and metrics. Use their detailed flame graphs to identify bottlenecks and latency in that app of yours. Start tracking the performance of your apps with a free trial at datadog.com/pythonpodcast. If you sign up for a trial and install the agent, Datadog will send you a free t-shirt.\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!\nYour host as usual is Tobias Macey and today I’m interviewing Nikita Sobolev about his work with DRY Python and Wemake Services\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by sharing your overarching philosophies or design aesthetics for writing maintainable software?\nWhat is your process for starting a new project, beginning at the design phase?\nWhat are some of the challenges or shortcomings that you see in the \"default\" way that most developers write Python?\nWhat is DRY Python is and how does it help in addressing those concerns?\n\nWhat was your motivation for creating these projects?\n\n\nThere are a number of different projects that are being built under the DRY Python umbrella. Can you list the ones that are currently active and outline how they fit together?\nWhat are some of the initial challenges that newcomers to the DRY Python libraries encounter?\nHow do you approach the design of the API and developer experience to make these development approaches more accessible?\nWhat have you seen in terms of real world impact on the maintainability and extensibility of projects that you have built on top of the DRY Python components?\nIn addition to DRY Python you are also involved with development of the wemake-python-styleguide. Can you describe that projects goal and how it got started?\n\nIf you make the linting too restrictive then developers are likely to just ignore or disable it. What have you found to be the right balance to which rules will fail a build and which are just informational?\nWhy do you push the responsibility for things like formatting onto the developer, rather than an autoformatter such as YAPF or Black?\n\n\nWhat are some of the other supporting technologies that you rely on during your development workflow?\nWhat are some of the elements that you think are missing in the common toolbox for Python developers?\n\nWhat tools are we lacking entirely?\n\n\nWhat are the cases where DRY Python is the wrong choice?\nWhat are your goals and plans for the future of DRY Python and the various Wemake libraries?\n\nKeep In Touch\n\nBlog\nsobolevn on GitHub\n\nPicks\n\nTobias\n\nThe Map To Everywhere\n\n\nNikita\n\nRussian Python Week\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nDRY Python\nWemake Services\nwemake-python-styleguide\nTurbogears 2\nDotenv Linter\nReturns\nWemake Python Package Cookiecutter Template\nTest Driven Development\nRequirements Analysis\nRESTs\nDjango Rest Framework\nClasses\nMonads\nFunctors\nScala\nKotlin\nHaskell\nPunq dependency injection library\nFlake8\nWemake Django Template\nFlake8 Baseline\nisort\nNitpick\nMypy\nDarglint\nPoetry\nPip Dependency Resolver\n\nPodcast Episode\n\n\nHypothesis\n\nPodcast Episode\n\n\nSchemathesis\nPytest Auto Hypothesis\nTypescript\nRust\nElixir\nZio Scala\nGitHub Sponsors\nDo Not Log blog post\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Python is an intuitive and flexible language, but that versatility can also lead to problematic designs if you’re not careful. Nikita Sobolev is the CTO of Wemake Services where he works on open source projects that encourage clean coding practices and maintainable architectures. In this episode he discusses his work on the DRY Python set of libraries and how they provide an accessible interface to functional programming patterns while maintaining an idiomatic Python interface. He also shares the story behind the wemake Python styleguide plugin for Flake8 and the benefits of strict linting rules to engender good development habits. This was a great conversation about useful practices to build software that will be easy and fun to work on.

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Announcements

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Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Nikita Sobolev about building Python projects that are more maintainable using DRY Python libraries to make functional programming patterns more intuitive.","date_published":"2020-07-20T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/935893e2-209f-4822-bbc7-0f5065492183.mp3","mime_type":"audio/mpeg","size_in_bytes":40583903,"duration_in_seconds":2862}]},{"id":"podlove-2020-07-14t11:38:50+00:00-f553a56eea87a4c","title":"The Past, Present, And Future Of The FLUFL: Barry Warsaw Shares His History With Python","url":"https://www.pythonpodcast.com/barry-warsaw-the-flufl-episode-271","content_text":"Summary\nBarry Warsaw has been a member of the Python community since the very beginning. His contributions to the growth of the language and its ecosystem are innumerable and diverse, earning him the title of Friendly Language Uncle For Life. In this episode he reminisces on his experiences as a core developer, a member of the Python Steering Committee, and his roles at Canonical and LinkedIn supporting the use of Python at those companies. In order to know where you are going it is always important to understand where you have been and this was a great conversation to get a sense of the history of how Python has gotten to where it is today.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nThis episode of Python Podcast is brought to you by Datadog. Do you have an app in production that is slower than you like? Is its performance all over the place (sometimes fast, sometimes slow)? Do you know why? With Datadog, you will. You can troubleshoot your app’s performance with Datadog’s end-to-end tracing and in one click correlate those Python traces with related logs and metrics. Use their detailed flame graphs to identify bottlenecks and latency in that app of yours. Start tracking the performance of your apps with a free trial at datadog.com/pythonpodcast. If you sign up for a trial and install the agent, Datadog will send you a free t-shirt.\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!\nYour host as usual is Tobias Macey and today I’m interviewing Barry Warsaw about his role in the Python community, past, present, and future.\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nFor anyone who isn’t familiar with you, how would you characterize your role in the Python language and community?\nWhat have been your main areas of focus in your role as a core developer?\n\nWhat are some of the other forms that your contributions to the language and community have taken?\n\n\nWhat are the contributions to Python that you are most proud of?\nLooking back at the past 25 years of Python, what do you find most interesting/surprising/exciting?\nHow has the focus of the community changed or evolved since you first began using it?\nWhat are you currently focused on in your role in the steering council?\nWhat are the aspects of the language and community that you think need greater attention?\nWhat are the core strengths of the language and community that you believe will carry it through the next 25 years?\nIn your current and previous roles you acted as a guiding force for Python. What are the main use cases for Python at LinkedIn?\n\nWhat kinds of projects are you involved with to support the other engineers in their use of Python?\n\n\nHow much of an impact has the invisible hand of the PSU had on the overall trajectory of Python?\nOutside of Python, what are the programming languages or communities that you look to for inspiration?\nWhat are your personal goals for the future of Python?\n\nKeep In Touch\n\nWebsite\nwarsaw on GitHub\nwarsaw on GitLab\nBlog\n@pumpichank on Twitter\n\nPicks\n\nTobias\n\nHanna TV Series\n\n\nBarry\n\nMidnight Gospel\nThe Expanse\n\nTV Series\nAudio Books\n\nFree 30 Day Audible Trial (Affiliate Link)\n\n\n\n\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nFLUFL PEP 401\nPython Steering Council\nThe PEP Talk episode\nUsenet\nBBS == Bulletin Board System\ncomp.lang.python\nNIST == National Institute of Standards and Technology\nCNRI == Corporation for National Research Initiatives\nBayPIGgies\nTcl/Tk\nPEP 572 := The Walrus Operator\n\"The Grand Renaming\"\nIETF == Internet Engineering Task Force\nRFC\nWebAssembly\nPython Software Foundation\n\nPodcast Episode\n\n\nPython Black Swans keynote by Russell Keith-Magee\n\nFollowup Podcast Episode\n\n\nEwa Jodlowska\nCanonical Launchpad\nMypy\n\nPodcast Episode\n\n\nPython Type Annotations\nIris Event Paging System\nOnCall Pager Rotation System\nShiv\nPyOxidizer\nRust\nFlake8\nisort\nBlack\nSphinx\nRead The Docs\n\nPodcast Episode\n\n\nSybil\nManuel\nDoctest\nPytest\nCoverage.py\nCargo package system\nTai Chi\nPython Core Mentorship\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Barry Warsaw has been a member of the Python community since the very beginning. His contributions to the growth of the language and its ecosystem are innumerable and diverse, earning him the title of Friendly Language Uncle For Life. In this episode he reminisces on his experiences as a core developer, a member of the Python Steering Committee, and his roles at Canonical and LinkedIn supporting the use of Python at those companies. In order to know where you are going it is always important to understand where you have been and this was a great conversation to get a sense of the history of how Python has gotten to where it is today.

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Announcements

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Interview

\n\n

Keep In Touch

\n\n

Picks

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Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Barry Warsaw about his experience as a core member of the Python development team from its early days through to today.","date_published":"2020-07-13T13:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/9deadae1-2f75-4554-a059-e2ad20ca57bf.mp3","mime_type":"audio/mpeg","size_in_bytes":41895137,"duration_in_seconds":3100}]},{"id":"podlove-2020-07-06t15:51:10+00:00-1bf31ddd1d132c1","title":"Pure Python Configuration Management With PyInfra","url":"https://www.pythonpodcast.com/pyinfra-configuration-management-episode-270","content_text":"Summary\nBuilding and managing servers is a challenging task. Configuration management tools provide a framework for handling the various tasks involved, but many of them require learning a specific syntax and toolchain. PyInfra is a configuration management framework that embraces the familiarity of Pure Python, allowing you to build your own integrations easily and package it all up using the same tools that you rely on for your applications. In this episode Nick Barrett explains why he built it, how it is implemented, and the ways that you can start using it today. He also shares his vision for the future of the project and you can get involved. If you are tired of writing mountains of YAML to set up your servers then give PyInfra a try today.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nThis portion of Podcast.__init__ is brought to you by Datadog. Do you have an app in production that is slower than you like? Is its performance all over the place (sometimes fast, sometimes slow)? Do you know why? With Datadog, you will. You can troubleshoot your app’s performance with Datadog’s end-to-end tracing and in one click correlate those Python traces with related logs and metrics. Use their detailed flame graphs to identify bottlenecks and latency in that app of yours. Start tracking the performance of your apps with a free trial at datadog.com/pythonpodcast. If you sign up for a trial and install the agent, Datadog will send you a free t-shirt.\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!\nYour host as usual is Tobias Macey and today I’m interviewing Nick Barrett about PyInfra, a pure Python framework for agentless configuration management\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what PyInfra is and its origin story?\nThere are a number of options for configuration management of various levels of complexity and language options. What are the features of PyInfra that might lead someone to choose it over other systems?\nWhat do you see as the major pain points in dealing with infrastructure today?\nFor someone who is using PyInfra to manage their servers, what is the workflow for building and testing deployments?\nHow do you handle enforcement of idempotency in the operations being performed?\nCan you describe how PyInfra is implemented?\n\nHow has its design or focus evolved since you first began working on it?\nWhat are some of the initial assumptions that you had at the outset which have been challenged or updated as it has grown?\n\n\nThe library of available operations seems to have a good baseline for deploying and managing services. What is involved in extending or adding operations to PyInfra?\nWith the focus of the project being on its use of pure Python and the easy integration of external libraries, how do you handle execution of python functions on remote hosts that requires external dependencies?\nWhat are some of the other options for interfacing with or extending PyInfra?\nWhat are some of the edge cases or points of confusion that users of PyInfra should be aware of?\nWhat has been the community response from developers who first encounter and trial PyInfra?\nWhat have you found to be the most interesting, unexpected, or challenging aspects of building and maintaining PyInfra?\nWhen is PyInfra the wrong choice for managing infrastructure?\nWhat do you have planned for the future of the project?\n\nKeep In Touch\n\nFizzadar on GitHub\nWebsite\n@Fizzadar on Twitter\nLinkedIn\n\nPicks\n\nTobias\n\nMy Spy\n\n\nNick\n\nDas Keyboard Ultimate\nKorean Short Ribs\nKimchi Fried Rice\n\n\n\nLinks\n\nPyInfra\nOxygem\nWordPress\nLua\nGary’s Mod\nJava\nAnsible\nSaltStack\nChef\nPuppet\nEC2\nBoto 3\nHashicorp Vault\nVagrant\nDocker\nTestinfra\n\nSaltStack Testinfra Plugin\n\n\nDockerfile\nIdempotence\nNginx\nPOSIX\ngevent\nJinja2\nClick\nZero Tier\nBSD\nAST Module\nRedBaron\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Building and managing servers is a challenging task. Configuration management tools provide a framework for handling the various tasks involved, but many of them require learning a specific syntax and toolchain. PyInfra is a configuration management framework that embraces the familiarity of Pure Python, allowing you to build your own integrations easily and package it all up using the same tools that you rely on for your applications. In this episode Nick Barrett explains why he built it, how it is implemented, and the ways that you can start using it today. He also shares his vision for the future of the project and you can get involved. If you are tired of writing mountains of YAML to set up your servers then give PyInfra a try today.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Nick Barrett about his work on PyInfra and how he uses it to maintain his servers with pure Python code that is easy to understand and execute.","date_published":"2020-07-06T13:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/3cfa2a54-4f5b-4ce3-8fd5-957b2281381c.mp3","mime_type":"audio/mpeg","size_in_bytes":32507769,"duration_in_seconds":2588}]},{"id":"podlove-2020-06-29t11:46:39+00:00-e061e2f4c5da4c6","title":"Build Your Own Domain Specific Language in Python With textX","url":"https://www.pythonpodcast.com/textx-domain-specific-language-episode-269","content_text":"Summary\nProgramming languages are a powerful tool and can be used to create all manner of applications, however sometimes their syntax is more cumbersome than necessary. For some industries or subject areas there is already an agreed upon set of concepts that can be used to express your logic. For those cases you can create a Domain Specific Language, or DSL to make it easier to write programs that can express the necessary logic with a custom syntax. In this episode Igor Dejanović shares his work on textX and how you can use it to build your own DSLs with Python. He explains his motivations for creating it, how it compares to other tools in the Python ecosystem for building parsers, and how you can use it to build your own custom languages.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!\nYour host as usual is Tobias Macey and today I’m interviewing Igor Dejanović about textX, a meta-language for building domain specific languges in Python\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what a domain specific language is and some examples of when you might need one?\nWhat is textX and what was your motivation for creating it?\nThere are a number of other libraries in the Python ecosystem for building parsers, and for creating DSLs. What are the features of textX that might lead someone to choose it over the other options?\nWhat are some of the challenges that face language designers when constructing the syntax of their DSL?\nBeyond being able to parse and process an arbitrary syntax, there are other concerns for consumers of the definition in terms of tooling. How does textX provide support to those end users?\nHow is textX implemented?\n\nHow has the design or goals of textX changed since you first began working on it?\n\n\nWhat is the workflow for someone using textX to build their own DSL?\n\nOnce they have defined the grammar, how do they distribute the generated interpreter for others to use?\n\n\nWhat are some of the common challenges that users of textX face when trying to define their DSL?\nWhat are some of the cases where a PEG parser is unable to unambiguously process a defined grammar?\nWhat are some of the most interesting/innovative/unexpected ways that you have seen textX used?\nWhat have you found to be the most interesting, unexpected, or challenging lessons that you have learned while building and maintaining textX and its associated projects?\nWhile preparing for this interview I noticed that you have another parser library in the form of Parglare. How has your experience working with textX informed your designs of that project?\n\nWhat lessons have you taken back from Parglare into textX?\n\n\nWhen is textX the wrong choice, and someone might be better served by another DSL library, different style of parser, or just hand-crafting a simple parser with a regex?\nWhat do you have planned for the future of textX?\n\nKeep In Touch\n\nWebsite\nigordejanovic on GitHub\n@dejanovicigor on Twitter\n\nPicks\n\nTobias\n\nwemake-python-styleguide\n\n\nIgor\n\nInteractive Fiction genre\n\nAwesome Interactive Fiction\nThe Interactive Fiction Database\nTADS\nInform 7\n\n\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\ntextX\nU of Novi Sad\nSerbia\nDSL course\nSecondary Notation\nDjango\nXtext\nEclipse\nPLY\nSLY\nPyParsing\nLark\nPEG Grammar\nLanguage Workbench\nLanguage Server Protocol\nVisual Studio Code\ntextX-LS\nArpeggio Parser\nContext-Free Grammar\npyTabs\nGuitar Tablatures\nParglare\nGLR parsing\nTEP 1\nEvennia\n\nPodcast Episode\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Programming languages are a powerful tool and can be used to create all manner of applications, however sometimes their syntax is more cumbersome than necessary. For some industries or subject areas there is already an agreed upon set of concepts that can be used to express your logic. For those cases you can create a Domain Specific Language, or DSL to make it easier to write programs that can express the necessary logic with a custom syntax. In this episode Igor Dejanović shares his work on textX and how you can use it to build your own DSLs with Python. He explains his motivations for creating it, how it compares to other tools in the Python ecosystem for building parsers, and how you can use it to build your own custom languages.

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Announcements

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Interview

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Keep In Touch

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Picks

\n\n

Closing Announcements

\n\n

Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"","date_published":"2020-06-29T20:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/c14853e8-f874-4409-8676-7fa7ac7e2ecc.mp3","mime_type":"audio/mpeg","size_in_bytes":45890015,"duration_in_seconds":3258}]},{"id":"podlove-2020-06-23t00:45:14+00:00-2abedbfb198b9be","title":"Adding Observability To Your Python Applications With OpenTelemetry","url":"https://www.pythonpodcast.com/opentelemetry-observability-episode-268","content_text":"Summary\nOnce you release an application into production it can be difficult to understand all of the ways that it is interacting with the systems that it integrates with. The OpenTracing project and its accompanying ecosystem of technologies aims to make observability of your systems more accessible. In this episode Austin Parker and Alex Boten explain how the correlation of tracing and metrics collection improves visibility of how your software is behaving, how you can use the Python SDK to automatically instrument your applications, and their vision for the future of observability as the OpenTelemetry standard gains broader adoption.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Austin Parker and Alex Boten about the OpenTelemetry project and its efforts to standardize the collection and analysis of observability data for your applications\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what OpenTelemetry is and some of the story behind it?\nHow do you define observability and in what ways is it separate from the \"traditional\" approach to monitoring?\nWhat are the goals of the OpenTelemetry project?\nFor someone who wants to begin using OpenTelemetry clients in their Python application, what is the process of integrating it into their application?\nHow does the definition and adoption of a cross-language standard for telemetry data benefit the broader software community?\n\nHow do you avoid the trap of limiting the whole ecosystem to the lowest common denominator?\n\n\nWhat types of information are you focused on collecting and analyzing to gain insights into the behavior of applications and systems?\n\nWhat are some of the challenges that are commonly faced in interpreting the collected data?\n\n\nWith so many implementations of the specification, how are you addressing issues of feature parity?\nFor the Python SDK, how is it implemented?\n\nWhat are some of the initial designs or assumptions that have had to be revised or reconsidered as it gains adoption?\n\n\nWhat is your approach to integration with the broader ecosystem of tools and frameworks in the Python community?\nWhat are some of the interesting or unexpected challenges that you have faced or lessons that you have learned while working on instrumentation of Python projects?\nOnce an application is instrumented, what are the options for delivering and storing the collected data?\nWhat are some of the most interesting, unexpected, or challenging lessons that you have learned while working on and with the OpenTelemetry ecosystem?\nWhat are some of the most interesting, innovative, or unexpected ways that you have seen components in the OpenTelemetry ecosystem used?\nWhen is OpenTelemetry the wrong choice?\nWhat is in store for the future of the OpenTelemetry project?\n\nKeep In Touch\n\nAustin\n\n@austinlparker on Twitter\naustinlparker on GitHub\n\n\nAlex\n\nLinkedIn\n@codeboten on Twitter\ncodeboten on GitHub\n\n\n\nPicks\n\nTobias\n\nPulumi\n\nPodcast Episode\n\n\n\n\nAustin\n\nHelm 3\n\n\nAlex\n\nAlgorithms To Live By: The Computer Science Of Everyday Decisions by Brian Christian and Tom Griffiths\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nOpenTelemetry\nLightstep\nOpenTracing\nOpenCensus\nDistributed Tracing\nJaeger\nZipkin\nObservability\nKubernetes\nSpring\nFlask\ngRPC\nStructlog\nFilebeat\nW3C Trace Context\nOpenTelemetry Python SDK\nOpenTelemetry Django\nOpenTelemetry Flask\nOpenTelemetry Collector\nOTLP == Open Telemetry Protocol\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Once you release an application into production it can be difficult to understand all of the ways that it is interacting with the systems that it integrates with. The OpenTracing project and its accompanying ecosystem of technologies aims to make observability of your systems more accessible. In this episode Austin Parker and Alex Boten explain how the correlation of tracing and metrics collection improves visibility of how your software is behaving, how you can use the Python SDK to automatically instrument your applications, and their vision for the future of observability as the OpenTelemetry standard gains broader adoption.

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Announcements

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Interview

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Keep In Touch

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Picks

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Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about using the OpenTelemetry SDK for Python to collect observability data and how it aids in understanding the behavior of complex systems.","date_published":"2020-06-22T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/d6d2a0de-c69d-438d-95ed-7da40584bc6d.mp3","mime_type":"audio/mpeg","size_in_bytes":49097709,"duration_in_seconds":3224}]},{"id":"podlove-2020-06-15t10:40:15+00:00-db35134ed0818eb","title":"Build A Personal Knowledge Store With Topic Modeling In Contextualize","url":"https://www.pythonpodcast.com/contextualize-topic-modeling-episode-267","content_text":"Summary\nOur thought patterns are rarely linear or hierarchical, instead following threads of related topics in unpredictable directions. Topic modeling is an approach to knowledge management which allows for forming a graph of associations to make capturing and organizing your thoughts more natural. In this episode Brett Kromkamp shares his work on the Contextualize project and how you can use it for building your own topic models. He explains why he wrote a new topic modeling engine, how it is architected, and how it compares to other systems for organizing information. Once you are done listening you can take Contextualize for a test run for free with his hosted instance.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Brett Kromkamp about Contextualise, a topic modeling application that helps you build a mind map for information-heavy projects\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what Contextualize is and some of the types of projects that it can be used for?\n\nWhat was your motivation for creating it?\n\n\nHow do you use topic maps in your own work and creative endeavors?\nThe space of personal note-taking and knowledge management is vast and varied. What does Contextualize do well that you have been unable to find or implement in other tools?\nFor someone using Contextualize, what does that workflow look like?\nHow are you approaching integration with different creative contexts (e.g. text editors, graphics editors, word processing, etc.)?\nCan you describe how Contextualize is implemented?\n\nHow has the design evolved since you first began working on it?\n\n\nIn the documentation for Contextualize it mentions that this is the latest in a string of topic mapping platforms that you have built. What are some of the lessons that you have learned from previous efforts that have influenced the design of this one?\nOne of the challenges with many knowledge management tools is that they are proscriptive in how to work with them. In what ways has your own preference for how to interact with information influenced the direction of Contextualize?\n\nBeing an open source application, how has its exposure to the public directed your software and user design?\n\n\nHow do you approach the challenge of reducing friction in adding content and relations while allowing for flexibility and context management?\nWhat are some of the projects that you are using Contextualize for?\nWhat are your thoughts on the utility of something like Contextualize for capturing and organizing the collective knowledge of a team of collaborators, whether in a work or casual context?\nWhat have you found to be the most interesting, complex, or complicated aspects of building a topic mapping platform?\nWhen is Contextualize the wrong choice?\nWhat do you have planned for the future of the project?\n\nKeep In Touch\n\nWebsite\n@brettkromkamp on Twitter\nbrettkromkamp on GitHub\n\nPicks\n\nTobias\n\nPydantic\n\nPodcast Episode\n\n\nMyPy\n\nPodcast Episode\n\n\n\n\nBrett\n\nBlack Lives Matter\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nContextualise\n\nGitHub Repository\n\n\nNorway\nIBM Rexx\nJava\nSemantic Web\nTopic Map\nISO standard for topic maps\nRDF\nSpain\nKnowledge Management\nGraph Database\nWorldbuilding\nRoam Research\nTopicDB\nTwitter Bootstrap\nHypergraph\nDigital Gardening\nNotion\nTiddlyWiki\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Our thought patterns are rarely linear or hierarchical, instead following threads of related topics in unpredictable directions. Topic modeling is an approach to knowledge management which allows for forming a graph of associations to make capturing and organizing your thoughts more natural. In this episode Brett Kromkamp shares his work on the Contextualize project and how you can use it for building your own topic models. He explains why he wrote a new topic modeling engine, how it is architected, and how it compares to other systems for organizing information. Once you are done listening you can take Contextualize for a test run for free with his hosted instance.

\n

Announcements

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Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about building and using the Contextualize topic modeling platform for building a personal knowledge base and working on information heavy projects.","date_published":"2020-06-15T08:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/adc60069-07fd-40a2-8072-414c5b336c3f.mp3","mime_type":"audio/mpeg","size_in_bytes":43663335,"duration_in_seconds":3486}]},{"id":"podlove-2020-06-08t11:26:30+00:00-5ec8b74c37c23de","title":"Open Source Product Analytics With PostHog","url":"https://www.pythonpodcast.com/posthog-product-analytics-episode-266","content_text":"Summary\nYou spend a lot of time and energy on building a great application, but do you know how it’s actually being used? Using a product analytics tool lets you gain visibility into what your users find helpful so that you can prioritize feature development and optimize customer experience. In this episode PostHog CTO Tim Glaser shares his experience building an open source product analytics platform to make it easier and more accessible to understand your product. He shares the story of how and why PostHog was created, how to incorporate it into your projects, the benefits of providing it as open source, and how it is implemented. If you are tired of fighting with your user analytics tools, or unwilling to entrust your data to a third party, then have a listen and then test out PostHog for yourself.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYou listen to this show because you love Python and want to keep your skills up to date, and machine learning is finding its way into every aspect of software engineering. Springboard has partnered with us to help you take the next step in your career by offering a scholarship to their Machine Learning Engineering career track program. In this online, project-based course every student is paired with a Machine Learning expert who provides unlimited 1:1 mentorship support throughout the program via video conferences. You’ll build up your portfolio of machine learning projects and gain hands-on experience in writing machine learning algorithms, deploying models into production, and managing the lifecycle of a deep learning prototype. Springboard offers a job guarantee, meaning that you don’t have to pay for the program until you get a job in the space. Podcast.__init__ is exclusively offering listeners 20 scholarships of $500 to eligible applicants. It only takes 10 minutes and there’s no obligation. Go to pythonpodcast.com/springboard and apply today! Make sure to use the code AISPRINGBOARD when you enroll.\nYour host as usual is Tobias Macey and today I’m interviewing Tim Glaser about PostHog, an open source platform for product analytics\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what PostHog is and what motivated you to build it?\nWhat are the goals of PostHog and who are the target audience?\nIn the description of PostHog it mentions being a product focused analytics platform, as opposed to session based. What are the meaningful differences between the two?\nCustomer analytics is a rather crowded market, with a large number of both commercial and open source offerings (e.g. Google Analytics, Heap, Matomo, Snowplow, etc.). How does PostHog fit in that landscape and what are the differentiating factors that would lead someone to select it over the alternativs?\nFor anyone interested in using PostHog, do you offer a migration path from other platforms?\nnecessary features for a customer analytics tool\nprivacy and security issues around analytics\nHow is PostHog implemented and how has its design evolved since you first began building it?\n\nreason for choosing Python\nbenefits of Django\n\n\nthoughts on introducing Channels\noption to include it as a pluggable Django app\nintegration points\ndata lake integration\nchallenges of providing understandable statistics and exposing options for detailed analysis\nHaving data about how users are interacting with your site or application is interesting, but how does it help in determining the useful actions to drive success?\nbusiness model and project governance\nWhat are the most complex, complicated, or misunderstood aspects of building a product analytics platform?\nWhat have you found to be the most interesting, unexpected, or challenging lessons that you have learned in the process of building PostHog?\nWhen is PostHog the wrong choice?\nWhat do you have planned for the future of PostHog?\n\nKeep In Touch\n\ntimgl on GitHub\nLinkedIn\n@timgl on Twitter\n\nPicks\n\nTobias\n\nHitchhiker’s Guide To The Galaxy\n\n\nTim\n\nTriumph Of The City by Edward Glaeser\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nPostHog\nMixPanel\nAmplitude\nHeap\n\nData Engineering Podcast Episode\n\n\nSnowplow\n\nData Engineering Podcast Episode\n\n\nLooker\n\nData Engineering Podcast Episode\n\n\nSnowflakeDB\n\nData Engineering Podcast Episode\n\n\nTableau\nDOM == Document Object Model for web pages\nDjango\nDjango Rest Framework\nReact.js\nKea state management for React.js\nRedux\nTypeScript\nDjango Stubs\nDjango Channels\nSentry\n\nPodcast Episode\n\n\nPluggable Django App\nPostgreSQL\nELT\nData Lake\nOptimizely\nFeature Flags\n\nPodcast Episode\n\n\nPostHog Roadmap\nPostHog Employee Handbook\nMatomo (formerly Piwik)\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

You spend a lot of time and energy on building a great application, but do you know how it’s actually being used? Using a product analytics tool lets you gain visibility into what your users find helpful so that you can prioritize feature development and optimize customer experience. In this episode PostHog CTO Tim Glaser shares his experience building an open source product analytics platform to make it easier and more accessible to understand your product. He shares the story of how and why PostHog was created, how to incorporate it into your projects, the benefits of providing it as open source, and how it is implemented. If you are tired of fighting with your user analytics tools, or unwilling to entrust your data to a third party, then have a listen and then test out PostHog for yourself.

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Announcements

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Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with PostHog CTO Tim Glaser on building an open source product analytics platform in Python and how it feeds back into the development process","date_published":"2020-06-08T07:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/5128aae9-21e2-4bff-928f-9514f6f751b2.mp3","mime_type":"audio/mpeg","size_in_bytes":34784547,"duration_in_seconds":2948}]},{"id":"podlove-2020-06-01t00:24:10+00:00-38a48fe6fa09017","title":"Extending The Life Of Python 2 Projects With Tauthon","url":"https://www.pythonpodcast.com/tauthon-python-2-fork-episode-265","content_text":"Summary\nThe divide between Python 2 and 3 lasted a long time, and in recent years all of the new features were added to version 3. To help bridge the gap and extend the viability of version 2 Naftali Harris created Tauthon, a fork of Python 2 that backports features from Python 3. In this episode he explains his motivation for creating it, the process of maintaining it and backporting features, and the ways that it is being used by developers who are unable to make the leap. This was an interesting look at how things might have been if the elusive Python 2.8 had been created as a more gentle transition.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!\nYou listen to this show because you love Python and want to keep your skills up to date, and machine learning is finding its way into every aspect of software engineering. Springboard has partnered with us to help you take the next step in your career by offering a scholarship to their Machine Learning Engineering career track program. In this online, project-based course every student is paired with a Machine Learning expert who provides unlimited 1:1 mentorship support throughout the program via video conferences. You’ll build up your portfolio of machine learning projects and gain hands-on experience in writing machine learning algorithms, deploying models into production, and managing the lifecycle of a deep learning prototype. Springboard offers a job guarantee, meaning that you don’t have to pay for the program until you get a job in the space. Podcast.__init__ is exclusively offering listeners 20 scholarships of $500 to eligible applicants. It only takes 10 minutes and there’s no obligation. Go to pythonpodcast.com/springboard and apply today! Make sure to use the code AISPRINGBOARD when you enroll.\nYour host as usual is Tobias Macey and today I’m interviewing Naftali Harris about his work on Tauthon, a fork of Python 2 that backports features from Python 3\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what Tauthon is and your motivations for creating it?\n\nWhat’s the story behind the name?\n\n\nWhat types of applications and environments are you using Tauthon in?\nHow much adoption of Tauthon have you seen?\n\nWhat are some of the different ways that your users are employing it?\n\n\nIs this the missing \"2.8\" release? In other words, is this intended to be a bridge for simplifying the migration of existing Python 2 code to Python 3, or as an extended support window for Python 2?\nWhat features have you backported from Python 3?\n\nWhat is your process for identifying and prioritizing features to bring into Tauthon?\n\n\nWhat is your workflow for implementing the backported functionality in Tauthon?\nWhat are some of the cases where you have had to compromise on the functionality or syntax of a feature that you have backported in order to fit into Python 2?\n\nWhat is your governing philosophy for how to manage syntax or behavior differences between Python 2 and 3?\nWhat have been the most challenging features to backport and maintain?\nWhat are some of the ways that Tauthon might break existing Python 2 code?\n\n\nWhat is the story for compatibility with libraries that are Python 3 only?\nWhat have you seen in terms of adoption of Tauthon?\n\nDo you have any sense of the commonalities among those users?\n\n\nWhat are some of the ecosystem challenges that faces users of Tauthon? (e.g. Pip support, package compatibility, etc.)\nWhat are some of the most interesting, unexpected, or challenging lessons that you have learned in the process of creating and maintaining Tauthon?\nWhat are your long-term plans for Tauthon, and how have they changed since you first started working on it?\n\nKeep In Touch\n\nWebsite\n@naftaliharris on Twitter\nnaftaliharris on GitHub\n\nPicks\n\nTobias\n\nDagster\nPyCon 2020 Online\n\n\nNaftali\n\nSentilink\nTimsort\nTim Peters\n\n\n\nLinks\n\nTauthon\nFunction Annotations\nTau\nNick Coghlan\nMyPy\n\nPodcast Episode\n\n\nMatrix Multiplier Operator\nPython 3.9 PEG Parser\nlazysorted\nnonlocal keyword\nValgrind\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

The divide between Python 2 and 3 lasted a long time, and in recent years all of the new features were added to version 3. To help bridge the gap and extend the viability of version 2 Naftali Harris created Tauthon, a fork of Python 2 that backports features from Python 3. In this episode he explains his motivation for creating it, the process of maintaining it and backporting features, and the ways that it is being used by developers who are unable to make the leap. This was an interesting look at how things might have been if the elusive Python 2.8 had been created as a more gentle transition.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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\n\n

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","summary":"An interview about the Tauthon project that backports Python 3 features to Python 2 as a way of extending the life of projects that haven't made the switch.","date_published":"2020-06-01T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/bb4470fb-67a9-4b48-a09b-2c89fe12d19a.mp3","mime_type":"audio/mpeg","size_in_bytes":30994880,"duration_in_seconds":1987}]},{"id":"podlove-2020-05-25t12:04:19+00:00-e66c7fb247b6039","title":"Dependency Management Improvements In Pip's Resolver","url":"https://www.pythonpodcast.com/pip-resolver-dependency-management-episode-264","content_text":"Summary\nDependency management in Python has taken a long and winding path, which has led to the current dominance of Pip. One of the remaining shortcomings is the lack of a robust mechanism for resolving the package and version constraints that are necessary to produce a working system. Thankfully, the Python Software Foundation has funded an effort to upgrade the dependency resolution algorithm and user experience of Pip. In this episode the engineers working on these improvements, Pradyun Gedam, Tzu-Ping Chung, and Paul Moore, discuss the history of Pip, the challenges of dependency management in Python, and the benefits that surrounding projects will gain from a more robust resolution algorithm. This is an exciting development for the Python ecosystem, so listen now and then provide feedback on how the new resolver is working for you.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show because you love Python and want to keep your skills up to date, and machine learning is finding its way into every aspect of software engineering. Springboard has partnered with us to help you take the next step in your career by offering a scholarship to their Machine Learning Engineering career track program. In this online, project-based course every student is paired with a Machine Learning expert who provides unlimited 1:1 mentorship support throughout the program via video conferences. You’ll build up your portfolio of machine learning projects and gain hands-on experience in writing machine learning algorithms, deploying models into production, and managing the lifecycle of a deep learning prototype. Springboard offers a job guarantee, meaning that you don’t have to pay for the program until you get a job in the space. Podcast.__init__ is exclusively offering listeners 20 scholarships of $500 to eligible applicants. It only takes 10 minutes and there’s no obligation. Go to pythonpodcast.com/springboard and apply today! Make sure to use the code AISPRINGBOARD when you enroll.\nYour host as usual is Tobias Macey and today I’m interviewing Tzu-ping Chung, Pradyun Gedam, and Paul Moore about their work to improve the dependency resolution capabilities of Pip and its user experience\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing the focus of the work that you are doing?\n\nWhat is the scope of the work, and what is the established criteria for when it is considered complete?\n\n\nWhat is your history with working on the Pip source code and what interests you most about this project?\nWhat are the main sources or manifestations of technical debt that exist in Pip as of today?\n\nHow does it currently handle dependency resolution?\n\n\nWhat are some of the workarounds that developers have had to resort to in the absence of a robust dependency resolver in Pip?\nHow is the new dependency resolver implemented?\n\nHow has your initial design evolved or shifted as you have gotten further along in its implementation?\n\n\nWhat are the pieces of information that the resolver will rely on for determining which packages and versions to install? (e.g. will it install setuptools > 45.x in a Python 2 virtualenv?)\nWhat are the new capabilities in Pip that will be enabled by this upgrade to the dependency resolver?\nWhat projects or features in the encompassing ecosystem will be unblocked with the introduction of this upgrade?\nWhat are some of the changes that users will need to make to adopt the updated Pip?\nHow do you anticipate the changes in Pip impacting the viability or adoption of Python and its ecosystem within different communities or industries?\nWhat are some of the additional changes or improvements that you would like to see in Pip or other core elements of the Python landscape?\nWhat are some of the most interesting, unexpected, or challenging lessons that you have learned while working on these updates to Pip?\n\nKeep In Touch\n\nPradyun\n\nWebsite\npradyunsg on GitHub\n@pradyunsg on Twitter\n\n\nPaul\n\npfmoore on GitHub\n\n\nTzu-Ping\n\nuranusjr on GitHub\nWebsite\n@uranusjr on Twitter\n\n\n\nPicks\n\nTzu-ping\n\nPython Launcher\nJoe Abercrombie author\nThe Shattered Sea Trilogy\nAnime\nPipX Standalone\n\n\nPaul\n\npipx\nBlack\nnox\ntox\nscoop\nNeil Gaiman\nGood Omens\n\nBook\nTV Series\n\n\n\n\nPradyun\n\nbecause my picks can be anything — things that have kept me sane in this lockdown world\n\nMusic: Chris Daughtry\nVideo Game: Parkitect\n\n\n\n\nTobias\n\nLanguage Server Protocol\nEmacs lsp-mode\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nPip\n\nPodcast interview with Donald Stufft\n\n\nMacdown\nTaiwan\nPipenv\nPyPI\n\nPodcast Episode\n\n\nTOML\nPython Package Metadata Standards\niBook G4\nAcorn Computer\ndistutils\neasy_install\nPython Eggs\nsetuptools\nPython Wheels\nCPAN\nConda\nInside The Cheeseshop\nGoogle Summer of Code\nZazo\nPEP517\npip-tools\nPoetry\nresolvelib\nSAT Solver\nTrove Classifiers\nPyPA\npyproject.toml\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Dependency management in Python has taken a long and winding path, which has led to the current dominance of Pip. One of the remaining shortcomings is the lack of a robust mechanism for resolving the package and version constraints that are necessary to produce a working system. Thankfully, the Python Software Foundation has funded an effort to upgrade the dependency resolution algorithm and user experience of Pip. In this episode the engineers working on these improvements, Pradyun Gedam, Tzu-Ping Chung, and Paul Moore, discuss the history of Pip, the challenges of dependency management in Python, and the benefits that surrounding projects will gain from a more robust resolution algorithm. This is an exciting development for the Python ecosystem, so listen now and then provide feedback on how the new resolver is working for you.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview about upgrading Pip's dependency resolution algorithm to improve the landscape of dependency management in Python.","date_published":"2020-05-25T19:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/7d0f6ba3-f391-4e6c-80f0-a61b94ce7dc4.mp3","mime_type":"audio/mpeg","size_in_bytes":54609941,"duration_in_seconds":4591}]},{"id":"podlove-2020-05-17t23:10:41+00:00-0e23a4b2d2b092c","title":"Easy Data Validation For Your Python Projects With Pydantic","url":"https://www.pythonpodcast.com/pydantic-data-validation-episode-263","content_text":"Summary\nOne of the most common causes of bugs is incorrect data being passed throughout your program. Pydantic is a library that provides runtime checking and validation of the information that you rely on in your code. In this episode Samuel Colvin explains why he created it, the interesting and useful ways that it can be used, and how to integrate it into your own projects. If you are tired of unhelpful errors due to bad data then listen now and try it out today.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show because you love Python and want to keep your skills up to date. Machine learning is finding its way into every aspect of software engineering. Springboard has partnered with us to help you take the next step in your career by offering a scholarship to their Machine Learning Engineering career track program. In this online, project-based course every student is paired with a Machine Learning expert who provides unlimited 1:1 mentorship support throughout the program via video conferences. You’ll build up your portfolio of machine learning projects and gain hands-on experience in writing machine learning algorithms, deploying models into production, and managing the lifecycle of a deep learning prototype. Springboard offers a job guarantee, meaning that you don’t have to pay for the program until you get a job in the space. Podcast.__init__ is exclusively offering listeners 20 scholarships of $500 to eligible applicants. It only takes 10 minutes and there’s no obligation. Go to pythonpodcast.com/springboard and apply today! Make sure to use the code AISPRINGBOARD when you enroll.\nYour host as usual is Tobias Macey and today I’m interviewing Samuel Colvin about Pydantic, a library for enforcing type hints at runtime\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what Pydantic is and what motivated you to create it?\nWhat are the main use cases that benefit from Pydantic?\nThere are a number of libraries in the Python ecosystem to handle various conventions or \"best practices\" for settings management. How does pydantic fit in that category and why might someone choose to use it over the other options?\nThere are also a number of libraries for defining data schemas or validation such as Marshmallow and Cerberus. How does Pydantic compare to the available options for those cases?\n\nWhat are some of the challenges, whether technical or conceptual, that you face in building a library to address both of these areas?\n\n\nThe 3.7 release of Python added built in support for dataclasses as a means of building containers for data with type validation. What are the tradeoffs of pydantic vs the built in dataclass functionality?\nHow much overhead does pydantic add for doing runtime validation of the modelled data?\nIn the documentation there is a nuanced point that you make about parsing vs validation and your choices as to what to support in pydantic. Why is that a necessary distinction to make?\n\nWhat are the limitations in terms of usage that you are accepting by choosing to allow for implicit conversion or potentially silent loss of precision in the parsed data?\nWhat are the benefits of punting on the strict validation of data out of the box?\n\n\nWhat has been your design philosophy for constructing the user facing API?\nHow is Pydantic implemented and how has the overall architecture evolved since you first began working on it?\n\nWhat have you found to be the most challenging aspects of building a library for managing the consistency of data structures in a dynamic language?\n\nWhat are some of the strengths and weaknesses of Python’s type system?\n\n\n\n\nWhat is the workflow for a developer who is using Pydantic in their code?\n\nWhat are some of the pitfalls or edge cases that they might run into?\n\n\nWhat is involved in integrating with other libraries/frameworks such as Django for web development or Dagster for building data pipelines?\nWhat are some of the more advanced capabilities or use cases of Pydantic that are less obvious?\nWhat are some of the features or capabilities of Pydantic that are often overlooked which you think should be used more frequently?\nWhat are some of the most interesting, innovative, or unexpected ways that you have seen Pydantic used?\nWhat are some of the most interesting, challenging, or unexpected lessons that you have learned through your work on or with Pydantic?\nWhen is Pydantic the wrong choice?\nWhat do you have planned for the future of the project?\n\nKeep In Touch\n\nsamuelcolvin on GitHub\nWebsite\nLinkedIn\n@samuel_colvin on Twitter\n\nPicks\n\nTobias\n\nDevil Sticks\n\n\nSamuel\n\nFlash Boys by Michael Lewis\nAlgorithms To Live By by Brian Christian and Tom Griffiths\nNGrok.com\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nPydantic\nMatlab\nC#\nFastAPI\n\nPodcast Episode\n\n\nMarshmallow\n\nPodcast Episode\n\n\nCerberus\n12 Factor App\nDjango\nPython Type Hints\nCython\n\nPodcast Episode\n\n\nMyPy\n\nPodcast Episode\n\n\nDuck Typing\nHaskell\nHigher Order Types\nPyCharm Pydantic Plugin\nDjango Rest Framework\nAvro\nParquet\nDagster\n\nData Engineering Podcast Episode\n\n\nStarlette\nFlask\nLudwig\nDeep Pavlov\nFast MRI\nReagent\nPynt\nOpen Source Has Failed article\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

One of the most common causes of bugs is incorrect data being passed throughout your program. Pydantic is a library that provides runtime checking and validation of the information that you rely on in your code. In this episode Samuel Colvin explains why he created it, the interesting and useful ways that it can be used, and how to integrate it into your own projects. If you are tired of unhelpful errors due to bad data then listen now and try it out today.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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\"\"

","summary":"An interview about the Pydantic project for fast and flexible data validation to reduce bugs in your Python projects with type hints.","date_published":"2020-05-18T08:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/7bdd544c-496c-459e-98db-cb11e8a2bf12.mp3","mime_type":"audio/mpeg","size_in_bytes":32653314,"duration_in_seconds":2834}]},{"id":"podlove-2020-05-10t23:28:46+00:00-9f4daea451f920b","title":"Managing Distributed Teams In The Age Of Remote Work","url":"https://www.pythonpodcast.com/sourcegraph-remote-work-episode-262","content_text":"Summary\nMore of us are working remotely than ever before, many with no prior experience with a remote work environment. In this episode Quinn Slack discusses his thoughts and experience of running Sourcegraph as a fully distributed company. He covers the lessons that he has learned in moving from partially to fully remote, the practices that have worked well in managing a distributed workforce, and the challenges that he has faced in the process. If you are struggling with your remote work situation then this conversation has some useful tips and references for further reading to help you be successful in the current environment.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou monitor your website to make sure that you’re the first to know when something goes wrong, but what about your data? Tidy Data is the DataOps monitoring platform that you’ve been missing. With real time alerts for problems in your databases, ETL pipelines, or data warehouse, and integrations with Slack, Pagerduty, and custom webhooks you can fix the errors before they become a problem. Go to pythonpodcast.com/tidydata today and get started for free with no credit card required.\nYour host as usual is Tobias Macey and today I’m interviewing Quinn Slack about his experience managing a fully remote company and useful tips for remote work\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by giving an overview of the team structure at Sourcegraph?\nYou recently moved to being fully remote. What was the motivating factor and how has it changed your personal workflow?\n\nWhat is your prior history with working remote?\n\n\nteam practices for visibility of progress\nimpact of remote teams on how code is written and organized\n\nreducing review burden by writing clearer code\n\n\nstructuring meetings when remote\npoints of friction for remote developer teams\nbenefits of being fully remote\nincentivizing documentation\ncompensation structure\n\nKeep In Touch\n\nLinkedIn\n@sqs on Twitter\nsqs on GitHub\n\nPicks\n\nTobias\n\nJoplin App\n\n\nQuinn\n\nSkunkworks by Ben Rich\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nSourcegraph\nQuinn’s Python Search Engine\nSourcegraph Employee Handbook\nGitlab\nGitlab Handbook\nZapier\nZapier Guide To Remote Work\nAutomattic\nAutomattic Blog On Distributed Work\nComments Showing Intent\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

More of us are working remotely than ever before, many with no prior experience with a remote work environment. In this episode Quinn Slack discusses his thoughts and experience of running Sourcegraph as a fully distributed company. He covers the lessons that he has learned in moving from partially to fully remote, the practices that have worked well in managing a distributed workforce, and the challenges that he has faced in the process. If you are struggling with your remote work situation then this conversation has some useful tips and references for further reading to help you be successful in the current environment.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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\"\"

","summary":"An interview with Sourcegraph's CEO about managing and growing a team of engineers in a fully remote work environment.","date_published":"2020-05-11T12:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/1b05708c-45d2-4bb9-be1e-6e71ea3f63a2.mp3","mime_type":"audio/mpeg","size_in_bytes":43172208,"duration_in_seconds":2925}]},{"id":"podlove-2020-05-03t22:07:34+00:00-0385b619f42c837","title":"Maintainable Infrastructure As Code In Pure Python With Pulumi","url":"https://www.pythonpodcast.com/pulumi-infrastructure-as-code-episode-261","content_text":"Summary\nAfter you write your application, you need a way to make it available to your users. These days, that usually means deploying it to a cloud provider, whether that’s a virtual server, a serverless platform, or a Kubernetes cluster. To manage the increasingly dynamic and flexible options for running software in production, we have turned to building infrastructure as code. Pulumi is an open source framework that lets you use your favorite language to build scalable and maintainable systems out of cloud infrastructure. In this episode Luke Hoban, CTO of Pulumi, explains how it differs from other frameworks for interacting with infrastructure platforms, the benefits of using a full programming language for treating infrastructure as code, and how you can get started with it today. If you are getting frustrated with switching contexts when working between the application you are building and the systems that it runs on, then listen now and then give Pulumi a try.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou monitor your website to make sure that you’re the first to know when something goes wrong, but what about your data? Tidy Data is the DataOps monitoring platform that you’ve been missing. With real time alerts for problems in your databases, ETL pipelines, or data warehouse, and integrations with Slack, Pagerduty, and custom webhooks you can fix the errors before they become a problem. Go to pythonpodcast.com/tidydata today and get started for free with no credit card required.\nYour host as usual is Tobias Macey and today I’m interviewing Luke Hoban about building and maintaining infrastructure as code with Pulumi\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing the concept of \"infrastructure as code\"?\nWhat is Pulumi and what is the story behind it?\n\nWhere does the name come from?\nHow does Pulumi compare to other infrastructure as code frameworks, such as Terraform?\n\n\nWhat are some of the common challenges in managing infrastructure as code?\n\nHow does use of a full programming language help in addressing those challenges?\nWhat are some of the dangers of using a full language to manage infrastructure?\n\nHow does Pulumi work to avoid those dangers?\n\n\n\n\nWhy is maintaining a record of the provisioned state of your infrastructure necessary, as opposed to relying on the state contained by the infrastructure provider?\n\nWhat are some of the design principles and constraints that developers should be considering as they architect their infrastructure with Pulumi?\n\n\nCan you describe how Pulumi is implemented?\n\nHow does Pulumi manage support for multiple languages while maintaining feature parity across them?\nHow do you manage testing and validation of the different providers?\n\n\nThe strength of any tool is largely measured in the ecosystem that exists around it, which is one of the reasons that Terraform has been so successful. How are you approaching the problem of bootstrapping the community and prioritizing platform support?\nCan you talk through the workflow of working with Pulumi to build and maintain a proper infrastructure?\nWhat are some of the ways to approach testing of infrastructure code?\n\nWhat does the CI/CD lifecycle for infrastructure look like?\n\n\nWhat are the limitations of infrastructure as code?\n\nHow do configuration management tools fit with frameworks such as Pulumi?\n\n\nThe core framework of Pulumi is open source, and your business model is focused around a managed platform for tracking state. How are you approaching governance of the project to ensure its continued viability and growth?\nWhat are some of the most interesting, innovative, or unexpected design patterns that you have seen your users include in their infrastructure projects?\nWhen is Pulumi the wrong choice?\nWhat do you have planned for the future of Pulumi?\n\nKeep In Touch\n\nLinkedIn\nlukehoban on GitHub\n@lukehoban on Twitter\n\nPicks\n\nTobias\n\nBookshelf App\n\n\nLuke\n\nGoBinaries.com\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nPulumi\nTerraform\nIronPython\nHCL == Hashicorp Config Language\nKubernetes\nTypeScript\nDevOps\nCloudFormation\nARM == Azure Resource Manager\nAWSx\nGCP == Google Cloud Platform\nPulumi SaaS\nSaltStack\n\nPodcast Episode\n\n\nAnsible\nElastic Beanstalk\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

After you write your application, you need a way to make it available to your users. These days, that usually means deploying it to a cloud provider, whether that’s a virtual server, a serverless platform, or a Kubernetes cluster. To manage the increasingly dynamic and flexible options for running software in production, we have turned to building infrastructure as code. Pulumi is an open source framework that lets you use your favorite language to build scalable and maintainable systems out of cloud infrastructure. In this episode Luke Hoban, CTO of Pulumi, explains how it differs from other frameworks for interacting with infrastructure platforms, the benefits of using a full programming language for treating infrastructure as code, and how you can get started with it today. If you are getting frustrated with switching contexts when working between the application you are building and the systems that it runs on, then listen now and then give Pulumi a try.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
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\"\"

","summary":"An interview about the open source Pulumi framework for building infrastructure as code in declarative Python to make your cloud systems easier to build and maintain.","date_published":"2020-05-04T08:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/a8778c86-c015-495e-9c12-67f42076f46e.mp3","mime_type":"audio/mpeg","size_in_bytes":48717489,"duration_in_seconds":3654}]},{"id":"podlove-2020-04-28t01:24:23+00:00-343943761d2e96e","title":"Teaching Python Machine Learning","url":"https://www.pythonpodcast.com/python-machine-learning-book-episode-260","content_text":"Summary\nPython has become a major player in the machine learning industry, with a variety of widely used frameworks. In addition to the technical resources that make it easy to build powerful models, there is also a sizable library of educational resources to help you get up to speed. Sebastian Raschka’s contribution of the Python Machine Learning book has come to be widely regarded as one of the best references for newcomers to the field. In this episode he shares his experiences as an author, his views on why Python is the right language for building machine learning applications, and the insights that he has gained from teaching and contributing to the field.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Sebastian Raschka about his experiences writing the popular Python Machine Learning book\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nHow did you get started in machine learning?\n\nWhat were the concepts that you found most difficult in your career with statistics and machine learning?\n\n\nOne of your notable contributions to the field is your book \"Python Machine Learning\". What inspired you to write the initial version?\n\nHow did you approach the challenge of striking the right balance of depth, breadth, and accessibility for the content?\nWhat was your process for determining which aspects of machine learning to include?\n\n\nYou have made 3 editions of the book from 2015 through December of 2019. In what ways has the book changed?\n\nWhat are the biggest changes to the ecosystem and approaches to ML in that timeframe?\n\n\nWhat are the fundamental challenges of developing machine learning projects that continue to present themselves?\n\nWhat new difficulties have arisen with the introduction of new technologies and the rise of deep learning?\n\n\nWhat are some of the ways that the Python language lends itself to analytical work?\n\nWhat are its shortcomings and how has the community worked around them?\nWhat do you see as the biggest risks to the popularity of Python in the data and analytics space?\n\n\nWhat are some of the common pitfalls that your readers and students face while learning about different aspects of machine learning?\nWhat are some of the industries that can benefit most from applications of machine learning?\nWhat are you most excited about in the applications or capabilities of machine learning?\n\nWhat are you most worried about?\n\n\n\nKeep In Touch\n\nWebsite\n@rasbt on Twitter\nrasbt on GitHub\nLinkedIn\n\nPicks\n\nTobias\n\nTrolls World Tour\n\n\nSebastian\n\nFFMPeg Normalize\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nPython Machine Learning (Packt)\n\nBuy On Amazon (affiliate link)\n\n\nUW Madison\nPascal\nDelphi\nR\nPerl\nBioinformatics\n\nSeq\n\nPodcast Episode\n\n\nBioPython\n\nPodcast Episode\n\n\n\n\nCodeCademy\nUdacity CS101\nAndrew Ng\nCoursera\nSupport-Vector Machine\nBayesian Statistics\nMatlab\nscikit-learn\nNumPy\nPandas\n\nPodcast Episode\n\n\nSebastian’s Blog\nPerceptron\nHeatmaps In R\nThe Hundred Page Machine Learning Book by Andriy Burkov\nImageNet\nRandom Forest\nLogistic Regression\nXGBoost\nTheano\nGenerative Adversarial Networks\nIs This Person Real / This Person Does Not Exist\nReinforcement Learning\nAlphaGo\nAlphaStar\nRay\nRLlib\nOpen AI\nGoogle DeepMind\nGoogle Colab\nCUDA\nJulia\nSebastian Raschka, Joshua Patterson, and Corey Nolet (2020). Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence. Information 2020, 11, 193\nSwift Language\nSwift for TensorFlow\nMatplotlib\nDifferential Privacy\nPrivacyNet\nYouTube recordings of Stat453: Introduction to Deep Learning and Generative Models (Spring 2020)\nffmpeg-normalize\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Python has become a major player in the machine learning industry, with a variety of widely used frameworks. In addition to the technical resources that make it easy to build powerful models, there is also a sizable library of educational resources to help you get up to speed. Sebastian Raschka’s contribution of the Python Machine Learning book has come to be widely regarded as one of the best references for newcomers to the field. In this episode he shares his experiences as an author, his views on why Python is the right language for building machine learning applications, and the insights that he has gained from teaching and contributing to the field.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview with Sebastian Raschka about his experience writing the Python Machine Learning book and keeping it up to date with changes in the industry.","date_published":"2020-04-27T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/4741316a-f593-4bda-b9c7-0bb497b8604d.mp3","mime_type":"audio/mpeg","size_in_bytes":42055127,"duration_in_seconds":2964}]},{"id":"podlove-2020-04-20t01:37:08+00:00-296b300801d719a","title":"Build The Next Generation Of Python Web Applications With FastAPI","url":"https://www.pythonpodcast.com/fastapi-web-application-framework-episode-259","content_text":"Summary\nPython has an embarrasment of riches when it comes to web frameworks, each with their own particular strengths. FastAPI is a new entrant that has been quickly gaining popularity as a performant and easy to use toolchain for building RESTful web services. In this episode Sebastián Ramirez shares the story of the frustrations that led him to create a new framework, how he put in the extra effort to make the developer experience as smooth and painless as possible, and how he embraces extensability with lightweight dependency injection and a straightforward plugin interface. If you are starting a new web application today then FastAPI should be at the top of your list.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Sebastián Ramirez about FastAPI, a framework for building production ready APIs in Python 3\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what FastAPI is?\n\nWhat are the main frustrations that you ran into with other frameworks that motivated you to create an entirely new one?\n\n\nWhat are some of the main use cases that FastAPI is designed for?\nMany web frameworks focus on managing the end-to-end functionality of a website, including the UI. Why did you focus on just API capabilities?\n\nWhat are the benefits of building an API only framework?\nIf you wanted to integrate a presentation layer, what would be involved in that effort?\n\n\nWhat API formats does FastAPI support?\n\nWhat would be involved in adding support for additional specifications such as GraphQL or JSON-LD?\n\n\nThere are a huge number of web frameworks available just in the Python ecosystem. How does FastAPI fit into that landscape and why might someone choose it over the other options?\nCan you share your design philosophy for the project?\n\nWhat are your main sources of inspiration for the framework?\nYou have also built the Typer CLI library which you refer to as the little sibling of FastAPI. How have your experiences building these two projects influenced their counterpart’s evolution?\n\n\nWhat are the benefits of incorporating type annotations into a web framework and in what ways do they manifest in its functionality?\nWhat is the workflow for a developer building a complex application in FastAPI?\nCan you describe how FastAPI itself is architected and how its design has evolved since you first began working on it?\n\nWhat are the extension points that are available for someone to build plugins for FastAPI?\n\n\nWhat are some of the challenges that you have faced in building an async framework that is leveraging the new ASGI specification?\nWhat are some sharp edges that users should keep an eye out for?\nWhat are some unique or underutilized features of FastAPI that users might not be aware of?\nWhat are some of the most interesting, unexpected, or innovative ways that you have seen FastAPI used?\nWhen is FastAPI the wrong choice?\nWhat are some of the most interesting, unexpected, or challenging lessons that you have learned in the process of building and maintaining FastAPI?\nWhat do you have planned for the future of the project?\n\nKeep In Touch\n@tiangolo on Twitter.\n@tiangolo on GitHub.\nPicks\n\nTobias\n\nOnce Upon A Time TV Show\n\n\nSebastián\n\nCloud Atlas Movie\nIsaac Asimov’s robot short stories\nPython devtools debug function\nasync compatible requests with HTTPX\nRescueTime for automatic time tracking\nJoplin for Notes\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nFastAPI\nTyper\nTyper CLI\nFastAPI Alternatives, Inspiration and Comparisons\nExplosion’s spaCy\nExplosion’s Prodigy\nStarlette\nPydantic\nUvicorn\nHypercorn\nfastapi-utils\n\nClass Based Views\n\n\nGrahQL Ariadne\nCoronavirus Tracker API\nTerminals from browser: termpair\nXPublish\nUber’s Ludwig\nNetflix Dispatch\nColombia\nBerlin Germany\nExplosion AI\nPython Type Annotations\nDjango Rest Framework\nFlask\nSwagger/OpenAPI\nSanic\nNodeJS\nJSON Schema\nOAuth2\nSwagger UI\nReDoc\nReact\nVueJS\nAngular\nREST == REpresentational State Transfer\nJSON-LD\nGo Language\nHug API framework\nClick CLI Framework\nFlask Blueprints\nTom Christie\n\nPodcast Interview\n\n\nDependency Injection\nASGI\n\nPodcast Episode\n\n\nWSGI\nThread Local Variables\nContext Vars\nOAUTH2 Scopes\nPipX\nXArray\nJAM Stack\nNextJS\nHugo\nGatsbyJS\nFastAPI Project Templates\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Python has an embarrasment of riches when it comes to web frameworks, each with their own particular strengths. FastAPI is a new entrant that has been quickly gaining popularity as a performant and easy to use toolchain for building RESTful web services. In this episode Sebastián Ramirez shares the story of the frustrations that led him to create a new framework, how he put in the extra effort to make the developer experience as smooth and painless as possible, and how he embraces extensability with lightweight dependency injection and a straightforward plugin interface. If you are starting a new web application today then FastAPI should be at the top of your list.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n

@tiangolo on Twitter.\n@tiangolo on GitHub.

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Picks

\n\n

Closing Announcements

\n\n

Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with the creator of FastAPI about his experience creating a framework to make building the new breed of web applications in Python fast, easy, and fun.","date_published":"2020-04-19T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/a9df5d59-674b-41dd-b10c-ff7b4926208d.mp3","mime_type":"audio/mpeg","size_in_bytes":48892801,"duration_in_seconds":3514}]},{"id":"podlove-2020-04-13t20:06:38+00:00-6b0e72c27dcdd67","title":"Distributed Computing In Python Made Easy With Ray","url":"https://www.pythonpodcast.com/ray-distributed-computing-episode-258","content_text":"Summary\nDistributed computing is a powerful tool for increasing the speed and performance of your applications, but it is also a complex and difficult undertaking. While performing research for his PhD, Robert Nishihara ran up against this reality. Rather than cobbling together another single purpose system, he built what ultimately became Ray to make scaling Python projects to multiple cores and across machines easy. In this episode he explains how Ray allows you to scale your code easily, how to use it in your own projects, and his ambitions to power the next wave of distributed systems at Anyscale. If you are running into scaling limitations in your Python projects for machine learning, scientific computing, or anything else, then give this a listen and then try it out!\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYour host as usual is Tobias Macey and today I’m interviewing Robert Nishihara about Ray, a framework for building and running distributed applications and machine learning\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what Ray is and how the project got started?\n\nHow did the environment of the RISE lab factor into the early design and development of Ray?\n\n\nWhat are some of the main use cases that you were initially targeting with Ray?\n\nNow that it has been publicly available for some time, what are some of the ways that it is being used which you didn’t originally anticipate?\n\n\nWhat are the limitations for the types of workloads that can be run with Ray, or any edge cases that developers should be aware of?\nFor someone who is building on top of ray, what is involved in either converting an existing application to take advantage of Ray’s parallelism, or creating a greenfield project with it?\nCan you describe how Ray itself is implemented and how it has evolved since you first began working on it?\nHow does the clustering and task distriubtion mechanism in Ray work?\nHow does the increased parallelism that Ray offers help with machine learning workloads?\n\nAre there any types of ML/AI that are easier to do in this context?\n\n\nWhat are some of the additional layers or libraries that have been built on top of the functionality of Ray?\nWhat are some of the most interesting, challenging, or complex aspects of building and maintaining Ray?\nYou and your co-founders recently announced the formation of Anyscale to support the future development of Ray. What is your business model and how are you approaching the governance of Ray and its ecosystem?\nWhat are some of the most interesting or unexpected projects that you have seen built with Ray?\nWhat are some cases where Ray is the wrong choice?\nWhat do you have planned for the future of Ray and Anyscale?\n\nKeep In Touch\n\nWebsite\n@robertnishihara on Twitter\nrobertnishihara on GitHub\n\nPicks\n\nTobias\n\nD&D Castle Ravenloft board game\nOne Deck Dungeon\n\n\nRobert\n\nThe Everything Store\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nRay\nAnyscale\nUC Berkeley\nRISELab\nMATLAB\nDeep Learning\nTheano\nTensorflow\nPyTorch\n\nPodcast Episode\n\n\nPhilip Moritz\nReinforcement Learning\nHyperparameter Tuning\nIPython Parallel\nAMPLab\nApache Spark\n\nData Engineering Podcast Episode\n\n\nActor Model\nHorovod(?)\nFlink\n\nData Engineering Podcast Episode\n\n\nSpark Streaming\nDask\n\nData Engineering Podcast Episode\n\n\ngRPC\nTune\nRust\nC++\nC\nApache Arrow\nWes McKinney\n\nPodcast Interview\n\n\nDataBricks\nMongoDB\nElastic\n\nData Engineering Podcast Episode\n\n\nConfluent\nEmbarassingly Parallel\nAnt Financial\nFlame Graph\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Distributed computing is a powerful tool for increasing the speed and performance of your applications, but it is also a complex and difficult undertaking. While performing research for his PhD, Robert Nishihara ran up against this reality. Rather than cobbling together another single purpose system, he built what ultimately became Ray to make scaling Python projects to multiple cores and across machines easy. In this episode he explains how Ray allows you to scale your code easily, how to use it in your own projects, and his ambitions to power the next wave of distributed systems at Anyscale. If you are running into scaling limitations in your Python projects for machine learning, scientific computing, or anything else, then give this a listen and then try it out!

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview about how Ray makes distributed computing in Python easy and accessible to simplify the process of scaling your machine learning workloads.","date_published":"2020-04-13T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/b214855f-add0-4f61-a08a-1baa79a44c98.mp3","mime_type":"audio/mpeg","size_in_bytes":27285776,"duration_in_seconds":2459}]},{"id":"podlove-2020-04-07t00:06:31+00:00-7f6d0bbf9215aeb","title":"Building The Seq Language For Bioinformatics","url":"https://www.pythonpodcast.com/seq-bioinformatics-language-episode-257","content_text":"Summary\nBioinformatics is a complex and computationally demanding domain. The intuitive syntax of Python and extensive set of libraries make it a great language for bioinformatics projects, but it is hampered by the need for computational efficiency. Ariya Shajii created the Seq language to bridge the divide between the performance of languages like C and C++ and the ecosystem of Python with built-in support for commonly used genomics algorithms. In this episode he describes his motivation for creating a new language, how it is implemented, and how it is being used in the life sciences. If you are interested in experimenting with sequencing data then give this a listen and then give Seq a try!\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on great conferences. And now, the events are coming to you, with no travel necessary! We have partnered with organizations such as ODSC, and Data Council. Upcoming events include the Observe 20/20 virtual conference on April 6th and ODSC East which has also gone virtual starting April 16th. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m interviewing Ariya Shajii about Seq, a programming language built for bioinformatics and inspired by Python\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what Seq is and your motivation for creating it?\n\nWhat was lacking in other languages or libraries for your use case that is made easier by creating a custom language?\nIf someone is already working in Python, possibly using BioPython, what might motivate them to consider migrating their work to Seq?\n\n\nCan you give an impression of the scope and nature of the tasks or projects that a biologist or geneticist might build with Seq?\nWhat was your process for identifying and prioritizing features and algorithms that would be beneficial to the target audience?\nFor someone using Seq can you describe their workflow and how it might differ from performing the same task in Python?\nHow is Seq implemented?\n\nWhat are some of the features that are included to simplify the work of bioinformatics?\nWhat was your process of designing the language and runtime?\nHow has the scope or direction of the project evolved since it was first conceived?\n\n\nWhat impact do you anticipate Seq having on the domain of bioinformatics and genomics?\nWhat have you found to be the most interesting, unexpected, and/or challenging aspects of building a language for this problem domain?\nWhat is in store for the future of Seq?\n\nKeep In Touch\n\narshajii on GitHub\nWebsite\n\nPicks\n\nTobias\n\nBoard Games\nLabyrinth Boardgame\nBoard Game Geek\n\n\nAriya\n\nBreakthrough documentary\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nSeq\nMIT CSAIL\nBioinformatics\nLLVM\nIntermediate Representation\nMatLab\nMoore’s Law\nBioPython\nSmith Waterman Algorithm\nHamming Distance\nPattern Matching in Functional Programming\nSIMD == Single Instruction Multiple Data\nComputational Genomics\nPhylogenetics\nSequence Read Archive public data set\nGoogle Cloud Life Sciences\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Bioinformatics is a complex and computationally demanding domain. The intuitive syntax of Python and extensive set of libraries make it a great language for bioinformatics projects, but it is hampered by the need for computational efficiency. Ariya Shajii created the Seq language to bridge the divide between the performance of languages like C and C++ and the ecosystem of Python with built-in support for commonly used genomics algorithms. In this episode he describes his motivation for creating a new language, how it is implemented, and how it is being used in the life sciences. If you are interested in experimenting with sequencing data then give this a listen and then give Seq a try!

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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\n\n

\"\"

","summary":"An interview about building the Seq language for bioinformatics, why it is necessary, and the inspiration that it draws from Python","date_published":"2020-04-06T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/2be177ba-a91b-4f16-9754-611f0e094af5.mp3","mime_type":"audio/mpeg","size_in_bytes":29846331,"duration_in_seconds":2185}]},{"id":"podlove-2020-03-30t22:43:57+00:00-7327862605b8955","title":"An Open Source Toolchain For Natural Language Processing From Explosion AI","url":"https://www.pythonpodcast.com/explosion-ai-natural-language-processing-episode-256","content_text":"Summary\nThe state of the art in natural language processing is a constantly moving target. With the rise of deep learning, previously cutting edge techniques have given way to robust language models. Through it all the team at Explosion AI have built a strong presence with the trifecta of SpaCy, Thinc, and Prodigy to support fast and flexible data labeling to feed deep learning models and performant and scalable text processing. In this episode founder and open source author Matthew Honnibal shares his experience growing a business around cutting edge open source libraries for the machine learning developent process.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on great conferences. And now, the events are coming to you, with no travel necessary! We have partnered with organizations such as ODSC, and Data Council. Upcoming events include the Observe 20/20 virtual conference on April 6th and ODSC East which has also gone virtual starting April 16th. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m interviewing Matthew Honnibal about the Thinc and Prodigy tools and an update on SpaCy\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by giving an overview of your mission at Explosion?\nWe spoke previously about your work on SpaCy. What has changed in the past 3 1/2 years?\n\nHow have recent innovations in language models such as BERT and GPT-2 influenced the direction or implementation of the project?\n\n\nWhen I last looked SpaCy only supported English and German, but you have added several new languages. What are the most challenging aspects of building the additional models?\n\nWhat would be required for supporting symbolic or right-to-left languages?\n\n\nHow has the ecosystem for language processing in Python shifted or evolved since you first introduced SpaCy?\nAnother project that you have released is Prodigy to support labelling of datasets. Can you talk through the motivation for creating it and describe the workflow for someone using it?\n\nWhat was lacking in the other annotation tools that you have worked with that you are trying to solve for in Prodigy?\n\n\nWhat are some of the most challenging or problematic aspects of labelling data sets for use in machine learning projects?\n\nWhat is a typical scale of data that can be reasonably handled by an individual or small team working with Prodigy?\n\nAt what point do you find that it makes sense to use a labeling service rather than generating the labels yourself?\n\n\n\n\nYour most recent project is Thinc for building and using deep learning models. What was the motivation for creating it and what problem does it solve in the ecosystem?\n\nHow does its design and usage compare to other deep learning frameworks such as PyTorch and Tensorflow?\nHow does it compare to projects such as Keras that abstract across those frameworks?\n\n\nHow do the SpaCy, Prodigy, and Thinc libraries work together?\nWhat are some of the biggest challenges that you are facing in building open source tools to meet the needs of data scientists and machine learning engineers?\nWhat are some of the most interesting or impressive projects that you have seen built with the tools your team is creating?\nWhat do you have planned for the future of Explosion, SpaCy, Prodigy, and Thinc?\n\nKeep In Touch\n\nLinkedIn\n@honnibal on Twitter\nhonnibal on GitHub\n\nPicks\n\nTobias\n\nOnward movie\n\n\nMatthew\n\nCoronavirus Preparedness\nRay\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nExplosion AI\nSpaCy\n\nPodcast Episode\n\n\nThinc\nProdigy\nNatural Language Processing\nPerl\nNLTK\nGPU == Graphics Processing Unit\nTPU == Tensor Processing Unit\nTransfer Learning\nAirflow\nLuigi\nPerceptron\nPyTorch\nTensorflow\nFunctional Programming\nMxNet\nKeras\nCuda\nC Language\nContinuous Integration\nBlackstone\nAllen AI Institute\nSciSpaCy\nHolmes\nSense2Vec\nFastAPI\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

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The state of the art in natural language processing is a constantly moving target. With the rise of deep learning, previously cutting edge techniques have given way to robust language models. Through it all the team at Explosion AI have built a strong presence with the trifecta of SpaCy, Thinc, and Prodigy to support fast and flexible data labeling to feed deep learning models and performant and scalable text processing. In this episode founder and open source author Matthew Honnibal shares his experience growing a business around cutting edge open source libraries for the machine learning developent process.

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Explosion AI co-founder about building a business around an open source tool chain for natural language processing","date_published":"2020-03-30T18:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/c870cb73-c1b1-45c6-b854-60fbd28fe8b2.mp3","mime_type":"audio/mpeg","size_in_bytes":43292460,"duration_in_seconds":3079}]},{"id":"podlove-2020-03-22t19:08:59+00:00-52c016ea327f27e","title":"A Flexible Open Source ERP Framework To Run Your Business","url":"https://www.pythonpodcast.com/tryton-open-source-erp-episode-255","content_text":"Summary\nRunning a successful business requires some method of organizing the information about all of the processes and activity that take place. Tryton is an open source, modular ERP framework that is built for the flexibility needed to fit your organization, rather than requiring you to model your workflows to match the software. In this episode core developers Nicolas Évrard and Cédric Krier are joined by avid user Jonathan Levy to discuss the history of the project, how it is being used, and the myriad ways that you can adapt it to suit your needs. If you are struggling to keep a consistent view of your business and ensure that all of the necessary workflows are being observed then listen now and give Tryton a try.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m interviewing Nicolas Évrard, Cédric Krier, and Jonathan Levy about Tryton\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what Tryton is and how it got started?\nWhat kinds of businesses is Tryton most suited to?\n\nWhat kinds of businesses is Tryton not a good fit for?\n\n\nWithin a business, who are the primary users of Tryton?\nCan you talk through a typical workflow for interacting with Tryton?\nWhat are some of the most complex or challenging aspects of modeling a business while maintaining a high degree of customizability?\nCan you describe how Tryton is architected and how its design has evolved since it was first started?\n\nIf you were to start over today, what would you do differently?\n\n\nThere are a number of plugins for Tryton. What kinds of functionality can be customized using the available interfaces?\n\nWhat is the process for building a custom module for Tryton?\n\n\nHow do you manage sustainability of the Tryton project?\nGiven the criticality of the Tryton platform, how do you approach ongoing stability and security of the project?\nWhat is involved in deploying and maintaining an installation of Tryton?\nWhat are some of the most interesting, innovative, or unexpected ways that you have seen Tryton used?\nWhat is in store for the future of Tryton?\n\nKeep In Touch\n\nNicolas\n\nnicoe on GitHub\n@nicoe on Twitter\n\n\nCédric\n\n@cedrickrier on Twitter\ncedk on GitHub\n\n\nJonathan\n\nLinkedIn\n\n\n\nPicks\n\nTobias\n\nAudio Books\n\nAudible free trial (Affiliate Link)\nOverdrive – ebooks and audiobooks from your local library\nPublic Domain Audiobooks\n\n\n\n\nNicolas\n\nCivilization VI\nFreeCiv\nThe 3 Body Problem\n\n\nCédric\n\nValérian and Laureline\n\n\nJonathan\n\nRoil.com\n\n\n\nLinks\n\nTryton\nB2CK\nTryton Foundation\nAdvocate Consulting Legal Group\nScheme\nLisp\nBelgium\nEuroPython Conference\nPlone\nZope\nVBA (Visual Basic for Applications)\nDjango\nOdoo\nERP == Enterprise Resource Planning\nSmall/Medium Enterprise (SME)\nGTK (Gnome ToolKit)\n3-Tier Application\nCookiecutter\nTryton Module Cookiecutter\nTryton Repository\nDocker\nGNU Health\nNereid\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

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Running a successful business requires some method of organizing the information about all of the processes and activity that take place. Tryton is an open source, modular ERP framework that is built for the flexibility needed to fit your organization, rather than requiring you to model your workflows to match the software. In this episode core developers Nicolas Évrard and Cédric Krier are joined by avid user Jonathan Levy to discuss the history of the project, how it is being used, and the myriad ways that you can adapt it to suit your needs. If you are struggling to keep a consistent view of your business and ensure that all of the necessary workflows are being observed then listen now and give Tryton a try.

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Interview

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Picks

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about the open source Tryton ERP framework and how its modular design can be adapted to fit your business needs","date_published":"2020-03-23T08:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/ba168523-8589-468e-a29f-85be40ca4f47.mp3","mime_type":"audio/mpeg","size_in_bytes":48277906,"duration_in_seconds":4053}]},{"id":"podlove-2020-03-16t15:18:10+00:00-bccb58b1c25a57c","title":"Getting A Handle On Portable C Extensions With hpy","url":"https://www.pythonpodcast.com/hpy-python-extension-episode-254","content_text":"Summary\nOne of the driving factors of Python’s success is the ability for developers to integrate with performant languages such as C and C++. The challenge is that the interface for those extensions is specific to the main implementation of the language. This contributes to difficulties in building alternative runtimes that can support important packages such as NumPy. To address this situation a team of developers are working to create the hpy project, a new interface for extension developers that is standardized and provides a uniform target for multiple runtimes. In this episode Antonio Cuni discusses the motivations for creating hpy, how it benefits the whole ecosystem, and ways to contribute to the effort. This is an exciting development that has the potential to unlock a new wave of innovation in the ways that you can run your Python code.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nAs a developer, maintaining a state of flow is key to your productivity. Don’t let something as simple as the wrong function ruin your day. Kite is the smartest completions engine available for Python, featuring a machine learning model trained by the brightest stars of GitHub. Featuring ranked suggestions sorted by relevance, offering up to full lines of code, and a programming copilot that offers up the documentation you need right when you need it. Get Kite for free today at getkite.com with integrations for top editors, including Atom, VS Code, PyCharm, Spyder, Vim, and Sublime.\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m interviewing Antonio Cuni about hpy, a project aiming to reimagine the C API for Python\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what the hpy project is and how it got started?\n\nWhat are the goals for the project?\nWho else is involved?\n\n\nHow much engagement have you had with CPython core contributors or the steering council?\nWho are the consumers of the current C API for the CPython implementation?\n\nWhat are some of the pain points or shortcomings for those consumers?\nWhat impact does that have for users of a given library that leverages C extensions?\n\n\nCan you talk through the structure of the hpy project?\n\nWhat are some of the design challenges that you are facing for determining the external API?\nWhat is involved in integrating the hpy interface into alternate runtimes such as PyPy or RustPython?\n\n\nWhat is the potential or observed performance impact for libraries that currently rely on the existing C API?\nHow has the vision and scope of this project been updated as you have gotten further along in the implementation?\nWhat are the downstream impacts that you anticipate in projects such as PyPy and Cython?\nWhat have you found to be the most challenging or contentious aspects of implementing hpy so far?\nWhat are some of the most interesting/unexpected/useful lessons that you have learned while working on hpy?\nWhat do you have planned for the near to medium term for hpy?\n\nKeep In Touch\n\nantocuni on GitHub\nWebsite\n@antocuni on Twitter\n\nPicks\n\nTobias\n\nPoetry\n\n\nAntonio\n\nCollapse: How Societies Choose To Fail Or Succeed by Jared Diamond\n\n\n\nLinks\n\nhpy\nPyPy\nAlex Martelli\n\nPodcast Interview\n\n\nPython C Extensions\nEuroPython\nVictor Stinner\nCython\n\nPodcast Episode\n\n\nArmin Rigo\nNumPy\nultrajson\nGIL == Global Interpreter Lock\nRustPython\n\nPodcast Episode\n\n\nGraalPython\nhpy-rust\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

One of the driving factors of Python’s success is the ability for developers to integrate with performant languages such as C and C++. The challenge is that the interface for those extensions is specific to the main implementation of the language. This contributes to difficulties in building alternative runtimes that can support important packages such as NumPy. To address this situation a team of developers are working to create the hpy project, a new interface for extension developers that is standardized and provides a uniform target for multiple runtimes. In this episode Antonio Cuni discusses the motivations for creating hpy, how it benefits the whole ecosystem, and ways to contribute to the effort. This is an exciting development that has the potential to unlock a new wave of innovation in the ways that you can run your Python code.

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Announcements

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Interview

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Keep In Touch

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Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about the hpy project for standardizing a portable interface for Python extensions and its potential to unlock innovation in the interpreter","date_published":"2020-03-16T19:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/9b522928-d3bb-4702-b65c-d1c8b8bc0666.mp3","mime_type":"audio/mpeg","size_in_bytes":24728652,"duration_in_seconds":2114}]},{"id":"podlove-2020-03-10t01:25:59+00:00-48f7743c0c651dd","title":"Open Source Machine Learning On Quantum Computers With Xanadu AI","url":"https://www.pythonpodcast.com/xanadu-quantum-computer-machine-learning-episode-253","content_text":"Summary\nQuantum computers promise the ability to execute calculations at speeds several orders of magnitude faster than what we are used to. Machine learning and artificial intelligence algorithms require fast computation to churn through complex data sets. At Xanadu AI they are building libraries to bring these two worlds together. In this episode Josh Izaac shares his work on the Strawberry Fields and Penny Lane projects that provide both high and low level interfaces to quantum hardware for machine learning and deep neural networks. If you are itching to get your hands on the coolest combination of technologies, then listen now and then try it out for yourself.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nAs a developer, maintaining a state of flow is key to your productivity. Don’t let something as simple as the wrong function ruin your day. Kite is the smartest completions engine available for Python, featuring a machine learning model trained by the brightest stars of GitHub. Featuring ranked suggestions sorted by relevance, offering up to full lines of code, and a programming copilot that offers up the documentation you need right when you need it. Get Kite for free today at getkite.com with integrations for top editors, including Atom, VS Code, PyCharm, Spyder, Vim, and Sublime.\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m interviewing Josh Izaac about how the work that he is doing at Xanadu AI to make it easier to build applications for quantum processors\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what you are working on at Xanadu AI?\n\nHow do the specifics of your quantum hardware influence the way in which developers need to build their algorithms? (e.g. as compared to DWave)\n\n\nWhat are some of the underlying principles that developers need to understand in order to take full advantage of the capabilities provided by quantum processors?\nCan you outline the different components and libraries that you are building to simplify the work of building machine learning/AI projects for quantum processors?\n\nWhat’s the story behind all of the Beatles references?\nHow do the different libraries fit together?\n\n\nWhat are some of the workloads and use cases that you and your customers are focused on?\nWhat are some of the most challenging aspects of designing a library that is accessible to developers while being able to take advantage of the underlying hardware?\nHow does the workflow for machine learning on quantum computers differ from what is being done in classical environments?\n\nGiven the magnitude of computational power and data processing that can be achieved in a quantum processor it seems that there is a potential for small bugs to have disproportionately large impacts. How can developers identify and mitigate potential sources of error in their algorithms?\n\n\nFor someone who is building an application or algorithm to be executed on a Xanadu processor, what does their workflow look like?\n\nWhat are some of the common errors or misconceptions that you have seen in customer code?\n\n\nCan you describe the design and implementation of the Penny Lane and Strawberry Fields libraries and how they have evolved since you first began working on them?\nWhat are some of the most ambitious or exciting use cases for quantum systems that you have seen?\nHow are you using the computational capabilities of your platform to feed back into the research and design of successive generations of hardware?\nWhat are some useful heuristics for determining whether it is worthwhile to build for a quantum processor rather than leveraging classical hardware?\nWhat are some of the most interesting/unexpected/useful lessons that you have learned while working on quantum algorithms and the libraries to support them?\nWhat is in store for the future of the Xanadu software ecosystem?\nWhat are your predictions for the near to medium term of quantum computing?\n\nKeep In Touch\n\njosh146 on GitHub\nWebsite\nLinkedIn\n\nPicks\n\nTobias\n\nKnives Out movie\n\n\nJosh\n\nBaking Sourdough Bread\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nXanadu AI\nStrawberry Fields\nPennyLane\nQuantum Physics\nASIC == Application Specific Integrated Circuit\nFPGA == Field Programmable Gate Array\nGPU == Graphics Processing Unit\nQuantum Photonics\nQubit\nTrapped Ions\nQuantum Optics\nCoherent Light\nHeisenberg’s Uncertainty Principle\nWave/Particle Duality\nContinuous Variable Quantum Computation\nNetworkX\nTensorflow\nThe Walrus\nRigetti Computing\nPyTorch\n\nPodcast Episode\n\n\nThe Walrus Operator (Assignment Expressions)\nFortran\nNumPy\nSciPy\nIPython\n\nPodcast Episode\n\n\nJax\nQuantum Machine Learning\nXanadu User Discussion Forum\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

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Quantum computers promise the ability to execute calculations at speeds several orders of magnitude faster than what we are used to. Machine learning and artificial intelligence algorithms require fast computation to churn through complex data sets. At Xanadu AI they are building libraries to bring these two worlds together. In this episode Josh Izaac shares his work on the Strawberry Fields and Penny Lane projects that provide both high and low level interfaces to quantum hardware for machine learning and deep neural networks. If you are itching to get your hands on the coolest combination of technologies, then listen now and then try it out for yourself.

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Interview

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Keep In Touch

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Picks

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Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about the open source libraries being created at Xanadu AI to power machine learning projects that run on quantum computers","date_published":"2020-03-09T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/bf8d34f2-fd40-41cf-9509-cb8592892482.mp3","mime_type":"audio/mpeg","size_in_bytes":35941296,"duration_in_seconds":3441}]},{"id":"podlove-2020-03-02t13:33:41+00:00-6a9cae1f50e6ec2","title":"The Advanced Python Task Scheduler","url":"https://www.pythonpodcast.com/apscheduler-python-task-scheduler-episode-252","content_text":"Summary\nMost long-running programs have a need for executing periodic tasks. APScheduler is a mature and open source library that provides all of the features that you need in a task scheduler. In this episode the author, Alex Grönholm, explains how it works, why he created it, and how you can use it in your own applications. He also digs into his plans for the next major release and the forces that are shaping the improved feature set. Spare yourself the pain of triggering events at just the right time and let APScheduler do it for you.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m interviewing Alex Grönholm about APScheduler, a library for scheduling tasks in your Python projects\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what APScheduler is and the main use cases that APScheduler is designed for?\n\nWhat was your movitvation for creating it?\n\n\nWhat is the workflow for integrating APScheduler into an application?\n\nIn the documentation it says not to run more than one instance of the scheduler, what are some strategies for scaling schedulers?\n\n\nWhat are some common architectures for applications that take advantage of APScheduler?\n\nWhat are some potential pitfalls that developers should be aware of?\n\n\nCan you describe how APScheduler is implemented and how its design has evolved since you first began working on it?\n\nWhat have you found to be the most complex or challenging aspects of building or using a scheduling framework?\n\n\nWhat are some of the most interesting/innovative/unexpected ways that you have seen APScheduler used?\nWhat are some of the features or capabilities that you have consciously left out?\n\nWhat design strategies or features of APScheduler are often overlooked or underappreciated?\n\n\nWhat are some of the most useful or interesting lessons that you have learned while building and maintaining APScheduler?\nWhen is APScheduler the wrong choice for managing task execution?\nWhat do you have planned for the future of the project?\n\nKeep In Touch\n\nagronholm on GitHub\n\nPicks\n\nTobias\n\nThe Data Exchange Podcast\n\n\nAlex\n\nTenacity\n\n\n\nLinks\n\nAPScheduler\nPHP\nJava\nECMAScript\nCelery\nERP == Enterprise Resource Planning\nCron Daemon\nRPyC\nZookeeper\n\nData Engineering Podcast Episode\n\n\nRethinkDB\nDaylight Saving Time\nFalsehoods Programmers Believe About Time\nPyTZ\nCelery Beats\nAsphalt Framework\n\nPodcast Episode\n\n\nAnyIO\nTwisted\n\nPodcast Episode\n\n\nPy2EXE\nPyInstaller\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Most long-running programs have a need for executing periodic tasks. APScheduler is a mature and open source library that provides all of the features that you need in a task scheduler. In this episode the author, Alex Grönholm, explains how it works, why he created it, and how you can use it in your own applications. He also digs into his plans for the next major release and the forces that are shaping the improved feature set. Spare yourself the pain of triggering events at just the right time and let APScheduler do it for you.

\n

Announcements

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Interview

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Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with the creator of APScheduler about how and why he built an open source task scheduler for Python projects that don't need distributed execution","date_published":"2020-03-02T08:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/e5b66271-7561-42e6-b21f-9d767e0e005b.mp3","mime_type":"audio/mpeg","size_in_bytes":19642507,"duration_in_seconds":1995}]},{"id":"podlove-2020-02-25t00:20:11+00:00-74568739c95c627","title":"Reducing The Friction Of Embedded Software Development With PlatformIO","url":"https://www.pythonpodcast.com/platformio-embedded-software-devleopment-episode-251","content_text":"Summary\nEmbedded software development is a challenging endeavor due to a fragmented ecosystem of tools. Ivan Kravets experienced the pain of programming for different hardware platforms when embroiled in a home automation project. As a result he built the PlatformIO ecosystem to reduce the friction encountered by engineers working with multiple microcontroller architectures. In this episode he describes the complexities associated with targeting multiple platforms, the tools that PlatformIO offers to simplify the workflow, and how it fits into the development process. If you are feeling the pain of working with different editing environments and build toolchains for various microcontroller vendors then give this interview a listen and then try it out for yourself.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m interviewing Ivan Kravets about PlatformIO, an open source ecosystem for IoT development including a cross-platform IDE, unified debugger, remote unit testing, and firmware updates.\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what PlatformIO is?\n\nWhat was your motivation for creating it?\nWhat are the aspects of embedded development that keep you interested and engaged in this space?\n\n\nWhat are some of the types of projects that someone might use PlatformIO to build?\nWhat are some of the common challenges that a developer might encounter when working on embedded systems?\n\nWhat are the additional complexities that get introduced as more hardware targets get added to a project?\n\n\nWhat is the workflow for someone using PlatformIO for embedded systems development?\nWhat are the different elements of PlatformIO and how do they simplify the work of building embedded systems projects?\nHow is PlatformIO implemented and how has the system design evolved since you first began working on it?\n\nWhat was your reason for selecting Python as the implementation language?\nIf you were to start over today what would you do differently?\n\n\nHow has the embedded hardware and software landscape changed since you first started work on PlatformIO?\n\nHow has that impacted your product direction?\n\n\nHow do developers handle testing and validation of their applications?\nHow does PlatformIO help with updating deployed devices with new firmware?\nWhat have been some of the most interesting/unexpected/innovative projects that you have seen built with PlatformIO?\nWhat have been some of the most interesting/unexpected/challenging aspects of building and maintaining PlatformIO?\nHow are you approaching sustainability of the project and business?\nWhat do you have planned for the future of PlatformIO?\n\nKeep In Touch\n\nLinkedIn\nWebsite\nivankravets on GitHub\n@ikravets on Twitter\n\nPicks\n\nTobias\n\nUMass Amherst Making Electricity From Thin Air\n\n\nIvan\n\nDon’t focus on the money side of your project, just focus on building a great product.\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nPlatformIO\nUkraine\nHome Automation\nHome Assistant\n\nPodcast Episode\n\n\nTwisted\n\nPodcast Episode\n\n\nZigbee Radio\nSerial I/O\nRS-232\nARM CPU Architecture\nRISC-V\nAVR Microcontrollers\nArduino\nTexas Instruments Launchpad\nEclipse IDE\nMCU == MicroController Unit\nVSCode\n\nPlatformIO Extension\n\n\nSCons\nMake\nRaspberry Pi\nESP8266\nMarlin 3D Printer Firmware\nESP Home\nZephyr Realtime Operating System\nWestern Digital\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Embedded software development is a challenging endeavor due to a fragmented ecosystem of tools. Ivan Kravets experienced the pain of programming for different hardware platforms when embroiled in a home automation project. As a result he built the PlatformIO ecosystem to reduce the friction encountered by engineers working with multiple microcontroller architectures. In this episode he describes the complexities associated with targeting multiple platforms, the tools that PlatformIO offers to simplify the workflow, and how it fits into the development process. If you are feeling the pain of working with different editing environments and build toolchains for various microcontroller vendors then give this interview a listen and then try it out for yourself.

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Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about the PlatformIO ecosystem and how it helps to reduce the pain of building embedded software applications that target multiple hardware vendors","date_published":"2020-02-24T22:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/fd4c4b56-1a29-4ccf-8b1a-0755d0950310.mp3","mime_type":"audio/mpeg","size_in_bytes":34582162,"duration_in_seconds":2809}]},{"id":"podlove-2020-02-18t14:12:27+00:00-492b748600b576b","title":"APIs, Sustainable Open Source and The Async Web With Tom Christie","url":"https://www.pythonpodcast.com/apis-sustainable-open-source-and-the-async-web-with-tom-christie","content_text":"Summary\nTom Christie is probably best known as the creator of Django REST Framework, but his contributions to the state the web in Python extend well beyond that. In this episode he shares his story of getting involved in web development, his work on various projects to power the asynchronous web in Python, and his efforts to make his open source contributions sustainable. This was an excellent conversation about the state of asynchronous frameworks for Python and the challenges of making a career out of open source.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m interviewing Tom Christie about the Encode organization and the work he is doing to drive the state of the art in async for Python\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what the Encode organization is and how it came to be?\n\nWhat are some of the other approaches to funding and sustainability that you have tried in the past?\nWhat are the benefits to the developers provided by an organization which you were unable to achieve through those other means?\nWhat benefits are realized by your sponsors as compared to other funding arrangements?\n\n\nWhat projects are part of the Encode organization?\nHow do you determine fund allocation for projects and participants in the organization?\nWhat is the process for becoming a member of the Encode organization and what benefits and responsibilities does that entail?\nA large number of the projects that are part of the organization are focused on various aspects of asynchronous programming in Python. Is that intentional, or just an accident of your own focus and network?\nFor those who are familiar with Python web programming in the context of WSGI, what are some of the practices that they need to unlearn in an async world, and what are some new capabilities that they should be aware of?\nBeyond Encode and your recent work on projects such as Starlette you are also well known as the creator of Django Rest Framework. How has your experience building and growing that project influenced your current focus on a technical, community, and professional level?\nNow that Python 2 is officially unsupported and asynchronous capabilities are part of the core language, what future directions do you foresee for the community and ecosystem?\n\nWhat are some areas of potential focus that you think are worth more attention and energy?\n\n\nWhat do you have planned for the future of Encode, your own projects, and your overall engagement with the Python ecosystem?\n\nKeep In Touch\n\nWebsite\ntomchristie on Github\n@_tomchristie on Twitter\n\nPicks\n\nTobias\n\nMaleficent: Mistress of Evil\nAbominable\n\n\nTom\n\nThe Lobster\nThe Master And His Emissary by Ian McGilchrist\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nEncode\nDjango Rest Framework\nStarlette\nZope\nDjango\nDjango Piston\nDjango Tastypie\nAndrew Godwin\nASGI\nDjango Channels\n\nPodcast Episode\n\n\nFlask\nPyramid\nSentry\n\nPodcast Episode\n\n\nTidelift\nUvicorn\nHTTPX\nTidelift\nOpen Collective\nStripe\nGithub Sponsors\nPython Software Foundation\n\nPodcast Episode\n\n\nFirebase\nDatabases\nORM\nHTTP3\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Tom Christie is probably best known as the creator of Django REST Framework, but his contributions to the state the web in Python extend well beyond that. In this episode he shares his story of getting involved in web development, his work on various projects to power the asynchronous web in Python, and his efforts to make his open source contributions sustainable. This was an excellent conversation about the state of asynchronous frameworks for Python and the challenges of making a career out of open source.

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Announcements

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Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Tom Christie about his experiences as an open source maintainer of API and asynchronous web frameworks in the Python ecosystem","date_published":"2020-02-18T09:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/6aad4141-c380-4071-9393-f99194ffcfed.mp3","mime_type":"audio/mpeg","size_in_bytes":32695291,"duration_in_seconds":2625}]},{"id":"podlove-2020-02-09t19:31:52+00:00-0ef66cd999f7d30","title":"Learning To Program Python By Building Video Games With Arcade","url":"https://www.pythonpodcast.com/arcade-python-video-games-episode-249","content_text":"Summary\nVideo games have been a vehicle for learning to program since the early days of computing. Continuing in that tradition, Paul Craven created the Arcade library as a modern alternative to PyGame for use in his classroom. In this episode he explains his motivations for starting a new framework for video game development, his view on the benefits of games in computer education, and how his students and the broader community are using it to build interesting and creative projects. If you are looking for a way to get new programmers engaged, or just want to experiment with building your own games, then this is the conversation for you. Give it a listen and then give Arcade a try for yourself.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m interviewing Paul Craven about Arcade, an easy-to-learn Python library for creating 2D video games\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what Arcade is?\n\nWhat inspired you to begin working on it?\n\n\nWho is your primary audience?\nAs an educator, what have you found to be most effective about using games as a vehicle for teaching programming?\n\nWhat elements of programming or computer science do you have difficulty in addressing within the context of a video game?\nFor someone who wants to move on from working on games to something like web development or data analytics, what elements of software design and structure are easily translated to other domains?\n\n\nCan you describe how Arcade is implemented and how the architecture has evolved since you first began working on it?\n\nIf you were to start over today, what would you do differently?\n\n\nWhat have you found to be the most interesting/unexpected/challenging aspects of building and maintaining Arcade?\nWhat are some of the most interesting/innovative/unexpected ways that you have seen Arcade used?\nWhen is Arcade the wrong platform, or at what point does someone need to move on from Arcade?\nWhat do you have planned for the future of Arcade?\n\nKeep In Touch\n\n@professorcraven on Twitter\npvcraven on GitHub\nFaculty Page\n\nPicks\n\nTobias\n\nOri And The Blind Forest\n\n\nPaul\n\nFahrenheit 451 by Ray Bradbury\n\n“Mistakes can be profited by Man, when i was young I showed my ignorance in people’s faces. They beat me with sticks. By the time I was forty my blunt instrument had been honed to a fine cutting point for me. If you hide your ignorance, no one will hit you and you’ll never learn.”\n\n\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nArcade\nSimpson College\nPyGame\nSDL\nOpenGL\nUnity\nUnreal Engine\nGoDot\nAutomate The Boring Stuff With Python\nMinesweeper\nPyglet\nSpatial Hashing\nTiled Map Editor\nPython Type Hints\nF Strings\nData Classes\nPyMunk\nFFMPEG\nPyWeek\n\nPodcast Episode\n\n\nPython Discord\nArcade Enhancement Requests\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Video games have been a vehicle for learning to program since the early days of computing. Continuing in that tradition, Paul Craven created the Arcade library as a modern alternative to PyGame for use in his classroom. In this episode he explains his motivations for starting a new framework for video game development, his view on the benefits of games in computer education, and how his students and the broader community are using it to build interesting and creative projects. If you are looking for a way to get new programmers engaged, or just want to experiment with building your own games, then this is the conversation for you. Give it a listen and then give Arcade a try for yourself.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about how the Arcade library is being used to teach computer science principles through building video games in Python","date_published":"2020-02-10T22:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/dc7d84e2-89e3-484e-a2c3-198f010af46d.mp3","mime_type":"audio/mpeg","size_in_bytes":30487345,"duration_in_seconds":2502}]},{"id":"podlove-2020-02-03t19:06:25+00:00-a80ad4f33d21990","title":"Build Your Own Personal Data Repository With Nostalgia","url":"https://www.pythonpodcast.com/nostalgia-personal-data-repository-episode-248","content_text":"Summary\nThe companies that we entrust our personal data to are using that information to gain extensive insights into our lives and habits while not always making those findings accessible to us. Pascal van Kooten decided that he wanted to have the same capabilities to mine his personal data, so he created the Nostalgia project to integrate his various data sources and query across them. In this episode he shares his motivation for creating the project, how he is using it in his day-to-day, and how he is planning to evolve it in the future. If you’re interested in learning more about yourself and your habits using the personal data that you share with the various services you use then listen now to learn more.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m interviewing Pascal van Kooten about his nostalgia project, a nascent framework for taking control of your personal data\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing your mission with the nostalgia project?\n\nHow did the topic of personal data management come to be a focus for you?\n\n\nWhat other options exist for users to be able to collect and manage their own data?\n\nWhat capabilities were lacking in those options that made you feel the need to build Nostalgia?\n\n\nWhat is your target audience for this set of projects?\nHow are you using Nostalgia in your own life?\n\nWhat are some of the insights that you have been able to gain as a result of integrating your data with Nostalgia?\n\n\nCan you describe the current architecture of the Nostalgia platform and how it has evolved since you began work on it?\n\nWhat are some of the assumptions that you are using to direct the focus of your development and interaction design?\n\n\nWhat are the minimum number of data sources needed to make this useful?\nWhat are some of the challenges that you are facing in collating and integrating different data sources?\nWhat are some of the drawbacks of using something like Nostalgia for managing your personal data?\nWhat are some of the most interesting/challenging/unexpected aspects of your work on Nostalgia so far?\nWhat do you have planned for the future of the project?\n\nKeep In Touch\n\nWebsite\nLinkedIn\n@kootenpv on Twitter\nkootenpv on GitHub\n\nPicks\n\nTobias\n\nJumanji: The Next Level\nJumanji\n\n\nPascal\n\nBup\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\ntimeliner\nqs_ledger\nNostalgia\nShrynk\nWhereami\nR Language\nDuck Duck Go\nCaddy\nPerkeep\nDark Programming Language\nPandas\n\nPodcast Episode\n\n\nNeo4J\nPandas Extension Arrays\n\nPodcast Episode\n\n\nParquet\n\nData Engineering Podcast Episode\n\n\nElectronJS\nZincbase\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

The companies that we entrust our personal data to are using that information to gain extensive insights into our lives and habits while not always making those findings accessible to us. Pascal van Kooten decided that he wanted to have the same capabilities to mine his personal data, so he created the Nostalgia project to integrate his various data sources and query across them. In this episode he shares his motivation for creating the project, how he is using it in his day-to-day, and how he is planning to evolve it in the future. If you’re interested in learning more about yourself and your habits using the personal data that you share with the various services you use then listen now to learn more.

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Announcements

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Interview

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Keep In Touch

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Picks

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Closing Announcements

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about how the Nostalgia project makes it possible to collect and integrate your personal data across sources and services to gain insights into your digital habits","date_published":"2020-02-03T21:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/6af54318-8fd8-4081-8222-e5526beaeb13.mp3","mime_type":"audio/mpeg","size_in_bytes":25319848,"duration_in_seconds":1977}]},{"id":"podlove-2020-01-27t02:35:16+00:00-ca9086b177f837d","title":"Simplifying Social Login For Your Web Applications","url":"https://www.pythonpodcast.com/python-social-auth-social-login-episode-247","content_text":"Summary\nA standard feature in most modern web applications is the ability to log in or register using accounts that you already own on other sites such as Google, Facebook, or Twitter. Building your own integrations for each service can be complex and time consuming, distracting you from the features that you and your users actually care about. Fortunately the Python social auth library makes it easy to support third party authentication with a large and growing number of services with minimal effort. In this episode Matías Aguirre discusses his motivation for creating the library, how he has designed it to allow for flexibility and ease of use, and the benefits of delegating identity and authentication to third parties rather than managing passwords yourself.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m interviewing Matías Aguirre about Python social auth and the complexities of third-party authentication\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what the Python social auth project is and your motivation for starting it?\nWhy might someone want to integrate with or rely on a third-party identity provider in their projects?\n\nWhat are some of the tradeoffs or drawbacks of implementing\n\n\nCan you describe the current architecture of the library and how it has evolved since you first began working on it?\nThere are a number of pre-built integrations with different web frameworks in the social auth github organization, but Django is the only one that has seen any commits recently. What are the contributing factors for that state of affairs?\nThere are a number of authentication protocols that you support. What are the common capabilities that they each support and what are some of the more challenging differences between them?\n\nHow have you implemented the interface for plugging different authentication mechanisms to allow for the variation between them while keeping the library code maintainable?\nWhat is involved in adding support for a new authentication provider or protocol?\n\n\nMany times authorization and authentication are conflated or used interchangeably. How does Python social auth address those concerns and what are the limitations of different mechanisms for defining permissions?\nFor someone who is using Python social auth, what is the workflow for integrating it with their application as a consumer?\nWhat are some of the most interesting/unexpected/innovative ways that you have seen Python social auth used?\nWhat are some of the most interesting/useful/unexpected lessons that you have learned in the process of building and maintaining Python social auth?\nWhen is Python social auth more effort than it’s worth?\nWhat do you have planned for the future of the project?\n\nKeep In Touch\n\nomab on GitHub\nWebsite\n@linuxaddict on Twitter\nLinkedIn\n\nPicks\n\nTobias\n\nJoker movie\n\n\nMatías\n\nSanic asynchronous web framework\nStar Trek Picard TV series\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nPython Social Auth\nUruguay\nDjango\nRuby on Rails\nMonkeyLearn\nSocial Authentication\nDjango Social Auth\nSalted and hashed passwords\nMagic Link Authentication\nOAuth\nOpenID\nSAML\nFastAPI\nSanic\nASGI\nWSGI\nAsyncIO\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

A standard feature in most modern web applications is the ability to log in or register using accounts that you already own on other sites such as Google, Facebook, or Twitter. Building your own integrations for each service can be complex and time consuming, distracting you from the features that you and your users actually care about. Fortunately the Python social auth library makes it easy to support third party authentication with a large and growing number of services with minimal effort. In this episode Matías Aguirre discusses his motivation for creating the library, how he has designed it to allow for flexibility and ease of use, and the benefits of delegating identity and authentication to third parties rather than managing passwords yourself.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about the Python social auth library and how it can be used to simplify implementing social login for your web applications","date_published":"2020-01-26T22:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/98fd88eb-bd7e-4285-ac37-07ef534ed772.mp3","mime_type":"audio/mpeg","size_in_bytes":27396974,"duration_in_seconds":2045}]},{"id":"podlove-2020-01-20t15:27:35+00:00-c404e96240616a2","title":"Building A Business On Building Data Driven Businesses","url":"https://www.pythonpodcast.com/redash-data-driven-dashboards-episode-246","content_text":"Summary\nIn order for an organization to be data driven they need easy access to their data and a simple way of sharing it. Arik Fraimovich built Redash as a way to address that need by connecting to any data source and building attractive dashboards on top of them. In this episode he shares the origin story of the project, his experiences running a business based on open source, and the challenges of working with data effectively.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m interviewing Arik Fraimovich about Redash, an open source business intelligence platform that helps you make sense of your data.\n\nInterview\n\n\nIntroductions\n\n\nHow did you get introduced to Python?\n\n\nCan you start by describing what Redash is and its origin story?\n\n\nWhat are the primary ways that it is used?\n\n\nThe business intelligence market is quite mature and has many commercial and open source projects to choose from. What are the aspects of Redash that have allowed you to be successful?\n\n\nWhat would you consider to be your closest competitors?\n\n\n\n\nWhat was your background with data before starting on Redash?\n\nWhat are some of the most notable lessons that you have learned about business intelligence since starting the project?\nHow has the landscape for business intelligence and data analysis changed since you began the project?\n\n\n\nBeyond just accessing data, Redash focuses on enabling visualization of the results. What types of visualizations do you support and how do you support users in choosing the most effective ways to represent the information?\n\n\nWhat are some of the common challenges that your users and customers encounter when communicating with data?\n\n\nOne of the critical aspects of enabling data access in an organization is the ability to collaborate on asking and answering questions. How do you approach that challenge in Redash?\n\n\nHow is Redash implemented and how has the overall design and architecture evolved since you first started working on it?\n\nHow do you manage the complexity of supporting so many different data sources?\nIf you were to start over today, what would you do differently?\n\n\n\nBeyond the code of Redash, you also have a business around providing it as a hosted service. What are some of the most interesting, challenging, or unexpected lessons that you have learned in the process of building and growing that service?\n\n\nHow do you approach the direction and governance of the open source project and balance that against the wants and needs of the community?\n\n\nWhat are some of the most interesting, innovative, or unexpected ways that you have seen Redash used?\n\n\nWhen is Redash the wrong platform to use?\n\n\nWhat do you have planned for the future of the Redash business and project?\n\n\nKeep In Touch\n\narikfr on GitHub\nWebsite\n@arikfr on Twitter\n\nPicks\n\nTobias\n\nData Engineering Podcast\n\n\nArik\n\nPeewee ORM\nAmazon ECS\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nRedash\nGoogle App Engine\nEverythingMe\nRedShift\nMetabase\n\nData Engineering Podcast Interview\n\n\nApache Superset\nElasticsearch\n\nData Engineering Podcast Interview\n\n\nTableau\nLooker\n\nData Engineering Podcast Interview\n\n\nPowerBI\nData Warehouse\nData Lake\nAthena\nSpark\n\nData Engineering Podcast Interview\n\n\nRedash Funnel Visualization\nStephen Few\nFlask\nSQLAlchemy\nRedis\nPostgreSQL\n\nData Engineering Podcast Interview\n\n\nCelery\nRQ\nTornado\nDjango ORM\nAngularJS\nReactJS\nNodeJS\nRedash Query Results Data Source\nIBM DB2\nRetool\nForest Admin\nGrafana\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

In order for an organization to be data driven they need easy access to their data and a simple way of sharing it. Arik Fraimovich built Redash as a way to address that need by connecting to any data source and building attractive dashboards on top of them. In this episode he shares the origin story of the project, his experiences running a business based on open source, and the challenges of working with data effectively.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about building the open source Redash project and the business around it, and how having a flexible data dashboarding tool helps build data driven organizations","date_published":"2020-01-20T11:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/8b6512d5-69da-4c6c-8e58-6aeee2bffb1f.mp3","mime_type":"audio/mpeg","size_in_bytes":25865939,"duration_in_seconds":2486}]},{"id":"podlove-2020-01-13t21:23:25+00:00-dd35d00d73e1afc","title":"Using Deliberate Practice To Level Up Your Python","url":"https://www.pythonpodcast.com/reuven-lerner-deliberate-practice-episode-245","content_text":"Summary\nAn effective strategy for teaching and learning is to rely on well structured exercises and collaboration for practicing the material. In this episode long time Python trainer Reuven Lerner reflects on the lessons that he has learned in the 5 years since his first appearance on the show, how his teaching has evolved, and the ways that he has incorporated more hands-on experiences into his lessons. This was a great conversation about the benefits of being deliberate in your approach to ongoing education in the field of technology, as well as having some helpful references for ways to keep your own skills sharp.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m pleased to welcome back Reuven Lerner to talk about the benefits of deliberate practice for learning and improving programming skills\n\nInterview\n\n\nIntroductions\n\n\nHow did you get introduced to Python?\n\n\nIn your first appearance on the show back in episode 2 we talked about your experience as a Python trainer. How has your teaching style evolved in the past 5 years?\n\nHow has the focus and scope of your training changed in that time period?\n\n\n\nWhat have you found to be some of the most helpful and effective tactics in your training?\n\n\nFrom the learner perspective, what are some strategies that you recommend for retaining information, particularly in the context of gaining technical knowledge?\n\n\nIn-person training vs. real-time online training vs. recorded videos, advantages and disadvantages of each.\n\n\nBlended learning, in which we combine aspects of the above\n\nBeyond in-person training, what are your preferred methods for learning and maintaining new skills?\n\n\n\nWhat is deliberate practice and how does it differ from the habits that many of us might default to?\n\nWhat are some of the resources that you provide for students of your trainings for practicing?\nWhat are some of the outside resources which you have found most useful or effective?\n\n\n\nKeep In Touch\n\nWebsite\nBlog\n@reuvenmlerner on Twitter\n\nPicks\n\nTobias\n\nThe Manager’s Path by Camille Fournier\n\n\nReuven\n\nLab Rats: How Silicon Valley Made Work Miserable For The Rest Of Us by Dan Lyons\n\n\n\nLinks\n\nDeliberate Practice\nReuven On Episode 2\nCGI == Common Gateway Interface\nLanguage Phrasebook\nJupyter Notebook\nWalrus Operator\n\nPyCon 2019 Presentation\n\n\nPython Bytes\nList Comprehension\nWeekly Python Exercise\nPython Morsels\nPyBites\nPractice Your Python\nPython Workout book by Reuven Lerner\nPyTest\nBrian Okken\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

An effective strategy for teaching and learning is to rely on well structured exercises and collaboration for practicing the material. In this episode long time Python trainer Reuven Lerner reflects on the lessons that he has learned in the 5 years since his first appearance on the show, how his teaching has evolved, and the ways that he has incorporated more hands-on experiences into his lessons. This was a great conversation about the benefits of being deliberate in your approach to ongoing education in the field of technology, as well as having some helpful references for ways to keep your own skills sharp.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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\"\"

","summary":"An interview with Reuven Lerner about how he uses deliberate practice in his Python training to help his students learn more effectively","date_published":"2020-01-13T16:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/bc93252e-4755-49ee-bf9a-5d5f1fea624b.mp3","mime_type":"audio/mpeg","size_in_bytes":40436960,"duration_in_seconds":2919}]},{"id":"podlove-2020-01-06t03:06:43+00:00-0c5681c577ffdc9","title":"Checking Up On Python's Role in DevOps","url":"https://www.pythonpodcast.com/devops-in-python-episode-244","content_text":"Summary\nPython has been part of the standard toolkit for systems administrators since it was created. In recent years there has been a shift in how servers are deployed and managed, and how code gets released due to the rise of cloud computing and the accompanying DevOps movement. The increased need for automation and speed of iteration has been a perfect use case for Python, cementing its position as a powerful tool for operations. In this episode Moshe Zadka reflects on his experiences using Python in a DevOps context and the book that he wrote on the subject. He also discusses the difference in what aspects of the language are useful as an introduction for system operators and where they can continue their learning.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m interviewing Moshe Zadke about his recent book DevOps In Python\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nHow did you gain experience in managing systems with Python?\nWhat is DevOps?\nWhat makes Python a good fit for managing systems?\nWhat is unique to the devops/sysadmin domain in terms of what software is used and what aspects of the language are useful?\nWhat are the main ways that Python is used for managing servers and infrastructure?\nWhat are some of the most notable changes in the ways that Python is used for server administration over the past several years?\nHow has Python3 impacted the lives of operators?\nWhat was your motivation for writing a book about Python focused specifically on DevOps and server automation?\nWhat are some of the tools that have been replaced in your own workflow over the years?\n\nKeep In Touch\n\nWebsite\nLinkedIn\n@moshezadka on Twitter\n\nPicks\n\nTobias\n\nSaltStack\n\nPodcast Episode\n\n\n\n\nMoshe\n\nAutomat\n\nPodcast Episode\n\n\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nDevOps In Python\nSurveyMonkey\nTwisted Episode\nDevOps\nB=hive\nCI/CD\nAmoeba OS\nPython OS module\nRequests\nCanary Deployments\nPost Mortem\nBash Shell\nZ Shell\nLinux\nUnix\nAWS\nBoto3\nGitHub\nGitLab\nDebian\nUbuntu\nCentOS\nPip\nPoetry\nPipenv\npip-tools\ndh-virtualenv\nDocker\nHyneck Schlaweck Presentation On Building Docker Images\nAnsible\nSaltStack\nChef\nPuppet\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Python has been part of the standard toolkit for systems administrators since it was created. In recent years there has been a shift in how servers are deployed and managed, and how code gets released due to the rise of cloud computing and the accompanying DevOps movement. The increased need for automation and speed of iteration has been a perfect use case for Python, cementing its position as a powerful tool for operations. In this episode Moshe Zadka reflects on his experiences using Python in a DevOps context and the book that he wrote on the subject. He also discusses the difference in what aspects of the language are useful as an introduction for system operators and where they can continue their learning.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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\n\n

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","summary":"An interview with Moshe Zadka about writing an introductory Python book focused on DevOps engineers and the myriad ways that it is used in modern technical operations","date_published":"2020-01-05T22:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/36aef357-7492-464d-8bf2-ee23fa0fb59b.mp3","mime_type":"audio/mpeg","size_in_bytes":25432815,"duration_in_seconds":2015}]},{"id":"podlove-2019-12-23t18:59:08+00:00-dd44ba106d31153","title":"Python's Built In IDE Isn't Just Sitting IDLE","url":"https://www.pythonpodcast.com/idle-python-ide","content_text":"Summary\nOne of the first challenges that new programmers are faced with is figuring out what editing environment to use. For the past 20 years, Python has had an easy answer to that question in the form of IDLE. In this episode Tal Einat helps us explore its history, the ways it is used, how it was built, and what is in store for its future. Even if you have never used the IDLE editor yourself, it is still an important piece of Python’s strength and history, and this conversation helps to highlight why that is.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m interviewing Tal Einat about the IDLE editor for Python, it’s history, and what is in store for its future\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nFor anyone who hasn’t used it, can you start by explaining what IDLE is?\nIDLE has been part of the standard library for Python for quite some time now. What was the motivation for adding it to the core of Python?\n\nHow has the evolution of our computing environment changed the motivation for maintaining IDLE and the use cases that it is most beneficial for?\n\n\nWhat are the benefits of including a basic editor in the default distribution of Python?\n\nWhat are some of the ways in which it is often used?\nWhat are the limiting factors that lead users to other IDEs or text editors?\n\n\nWhat role do you think IDLE has played in the growth of Python?\nWhat was your motivation for getting involved as a Python contributor and working on the implementation of IDLE?\nHow is IDLE implemented and what are some of the ways that it has evolved since its initial introduction?\n\nHow well has the code for IDLE aged as new features and capabilities are added to the language?\n\n\nWhat are some of the integration points available for extending IDLE?\nWhat are some of the most interesting or innovative ways that you have seen IDLE used and extended?\nWhat is planned for the future of the IDLE module?\n\nKeep In Touch\n\nLinkedIn\n@TalEinat on Twitter\ntaleinat on GitHub\n\nPicks\n\nTobias\n\nMr. Robot\n\n\nTal\n\nCaptain Fantastic\nThe Lesson To Unlearn article by Paul Graham\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nIDLE\nFullProof\nIsrael\n\nMandatory Military Service\n\n\nEric Idle\nMonty Python\nVisual Studio\nIDLE-fork\nVi\nEmacs\nSublime Text\nVisual Studio Code\nREPL == Read Eval Print Loop\nTcl/Tk\nTkinter\nRPC == Remote Procedure Call\nIDLEx\nVPython\n\nPodcast Episode\n\n\nPython Turtle\nSVN (Subversion)\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

One of the first challenges that new programmers are faced with is figuring out what editing environment to use. For the past 20 years, Python has had an easy answer to that question in the form of IDLE. In this episode Tal Einat helps us explore its history, the ways it is used, how it was built, and what is in store for its future. Even if you have never used the IDLE editor yourself, it is still an important piece of Python’s strength and history, and this conversation helps to highlight why that is.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
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","summary":"An episode about the IDLE package built into Python and how it reduces the friction associated with learning to program by having an easy to use IDE","date_published":"2019-12-23T18:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/858e52b4-2b63-46cd-930c-feb9e16fce13.mp3","mime_type":"audio/mpeg","size_in_bytes":28479747,"duration_in_seconds":2193}]},{"id":"podlove-2019-12-16t14:44:27+00:00-ac6e0e4a0a60ecb","title":"Riding The Rising Tides Of Python","url":"https://www.pythonpodcast.com/pete-fein-episode-242","content_text":"Summary\nThe past two decades have seen massive growth in the language, community, and ecosystem of Python. The career of Pete Fein has occurred during that same period and his use of the language has paralleled some of the major shifts in focus that have occurred. In this episode he shares his experiences moving from a trader writing scripts, through the rise of the web, to the current renaissance in data. He also discusses how his engagement with the community has evolved, why he hasn’t needed to use any other languages in his career, and what he is keeping an eye on for the future.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m interviewing Pete Fein about his voyage on the rising tide of Python\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nI understand that you have used Python exclusively in your professional life. What other languages have you been exposed to and taken inspiration from?\nWhat are some of the projects that you have been involved with which you are most proud of?\nHow has the community and your involvement with it changed over the years?\n\nIn your experience, how has the growth in the size and breadth of the community impacted its accessibility to newcomers?\n\n\nYou have been using Python and participating in the community for quite some time now, and there have been significant changes in both within that period. What are some of the most significant technological shifts that you have noticed and been a part of?\n\nHow have those shifts influenced the direction of your career?\n\n\nAs you have moved through the different phases of your career with different areas of focus, what are some of the aspects of the work which have remained constant?\n\nWhat have been the biggest differences across the different problem domains?\n\n\nWhat are some of the aspects of the language or its ecosystem which you feel are lacking or don’t get enough attention?\nWhat are some of the industry trends which you are keeping a close eye on and how do you anticipate them influencing the direction of the community and your career in the upcoming years?\n\nKeep In Touch\n\nConsulting Website\nPersonal Website\n@wearpants on Twitter\nLinkedIn\nwearpants on GitHub\n\nPicks\n\nTobias\n\nMatomo Analytics\n\n\nPete\n\nFastAPI\nPyDantic\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nChicago\nScheme\nStructure and Interpretation of Computer Programs\nDavid Beazley\n\nPodcast Episode\n\n\nTwiggy logging library\nJesse Noller\nLog4J\nDebian\nRedHat\nStructLog\nElliot\n\nPodcast Episode\n\n\nLogbook\nArmin Ronacher\n\nPodcast Episode\n\n\nPittsburgh Python Meetup\nBoltons library\nElixir\nChiPy Chicago Python user group\nSubversion\nRuby On Rails\nDjango\nData Engineering\nData Engineering Podcast\nInternet of Things\nPittsburgh\nArtificial Pancreas Project\nEric Holscher\nRead The Docs\n\nPodcast Episode\n\n\nCircuit Playground Express\nCircuitPython\n\nPodcast Episode\n\n\nRust Language\nPyOhio\nPyGotham\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

The past two decades have seen massive growth in the language, community, and ecosystem of Python. The career of Pete Fein has occurred during that same period and his use of the language has paralleled some of the major shifts in focus that have occurred. In this episode he shares his experiences moving from a trader writing scripts, through the rise of the web, to the current renaissance in data. He also discusses how his engagement with the community has evolved, why he hasn’t needed to use any other languages in his career, and what he is keeping an eye on for the future.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview with Pete Fein about his career in Python and how it has evolved alongside the shifting focus of the community","date_published":"2019-12-16T09:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/146e9939-5d3c-4d12-a394-85263910f0d6.mp3","mime_type":"audio/mpeg","size_in_bytes":29611265,"duration_in_seconds":2656}]},{"id":"podlove-2019-12-09t01:34:04+00:00-09fa9ccb16cee29","title":"Debugging Python Projects With PySnooper","url":"https://www.pythonpodcast.com/pysnooper-python-debugging-episode-241","content_text":"Summary\nDebugging is a painful but necessary practice in software development. The tools that are available in Python range from the built-in debugger, to tools integrated with your coding environment, to the trusty print function. In this episode Ram Rachum describes his work on PySnooper and how it can be used to speed up your problem solving in complex or legacy applications.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, or running your build servers, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media and the Python Software Foundation. Upcoming events include the Software Architecture Conference in NYC and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m interviewing Ram Rachum about PySnooper, an alternative approach to debugging your python projects\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nHow do developers normally debug their code, and what need does PySnooper address that isn’t addressed by the established methods?\nWhat is the workflow for using PySnooper for investigating or debugging a project? (This will probably be answered in the answer to the question above)\nWhat are some of the pieces of information that it surfaces and how do they aid the developer in directing their investigation?\nWhat were some of the projects that you were testing it with and how did they influence the direction that you took PySnooper?\nCan you describe how PySnooper is implemented and some of the ways that it has evolved since you first began working on it?\nWhat are some of the initial goals that you had for the project which you have since abandoned as either not useful or too challenging to implement?\nWhat are some of the edge cases or technical challenges that you have encountered while working on PySnooper, either in Python itself or in the tool?\nThere is another project called Snoop which builds on top of your work on PySnooper to add some extra functionality and developer ergonomics. What, if anything, was your reaction to it and how has it influenced your work on PySnooper?\nOne of the notable aspects of your work on PySnooper is the amount of attention that it garnered shortly after you published it. How has that visibility affected the long-term popularity and use of PySnooper?\nWhat have been some of the most interesting, unexpected, or difficult aspects of creating, maintaining, and promoting PySnooper?\nWhat do you have planned for the future of the project?\n\nKeep In Touch\n\ncool-RR on GitHub\nPersonal Website\nConsulting Website\n\nPicks\n\nTobias\n\nPyCon US\n\nCall for proposals\nRegistration\n\n\n\n\nRam\nNonviolent communication\n\nLinks\n\n\nPySnooper\n\n\nRam’s Python workshops\n\n\nThe PyWeb-IL meetup\n\n\nBlueVine’s career page Submit your CV to Ram’s email mailto:ram@rachum.com\n\n\nTel Aviv Israel\n\n\nPaul Graham\n\n\nY Combinator startup accelerator\n\n\nWing IDE\n\n\nPyCharm\n\n\nsys.settrace\n\n\nPython f_trace\n\n\ncoverage.py\n\nPodcast.init Interview\n\n\n\nPEP == Python Enhancement Proposal\n\nPodcast Episode\n\n\n\nsnoop project\n\n\nAlex Hall\n\n\npdb\n\n\npudb\n\n\npdb++\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n\n\n","content_html":"

Summary

\n

Debugging is a painful but necessary practice in software development. The tools that are available in Python range from the built-in debugger, to tools integrated with your coding environment, to the trusty print function. In this episode Ram Rachum describes his work on PySnooper and how it can be used to speed up your problem solving in complex or legacy applications.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n
\n\n

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","summary":"An interview about building PySnooper to simplify debugging complex and legacy python projects","date_published":"2019-12-08T20:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/c30d3f4a-6f20-499d-8132-9c23221d6557.mp3","mime_type":"audio/mpeg","size_in_bytes":26918863,"duration_in_seconds":2730}]},{"id":"podlove-2019-12-03t03:22:15+00:00-2dfc066de54f8eb","title":"Making Complex Software Fun And Flexible With Plugin Oriented Programming","url":"https://www.pythonpodcast.com/plugin-oriented-programming-episode-240","content_text":"Summary\nStarting a new project is always exciting because the scope is easy to understand and adding new features is fun and easy. As it grows, the rate of change slows down and the amount of communication necessary to introduce new engineers to the code increases along with the complexity. Thomas Hatch, CTO and creator of SaltStack, didn’t want to accept that as an inevitable fact of software, so he created a new paradigm and a proof-of-concept framework to experiment with it. In this episode he shares his thoughts and findings on the topic of plugin oriented programming as a way to build and scale complex projects while keeping them fun and flexible.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m interviewing Thomas Hatch about his work on the POP library and how he is using plugin oriented programming in his work at SaltStack\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by giving your definition of Plugin Oriented Programming and your thoughts on what benefits it provides?\nYou created the POP library as a framework for enabling developers to incorporate this pattern into their own projects. What capabilities does that framework provide and what was your motivation for creating it?\n\nHow has your work on Salt influenced your thinking on how to implement plugins for software projects?\nHow does POP fit into the future of the SaltStack project?\n\n\nWhat are some of the advanced patterns or paradigms that the POP model allows for?\nCan you describe how the POP library itself is implemented and some of the ways that its design has evolved since you first began experimenting with it?\n\nWhat are some of the languages or libraries that you have looked at for inspiration in your design and philosophy around this development pattern?\n\n\nFor someone who is building a project on top of POP what does their workflow look like and what are some of the up-front design considerations they should be thinking of?\nHow do you define and validate the contract exposed by or expected from a plugin subsystem?\nOne of the interesting capabilities that you highlight in the documentation is the concept of merging applications. What are your thoughts on the challenges that an engineer might face when merging library or microservice applications built with POP into a single deployable artifact?\n\nWhat would be involved in going the other direction to split a single application into independently runnable microservices?\n\n\nWhen extracting common functionality from a group of existing applications, what are the relative merits of creating a plugin sybsystem vs writing a library?\nHow does the system design of a POP application impact the available range of communication patterns for software and the teams building it?\nWhat are some antipatterns that you anticipate for teams building their projects on top of POP?\nIn the documentation you mention that POP is just an example implementation of the broader pattern and that you hope to see other languages and developer communities adopt it. What are some of the barriers to adoption that you foresee?\nWhat are some of the limitations of POP or cases where you would recommend against following this paradigm?\nWhat are some of the most interesting, innovative, or unexpected ways that you have seen POP used?\nWhat have been some of the most interesting, unexpected, or challenging aspects of building POP?\nWhat do you have planned for the future of the POP library, or any applications where you plan to employ this pattern?\n\nKeep In Touch\n\nthatch45 on GitHub\n@thatch45 on Twitter\n\nPicks\n\nTobias\n\nThe Man In The High Castle TV series\n\n\nThomas\n\nJack Ryan TV Series\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nEpisode 1\nPOP\nSaltStack\nRuby\nMicroservices\nLinus Torvalds\nSaltConf\nSaltStack Thorium\nSalt Beacons\nSalt Reactors\nSalt Grains\nIdem\nAsyncIO\nNim\nOCaml\nJulia\nLLVM\nObject Oriented Programming\nGo Language\nRust\nRBAC == Role Based Access Control\nThe Mythical Man Month\nLinux Kernel\nHeist\nUmbra\nFlow Programming\nMagic The Gathering\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Starting a new project is always exciting because the scope is easy to understand and adding new features is fun and easy. As it grows, the rate of change slows down and the amount of communication necessary to introduce new engineers to the code increases along with the complexity. Thomas Hatch, CTO and creator of SaltStack, didn’t want to accept that as an inevitable fact of software, so he created a new paradigm and a proof-of-concept framework to experiment with it. In this episode he shares his thoughts and findings on the topic of plugin oriented programming as a way to build and scale complex projects while keeping them fun and flexible.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
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","summary":"An interview about plugin oriented programming as a paradigm to make development of large and complex software more fun and flexible","date_published":"2019-12-02T22:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/df0121c2-b71c-4fd7-901a-436259dde05e.mp3","mime_type":"audio/mpeg","size_in_bytes":52781804,"duration_in_seconds":3757}]},{"id":"podlove-2019-11-26t12:28:20+00:00-f9be2ee22fd8694","title":"Faster And Safer Software Development With Feature Flags","url":"https://www.pythonpodcast.com/feature-flags-episode-239","content_text":"Summary\nAny software project that is worked on or used by multiple people will inevitably reach a point where certain capabilities need to be turned on or off. In this episode Pete Hodgson shares his experience and insight into when, how, and why to use feature flags in your projects as a way to enable that practice. In addition to the simple on and off controls for certain logic paths, feature toggles also allow for more advanced patterns such as canary releases and A/B testing. This episode has something useful for anyone who works on software in any language.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m interviewing Pete Hodgson about the concept of feature flags and how they can benefit your development workflow\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what a feature flag is?\n\nWhat was your first experience with feature flags and how did it affect your approach to software development?\n\n\nWhat are some of the ways that feature flags are used?\n\nWhat are some antipatterns that you have seen for teams using feature flags?\n\n\nWhat are some of the alternative development practices that teams will employ to achieve the same or similar outcomes to what is possible with feature flags?\nCan you describe some of the different approaches to implementing feature flags in an application?\n\nWhat are some of the common pitfalls or edge cases that teams run into when building an in-house solution?\nWhat are some useful considerations when making a build vs. buy decision for a feature toggling service?\n\n\nWhat are some of the complexities that get introduced by feature flags for mantaining application code over the long run?\nWhat have you found to be useful or effective strategies for cataloging and documenting feature toggles in an application, particularly if they are long lived or for open source applications where there is no institutional context?\nCan you describe some of the lifecycle considerations for feature flags, and how the design, implementation, or use of them changes for short-lived vs long-lived use cases?\nWhat are some cases where the overhead of implementing and maintaining a feature flag infrastructure outweighs the potential benefit?\nWhat advice or references do you recommend for anyone who is interested in using feature flags for their own work?\n\nKeep In Touch\n\nWebsite\n@ph1 on Twitter\nmoredip on GitHub\n\nPicks\n\nTobias\n\nCircuit Playground Express\n\nCircuitPython Episode\n\n\n\n\nPete\n\nAccelerate by Nicole Forsgren, Jez Humble, and Gene Kim\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nPerl\nRuby\nDjango\nFeature Flag\nPete’s Blog Post On Feature Flags\nThoughtworks\nContinuous Delivery\nContinuous Delivery Book\nTrunk Based Development\nBranch By Abstraction\nTechnical Debt\nStrategy Pattern\nPolymorphism\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Any software project that is worked on or used by multiple people will inevitably reach a point where certain capabilities need to be turned on or off. In this episode Pete Hodgson shares his experience and insight into when, how, and why to use feature flags in your projects as a way to enable that practice. In addition to the simple on and off controls for certain logic paths, feature toggles also allow for more advanced patterns such as canary releases and A/B testing. This episode has something useful for anyone who works on software in any language.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview about how using feature flags in your projects allows for faster development and safer releases","date_published":"2019-11-26T07:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/f6e28ac2-21a8-4cbf-ac6d-792c09bb84f5.mp3","mime_type":"audio/mpeg","size_in_bytes":48938831,"duration_in_seconds":3688}]},{"id":"podlove-2019-11-18t21:33:12+00:00-4abc8106437a358","title":"From Simple Script To Beautiful Web Application With Streamlit","url":"https://www.pythonpodcast.com/streamlit-web-application-episode-238","content_text":"Summary\nBuilding well designed and easy to use web applications requires a significant amount of knowledge and experience across a range of domains. This can act as an impediment to engineers who primarily work in so-called back-end technologies such as machine learning and systems administration. In this episode Adrien Treuille describes how the Streamlit framework empowers anyone who is comfortable writing Python scripts to create beautiful applications to share their work and make it accessible to their colleagues and customers. If you have ever struggled with hacking together a simple web application to make a useful script self-service then give this episode a listen and then go experiment with how Streamlit can level up your work.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nHaving all of your logs and event data in one place makes your life easier when something breaks, unless that something is your Elastic Search cluster because it’s storing too much data. CHAOSSEARCH frees you from having to worry about data retention, unexpected failures, and expanding operating costs. They give you a fully managed service to search and analyze all of your logs in S3, entirely under your control, all for half the cost of running your own Elastic Search cluster or using a hosted platform. Try it out for yourself at pythonpodcast.com/chaossearch and don’t forget to thank them for supporting the show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m interviewing Adrien Treuille about Streamlit, an open source app framework built for machine learning and data science teams\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what Streamlit is and its origin story?\nWhat are some of the types of applications that are commonly built by data teams and who are the typical consumers of those projects?\nWhat are some of the challenges or complications that are unique to this problem space?\nWhat are some of the complications or challenges that you have faced to integrate Streamlit with so many different machine learning frameworks?\nCan you describe the technical implementation of Streamlit and how it has evolved since you began working on it?\n\nHow did you approach the design of the API and development workflow to tailor it for the needs and capabilities of machine learning engineers?\nIf you were to start the project from scratch today what would you do differently?\n\n\nWhat is a typical workflow for someone working on a machine learning application and how does Streamlit fit in?\n\nWhat are some of the types of tools or processes that it replaces?\n\n\nWhat are some of the most interesting or unexpected ways that you have seen Streamlit used?\nWhat have you found to be some of the most challenging or unexpected aspects of building and evolving Streamlit?\nHow do you see Python evolving in light of Streamlit and other work in the machine learning space?\nWhat do you have in store for the future of Streamlit or any adjacent products and services?\nHow are you approaching the governance and sustainability of the Streamlit open source project?\n\nKeep In Touch\n\nWebsite\nLinkedIn\n@myelbows on Twitter\ntreuille on GitHub\n\nPicks\n\nTobias\n\nThe Book Of Why by Judea Pearl\n\n\nAdrien\n\nNo Self, No Problem by Anam Thubten\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nStreamlit\n\nForum\nGitHub\nTwitter\n\n\nCarnegie Mellon University\nGoogle X\nZoox\nIBM\nCornell University\nNumPy\nSciPy\nMachine Learning Engineer\nJupyter\nDeckGL\nMatplotlib\nPlotly\nSeaborn\nAltair\nPyTorch\nTensorflow\nProtocol Buffers\nStreamlit for teams\nHeroku\nEC2\nReact JS\nAwesome Streamlit\nFlask\nPlotly Dash\nVoila\nNeurIPS\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Building well designed and easy to use web applications requires a significant amount of knowledge and experience across a range of domains. This can act as an impediment to engineers who primarily work in so-called back-end technologies such as machine learning and systems administration. In this episode Adrien Treuille describes how the Streamlit framework empowers anyone who is comfortable writing Python scripts to create beautiful applications to share their work and make it accessible to their colleagues and customers. If you have ever struggled with hacking together a simple web application to make a useful script self-service then give this episode a listen and then go experiment with how Streamlit can level up your work.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview about how the Streamlit framework simplifies the work of creating intuitive and attractive web applications out of Python scripts","date_published":"2019-11-18T16:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/154dda85-a267-4753-a6ce-a3e67201152a.mp3","mime_type":"audio/mpeg","size_in_bytes":38165818,"duration_in_seconds":2941}]},{"id":"podlove-2019-11-11t22:47:44+00:00-53de695c05f52e8","title":"Automate Your Server Security With GrapheneX","url":"https://www.pythonpodcast.com/graphenex-server-security-episode-237","content_text":"Summary\nThe internet is rife with bots and bad actors trying to compromise your servers. To counteract these threats it is necessary to diligently harden your systems to improve server security. Unfortunately, the hardening process can be complex or confusing. In this week’s episode 18 year old Orhun Parmaksiz shares the story of how he and his friends created the GrapheneX framework to simplify the process of securing and maintaining your servers using the power and flexibility of Python. If you run your own software then this is definitely worth a listen.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nHaving all of your logs and event data in one place makes your life easier when something breaks, unless that something is your Elastic Search cluster because it’s storing too much data. CHAOSSEARCH frees you from having to worry about data retention, unexpected failures, and expanding operating costs. They give you a fully managed service to search and analyze all of your logs in S3, entirely under your control, all for half the cost of running your own Elastic Search cluster or using a hosted platform. Try it out for yourself at pythonpodcast.com/chaossearch and don’t forget to thank them for supporting the show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m interviewing Orhun Parmaksiz about GrapheneX, a framework for simplifying the process of hardening your servers\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what we mean when we talk about hardening of servers?\nWhat are the common ways of hardening a system, which techniques can we use for this purpose?\nWhat are some of the high level categories of threats that operators should be considering?\nWhat is GrapheneX and what was your motivation for creating it?\n\nHow does GrapheneX aid users in the process of increasing the security of their infrastructure?\nIs any extra operating system knowledge required for using GrapheneX?\n\n\nCan you talk through the workflow for someone using GrapheneX to harden their systems?\n\nWhat options does it support for managing deployment across a fleet of servers?\n\n\nSome security controls can actually prevent proper operation of the applications and services that are deployed on a server. How do you approach preventing those scenarios or educating the users in determining which controls are appropriate?\nWhy did you choose Python for a project like GrapheneX?\nHow is GrapheneX implemented?\n\nHow has the design evolved since you first began working on it?\nIf you were to start the project over today, what would you do differently?\n\n\nDo you accept contributions to the framework? If so, what kind of contributions are needed for improving GrapheneX?\nFor someone who is interested in adding a new module to the framework, what is involved?\nWhat have you found to be the most interesting or challenging aspects of your work on GrapheneX?\nWhat, if any, aspects of server security have you consciously avoided implementing in GrapheneX?\nWhat are your future plans about the GrapheneX?\n\nKeep In Touch\n\nOrhun\n\nGitHub\nTwitter\nLinkedIn\n\n\n\nPicks\n\nTobias\n\nChess\n\n\nOrhun\n\nCreeping in My Soul by Cryoshell\nGravity Hurts by Cryoshell\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nGrapheneX\n\nGitHub\nWebsite\nPyPI\nTwitter\nTrello\n\n\nGraphene\nNew Modules for GNU/Linux & Windows (Issue)\nFlask\n\nFlask-SocketIO\n\n\nReact\ntrimstray/linux-hardening-checklist\nThe Windows Server Hardening Checklist\nFirewall\n\nWindows Firewall\nLinux iptables\n\n\nPCI-DSS 2.2 requirement- server hardening standards\nCIS Benchmarks\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

The internet is rife with bots and bad actors trying to compromise your servers. To counteract these threats it is necessary to diligently harden your systems to improve server security. Unfortunately, the hardening process can be complex or confusing. In this week’s episode 18 year old Orhun Parmaksiz shares the story of how he and his friends created the GrapheneX framework to simplify the process of securing and maintaining your servers using the power and flexibility of Python. If you run your own software then this is definitely worth a listen.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview about the GrapheneX framework and how it helps to automate your server security with hardening best practices","date_published":"2019-11-11T17:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/e201a94f-a052-4ffe-87d9-bdb227758f58.mp3","mime_type":"audio/mpeg","size_in_bytes":25338239,"duration_in_seconds":2141}]},{"id":"podlove-2019-11-03t12:26:35+00:00-67f8a15f7c6eb4f","title":"Accelerating The Adoption Of Python At Wayfair","url":"https://www.pythonpodcast.com/python-adoption-wayfair-episode-236","content_text":"Summary\nLarge companies often have a variety of programming languages and technologies being used across departments to keep the business running. Python has been gaining ground in these environments because of its flexibility, ease of use, and developer productivity. In order to accelerate the rate of adoption at Wayfair this week’s guest Jonathan Biddle started a team to work with other engineering groups on their projects and show them how best to take advantage of the benefits of Python. In this episode he explains their operating model, shares their success stories, and provides advice on the pitfalls to avoid if you want to follow in his footsteps. This is definitely worth a listen if you are using Python in your work or would like to aid in its adoption.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nHaving all of your logs and event data in one place makes your life easier when something breaks, unless that something is your Elastic Search cluster because it’s storing too much data. CHAOSSEARCH frees you from having to worry about data retention, unexpected failures, and expanding operating costs. They give you a fully managed service to search and analyze all of your logs in S3, entirely under your control, all for half the cost of running your own Elastic Search cluster or using a hosted platform. Try it out for yourself at pythonpodcast.com/chaossearch and don’t forget to thank them for supporting the show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m interviewing Jonathan Biddle about his work to encourage and empower Wayfair engineers in their use of Python\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing the mission statement for you and your team at Wayfair?\n\nWhat is the origin story for how your group got started?\n\nHow and where was Python being used within Wayfair at the time?\n\n\n\n\nWhat are the primary languages that are used throughout Wayfair?\n\nWhat is involved in the selection process for a language and technology stack for new projects within Wayfair?\n\n\nCan you describe how and why you work with different groups throughout Wayfair?\nWhat are some of the common misconceptions or barriers that you encounter when working with other engineering and product teams about how and where Python will be useful?\nHow large is your team currently and what is the length of a typical engagement?\n\nHow has the scale and scope of your work changed since your group was first formed?\n\n\nHow many different product teams have you worked with at this point and what are some of the notable outcomes?\nWhat are some of the most challenging aspects, both technical and organizational, of educating other engineers on when and how to use Python?\nCan you share some examples of engagements that you would classify as a failure?\n\nWhat lessons have you learned from those situations?\n\n\nWhat advice do you have for other groups or organizations who may be considering or actively launching similar initiatives?\n\nKeep In Touch\n\nWebsite\nLinkedIn\n@jonbiddle on Twitter\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nPicks\n\nTobias\n\nLearning Bayesian Statistics Podcast\n\n\nJonathan\n\nPyDantic\nFastAPI\nMKDocs\n\n\n\nLinks\n\nWayfair\nZope\nDjango\nPHP\nJava\nJavascript\n.NET\nKafka\nJack Diederich – Stop Writing Classes\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Large companies often have a variety of programming languages and technologies being used across departments to keep the business running. Python has been gaining ground in these environments because of its flexibility, ease of use, and developer productivity. In order to accelerate the rate of adoption at Wayfair this week’s guest Jonathan Biddle started a team to work with other engineering groups on their projects and show them how best to take advantage of the benefits of Python. In this episode he explains their operating model, shares their success stories, and provides advice on the pitfalls to avoid if you want to follow in his footsteps. This is definitely worth a listen if you are using Python in your work or would like to aid in its adoption.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Closing Announcements

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
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\"\"

","summary":"An interview about how the auxiliary engineering team at Wayfair is championing the use of Python across the organization","date_published":"2019-11-03T08:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/1dcbbc6d-b03e-4fe0-9da1-a1808aaf0440.mp3","mime_type":"audio/mpeg","size_in_bytes":32210277,"duration_in_seconds":2522}]},{"id":"podlove-2019-10-28t21:01:05+00:00-8abb21f229f27e3","title":"Building Quantum Computing Algorithms In Python","url":"https://www.pythonpodcast.com/ocean-sdk-quantum-computing-episode-235","content_text":"Summary\nQuantum computers are the biggest jump forward in processing power that the industry has seen in decades. As part of this revolution it is necessary to change our approach to algorithm design. D-Wave is one of the companies who are pushing the boundaries in quantum processing and they have created a Python SDK for experimenting with quantum algorithms. In this episode Alexander Condello explains what is involved in designing and implementing these algorithms, how the Ocean SDK helps you in that endeavor, and what types of problems are well suited to this approach.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m interviewing Alex Condello about the Ocean SDK from D-Wave for building quantum algorithms in Python\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by giving a high-level overview of quantum computing?\nWhat is the Ocean SDK and how does it fit into the business model for D-Wave?\nWhat are some of the problem types that a quantum processor is uniquely well suited for?\n\nHow does the overall system design for a quantum computer compare to that of the Von Neumann architecture that is common for the machines that we are all familiar with?\n\n\nWhat are some of the differences in algorithm design when programming for a quantum processor?\n\nIs there any specialized background knowledge that is necessary for making effective use of the QPU’s capabilities?\nWhat are some of the common difficulties that you have seen users struggle with?\nHow does the Ocean SDK assist the developer in implementing and understanding the patterns necessary for Quantum algorithms?\n\n\nWhat was the motivation for choosing Python as the target language for an SDK to attract developers to experiment with quantum algorithms?\nCan you describe how the SDK is implemented and some of the integrations that are necessary for being able to operate on a quantum processor?\n\nWhat have you found to be some of the most interesting, challenging, or unexpected aspects of your work on the Ocean software stack?\nHow do you handle the abstraction of the execution context to allow for replicating the program behavior on CPU/GPU vs QPU\n\n\nIs there any potential for quantum computing to impact research in previously intractable computer science research, such as the P vs NP problem?\nWhat are your current scaling limits in terms of providing compute to customers for their problems?\nWhat are some of the most interesting, innovative, or unexpected ways that you have seen developers use the Ocean SDK and quantum processors?\nWhat are you most excited for as you look to the future capabilities of quantum systems?\n\nWhat are some of the upcoming challenges that you anticipate for the quantum computing industry?\n\n\n\nKeep In Touch\n\narcondello on GitHub\n\nPicks\n\nTobias\n\nQuTip Podcast Interview\n\n\nAlex\n\nCython\n\nPodcast Interview\n\n\n\n\n\nLinks\n\nOcean SDK\nD-Wave\nQuantum Computing\nQuantum Annealing\nQuantum Superposition\nQubit\nD-Wave Leap\nVon Neumann Architecture\nCuda\nLinear Programming\nD-Wave ML Papers\nD-Wave NetworkX\nMaximum Cut Problem\nIsing Problem\nLos Alamos National Laboratory\nVertex Cover Problem\nD-Wave Hybrid\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

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Quantum computers are the biggest jump forward in processing power that the industry has seen in decades. As part of this revolution it is necessary to change our approach to algorithm design. D-Wave is one of the companies who are pushing the boundaries in quantum processing and they have created a Python SDK for experimenting with quantum algorithms. In this episode Alexander Condello explains what is involved in designing and implementing these algorithms, how the Ocean SDK helps you in that endeavor, and what types of problems are well suited to this approach.

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about the Ocean SDK from D-Wave and how it is used to write and test algorithms for quantum computing processors","date_published":"2019-10-28T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/63ee3311-b599-4ac7-864c-eb3c7137d25d.mp3","mime_type":"audio/mpeg","size_in_bytes":28460082,"duration_in_seconds":2174}]},{"id":"podlove-2019-10-21t02:22:58+00:00-b0b8ab1000c8973","title":"Illustrating The Landscape And Applications Of Deep Learning","url":"https://www.pythonpodcast.com/deep-learning-illustrated-episode-234","content_text":"Summary\nDeep learning is a phrase that is used more often as it continues to transform the standard approach to artificial intelligence and machine learning projects. Despite its ubiquity, it is often difficult to get a firm understanding of how it works and how it can be applied to a particular problem. In this episode Jon Krohn, author of Deep Learning Illustrated, shares the general concepts and useful applications of this technique, as well as sharing some of his practical experience in using it for his work. This is definitely a helpful episode for getting a better comprehension of the field of deep learning and when to reach for it in your own projects.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m interviewing Jon Krohn about his recent book, deep learning illustrated\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by giving a brief description of what we’re talking about when we say deep learning and how you got involved with the field?\n\nHow does your background in neuroscience factor into your work on designing and building deep learning models?\n\n\nWhat are some of the ways that you leverage deep learning techniques in your work?\nWhat was your motivation for writing a book on the subject?\n\nHow did the idea of including illustrations come about and what benefit do they provide as compared to other books on this topic?\n\n\nWhile planning the contents of the book what was your thought process for determining the appropriate level of depth to cover?\n\nHow would you characterize the target audience and what level of familiarity and proficiency in employing deep learning do you wish them to have at the end of the book?\n\n\nHow did you determine what to include and what to leave out of the book?\n\nThe sequencing of the book follows a useful progression from general background to specific uses and problem domains. What were some of the biggest challenges in determining which domains to highlight and how deep in each subtopic to go?\n\n\nBecause of the continually evolving nature of the field of deep learning and the associated tools, how have you guarded against obsolescence in the content and structure of the book?\n\nWhich libraries did you focus on for your examples and what was your selection process?\n\nNow that it is published, is there anything that you would have done differently?\n\n\n\n\nOne of the critiques of deep learning is that the models are generally single purpose. How much flexibility and code reuse is possible when trying to repurpose one model pipeline for a slightly different dataset or use case?\n\nI understand that deployment and maintenance of models in production environments is also difficult. What has been your experience in that regard, and what recommendations do you have for practitioners to reduce their complexity?\n\n\nWhat is involved in actually creating and using a deep learning model?\n\nCan you go over the different types of neurons and the decision making that is required when selecting the network topology?\n\n\nIn terms of the actual development process, what are some useful practices for organizing the code and data that goes into a model, given the need for iterative experimentation to achieve desired levels of accuracy?\nWhat is your personal workflow when building and testing a new model for a new use case?\nWhat are some of the limitations of deep learning and cases where you would recommend against using it?\nWhat are you most excited for in the field of deep learning and its applications?\n\nWhat are you most concerned by?\n\n\nDo you have any parting words or closing advice for listeners and potential readers?\n\nKeep In Touch\n\nWebsite\n@jonkrohnlearns on Twitter\njonkrohn on GitHub\n\nPicks\n\nTobias\n\nSpurious Correlations\n\n\nJon\n\nData Elixir Newsletter\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nUntapt\nDeep Learning Illustrated\nPearson\nColumbia University\nNew York City Data Science Academy\nNIH (National Institutes of Health)\nOxford Uniersity\nMatlab\nR Language\nNeuroscience\nArtificial Neural Network\nDeep Learning\nNatural Language Processing\nComputer Vision\nGenerative Adversarial Networks\nDeep Learning by Ian Goodfellow, et al.\nHands On Machine Learning by Aurélien Géron\nO’Reilly Online Learning\nTransfer Learning\nKeras\nTensorflow\nPyTorch\nGary Marcus\nJudea Pearl\nArtificial General Intelligence\nExplainable AI\nYuval Noah Harrari\n\nSapiens\nHome Deus\n\n\nWait But Why?\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

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Deep learning is a phrase that is used more often as it continues to transform the standard approach to artificial intelligence and machine learning projects. Despite its ubiquity, it is often difficult to get a firm understanding of how it works and how it can be applied to a particular problem. In this episode Jon Krohn, author of Deep Learning Illustrated, shares the general concepts and useful applications of this technique, as well as sharing some of his practical experience in using it for his work. This is definitely a helpful episode for getting a better comprehension of the field of deep learning and when to reach for it in your own projects.

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Announcements

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Interview

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Keep In Touch

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Picks

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Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with author Jon Krohn about his experience using and writing about deep learning for real world applications","date_published":"2019-10-21T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/eb797720-8419-478f-ae06-29fafa728da0.mp3","mime_type":"audio/mpeg","size_in_bytes":38960275,"duration_in_seconds":3381}]},{"id":"podlove-2019-10-14t23:09:20+00:00-b2f3b95ba6a91a6","title":"Andrew's Adventures In Coderland","url":"https://www.pythonpodcast.com/adventures-in-coderland-episode-233","content_text":"Summary\nSoftware development is a unique profession in many ways, and it has given rise to its own subculture due to the unique sets of challenges that face developers. Andrew Smith is an author who is working on a book to share his experiences learning to program, and understand the impact that software is having on our world. In this episode he shares his thoughts on programmer culture, his experiences with Python and other language communities, and how learning to code has changed his views on the world. It was interesting getting an anthropological perspective from a relative newcomer to the world of software.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, Data Council in Barcelona, and the Data Orchestration Summit. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m interviewing Andrew Smith about his anthropological study of software engineering culture in his upcoming book Adventures In Coderland.\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing the scope and intent of your work on Adventures In Coderland?\nWhat was your motivation for embarking on this particular project?\nPrior to the start of your research for this book, what was your level of familiarity with software development as a discipline and a cultural phenomenon?\nHow are you approaching the research for this book and to what level of detail are you trying to address the problem space?\nWhat are some of the most striking contrasts that you have identified between software engineers and coding culture as it compares to that of a layperson?\nWe met at the most recent PyCon US, which I understand you attended as a means of conducting research for your book. What are some of the notable aspects of the Python community that you discovered while you were attending?\nWhat are some of the other programming communities that you have engaged with?\n\nWhat are some of the differentiating factors that you have noticed between the communities that you have interacted with?\n\n\nWhat are some of the most surprising discoveries that you have made in the process of writing this book?\nWhat is your metric for determining when you have gathered enough raw material to complete the book?\nNow that you have delved into the peculiarities of \"coderland\", how has it changed your own outlook on both the software industry, and society at large?\nWhat advice do you have for the engineers who are listening as it pertains to your experiences in writing your book?\n\nKeep In Touch\n\nWebsite\n@wiresmith on Twitter\n\nPicks\n\nTobias\n\nThroughline Podcast\n\n\nAndrew\n\n20 Thousand Hertz Podcast\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinksj\n\nAdventures In Coderland\nhttps://us.pycon.org?utm_source=rss&utm_medium=rss\nNicholas Tollervey\n1843 Magazine\nThe Economist\nFree Code Camp\nCode Golf\nMoon Dust book about the astronauts who first landed on the moon\nThe Face magazine\nThe Observer\nThe Guardian\nCharlie Duke\nTotally Wired\nCode For America\nSupercollider programming environment\nSonicPi\nGeorge Boole\nFMRI (Functional Magnetic Resonance Imaging)\nRuby Language\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Software development is a unique profession in many ways, and it has given rise to its own subculture due to the unique sets of challenges that face developers. Andrew Smith is an author who is working on a book to share his experiences learning to program, and understand the impact that software is having on our world. In this episode he shares his thoughts on programmer culture, his experiences with Python and other language communities, and how learning to code has changed his views on the world. It was interesting getting an anthropological perspective from a relative newcomer to the world of software.

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Interview

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Keep In Touch

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Picks

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Closing Announcements

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with author Andrew Smith about his anthropological exploration of software communities and how software is shaping the world","date_published":"2019-10-14T19:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/dfb4dbc0-a853-4723-86ea-9e55e8e287d2.mp3","mime_type":"audio/mpeg","size_in_bytes":43664429,"duration_in_seconds":3626}]},{"id":"podlove-2019-10-08t00:38:20+00:00-353a46cec7649ea","title":"Network Automation At Enterprise Scale With Python","url":"https://www.pythonpodcast.com/enms-network-automation-episode-232","content_text":"Summary\nDesigning and maintaining enterprise networks and the associated hardware is a complex and time consuming task. Network automation tools allow network engineers to codify their workflows and make them repeatable. In this episode Antoine Fourmy describes his work on eNMS and how it can be used to automate enterprise grade networks. He explains how his background in telecom networking led him to build an open source platform for network engineers, how it is architected, and how you can use it for creating your own workflows. This is definitely worth listening to as a way to gain some appreciation for all of the work that goes on behind the scenes to make the internet possible.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m interviewing Antoine Fourmy about eNMS, an enterprise-grade vendor-agnostic network automation platform.\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what eNMS is\nWhat was your motivation for creating it?\nWho are the target users of eNMS and how much background knowledge of network management is required to be effective with it?\nWhat are some of the alternative tools that exist in this space and why might a network operator choose to use eNMS in their place?\nWhat are some of the most challenging aspects of network creation and maintenance and how does eNMS assist with them?\nWhat are some of the mundane and/or error-prone tasks that can be replaced or automated with eNMS?\nWhat are some of the additional features that come into play for more complex networking tasks?\nCan you describe the system architecture of eNMS and how it has evolved since you first began working on it?\neNMS is an impressive project that looks to have a substantial amount of polish. How large is the overall community of users and contributors?\n\nFor someone who wants to get involved in contributing to eNMS what are some of the types of skills and background that would be helpful?\n\n\nWhat are some of the most innovative/unexpected ways that you have seen eNMS used?\nWhen is eNMS the wrong choice?\nWhat do you have planned for the future of the project?\n\nKeep In Touch\n\nWebsite\nLinkedIn\nafourmy on GitHub\n\nPicks\n\nTobias\n\nTedeschi Trucks Band\n\n\nAntoine\n\nCheckIO\n\nPodcast Episode\n\n\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\neNMS\nOrange\nNetmiko\nNAPALM\n\nPodcast Episode\n\n\nParamiko\nAnsible\nRequests\nOpenNMS\nLibreNMS\nAnsible Tower\nRundeck\nSaltStack\n\nPodcast Episode\n\n\nStackStorm\n\nPodcast Episode\n\n\nSaltStack Proxy Minions\nHashicorp Vault\nVirtualBox\nFlask\nDjango\nSQLAlchemy\nAPScheduler\nDocker\n\nPodcast Episode\n\n\nRedis\nCelery\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

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Designing and maintaining enterprise networks and the associated hardware is a complex and time consuming task. Network automation tools allow network engineers to codify their workflows and make them repeatable. In this episode Antoine Fourmy describes his work on eNMS and how it can be used to automate enterprise grade networks. He explains how his background in telecom networking led him to build an open source platform for network engineers, how it is architected, and how you can use it for creating your own workflows. This is definitely worth listening to as a way to gain some appreciation for all of the work that goes on behind the scenes to make the internet possible.

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Announcements

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Interview

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Keep In Touch

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Picks

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Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about building the eNMS platform for empowering enterprise network engineers to take advantage of automated workflows","date_published":"2019-10-07T20:45:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/4f94fe15-0ca7-48db-9141-b3dcbbfa39c3.mp3","mime_type":"audio/mpeg","size_in_bytes":24869748,"duration_in_seconds":2077}]},{"id":"podlove-2019-09-29t11:18:21+00:00-fad0a9dac7ec8ca","title":"Building A Modern Discussion Forum In Python To Support Healthy Communities","url":"https://www.pythonpodcast.com/misago-discussion-forum-episode-231","content_text":"Summary\nBuilding and sustaining a healthy community requires a substantial amount of effort, especially online. The design and user experience of the digital space can impact the overall interactions of the participants and guide them toward respectful conversation. In this episode Rafał Pitoń shares his experience building the Misago platform for creating community forums. He explains his motivation for creating the project, the lessons he has learned in the process, and how it is being used by himself and others. This was a great conversation about how technology is just a means, and not the end in itself.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, Data Council in Barcelona, and the Data Orchestration Summit. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m interviewing Rafał Pitoń about Misago, a fully featured modern forum application that is fast, scalable, and responsive\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what Misago is and your motivation for creating it?\n\nHow does it compare to other modern forum options such as Discourse and Flarum?\n\n\nHow did you generate and prioritize the set of features that you have implemented and what are the main capabilities that are still on your roadmap?\nIs Misago intended to be run in isolation, or does it allow for integrating into a larger Django project?\n\nIs there any support for multi-tenancy?\n\n\nHow is Misago itself implemented and how has the architecture evolved since you first began working on it?\n\nIf you were to start it today, what are some of the choices that you would make differently?\n\n\nWhat are the extension points that developers can hook into for adding custom functionality?\nIn addition to the technical challenges, managing a forum involves a fair amount of social challenges. How does Misago help with management of a healthy community?\n\nHow do different design elements factor into promoting healthy conversation and sustainable engagement?\nWhat are some of the aspects of community management and the accompanying platform features that enable them which aren’t initially obvious?\n\n\nFor someone who wants to use Misago, what is involved in deploying and configuring it?\n\nWhat are some of the routine maintenance tasks that they should be aware of?\n\n\nWhat are some of the most interesting or unexpected ways that you have seen Misago used?\nWhat have you found to be the most interesting, unexpected, and challenging aspects of building and maintaining a forum platform?\nWhat do you have planned for the future of Misago?\n\nKeep In Touch\n\nrafalp on GitHub\n@RafalPiton on Twitter\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nPicks\n\nTobias\n\nFear Innoculum by Tool\n\n\nRafał\n\ngithub.com/encode\nAriadne GraphQL Library\n\n\n\nLinks\n\nMisago\nPoland\nMirumee\n\nSaleor Episode\n\n\nPHP\nDiscourse\nFlarum\nMySQL\nPostgreSQL\n\nData Engineering Podcast Interview\n\n\njQuery\nDJango Rest Framework\nEmberJS\nMithrilJS\nAngularJS\nReactJS\nPHPBB\nCelery\nGDPR == General Data Privacy Regulation\nDocker\nmisago_docker\nVPS == Virtual Private Server\nNginx\nStarlette Async API framework\nAriadne GraphQL Library\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Building and sustaining a healthy community requires a substantial amount of effort, especially online. The design and user experience of the digital space can impact the overall interactions of the participants and guide them toward respectful conversation. In this episode Rafał Pitoń shares his experience building the Misago platform for creating community forums. He explains his motivation for creating the project, the lessons he has learned in the process, and how it is being used by himself and others. This was a great conversation about how technology is just a means, and not the end in itself.

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Announcements

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Interview

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Keep In Touch

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Closing Announcements

\n\n

Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about the history and implementation of the Misago discussion forum platform, and how it can be used to grow healthy online communities.","date_published":"2019-09-30T20:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/8b42fb2f-27ab-4dff-8364-45553185432c.mp3","mime_type":"audio/mpeg","size_in_bytes":38506251,"duration_in_seconds":3142}]},{"id":"podlove-2019-09-23t12:42:43+00:00-01978bb18676266","title":"Exploratory Data Analysis Made Easy At The Command Line","url":"https://www.pythonpodcast.com/visidata-exploratory-data-analysis-episode-230","content_text":"Summary\nThere are countless tools and libraries in Python for data scientists to perform powerful analyses, but they often have a setup cost that acts as a barrier to ad-hoc exploration of data. Visidata is a command line application that eliminates the friction involved with starting the discovery process. In this episode Saul Pwanson explains his motivation for creating it, why a terminal environment is a useful place for this work, and how you can use Visidata for your own work. If you have ever avoided looking at a data set because you couldn’t be bothered with the boilerplate for a Jupyter notebook, then Visidata is the perfect addition to your toolbox.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, and Data Council. Upcoming events include the Strata Data conference, the combined events of the Data Architecture Summit and Graphorum, and Data Council in Barcelona. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m interviewing Saul Pwanson about Visidata, a terminal oriented interactive multitool for tabular data\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what Visidata is and how the project got started?\n\nWhat are the main use cases for Visidata?\nWhat are some tools that it has replaced in your workflow?\n\n\nCan you talk through a typical workflow for data exploration and analysis with Visidata?\nOne of the capabilities that you mention on the website is quickly opening large files. What are some strategies that you have used to enable performant access for files that might crash a typical editor (e.g. Vim, Emacs)?\nCan you describe how Visidata is implemented and how it has evolved since you started working on it (including the upcoming 2.0 release)?\n\nWhat libraries or language features have proven most useful?\n\n\nWhy did you choose to implement Visidata as a terminal only tool and what constraints does that bring with it?\n\nWhat are some of the most challenging aspects of building a terminal UI for data exploration and analysis?\nBecause of its manifestation as a terminal/CLI application it relies heavily on keyboard bindings. How do you approach key assignments to ensure a consistent and intuitive user experience?\n\n\nWhat are some of the types of analysis that Visidata can be used for out of the box?\nWhat are some of the most interesting/unexpected/innovative ways that you have seen Visidata used?\nHow much community adoption have you seen and how do you approach project governance as a solo developer?\nWhat do you have planned for the future of Visidata?\n\nKeep In Touch\n\nWebsite\nsaulpw on GitHub\n@saulfp on Twitter\nLinkedIn\n\nPicks\n\nTobias\n\nData Is Plural newsletter\n\n\nSaul\n\nTMate\nMosh – The Mobile Shell\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nVisidata\nF5 Networks\nHDF5\nPyTables\n\nPodcast Interview\n\n\nvgit\nvping\nJeremy Singer-Vine\ndata.boston.gov\nRecurse Center\nCurses\ndateutil\ndecorators\nElectron\nOpenRefine\nTmux\nVisicalc\nWindows Subsystem For Linux\nSaul’s Lightning Talk\nThe Book of Visidata\nWhere In The World Is Carmen San Diego\nOh My Zsh\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

There are countless tools and libraries in Python for data scientists to perform powerful analyses, but they often have a setup cost that acts as a barrier to ad-hoc exploration of data. Visidata is a command line application that eliminates the friction involved with starting the discovery process. In this episode Saul Pwanson explains his motivation for creating it, why a terminal environment is a useful place for this work, and how you can use Visidata for your own work. If you have ever avoided looking at a data set because you couldn’t be bothered with the boilerplate for a Jupyter notebook, then Visidata is the perfect addition to your toolbox.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview about building and using the Visidata tool for exploratory data analysis in your terminal","date_published":"2019-09-23T08:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/62cd4765-8106-46f5-8a03-12cf6ee982ec.mp3","mime_type":"audio/mpeg","size_in_bytes":36542778,"duration_in_seconds":3170}]},{"id":"podlove-2019-09-17t22:05:16+00:00-f879990cbe52be7","title":"Cultivating The Python Community In Argentina","url":"https://www.pythonpodcast.com/facundo-batista-python-community-argentina-episode-229","content_text":"Summary\nThe Python community in Argentina is large and active, thanks largely to the motivated individuals who manage and organize it. In this episode Facundo Batista explains how he helped to found the Python user group for Argentina and the work that he does to make it accessible and welcoming. He discusses the challenges of encompassing such a large and distributed group, the types of events, resources, and projects that they build, and his own efforts to make information free and available. He is an impressive individual with a substantial list of accomplishments, as well as exhibiting the best of what the global Python community has to offer.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, and Data Council. Upcoming events include the O’Reilly AI conference, the Strata Data conference, the combined events of the Data Architecture Summit and Graphorum, and Data Council in Barcelona. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m interviewing Facundo Batista about his experiences founding and fostering the Argentinian Python community, working as a core developer, and his career in Python\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nWhat was your motivation for organizing a Python user group in Argentina?\nHow does the geography and culture of Argentina influence the focus of the community?\nArgentina is a fairly large country. What is the reasoning for having the user group encompass the whole nation and how is it organized to provide access to everyone?\nWhat are some notable projects that have been built by or for members of PyAr?\n\nWhat are some of the challenges that you faced while building CDPedia and what aspects of it are you most proud of?\n\n\nHow did you get started as a core developer?\n\nWhat areas of the language and runtime have you been most involved with?\n\n\nAs a core developer, what are some of the most interesting/unexpected/challenging lessons that you have learned?\nWhat other languages do you currently use and what is it about Python that has motivated you to spend so much of your attention on it?\nWhat are some of the shortcomings in Python that you would like to see addressed in the future?\nOutside of CPython, what are some of the projects that you are most proud of?\nHow has your involvement with core development and PyAr influenced your life and career?\n\nKeep In Touch\n\n@facundobatista on Twitter\nBlog\n\nPicks\n\nTobias\n\nDictionary of Difficult Words\n\n\nFacundo\n\nFades\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nPyAr\nArgentina\nPyAr Mailing List\nPyAr Telegram\nPyCon Argentina\nBuenos Aires\nCordoba\nRosario\nMendoza\nCDPedia\nPyCamp\nPSF == Python Software Foundation\nWikipedia\nInternet Archive\nDecimal Module\n\nPEP 327\n\n\nTim Peters\nCanonical\nTennis\nFades\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

The Python community in Argentina is large and active, thanks largely to the motivated individuals who manage and organize it. In this episode Facundo Batista explains how he helped to found the Python user group for Argentina and the work that he does to make it accessible and welcoming. He discusses the challenges of encompassing such a large and distributed group, the types of events, resources, and projects that they build, and his own efforts to make information free and available. He is an impressive individual with a substantial list of accomplishments, as well as exhibiting the best of what the global Python community has to offer.

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Announcements

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Interview

\n\n

Keep In Touch

\n\n

Picks

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Closing Announcements

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with Facundo Batista about his experiences building and growing the Python community across Argentina","date_published":"2019-09-18T08:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/e8bac79b-9709-4398-8ab9-6a0eda6caa73.mp3","mime_type":"audio/mpeg","size_in_bytes":27661387,"duration_in_seconds":2506}]},{"id":"podlove-2019-09-09t14:02:24+00:00-37dc925da0999f6","title":"Python Powered Journalistic Freedom With SecureDrop","url":"https://www.pythonpodcast.com/securedrop-whistleblower-platform-episode-228","content_text":"Summary\nThe internet has made it easier than ever to share information, but at the same time it has increased our ability to track that information. In order to ensure that news agencies are able to accept truly anonymous material submissions from whistelblowers, the Freedom of the Press foundation has supported the ongoing development and maintenance of the SecureDrop platform. In this episode core developers of the project explain what it is, how it protects the privacy and identity of journalistic sources, and some of the challenges associated with ensuring its security. This was an interesting look at the amount of effort that is required to avoid tracking in the modern era.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, and Data Council. Upcoming events include the O’Reilly AI conference, the Strata Data conference, the combined events of the Data Architecture Summit and Graphorum, and Data Council in Barcelona. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m interviewing Jen Helsby and Kushal Das about SecureDrop, a secure platform for submitting and receiving documents anonymously\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what SecureDrop is and how it got started?\n\nHow did you get involved in the project?\n\n\nCan you give some background on where and why it is useful?\nFor someone using a running instance, what does their workflow look like?\n\nWhat are some of the ways that you minimize user experience hurdles to prevent them from circumventing the security through laziness or apathy?\n\n\nI was a bit surprised to see the references to the messaging system that is included. Why is that an important feature?\nWhat form do the submissions generally take and what are the limits on formats that you can accept?\nHow is the system itself architected and how has the design evolved since the first implementation?\nIn terms of the security protocols and technologies that are implemented, what factors are you considering as you develop the project?\n\nWhat are the weak points or edge cases that could lead to compromise and how do you guard against them?\n\n\nIn terms of the deployment and maintenance of a SecureDrop instance, how much technological sophistication is necessary for the organization running it, and how much effort do you put into simplifying it?\nWhat are some of the notable uses of a SecureDrop deployment and what motivates you to continue working on it?\nWhat are the most interesting/innovative/unexpected uses of SecureDrop that you have seen?\nHow do you approach the sustainability of the platform?\nWhat have you found most challenging/interested/unexpected in your work on SecureDrop?\nWhat is in store for the future of the project?\n\nKeep In Touch\n\nJen\n\n@redshiftzero on Twitter\nredshiftzero on GitHub\nBlog\n\n\nKushal\n\nWebsite\n@kushaldas on Twitter\nkushaldas on GitHub\n\n\n\nPicks\n\nTobias\n\nLaser Tag\n\n\nKushal\n\nPermanent Record by Edward Snowden\n\n\nJen\n\nPermanent Record by Edward Snowden\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nSecureDrop\nAaron Swartz\nFreedom Of The Press Foundation\nSecureDrop Directory\nTOR Browser\nTOR == The Onion Router\nTails OS\nUbuntu\nIDS == Intrusion Detection System\nAnsible\nDEF CON\nMozilla Open Source Support (MOSS)\nTestinfra\nFlask\nMolecule unit test library for Ansible\nBandit\nSafety\nQubes OS\nQt\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

The internet has made it easier than ever to share information, but at the same time it has increased our ability to track that information. In order to ensure that news agencies are able to accept truly anonymous material submissions from whistelblowers, the Freedom of the Press foundation has supported the ongoing development and maintenance of the SecureDrop platform. In this episode core developers of the project explain what it is, how it protects the privacy and identity of journalistic sources, and some of the challenges associated with ensuring its security. This was an interesting look at the amount of effort that is required to avoid tracking in the modern era.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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\"\"

","summary":"An interview about the SecureDrop platform that enables whistleblowers to share information safely","date_published":"2019-09-09T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/c33364ec-1bc7-4628-8800-fbbcf6177b0f.mp3","mime_type":"audio/mpeg","size_in_bytes":29049348,"duration_in_seconds":2302}]},{"id":"podlove-2019-09-02t15:53:48+00:00-0f7c62662230499","title":"Combining Python And SQL To Build A PyData Warehouse","url":"https://www.pythonpodcast.com/pydata-warehouse-episode-227","content_text":"Summary\nThe ecosystem of tools and libraries in Python for data manipulation and analytics is truly impressive, and continues to grow. There are, however, gaps in their utility that can be filled by the capabilities of a data warehouse. In this episode Robert Hodges discusses how the PyData suite of tools can be paired with a data warehouse for an analytics pipeline that is more robust than either can provide on their own. This is a great introduction to what differentiates a data warehouse from a relational database and ways that you can think differently about running your analytical workloads for larger volumes of data.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nTaking a look at recent trends in the data science and analytics landscape, it’s becoming increasingly advantageous to have a deep understanding of both SQL and Python. A hybrid model of analytics can achieve a more harmonious relationship between the two languages. Read more about the Python and SQL Intersection in Analytics at mode.com/init. Specifically, we’re going to be focusing on their similarities, rather than their differences.\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, and Data Council. Upcoming events include the O’Reilly AI conference, the Strata Data conference, the combined events of the Data Architecture Summit and Graphorum, and Data Council in Barcelona. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m interviewing Robert Hodges about how the PyData ecosystem can play nicely with data warehouses\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nTo start with, can you give a quick overview of what a data warehouse is and how it differs from a \"regular\" database for anyone who isn’t familiar with them?\n\nWhat are the cases where a data warehouse would be preferable and when are they the wrong choice?\n\n\nWhat capabilities does a data warehouse add to the PyData ecosystem?\nFor someone who doesn’t yet have a warehouse, what are some of the differentiating factors among the systems that are available?\nOnce you have a data warehouse deployed, how does it get populated and how does Python fit into that workflow?\nFor an analyst or data scientist, how might they interact with the data warehouse and what tools would they use to do so?\nWhat are some potential bottlenecks when dealing with the volumes of data that can be contained in a warehouse within Python?\n\nWhat are some ways that you have found to scale beyond those bottlenecks?\n\n\nHow does the data warehouse fit into the workflow for a machine learning or artificial intelligence project?\nWhat are some of the limitations of data warehouses in the context of the Python ecosystem?\nWhat are some of the trends that you see going forward for the integration of the PyData stack with data warehouses?\n\nWhat are some challenges that you anticipate the industry running into in the process?\n\n\nWhat are some useful references that you would recommend for anyone who wants to dig deeper into this topic?\n\nKeep In Touch\n\nLinkedIn\nhodgesrm on GitHub\n\nPicks\n\nTobias\n\nFoundations Of Architecting Data Solutions: Managing Successful Data Projects by Ted Malaska & Jonathan Seidman\n\n\nRobert\n\nReading old academic papers such as CStore\nPython Machine Learning by Sebastian Raschka\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nAltinity\nClickhouse\n\nData Engineering Podcast Interview\n\n\nMySQL\nData Warehouse\nColumn Oriented Database\nSIMD == Single Instruction Multiple Data\nPostgreSQL\n\nData Engineering Podcast Episode\n\n\nMicrosoft SQL Server\nPandas\nNumPy\nTensorflow\nJupyter\nData Sampling\nDask\n\nData Engineering Podcast\n\n\nRay\nMap/Reduce\nVertica\nSharding\nHadoop\nSnowflakeDB\nDelta Lake\n\nData Engineering Podcast Episode\n\n\nBigQuery\nRedShift\nSnowflake Data Sharing\nOracleDB\nKubernetes\nDBT\n\nData Engineering Podcast Episode\n\n\nCSV\nParquet\n\nData Engineering Podcast Episode\n\n\nKafka\nUC Davis\nWeb Scraping\nClickhouse Python Driver\nSQLAlchemy\n\nAltinity Blog Post\n\n\nMaterialized View\nPyTorch\n\nPodcast Interview\n\n\nscikit-learn\nSpark\n\nData Engineering Podcast Interview\n\n\nBigQuery ML\nApache Arrow\nWes McKinney\n\nPodcast Interview\n\n\nUser Defined Function\nKDB\nCStore Paper by Dr. Michael Stonebraker, et al\nKinetica\nMapD/OmniSci\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

The ecosystem of tools and libraries in Python for data manipulation and analytics is truly impressive, and continues to grow. There are, however, gaps in their utility that can be filled by the capabilities of a data warehouse. In this episode Robert Hodges discusses how the PyData suite of tools can be paired with a data warehouse for an analytics pipeline that is more robust than either can provide on their own. This is a great introduction to what differentiates a data warehouse from a relational database and ways that you can think differently about running your analytical workloads for larger volumes of data.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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\n\n

\"\"

","summary":"An interview about how data warehouses fit into the PyData ecosystem for advanced analytics on big data","date_published":"2019-09-02T12:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/dc182c98-107c-4301-8aa8-d76ee08a7e7d.mp3","mime_type":"audio/mpeg","size_in_bytes":34420595,"duration_in_seconds":2624}]},{"id":"podlove-2019-08-26t14:26:31+00:00-5444a212fed060b","title":"AI Driven Automated Code Review With DeepCode","url":"https://www.pythonpodcast.com/deepcode-automated-code-review-episode-226","content_text":"Summary\nSoftware engineers are frequently faced with problems that have been fixed by other developers in different projects. The challenge is how and when to surface that information in a way that increases their efficiency and avoids wasted effort. DeepCode is an automated code review platform that was built to solve this problem by training a model on a massive array of open sourced code and the history of their bug and security fixes. In this episode their CEO Boris Paskalev explains how the company got started, how they build and maintain the models that provide suggestions for improving your code changes, and how it integrates into your workflow.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, and Data Council. Upcoming events include the O’Reilly AI conference, the Strata Data conference, the combined events of the Data Architecture Summit and Graphorum, and Data Council in Barcelona. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nYour host as usual is Tobias Macey and today I’m interviewing Boris Paskalev about DeepCode, an automated code review platform for detecting security vulnerabilities in your projects\n\nInterview\n\nIntroductions\nCan you start by explaining what DeepCode is and the story of how it got started?\nHow is the DeepCode platform implemented?\nWhat are the current languages that you support and what was your guiding principle in selecting them?\n\nWhat languages are you targeting next?\nWhat is involved in maintaining support for languages as they release new versions with new features?\n\nHow do you ensure that the recommendations that you are making are not using languages features that are not available in the runtimes that a given project is using?\n\n\n\n\nFor someone who is using DeepCode, how does it fit into their workflow?\nCan you explain the process that you use for training your models?\n\nHow do you curate and prepare the project sources that you use to power your models?\n\nHow much domain expertise is necessary to identify the faults that you are trying to detect?\nWhat types of labelling do you perform to ensure that the resulting models are focusing on the proper aspects of the source repositories?\n\n\n\n\nHow do you guard against false positives and false negatives in your analysis and recommendations?\nDoes the code that you are analyzing and the resulting fixes act as a feedback mechanism for a reinforcement learning system to update your models?\n\nHow do you guard against leaking intellectual property of your scanned code when surfacing recommendations?\n\n\nWhat have been some of the most interesting/unexpected/challenging aspects of building the DeepCode product?\nWhat do you have planned for the future of the platform and business?\n\nKeep In Touch\n\nLinkedIn\n\nPicks\n\nTobias\n\nRedwall Series by Brian Jacques\n\n\nBoris\n\nArtifical Intelligence\nGet outside\n\n\n\nClosing Announcements\n\nThank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.\nVisit the site to subscribe to the show, sign up for the mailing list, and read the show notes.\nIf you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\n\nLinks\n\nDeepCode\nZurich, Switzerland\nBigCode\nETH Zurich\nDatalog\nF Strings\nData Classes\nDeepCode Research\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Software engineers are frequently faced with problems that have been fixed by other developers in different projects. The challenge is how and when to surface that information in a way that increases their efficiency and avoids wasted effort. DeepCode is an automated code review platform that was built to solve this problem by training a model on a massive array of open sourced code and the history of their bug and security fixes. In this episode their CEO Boris Paskalev explains how the company got started, how they build and maintain the models that provide suggestions for improving your code changes, and how it integrates into your workflow.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Closing Announcements

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
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","summary":"An interview about building a machine learning engine for finding and fixing software defects at DeepCode","date_published":"2019-08-26T11:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/c87b45f6-eeaa-4da4-8fee-a68577ead99a.mp3","mime_type":"audio/mpeg","size_in_bytes":27993050,"duration_in_seconds":1995}]},{"id":"podlove-2019-08-19t19:50:57+00:00-fe47b0be0a965a9","title":"Security, UX, and Sustainability For The Python Package Index","url":"https://www.pythonpodcast.com/pypi-improvements-episode-225","content_text":"Summary\nPyPI is a core component of the Python ecosystem that most developer’s have interacted with as either a producer or a consumer. But have you ever thought deeply about how it is implemented, who designs those interactions, and how it is secured? In this episode Nicole Harris and William Woodruff discuss their recent work to add new security capabilities and improve the overall accessibility and user experience. It is a worthwhile exercise to consider how much effort goes into making sure that we don’t have to think much about this piece of infrastructure that we all rely on.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, and Data Counsil. Upcoming events include the O’Reilly AI conference, the Strata Data conference, the combined events of the Data Architecture Summit and Graphorum, and Data Council in Barcelona. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Nicole Harris and William Woodruff about the work they are doing on the PyPI service to improve the security and utility of the package repository that we all rely on\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by sharing how you each got involved in working on PyPI?\n\nWhat was the state of the system at the time that you first began working on it?\n\n\nOnce you committed to working on PyPI how did you each approach the process of identifying and prioritizing the work that needed to be done?\n\nWhat were the most significant issues that you were faced with at the outset?\n\n\nHow often have the issues that you each focused on overlapped at the cross section of UX and security?\n\nHow do you balance the tradeoffs that exist at that boundary?\n\n\nWhat is the surface area of the domains that you are each working in? (e.g. web UI, system API, data integrity, platform support, etc.)\n\nWhat are some of the pain points or areas of confusion from a user perspective that you have dealt with in the process of improving the platform?\n\n\nWhat have been the most notable features or improvements that you have each introduced to PyPI?\n\nWhat were the biggest challenges with implementing or integrating those changes?\n\n\nHow do you approach introducing changes to PyPI given the volume of traffic that it needs to support and the level of importance that it serves in the community?\nWhat are some examples of attack vectors that exist as a result of the nature of the PyPI platform and what are you most concerned by?\nHow does poor accessibility or user experience impact the utility of PyPI and the community members who interact with it?\nWhat have you found to be the most interesting/challenging/unexpected aspects of working on Warehouse?\n\nWhat are some of the most useful lessons that you have learned in the process?\n\n\nWhat do you have planned for future improvements to the platform?\n\nHow can the listeners get involved and help out?\n\n\nHow was this work funded?\n\nKeep In Touch\n\nNicole\n\n@nlhkabu on Twitter\nWebsite\nIf you’re using CI to upload to PyPI and would like to speak with Nicole please book a time here\nIf you’re using assistive technology and would like to speak with Nicole please book a time here\n\n\nWilliam\n\n@8x5clPW2\nWebsite\nEmail\nPlease get in touch if you’d like to work with Trail of Bits on your next security project!\n\n\n\nPicks\n\nTobias\n\nThe Expanse TV Series\n\n\nNicole\n\nThe Great Hack documentary\n\n\nWilliam\n\nAbraham Lincoln Autobiography by Carl Sandburg\n\n\n\nLinks\n\nPyPI\nWarehouse\n\nIssue Tracker\nGood First Issues\n\n\nPeopleDoc\nTrail of Bits\nOSQuery\nDjango\nRuby\nPython Software Foundation\n\nPython Packaging Working Group\nPodcast Episode\n\n\nDonald Stufft\n\nPodcast Episode\n\n\nUX (User Experience) Design\nOTF == Open Technology Fund\nBootstrap\nTOTP\nWebauthN\nYubikey\nChangeset Consulting\nSumana Harihareswara\nWCAG (Web Content Accessibility Guidelines) 2.0\nMacaroon Security Tokens\nDocker Compose\nMOSS = Mozilla Open Source Support\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

PyPI is a core component of the Python ecosystem that most developer’s have interacted with as either a producer or a consumer. But have you ever thought deeply about how it is implemented, who designs those interactions, and how it is secured? In this episode Nicole Harris and William Woodruff discuss their recent work to add new security capabilities and improve the overall accessibility and user experience. It is a worthwhile exercise to consider how much effort goes into making sure that we don’t have to think much about this piece of infrastructure that we all rely on.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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\"\"

","summary":"An interview about improving the security and user experience of the Python Package Index","date_published":"2019-08-19T16:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/39d073d0-782c-440e-818b-735319032b81.mp3","mime_type":"audio/mpeg","size_in_bytes":39971205,"duration_in_seconds":3098}]},{"id":"podlove-2019-08-12t13:17:34+00:00-1e87abbfd5f68f8","title":"Learning To Program In Python With CodeGrades","url":"https://www.pythonpodcast.com/codegrades-learn-to-program-episode-224","content_text":"Summary\nWith the increasing role of software in our world there has been an accompanying focus on teaching people to program. There are numerous approaches that have been attempted to achieve this goal with varying levels of success. Nicholas Tollervey has begun a new effort that blends the approach adopted by musicians and martial artists that uses a series of grades to provide recognition for the achievements of students. In this episode he explains how he has structured the study groups, syllabus, and evaluations to help learners build projects based on their interests and guide their own education while incorporating useful skills that are necessary for a career in software. If you are interested in learning to program, teach others, or act as a mentor then give this a listen and then get in touch with Nicholas to help make this endeavor a success.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYour host as usual is Tobias Macey and today Nicholas Tollervey is back to talk about his work on CodeGrades, a new effort that he is building to blend his backgrounds in music, education, and software to help teach kids of all ages how to program.\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what CodeGrades is and what motivated you to start this project?\n\nHow does it differ from other approaches to teaching software development that you have encountered?\nIs there a particular age or level of background knowledge that you are targeting with the curriculum that you are developing?\n\n\nWhat are the criteria that you are measuring against and how does that criteria change as you progress in grade levels?\nFor someone who completes the full set of levels, what level of capability would you expect them to have as a developer?\nGiven your affiliation with the Python community it is understandable that you would target that language initially. What would be involved in adapting the curriculum, mentorship, and assessments to other languages?\n\nIn what other ways can this idea and platform be adapted to accomodate other engineering skills? (e.g. system administration, statistics, graphic design, etc.)\n\n\nWhat interesting/exciting/unexpected outcomes and lessons have you found while iterating on this idea?\nFor engineers who would like to be involved in the CodeGrades platform, how can they contribute?\nWhat challenges do you anticipate as you continue to develop the curriculum and mentor networks?\nHow do you envision the future of CodeGrades taking ship in the medium to long term?\n\nKeep In Touch\n\nntoll on GitHub\nWebsite\n@ntoll on Twitter\n\nPicks\n\nTobias\n\nParsy\nNevermoor: The Trials of Morrigan Crow\n\n\nNicholas\n\nKivy\nWittgenstein: The Duty Of Genious\nThe Hitchhiker’s Guide To The Galaxy by Douglas Adams\n\n\n\nLinks\n\nCodeGrades\nBlog Post\nC#\n.NET\nLondon\nIronPython\nMusical Grades\nAutodidact\nLambda School\nHow To Draw An Owl\nDunder (double underscore) methods\nDuck Typing\nImpostor Syndrome\nDjango Girls\nMu Editor\nBaroque Music\nChamber Music\nPyData\nAdafruit\nCircuitPython\n\nPodcast Interview\n\n\nPyPortal\nHypercard\nPypercard\nKivy\n\nPodcast Interview\n\n\nAlan Turing\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

With the increasing role of software in our world there has been an accompanying focus on teaching people to program. There are numerous approaches that have been attempted to achieve this goal with varying levels of success. Nicholas Tollervey has begun a new effort that blends the approach adopted by musicians and martial artists that uses a series of grades to provide recognition for the achievements of students. In this episode he explains how he has structured the study groups, syllabus, and evaluations to help learners build projects based on their interests and guide their own education while incorporating useful skills that are necessary for a career in software. If you are interested in learning to program, teach others, or act as a mentor then give this a listen and then get in touch with Nicholas to help make this endeavor a success.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview about combining music education with learning to program with CodeGrades for more confident and capable software developers","date_published":"2019-08-12T09:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/c7868545-02d1-40c3-bdc3-e3ba2feafe28.mp3","mime_type":"audio/mpeg","size_in_bytes":50585924,"duration_in_seconds":3842}]},{"id":"podlove-2019-08-05t21:11:30+00:00-c9e9c199a79b66d","title":"Build Your Own Knowledge Graph With Zincbase","url":"https://www.pythonpodcast.com/zincbase-knowledge-graph-episode-223","content_text":"Summary\nComputers are excellent at following detailed instructions, but they have no capacity for understanding the information that they work with. Knowledge graphs are a way to approximate that capability by building connections between elements of data that allow us to discover new connections among disparate information sources that were previously uknown. In our day-to-day work we encounter many instances of knowledge graphs, but building them has long been a difficult endeavor. In order to make this technology more accessible Tom Grek built Zincbase. In this episode he explains his motivations for starting the project, how he uses it in his daily work, and how you can use it to create your own knowledge engine and begin discovering new insights of your own.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nAnd to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. With such an intuitive tool it’s easy to make sure that everyone in the business is on the same page. Podcast.init listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Tom Grek about knowledge graphs, when they’re useful, and his project Zincbase that makes them easier to build\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what a knowledge graph is and some of the ways that they are used?\n\nHow did you first get involved in the space of knowledge graphs?\n\n\nYou have built the Zincbase project for building and querying knowledge graphs. What was your motivation for creating this project and what are some of the other tools that are available to perform similar tasks?\nCan you describe how Zincbase is implemented and some of the ways that it has evolved since you first began working on it?\n\nWhat are some of the assumptions that you had at the outset of the project which have been challenged or updated in the process of working on and with it?\n\n\nWhat are some of the common challenges when building or using knowledge graphs?\nHow has the domain of knowledge graphs changed in recent years as new approaches to entity resolution and data processing have been introduced?\nCan you talk through a use case and workflow for using Zincbase to design and populate a knowledge graph?\nWhat are some of the ways that you are using Zincbase in your own projects?\nWhat have you found to be the most challenging/interesting/unexpected lessons that you have learned in the process of building and maintaining Zincbase?\nWhat do you have planned for the future of the project?\n\nKeep In Touch\n\ntomgrek on GitHub\nWebsite\n@tomgrek on Twitter\nLinkedIn\n\nPicks\n\nTobias\n\nBanana Blueberry Oat Bars\n\n\nTom\n\nPickled Habañero\n\n\n\nLinks\n\nZincbase\nCommodore 64\nElectronic Engineering\nArtificial Intelligence\nPrimer.ai\nArtificial General Intelligence\nMatlab\nIPython\nNumPy\nExcel\nJupyter\nPandas\nKnowledge Graph\n\nData Engineering Podcast Episode About Enigma Knowledge Graph\n\n\nThe Matrix\nKeanu Reeves\nOntology\nSemantic Web\nWord2Vec\nSparQL\nNeo4J\nGraph Database\n\nData Engineering Podcast Episode About DGraph\n\n\nAWS Neptune\nPostgreSQL\n\nData Engineering Podcast Episode\n\n\nDask\n\nData Engineering Podcast Episode\n\n\nBBC Micro\nBASIC\nProlog\nNLP\nELMO\nBERT\nGPT-2\nWinograd Schema Challenge\nPyTorch BigGraph\nAmpligraph\nSpaCy\n\nPodcast.__init__ Episode\n\n\nAI Winter\nPyTorch\n\nPodcast Episode\n\n\nscikit-learn\nNetworkX\nSciPy\nCircleCI\nRead The Docs\n\nPodcast Episode\n\n\nProject Gutenberg\nAllen NLP\nDoctest\nReinforcement Learning\nMetacognition\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Computers are excellent at following detailed instructions, but they have no capacity for understanding the information that they work with. Knowledge graphs are a way to approximate that capability by building connections between elements of data that allow us to discover new connections among disparate information sources that were previously uknown. In our day-to-day work we encounter many instances of knowledge graphs, but building them has long been a difficult endeavor. In order to make this technology more accessible Tom Grek built Zincbase. In this episode he explains his motivations for starting the project, how he uses it in his daily work, and how you can use it to create your own knowledge engine and begin discovering new insights of your own.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about how Zincbase makes knowledge graphs easier to build and when they are useful","date_published":"2019-08-05T17:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/b7533537-92a4-4e09-b0ad-5cd87ff1f7ac.mp3","mime_type":"audio/mpeg","size_in_bytes":32914422,"duration_in_seconds":2924}]},{"id":"podlove-2019-07-29t02:02:05+00:00-93517d67fb395cc","title":"Docker Best Practices For Python In Production","url":"https://www.pythonpodcast.com/docker-python-production-episode-222","content_text":"Summary\nDocker is a useful technology for packaging and deploying software to production environments, but it also introduces a different set of complexities that need to be understood. In this episode Itamar Turner-Trauring shares best practices for running Python workloads in production using Docker. He also explains some of the security implications to be aware of and digs into ways that you can optimize your build process to cut down on wasted developer time. If you are using Docker, thinking about using it, or just heard of it recently then it is worth your time to listen and learn about some of the cases you might not have considered.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nTo connect with the startups that are shaping the future and take advantage of the opportunities that they provide, check out Angel List where you can invest in innovative business, find a job, or post a position of your own. Sign up today at pythonpodcast.com/angel and help support this show.\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Itamar Turner-Trauring about what you need to know about running Python workloads in Docker\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nFor anyone who is unfamiliar with it, can you describe what Docker is and the benefits that it can provide?\nWhat was your motivation for dedicating so much time and energy to the specific area of using Docker for Python production usage?\nWhat are some of the common issues that developers and operations engineers run into when dealing with Docker and its build system?\nWhat are some of the issues that are specific to Python that you have run into when using Docker?\nHow does the ecosystem for Python in containers compare to other languages that you are familiar with?\nWhat are some of the security issues that engineers are likely to run into when using some of the advice and pre-existing containers that are publicly available?\nOne of the issues that you call out is the speed of container builds. What are some of the contributing factors that lead to such slow packaging times?\n\nCan you talk through some of the aspects of multi-layer packages and useful ways to take proper advantage of them?\n\n\nThere have been some recent projects that attempt to work around the shortcomings of the Dockerfile itself. What are your thoughts on that overall effort and any specific tools that you have experimented with?\nWhen is Docker the wrong choice for a production environment?\n\nWhat are some useful alternatives to Docker, for Python specifically and for software distribution in general that you have had good luck with?\n\n\n\nKeep In Touch\n\nWebsite\n@itamarst on Twitter\nitamarst on GitHub\n\nPicks\n\nTobias\n\nShazam Movie\n\n\nItamar\n\nVeronica Mars\n\n\n\nLinks\n\nItamar’s Best Practices Guide\nDocker\nZope\nGitLab CI\nHeresy In The Church Of Docker\nPoetry\nPipenv\nDockerfile\n40 Years of DSL Disasters (Slides)\nUbuntu\nDebian\nDocker Layers\nBitnami\nAlpine Linuxhttps://alpinelinux.org?utm_source=rss&utm_medium=rss\nPodMan\nNix\nHeroku Buildpacks\nItamar’s Docker Template\nHashicorp Packer\nRkt\nSolaris Zones\nBSD Jails\nPyInstaller\nSnap\nFlatPak\nConda\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Docker is a useful technology for packaging and deploying software to production environments, but it also introduces a different set of complexities that need to be understood. In this episode Itamar Turner-Trauring shares best practices for running Python workloads in production using Docker. He also explains some of the security implications to be aware of and digs into ways that you can optimize your build process to cut down on wasted developer time. If you are using Docker, thinking about using it, or just heard of it recently then it is worth your time to listen and learn about some of the cases you might not have considered.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview covering what you need to know about running your Python projects in production with Docker","date_published":"2019-07-28T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/209cd2b5-ba04-47cc-94b7-273111009dbf.mp3","mime_type":"audio/mpeg","size_in_bytes":31239100,"duration_in_seconds":2640}]},{"id":"podlove-2019-07-22t21:40:38+00:00-a0aa37a3e9d64b6","title":"Protecting The Future Of Python By Hunting Black Swans","url":"https://www.pythonpodcast.com/python-potential-black-swans-episode-221","content_text":"Summary\nThe Python language has seen exponential growth in popularity and usage over the past decade. This has been driven by industry trends such as the rise of data science and the continued growth of complex web applications. It is easy to think that there is no threat to the continued health of Python, its ecosystem, and its community, but there are always outside factors that may pose a threat in the long term. In this episode Russell Keith-Magee reprises his keynote from PyCon US in 2019 and shares his thoughts on potential black swan events and what we can do as engineers and as a community to guard against them.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nAnd to grow your professional network and find opportunities with the startups that are changing the world then Angel List is the place to go. Go to pythonpodcast.com/angel to sign up today.\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Upcoming events include the O’Reilly AI Conference, the Strata Data Conference, and the combined events of the Data Architecture Summit and Graphorum. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Russell Keith-Magee about potential black swans for the Python language, ecosystem, and community and what we can do about them\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what a Black Swan is in the context of our conversation?\nYou were the opening keynote for PyCon this year, where you talked about some of the potential challenges facing Python. What motivated you to choose this topic for your presentation?\nWhat effect did your talk have on the overall tone and focus of the conversations that you experienced during the rest of the conference?\n\nWhat were some of the most notable or memorable reactions or pieces of feedback that you heard?\n\n\nWhat are the biggest potential risks for the Python ecosystem that you have identified or discussed with others?\nWhat is your overall sentiment about the potential for the future of Python?\nAs developers and technologists, does it really matter if Python continues to be a viable language?\nWhat is your personal wish list of new capabilities or new directions for the future of the Python language and ecosystem?\nFor listeners to this podcast and members of the Python community, what are some of the ways that we can contribute to the long-term success of the language?\n\nKeep In Touch\n\nBeeWare\nfreakboy3742 on GitHub\n@freakboy3742 on Twitter\nWebsite\n\nPicks\n\nTobias\n\nJethro Tull\n\n\nRussell\n\npytest-tldr\npdbpp\n\n\n\nLinks\n\nPyCon 2019 Keynote Presentation\nPerth\nWestern Australia\nDjango\nBeeWare\nRedHat\nEmacs\nVim\nLisp\nGlyph\nTwisted\nCal Henderson\nFlickr\nSlack\nBlack Swan\n\nAnimal\nBook\nMetaphor\n\n\nNassim Nicholas Taleb\nPyCon US\nEwa Jodlowska\nPython Software Foundation\n\nPodcast Interview\n\n\nSwift\nJavaScript\nDjango Girls\nBriefcase packaging tool\nPyPy\nWeb Assembly (WASM)\nCOBOL\nTidelift\nCricket unit test runner\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

The Python language has seen exponential growth in popularity and usage over the past decade. This has been driven by industry trends such as the rise of data science and the continued growth of complex web applications. It is easy to think that there is no threat to the continued health of Python, its ecosystem, and its community, but there are always outside factors that may pose a threat in the long term. In this episode Russell Keith-Magee reprises his keynote from PyCon US in 2019 and shares his thoughts on potential black swan events and what we can do as engineers and as a community to guard against them.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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\"\"

","summary":"An interview about identifying areas of weakness in the Python ecosystem and how to address them in the interest of the future for the language and its community","date_published":"2019-07-22T17:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/fd0fb463-bd34-4243-8abd-491af39353aa.mp3","mime_type":"audio/mpeg","size_in_bytes":35644182,"duration_in_seconds":3275}]},{"id":"podlove-2019-07-15t01:32:09+00:00-fed3d5ced302984","title":"A Modern Open Source Project Management Platform","url":"https://www.pythonpodcast.com/taiga-project-management-episode-220","content_text":"Summary\nProject management is a discipline that has been through many incarnations, spawning an entire industry of businesses and tools. The challenge is to build a platform that is sufficiently powerful and adaptable to fit the workflow of your teams, while remaining opinionated enough to be useful. It also helps to have an open and extensible platform that can be customized as needed. In this episode Pablo Ruiz Múzquiz explains the motivation for creating the open source tool Taiga, how it compares to the other options in the market, and how you can use it for your own projects. He also discusses the challenges inherent to project management tools, his philosophies on what makes a project successful, and how to manage your team workflows to be most effective. It was helpful learning from Pablo’s long experience in the software industry and managing teams of various sizes.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Pablo Ruiz Múzquiz about Taiga, a project management platform for agile developers & designers and project managers who want a beautiful tool that makes work truly enjoyable\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what Taiga is and the reason for building it?\n\nProject management platforms have been available for a long time. Can you describe how Taiga fits into that market and what makes it stand out?\n\n\nCan you describe how you view project management and some of the unique challenges that it poses when building a tool for it?\n\nHow do the requirements differ between project management for software teams vs other disciplines?\n\n\nHow is Taiga implemented and how has the system design evolved since it was first started?\nFor someone who is using Taiga can you talk through the features of the platform and how it fits into a typical workflow?\nHow do you maintain a balance between usability and structure in managing project workflows against flexibility and customization?\nWithin an engineering team how do you view the responsibility for driving and maintaining the lifecycle of a project?\nWhat are the most common points of friction within a project management workflow and how are you working to address them in Taiga?\n\nOnboarding and discovery for a new contributor in a given project is often painful. What are some steps that a project manager or product team can take to make that process more palatable?\n\n\nHow has the landscape of project management practices and tools changed since you first began working on Taiga and how has that influenced your roadmap?\nWhat have been the most challenging or difficult aspects of building and growing the Taiga project and community?\n\nWhat lessons have you learned in the process that have been particularly valuable or unexpected?\n\n\nWhat are some of the most interesting/unexpected/innovative ways that you have seen Taiga used?\nWhen is Taiga the wrong choice for a given project or team?\nWhat do you have planned for the future of Taiga?\n\nAdded by Pablo\n\nWhy did you choose AGPLv3 for a license?\nHow can Taiga integrate itself with other platforms that are typically used by teams?\n\nKeep In Touch\n\n@diacritica on Twitter\nLinkedIn\nWebsite\n\nPicks\n\nTobias\n\nMarchway Hydration Pack\n\n\nPablo\n\nArchery\n3D Archery\n\n\n\nLinks\n\nTaiga\nMadrid, Spain\nTraditional Archery\nKaleidos\nPerl\nMonty Python\nBlender\nAgile\nProject Management\nRedmine\nTrac\nAgile Manifesto\nREST\nDjango\nAngularJS\nDjango REST Framework\nScrum\nKanban\nTaiga Mobile App\nWebhooks\nAGPLv3\nFOSDEM\nIocaine\nThe Princess Bride\nTaiga Tribe\nFedora\nAtlassian\nJira\nTrello\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Project management is a discipline that has been through many incarnations, spawning an entire industry of businesses and tools. The challenge is to build a platform that is sufficiently powerful and adaptable to fit the workflow of your teams, while remaining opinionated enough to be useful. It also helps to have an open and extensible platform that can be customized as needed. In this episode Pablo Ruiz Múzquiz explains the motivation for creating the open source tool Taiga, how it compares to the other options in the market, and how you can use it for your own projects. He also discusses the challenges inherent to project management tools, his philosophies on what makes a project successful, and how to manage your team workflows to be most effective. It was helpful learning from Pablo’s long experience in the software industry and managing teams of various sizes.

\n

Announcements

\n\n

Interview

\n\n

Added by Pablo

\n
    \n
  1. Why did you choose AGPLv3 for a license?
  2. \n
  3. How can Taiga integrate itself with other platforms that are typically used by teams?
  4. \n
\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
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\"\"

","summary":"An interview about the modern, open source project management platform Taiga, and how it can contribute to successful projects","date_published":"2019-07-14T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/ba8b8b71-e8c5-4500-9874-f852d2507d9e.mp3","mime_type":"audio/mpeg","size_in_bytes":58144320,"duration_in_seconds":4145}]},{"id":"podlove-2019-07-08t02:15:15+00:00-e2fdae55054f0d7","title":"Domain Driven Design For Python","url":"https://www.pythonpodcast.com/domain-driven-design-episode-219","content_text":"Summary\nWhen your software projects start to scale it becomes a greater challenge to understand and maintain all of the pieces. In this episode Henry Percival shares his experiences working with domain driven design in large Python projects. He explains how it is helpful, and how you can start using it for your own applications. This was an informative conversation about software architecture patterns for large organizations and how they can be used by Python developers.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nAnd to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. With such an intuitive tool it’s easy to make sure that everyone in the business is on the same page. Podcast.init listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Harry Percival about domain driven design and enterprise application architecture in Python\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what \"application architecture\" is and how it compares to the types of application designs that Python developers and teams typically rely on? how does it contrast with \"enterprise architecture\"?\n\nWhat are the influences that tend to lead engineers into sub-optimal architectures and how can they guard against them?\n\n\nOne of the core concepts in this problem space is that of \"domain driven design\". Can you unpack that term and explain the benefits that it provides to software architecture?\nWhat are some of the other concepts that are common among application architecture patterns?\nWhat are some of the common points of confusion among engineers who are first working with DDD?\nIs there any particular size or scope of project and organization that merits the approach of domain driven design or is it applicable even at small scales of complexity and team size?\nNow that we’ve convinced everyone that they should be using DDD can you talk through the steps involved in identifying and encapsulating the various implementation details that they will need to work through?\n\nHow does that process change when dealing with an existing application as opposed to a \"greenfield\" project?\n\n\nHow do Python language constructs and libraries impact the approach to implementation of application architecture patterns as compared to more traditional \"enterprise\" languages such as Java and C#?\nWhat are some of the architectural anti-patterns to watch out for when implementing DDD?\nOn any given team, who is responsible for identifying and ensuring adherence to proper architectural principles?\nAre there any publicly visible projects that implement DDD which listeners can look at and learn from?\nTo help Python developers in their efforts to learn and implement DDD and other aspects of enterprise architecture you have been working on a book. Can you talk about your motivation for that undertaking, what listeners can expect to learn when the read it, and any challenges that you have encountered in the process?\nWhat are some trends in terms of system design and architecture, or technology influences, that you are keeping an eye on?\n\nKeep In Touch\n\n@hjwp on Twitter\nhjwp on GitHub\nWebsite\nLinkedIn\n\nPicks\n\nTobias\n\nDragon Pearl by Yoon Ha Lee\n\n\nHarry\n\nTremé\nWhy We Sleep: Unlocking The Power Of Sleep and Dreams by Matthew Walker PhD\n\n\n\nLinks\n\nMADE\nObey The Testing Goat\nPython Anywhere\nXP (eXtreme Programming)\nDjango\nDive Into Python\nDomain Driven Design\nDesign Patterns\nGang Of Four Book\nMVC (Model View Controller)\nMicroservices\nµCon\n\"Uncle\" Bob Martin\nClean Architecture book\nPython LEAP Book\nDependency Injection\nInversion Of Control\nTest Pyramid\nGary Bernhardt\n\nPodcast Interview\nFunctional Core, Imperative Shell\n\n\nHarry’s Blog\nThe \"Blue\" Book by Eric Evans\nGartner Hype Cycle\nThe Clean Architecture In Python by Leonardo Giordani\nDRY Python\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

When your software projects start to scale it becomes a greater challenge to understand and maintain all of the pieces. In this episode Henry Percival shares his experiences working with domain driven design in large Python projects. He explains how it is helpful, and how you can start using it for your own applications. This was an informative conversation about software architecture patterns for large organizations and how they can be used by Python developers.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview about software architecture patterns in Python for large and complex systems","date_published":"2019-07-07T23:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/6b7481b3-fcba-4d82-8dc1-a82e3c6bc112.mp3","mime_type":"audio/mpeg","size_in_bytes":44017086,"duration_in_seconds":3341}]},{"id":"podlove-2019-07-01t20:56:07+00:00-b72046b8acfe1c7","title":"Open Source Automated Machine Learning With MindsDB","url":"https://www.pythonpodcast.com/mindsdb-automated-machine-learning-episode-218","content_text":"Summary\nMachine learning is growing in popularity and capability, but for a majority of people it is still a black box that we don’t fully understand. The team at MindsDB is working to change this state of affairs by creating an open source tool that is easy to use without a background in data science. By simplifying the training and use of neural networks, and making their logic explainable, they hope to bring AI capabilities to more people and organizations. In this interview George Hosu and Jorge Torres explain how MindsDB is built, how to use it for your own purposes, and how they view the current landscape of AI technologies. This is a great episode for anyone who is interested in experimenting with machine learning and artificial intelligence. Give it a listen and then try MindsDB for yourself.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nAnd to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. With such an intuitive tool it’s easy to make sure that everyone in the business is on the same page. Podcast.init listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing George Hosu and Jorge Torres about MindsDB, a framework for streamlining the use of neural networks\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what MindsDB is and the problem that it is trying to solve?\n\nWhat was the motivation for creating the project?\n\n\nWho is the target audience for MindsDB?\nBefore we go deep into MindsDB can you explain what a neural network is for anyone who isn’t familiar with the term?\nFor someone who is using MindsDB can you talk through their workflow?\n\nWhat are the types of data that are supported for building predictions using MindsDB?\nHow much cleaning and preparation of the data is necessary before using it to generate a model?\nWhat are the lower and upper bounds for volume and variety of data that can be used to build an effective model in MindsDB?\n\n\nOne of the interesting and useful features of MindsDB is the built in support for explaining the decisions reached by a model. How do you approach that challenge and what are the most difficult aspects?\nOnce a model is generated, what is the output format and can it be used separately from MindsDB for embedding the prediction capabilities into other scripts or services?\nHow is MindsDB implemented and how has the design changed since you first began working on it?\n\nWhat are some of the assumptions that you made going into this project which have had to be modified or updated as it gained users and features?\n\n\nWhat are the limitations of MindsDB and what are the cases where it is necessary to pass a task on to a data scientist?\nIn your experience, what are the common barriers for individuals and organizations adopting machine learning as a tool for addressing their needs?\nWhat have been the most challenging, complex, or unexpected aspects of designing and building MindsDB?\nWhat do you have planned for the future of MindsDB?\n\nKeep In Touch\n\nGeorge\n\nBlog\nGeorge3d6 on GitHub\n@Cerebralab2 on Twitter\nLinkedIn\n\n\nJorge\n\nLinkedIn\n\n\nMindsDB\n\nWebsite\n@mindsdb on Twitter\nmindsdb on GitHub\n\n\n\nPicks\n\nTobias\n\nBose QuietComfort 25 noise cancelling headphones\n\n\nGeorge\n\nOpen CourseWare – Brain And Cognitive Sciences\nCerebralab Blog\n\n\nJorge\n\nLightwood\nMKDocs with Google Material Templates\n\n\n\nLinks\n\nMindsDB\n\nGitHub\n\n\n3Blue1Brown – Neural Networks\nThink Bayes\nBackpropagation\nReverse Automatic Differentiation\nLudwig deep learning toolbox\nLightwood\nTensorflow\nPyTorch\n\nPodcast Interview\n\n\nAerospike\nscikit-learn\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Machine learning is growing in popularity and capability, but for a majority of people it is still a black box that we don’t fully understand. The team at MindsDB is working to change this state of affairs by creating an open source tool that is easy to use without a background in data science. By simplifying the training and use of neural networks, and making their logic explainable, they hope to bring AI capabilities to more people and organizations. In this interview George Hosu and Jorge Torres explain how MindsDB is built, how to use it for your own purposes, and how they view the current landscape of AI technologies. This is a great episode for anyone who is interested in experimenting with machine learning and artificial intelligence. Give it a listen and then try MindsDB for yourself.

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Interview

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Keep In Touch

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Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about building an open source automated machine learning tool at MindsDB","date_published":"2019-07-01T17:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/57ee1a6c-7400-49ee-bc56-1f71fe7ebb06.mp3","mime_type":"audio/mpeg","size_in_bytes":43277480,"duration_in_seconds":3490}]},{"id":"podlove-2019-06-24t02:21:32+00:00-2f2db633b45a344","title":"Behind The Scenes At The Python Software Foundation","url":"https://www.pythonpodcast.com/python-software-foundation-episode-217","content_text":"Summary\nOne of the secrets of the success of Python the language is the tireless efforts of the people who work with and for the Python Software Foundation. They have made it their mission to ensure the continued growth and success of the language and its community. In this episode Ewa Jodlowska, the executive director of the PSF, discusses the history of the foundation, the services and support that they provide to the community and language, and how you can help them succeed in their mission.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nAnd to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. With such an intuitive tool it’s easy to make sure that everyone in the business is on the same page. Podcast.init listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.\nBots and automation are taking over whole categories of online interaction. Discover.bot is an online community designed to serve as a platform-agnostic digital space for bot developers and enthusiasts of all skill levels to learn from one another, share their stories, and move the conversation forward together. They regularly publish guides and resources to help you learn about topics such as bot development, using them for business, and the latest in chatbot news. For newcomers to the space they have the Beginners Guide To Bots that will teach you the basics of how bots work, what they can do, and where they are developed and published. To help you choose the right framework and avoid the confusion about which NLU features and platform APIs you will need they have compiled a list of the major options and how they compare. Go to pythonpodcast.com/discoverbot today to get started and thank them for their support of the show.\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Ewa Jodlowska about the Python Software Foundation and the role that it serves in the language and community\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what the PSF is for anyone who isn’t familiar with it?\n\nHow did you get involved with the PSF and what is your current role?\n\n\nWhat was the motivation for creating the PSF?\nWhat are the primary responsibilities of the PSF?\n\nHow has the scope and scale of the responsibilities for the PSF shifted in the years since its foundation?\n\n\nWhat is the relationship between the PSF and the language core developers?\nWhat are some reasons that someone would want to become a member of the PSF and what is involved in gaining membership?\nWhat are the challenges confronted by you and the PSF, currently and in the recent past?\nWhat are you most worried about and most proud of in the PSF, the core language, or the community?\nWhat challenges or changes do you foresee for the PSF in the near to medium future?\nWhat are some of the most interesting/unexpected/challenging lessons that you have learned while working with the PSF?\nHow are the PSF and the PSU (Python Secret Underground) related?\nOutside of the PSF, how can the community contribute to the health and longevity of the language, its ecosystem, and its community?\n\nKeep In Touch\n\nEwa\n\n@ewa_jodlowska on Twitter\nEmail\n\n\nThe Python Software Foundation\n\nWebsite\n@thepsf on Twitter\nBlog\n\n\n\nPicks\n\nTobias\n\nRussell Keith-Magee PyCon 2019 Keynote\n\n\nEwa\n\nDonate To The PSF\n\n\n\nLinks\n\nThe PSF\nInformix\nPHP\nPyCon\nPyLadies\nPyPI\nDenmark\nPSF Mission Statement\nChiPy\nBrett Cannon PyCon 2018 Keynote\nMozilla Open Source Support Fund\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

One of the secrets of the success of Python the language is the tireless efforts of the people who work with and for the Python Software Foundation. They have made it their mission to ensure the continued growth and success of the language and its community. In this episode Ewa Jodlowska, the executive director of the PSF, discusses the history of the foundation, the services and support that they provide to the community and language, and how you can help them succeed in their mission.

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Announcements

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Interview

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Keep In Touch

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Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about how the Python Software Foundation helps the language and community and how you can help them in their mission","date_published":"2019-06-23T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/6899be5c-63f6-47e5-98a9-a66efe12682c.mp3","mime_type":"audio/mpeg","size_in_bytes":27625149,"duration_in_seconds":2251}]},{"id":"podlove-2019-06-15t13:17:35+00:00-6fedba6d17ecf83","title":"Algorithmic Trading In Python Using Open Tools And Open Data","url":"https://www.pythonpodcast.com/quantconnect-algorithmic-trading-episode-216","content_text":"Summary\nAlgorithmic trading is a field that has grown in recent years due to the availability of cheap computing and platforms that grant access to historical financial data. QuantConnect is a business that has focused on community engagement and open data access to grant opportunities for learning and growth to their users. In this episode CEO Jared Broad and senior engineer Alex Catarino explain how they have built an open source engine for testing and running algorithmic trading strategies in multiple languages, the challenges of collecting and serving currrent and historical financial data, and how they provide training and opportunity to their community members. If you are curious about the financial industry and want to try it out for yourself then be sure to listen to this episode and experiment with the QuantConnect platform for free.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nAnd to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. With such an intuitive tool it’s easy to make sure that everyone in the business is on the same page. Podcast.init listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.\nThe Python Software Foundation is the lifeblood of the community, supporting all of us who want to run workshops and conferences, run development sprints or meetups, and ensuring that PyCon is a success every year. They have extended the deadline for their 2019 fundraiser until June 30th and they need help to make sure they reach their goal. Go to pythonpodcast.com/psf today to make a donation. If you’re listening to this after June 30th of 2019 then consider making a donation anyway!\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Jared Broad and Alex Catarino about QuantConnect, a platform for building and testing algorithmic trading strategies on open data and cloud resources\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what QuantConnect is and how the business got started?\nWhat is your mission for the company?\nI know that there are a few other entrants in this market. Can you briefly outline how you compare to the other platforms and maybe characterize the state of the industry?\nWhat are the main ways that you and your customers use Python?\nFor someone who is new to the space can you talk through what is involved in writing and testing a trading algorithm?\nCan you talk through how QuantConnect itself is architected and some of the products and components that comprise your overall platform?\nI noticed that your trading engine is open source. What was your motivation for making that freely available and how has it influenced your design and development of the project?\nI know that the core product is built in C# and offers a bridge to Python. Can you talk through how that is implemented?\n\nHow do you address latency and performance when bridging those two runtimes given the time sensitivity of the problem domain?\n\n\nWhat are the benefits of using Python for algorithmic trading and what are its shortcomings?\n\nHow useful and practical are machine learning techniques in this domain?\n\n\nCan you also talk through what Alpha Streams is, including what makes it unique and how it benefits the users of your platform?\nI appreciate the work that you are doing to foster a community around your platform. What are your strategies for building and supporting that interaction and how does it play into your product design?\nWhat are the categories of users who tend to join and engage with your community?\nWhat are some of the most interesting, innovative, or unexpected tactics that you have seen your users employ?\nFor someone who is interested in getting started on QuantConnect what is the onboarding process like?\n\nWhat are some resources that you would recommend for someone who is interested in digging deeper into this domain?\n\n\nWhat are the trends in quantitative finance and algorithmic trading that you find most exciting and most concerning?\nWhat do you have planned for the future of QuantConnect?\n\nKeep In Touch\n\nJared\n\nLinkedIn\n@jaredbroad on Twitter\n\n\nAlex\n\nAlexCatarino on GitHub\nLinkedIn\n@AlexCatx on Twitter\n\n\nQuantConnect\n\n@QuantConnect on Twitter\nWebsite\n\n\n\nPicks\n\nTobias\n\nGood Omens book and miniseries\n\n\nJared\n\nChernobyl HBO Series\n\n\nAlex\n\nThe 100\n\n\n\nLinks\n\nQuantConnect\nLEAN algorithm engine\nAlpha Streams\nGoogle Spanner\nPyCharm\nVisual Studio Code\nIronPython\nNumPy\nSymPy\nPandas\nPythonNet\nTensorflow\nKeras\nUdemy\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Algorithmic trading is a field that has grown in recent years due to the availability of cheap computing and platforms that grant access to historical financial data. QuantConnect is a business that has focused on community engagement and open data access to grant opportunities for learning and growth to their users. In this episode CEO Jared Broad and senior engineer Alex Catarino explain how they have built an open source engine for testing and running algorithmic trading strategies in multiple languages, the challenges of collecting and serving currrent and historical financial data, and how they provide training and opportunity to their community members. If you are curious about the financial industry and want to try it out for yourself then be sure to listen to this episode and experiment with the QuantConnect platform for free.

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Announcements

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Interview

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Keep In Touch

\n\n

Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about building an open source engine and an open data platform for algorithmic trading and the power of community at QuantConnect","date_published":"2019-06-16T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/87ef2177-9e0a-40cc-b070-2964aafa0783.mp3","mime_type":"audio/mpeg","size_in_bytes":32118714,"duration_in_seconds":3043}]},{"id":"podlove-2019-06-08t22:47:56+00:00-2985ac7bb4ec2c4","title":"Web Application Development Entirely In Python With Anvil","url":"https://www.pythonpodcast.com/anvil-web-application-development-episode-215","content_text":"Summary\nThe knowledge and effort required for building a fully functional web application has grown at an accelerated rate over the past several years. This introduces a barrier to entry that excludes large numbers of people who could otherwise be producing valuable and interesting services. To make the onramp easier Meredydd Luff and Ian Davies created Anvil, a platform for full stack web development in pure Python. In this episode Meredydd explains how the Anvil platform is built and how you can use it to build and deploy your own projects. He also shares some examples of people who were able to create profitable businesses themselves because of the reduced complexity. It was interesting to get Meredydd’s perspective on the state of the industry for web development and hear his vision of how Anvil is working to make it available for everyone.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nAnd to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. With such an intuitive tool it’s easy to make sure that everyone in the business is on the same page. Podcast.init listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.\nBots and automation are taking over whole categories of online interaction. Discover.bot is an online community designed to serve as a platform-agnostic digital space for bot developers and enthusiasts of all skill levels to learn from one another, share their stories, and move the conversation forward together. They regularly publish guides and resources to help you learn about topics such as bot development, using them for business, and the latest in chatbot news. For newcomers to the space they have the Beginners Guide To Bots that will teach you the basics of how bots work, what they can do, and where they are developed and published. To help you choose the right framework and avoid the confusion about which NLU features and platform APIs you will need they have compiled a list of the major options and how they compare. Go to pythonpodcast.com/discoverbot today to get started and thank them for their support of the show.\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.\nThe Python Software Foundation is the lifeblood of the community, supporting all of us who want to run workshops and conferences, run development sprints or meetups, and ensuring that PyCon is a success every year. They have extended the deadline for their 2019 fundraiser until June 30th and they need help to make sure they reach their goal. Go to pythonpodcast.com/psf today to make a donation. If you’re listening to this after June 30th of 2019 then consider making a donation anyway!\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Meredydd Luff about Anvil, platform for building full stack web applications entirely in Python\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what Anvil is and the story of how and why you created it?\nWeb applications come in a vast array of styles. What are the primary formats of web applications that Anvil supports building and what are its limitations?\nAre there certain categories of users that tend to gravitate toward Anvil?\n\nHow do you approach user experience design and overall usability given the varied backgrounds of your customers?\n\n\nFor someone who wants to use Anvil can you talk through a typical workflow and highlight the different components of the platform?\nCan you describe how Anvil itself is implemented and how it has evolved since you first began working on it?\n\nFor the javascript transpilation, are you using an existing project such as Transcrypt or PyJS, or did you develop your own?\n\n\nGiven that the Python dependencies on your servers are managed by how, how do you approach version upgrades to avoid breaking your customer’s applications?\nWhat are the main assumptions that you had going into the project and how have those assumptions been challenged or updated in the process of growing the business?\nWhat have been some of the biggest challenges that you have faced in the process of building and growing Anvil?\n\nWhat are some of the edge cases that you have run into while developing Anvil? (e.g. browser APIs, javascript <-> Python impedance mismatch, etc.)\n\n\nCan you talk through how you manage deployments of your customer’s applications?\nWhat are some of the features of Anvil that are often overlooked, under-utilized, or misunderstood which you think users would benefit from knowing about?\nWhat are some of the most interesting/innovative/unexpected ways that you have seen Anvil used?\nWhat are the limitations of Anvil and when is it the wrong choice?\nWhat do you have planned for the future of Anvil?\n\nKeep In Touch\n\n@meredydd on Twitter\nLinkedIn\nWebsite\nmeredydd on GitHub\n\nPicks\n\nTobias\n\nPipx\n\n\nMeredydd\n\nSkulpt\nPython in the Browser implementations generally\n\n\n\nLinks\n\nAnvil\nDelphi\nVisual Basic\nHuman-Computer Interaction\nAmazon RDS (Relational Database Service)\nBokeh\n\nPodcast Interview\n\n\nPlotly\nRaspberry Jam by the Raspberry Pi Foundation\nPyCharm\nWebsockets\nSkulpt\nComparing implementations of Python in the Browser on Python Tips\nBrython\nThe Matrix\nPyodide\nHow Skulpt works (PyCon 2017 Lightning Talk)\nHow Anvil’s autocompleter works (PyCon UK 2017 Lightning Talk)\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n\n\n","content_html":"

Summary

\n

The knowledge and effort required for building a fully functional web application has grown at an accelerated rate over the past several years. This introduces a barrier to entry that excludes large numbers of people who could otherwise be producing valuable and interesting services. To make the onramp easier Meredydd Luff and Ian Davies created Anvil, a platform for full stack web development in pure Python. In this episode Meredydd explains how the Anvil platform is built and how you can use it to build and deploy your own projects. He also shares some examples of people who were able to create profitable businesses themselves because of the reduced complexity. It was interesting to get Meredydd’s perspective on the state of the industry for web development and hear his vision of how Anvil is working to make it available for everyone.

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Announcements

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Interview

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Keep In Touch

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Picks

\n\n

Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about how Anvil simplifies web application development and how to build a full stack web app in Python","date_published":"2019-06-09T23:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/fc93830e-dd21-4d2b-8f73-957c797ccd67.mp3","mime_type":"audio/mpeg","size_in_bytes":48387302,"duration_in_seconds":3450}]},{"id":"podlove-2019-06-04t04:19:56+00:00-9c092e500b3525b","title":"Building A Business On Serverless Technology","url":"https://www.pythonpodcast.com/datacoral-serverless-technology-episode-214","content_text":"Summary\nServerless computing is a recent category of cloud service that provides new options for how we build and deploy applications. In this episode Raghu Murthy, founder of DataCoral, explains how he has built his entire business on these platforms. He explains how he approaches system architecture in a serverless world, the challenges that it introduces for local development and continuous integration, and how the landscape has grown and matured in recent years. If you are wondering how to incorporate serverless platforms in your projects then this is definitely worth your time to listen to.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nAnd to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. With such an intuitive tool it’s easy to make sure that everyone in the business is on the same page. Podcast.init listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.\nBots and automation are taking over whole categories of online interaction. Discover.bot is an online community designed to serve as a platform-agnostic digital space for bot developers and enthusiasts of all skill levels to learn from one another, share their stories, and move the conversation forward together. They regularly publish guides and resources to help you learn about topics such as bot development, using them for business, and the latest in chatbot news. For newcomers to the space they have the Beginners Guide To Bots that will teach you the basics of how bots work, what they can do, and where they are developed and published. To help you choose the right framework and avoid the confusion about which NLU features and platform APIs you will need they have compiled a list of the major options and how they compare. Go to pythonpodcast.com/discoverbot today to get started and thank them for their support of the show.\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.\nThe Python Software Foundation is the lifeblood of the community, supporting all of us who want to run workshops and conferences, run development sprints or meetups, and ensuring that PyCon is a success every year. They have extended the deadline for their 2019 fundraiser until June 30th and they need help to make sure they reach their goal. Go to pythonpodcast.com/psf2019 today to make a donation. If you’re listening to this after June 30th of 2019 then consider making a donation anyway!\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Raghu Murthy from DataCoral about his experience building and deploying a personalized SaaS platform on top of serverless technologies\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by giving a brief overview of DataCoral?\nBefore we get too deep can you share your definition of what types of technologies fall under the umbrella of \"serverless\"?\nHow are you using serverless technologies at DataCoral?\n\nHow has your usage evolved as your business and the underlying technologies have evolved?\n\n\nHow do serverless technologies impact your approach to application architecture?\nWhat are some of the main benefits for someone to target services such as Lambda?\n\nWhat is your litmus test for determining whether a given project would be a good fit for a Function as a Service platform?\n\n\nWhat are the most challenging aspects of running code on Lambda?\n\nWhat are some of the major design differences between running on Lambda vs the more familiar server-oriented paradigms?\nWhat are some of the other services that are most commonly used alongside Function as as Service (e.g. Lambda) to build full featured applications?\n\n\nWith serverless function platforms there is the cold start problem, can you explain what that means and some application design patterns that can help mitigate it?\nWhen building on cloud-based technologies, especially proprietary ones, local development can be a challenge. How are you handling that issue at DataCoral?\nIn addition to development this new deployment paradigm upends some of the traditional approaches to CI/CD. How are you approaching testing and deployment of your services?\n\nHow do you identify and maintain dependency graphs between your various microservices?\n\n\nIn addition to deployment, it is also necessary to track performance characteristics and error events across service boundaries. How are you managing observability and alerting in your product?\nWhat are you most excited for in the serverless space that listeners should know about?\n\nKeep In Touch\n\nLinkedIn\nMedium\n\nPicks\n\nTobias\n\nAvengers Endgame\n\n\nRaghu\n\nGolden State Warriors\n\n\n\nLinks\n\nDataCoral\n\nData Engineering Podcast Interview\n\n\nPerl\nAirflow\n\nPodcast Interview\n\n\nServerless Computing\nDynamoDB\nAurora\nSNS\nSQS\nLambda\nS3\nAPI Gateway\nEMR\nApache Hive\nAWS Glue\nRedShift\nSnowflakeDB\nHadoop\nFunction As A Service\nDistributed Systems\nConway’s Law\nSRE == Site Reliability Engineer\nRollbar\nAWS Batch\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Serverless computing is a recent category of cloud service that provides new options for how we build and deploy applications. In this episode Raghu Murthy, founder of DataCoral, explains how he has built his entire business on these platforms. He explains how he approaches system architecture in a serverless world, the challenges that it introduces for local development and continuous integration, and how the landscape has grown and matured in recent years. If you are wondering how to incorporate serverless platforms in your projects then this is definitely worth your time to listen to.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about the landscape of serverless technologies and how they impact software design and development","date_published":"2019-06-04T00:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/d19aa7ae-d3d3-4e3b-a096-7d7cdf038e0a.mp3","mime_type":"audio/mpeg","size_in_bytes":35431343,"duration_in_seconds":2833}]},{"id":"podlove-2019-05-27t11:31:16+00:00-94daba6601e8e49","title":"A Data Catalog For Your PyData Projects","url":"https://www.pythonpodcast.com/intake-data-catalog-episode-213","content_text":"Summary\nOne of the biggest pain points when working with data is getting is dealing with the boilerplate code to load it into a usable format. Intake encapsulates all of that and puts it behind a single API. In this episode Martin Durant explains how to use the Intake data catalogs for encapsulating source information, how it simplifies data science workflows, and how to incorporate it into your projects. It is a lightweight way to enable collaboration between data engineers and data scientists in the PyData ecosystem.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Martin Durant about Intake, a lightweight package for finding, investigating, loading and disseminating data\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what Intake is and the story behind its creation?\n\nCan you outline some of the other projects and products that intersect with the functionality of Intake and describe where it fits in terms of use case and capabilities? (e.g. Quilt Data, Arrow, Data Retriever)\n\n\nCan you describe the workflows for using Intake, both from the data scientist and the data engineer perspective?\nOne of the persistent challenges in working with data is that of cataloging and discovery of what already exists. In what ways does Intake address that problem?\n\nDoes it have any facilities for capturing and exposing data lineage?\n\n\nFor someone who needs to customize their usage of Intake, what are the extension points and what is involved in building a plugin?\nCan you describe how Intake is implemented and how it has evolved since it first started?\n\nWhat are some of the most challenging, complex, or novel aspects of the Intake implementation?\n\n\nIntake focuses primarily on integrating with the PyData ecosystem (e.g. NumPy, Pandas, SciPy, etc.). What are some other communities that are, or could be, benefiting from the work being done on Intake?\n\nWhat are some of the assumptions that are baked into Intake that would need to be modified to make it more broadly applicable?\n\n\nWhat are some of the assumptions that were made going into this project that have needed to be reconsidered after digging deeper into the problem space?\nWhat are some of the most interesting/unexpected/innovative ways that you have seen Intake leveraged?\nWhat are your plans for the future of Intake?\n\nKeep In Touch\n\nmartindurant on GitHub\nWebsite\n@martin_durant_ on Twitter\n\nPicks\n\nTobias\n\nUbersuggest SEO tool\n\n\n\nLinks\n\nIntake\nAnaconda\nDask\n\nData Engineering Podcast Interview\n\n\nFast Parquet\nIDL\nSpace Telescope Institute\nBlaze\nQuilt Data\n\nPodcast Interview\n\n\nArrow\nData Retriever\n\nPodcast Interview\n\n\nParquet\n\nData Engineering Podcast Interview\n\n\nDataFrame\nApache Spark\nDremio\n\nData Engineering Podcast Interview\n\n\nDat Project – distributed peer-to-peer data sharing\n\nData Engineering Podcast Interview\n\n\nGeoPandas\nXArray\nSolr\nStreamz\nPyViz\nS3FS\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

One of the biggest pain points when working with data is getting is dealing with the boilerplate code to load it into a usable format. Intake encapsulates all of that and puts it behind a single API. In this episode Martin Durant explains how to use the Intake data catalogs for encapsulating source information, how it simplifies data science workflows, and how to incorporate it into your projects. It is a lightweight way to enable collaboration between data engineers and data scientists in the PyData ecosystem.

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Announcements

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Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about how the Intake project makes it easier to load and analyze your data","date_published":"2019-05-27T07:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/ac5d0cee-d2c6-49da-83bb-bdbce48e09f2.mp3","mime_type":"audio/mpeg","size_in_bytes":36238783,"duration_in_seconds":3001}]},{"id":"podlove-2019-05-20t01:53:34+00:00-ef97aee880a6ba7","title":"Hardware Hacking Made Easy With CircuitPython","url":"https://www.pythonpodcast.com/circuitpython-hardware-hacking-episode-212","content_text":"Summary\nLearning to program can be a frustrating process, because even the simplest code relies on a complex stack of other moving pieces to function. When working with a microcontroller you are in full control of everything so there are fewer concepts that need to be understood in order to build a functioning project. CircuitPython is a platform for beginner developers that provides easy to use abstractions for working with hardware devices. In this episode Scott Shawcroft explains how the project got started, how it relates to MicroPython, some of the cool ways that it is being used, and how you can get started with it today. If you are interested in playing with low cost devices without having to learn and use C then give this a listen and start tinkering!\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Scott Shawcroft about CircuitPython, the easiest way to program microcontrollers\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what CircuitPython is and how the project got started?\n\nI understand that you work at Adafruit and I know that a number of their products support CircuitPython. What other runtimes do you support?\n\n\nMicrocontrollers have typically been the domain of C because of the resource and performance constraints. What are the benefits of using Python to program hardware devices?\nWith the wide availability of powerful computing platforms, what are the benefits of experimenting with microcontrollers and their peripherals?\nI understand that CircuitPython is a friendly fork of MicroPython. What have you changed in your version?\n\nHow do you structure your development to avoid conflicts with the upstream project?\nWhat are some changes that you have contributed back to MicroPython?\n\n\nWhat are some of the features of CircuitPython that make it easier for users to interact with sensors, motors, etc.?\nCircuitPython provides an easy on-ramp for experimenting with hardware projects. Is there a point where a user will outgrow it and need to move to a different language or framework?\nWhat are some of the most interesting/innovative/unexpected projects that you have seen people build using CircuitPython?\n\nAre there any cases of someone building and shipping a production grade project in CircuitPython?\n\n\nWhat have been some of the most interesting/challenging/unexpected aspects of building and maintaining CircuitPython?\nWhat is in store for the future of the project?\n\nKeep In Touch\n\n@tannewt on Twitter\nWebsite\ntannewt on GitHub\n\nPicks\n\nTobias\n\nWings Of Fire book series\n\n\nScott\n\nBrandon Sanderson\nThe Wheel Of Time Series\nMist Born\n\n\n\nLinks\n\nAdafruit\nCircuitPython\nMicroPython\n\nPodcast Interview\n\n\nMicrocontroller\nArduino\nMicrosoft MakeCode\nNodeBots\nEspruino\nI2C\nHackspace Magazine\nAdafruit Blinka\nlearn.adafruit.com\nScott Hanselman Blog Post\nReflow Oven\nAdafruit Crickit – Creative Robotics Platform\nAdabox\nMoore’s Law\nSparkFun\nDigiKey\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Learning to program can be a frustrating process, because even the simplest code relies on a complex stack of other moving pieces to function. When working with a microcontroller you are in full control of everything so there are fewer concepts that need to be understood in order to build a functioning project. CircuitPython is a platform for beginner developers that provides easy to use abstractions for working with hardware devices. In this episode Scott Shawcroft explains how the project got started, how it relates to MicroPython, some of the cool ways that it is being used, and how you can get started with it today. If you are interested in playing with low cost devices without having to learn and use C then give this a listen and start tinkering!

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about recapturing the creativity and excitement of the early years of computing by using Python to experiment with microcontrollers","date_published":"2019-05-19T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/b4efe80e-4393-4565-bdce-29f2d6a8e316.mp3","mime_type":"audio/mpeg","size_in_bytes":39988592,"duration_in_seconds":3245}]},{"id":"podlove-2019-05-12t22:05:26+00:00-333a4819bcbc3c5","title":"Building A Privacy Preserving Voice Assistant","url":"https://www.pythonpodcast.com/snips-voice-assistant-episode-211","content_text":"Summary\nBeing able to control a computer with your voice has rapidly moved from science fiction to science fact. Unfortunately, the majority of platforms that have been made available to consumers are controlled by large organizations with little incentive to respect users’ privacy. The team at Snips are building a platform that runs entirely off-line and on-device so that your information is always in your control. In this episode Adrien Ball explains how the Snips architecture works, the challenges of building a speech recognition and natural language understanding toolchain that works on limited resources, and how they are tackling issues around usability for casual consumers. If you have been interested in taking advantage of personal voice assistants, but wary of using commercially available options, this is definitely worth a listen.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Adrien Ball about SNIPS, a set of technologies to make voice controlled systems that respect user’s privacy\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what the Snips is and how it got started?\nFor someone who wants to use Snips can you talk through the onboarding proces?\n\nOne of the interesting features of your platform is the option for automated training data generation. Can you explain how that works?\n\n\nCan you describe the overall architecture of the Snips platform and how it has evolved since you first began working on it?\nTwo of the main components that can be used independently are the ASR (Automated Speech Recognition) and NLU (Natural Language Understanding) engines. Each of those have a number of competitors in the market, both open source and commercial. How would you describe your overall position in the market for each of those projects?\nI know that one of the biggest challenges in conversational interfaces is maintaining context for multi-step interactions. How is that handled in Snips?\nFor the NLU engine, you recently ported it from Python to Rust. What was your motivation for doing so and how would you characterize your experience between the two languages?\n\nAre you continuing to maintain both implementations and if so how are you maintaining feature parity?\n\n\nHow do you approach the overall usability and user experience, particularly for non-technical end users?\n\nHow is discoverability handled (e.g. finding out what capabilities/skills are available)\n\n\nOne of the compelling aspects of Snips is the ability to deploy to a wide variety of devices, including offline support. Can you talk through that deployment process, both from a user perspective and how it is implemented under the covers?\n\nWhat is involved in updating deployed models and keeping track of which versions are deployed to which devices?\n\n\nWhat is involved in adding new capabilities or integrations to the Snips platform?\nWhat are the limitations of running everything offline and on-device?\n\nWhen is Snips the wrong choice?\n\n\nIn the process of building and maintaining the various components of Snips, what have been some of the most useful/interesting/unexpected lessons that you have learned?\n\nWhat have been the most challenging aspects?\n\n\nWhat are some of the most interesting/innovative/unexpected ways that you have seen the Snips technologies used?\nWhat is in store for the future of Snips?\n\nKeep In Touch\n\nLinkedIn\nadrienball on GitHub\n@adrien_ball on Medium\n@adrien_ball on Twitter\n\nPicks\n\nTobias\n\nChrome OS\n\n\nAdrien\n\nGoogle I/O\nFacebook F8\nUser Privacy\n\n\n\nLinks\n\nSnips\n2048 Game\nSmart Cities\nRaspberry Pi\nWikiData\nMQTT\nGoogle Assistant\nAmazon Alexa\nMicrosoft Cortana\nMozilla Common Voice\nRust Language\nSnips Hermes messaging library\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Being able to control a computer with your voice has rapidly moved from science fiction to science fact. Unfortunately, the majority of platforms that have been made available to consumers are controlled by large organizations with little incentive to respect users’ privacy. The team at Snips are building a platform that runs entirely off-line and on-device so that your information is always in your control. In this episode Adrien Ball explains how the Snips architecture works, the challenges of building a speech recognition and natural language understanding toolchain that works on limited resources, and how they are tackling issues around usability for casual consumers. If you have been interested in taking advantage of personal voice assistants, but wary of using commercially available options, this is definitely worth a listen.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about how the Snips team are building an offline first voice assistant that respects your privacy","date_published":"2019-05-12T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/82fa8662-adf8-4a8a-8830-4107d185badd.mp3","mime_type":"audio/mpeg","size_in_bytes":32872793,"duration_in_seconds":3387}]},{"id":"podlove-2019-05-07t01:25:22+00:00-e56b741bb127626","title":"Hacking The Government With The USDS","url":"https://www.pythonpodcast.com/usds-government-software-episode-210","content_text":"Summary\nThe U.S. government has a vast quantity of software projects across the various agencies, and many of them would benefit from a modern approach to development and deployment. The U.S. Digital Services Agency has been tasked with making that happen. In this episode the current director of engineering for the USDS, David Holmes, explains how the agency operates, how they are using Python in their efforts to provide the greatest good to the largest number of people, and why you might want to get involved. Even if you don’t live in the U.S.A. this conversation is worth listening to so you can see an interesting model of how to improve government services for everyone.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nBots and automation are taking over whole categories of online interaction. Discover.bot is an online community designed to serve as a platform-agnostic digital space for bot developers and enthusiasts of all skill levels to learn from one another, share their stories, and move the conversation forward together. They regularly publish guides and resources to help you learn about topics such as bot development, using them for business, and the latest in chatbot news. For newcomers to the space they have the Beginners Guide To Bots that will teach you the basics of how bots work, what they can do, and where they are developed and published. To help you choose the right framework and avoid the confusion about which NLU features and platform APIs you will need they have compiled a list of the major options and how they compare. Go to pythonpodcast.com/discoverbot today to get started and thank them for their support of the show.\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing David Holmes about his work at the US Digital Services organization\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what the USDS is and how you got involved with it?\nThe terminology that is used around \"Tours of Service\" is interesting. Can you explain what that entails?\n\nrelocation\nwhat if you have a house and career?\n\n\nCan you explain the model of how the USDS works?\n\nWhat is involved in staffing a new project?\nWhat is your typical toolkit, and how does that vary with the specific departments that you are working with?\n\n\nWhat are some of the most interesting projects that you and the team at USDS have worked on?\nWhat are some of the most challenging projects that you have been involved with?\nWhat are some projects that you hope to be asked to work on?\n\nKeep In Touch\n\ndavideholmes on GitHub\n\nPicks\n\nTobias\n\nCaptain Marvel movie\n\n\nDavid\n\nAvengers: Endgame\nGame Of Thrones television series\n\n\n\nLinks\n\nUS Digital Services\nUS Digital Services Job Application\nUS Digital Services Projects\n18F\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

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The U.S. government has a vast quantity of software projects across the various agencies, and many of them would benefit from a modern approach to development and deployment. The U.S. Digital Services Agency has been tasked with making that happen. In this episode the current director of engineering for the USDS, David Holmes, explains how the agency operates, how they are using Python in their efforts to provide the greatest good to the largest number of people, and why you might want to get involved. Even if you don’t live in the U.S.A. this conversation is worth listening to so you can see an interesting model of how to improve government services for everyone.

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Interview

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Keep In Touch

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Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about how the US Digital Services Agency consults with the government to help them build better software","date_published":"2019-05-06T21:30:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/a37d3106-8665-498b-ab19-892179856269.mp3","mime_type":"audio/mpeg","size_in_bytes":25063891,"duration_in_seconds":2043}]},{"id":"podlove-2019-04-29t01:08:14+00:00-df3e6d4925e343b","title":"Probabilistic Modeling In Python (And What That Even Means)","url":"https://www.pythonpodcast.com/pymc3-probabilistic-modeling-episode-209","content_text":"Summary\nMost programming is deterministic, relying on concrete logic to determine the way that it operates. However, there are problems that require a way to work with uncertainty. PyMC3 is a library designed for building models to predict the likelihood of certain outcomes. In this episode Thomas Wiecki explains the use cases where Bayesian statistics are necessary, how PyMC3 is designed and implemented, and some great examples of how it is being used in real projects.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Thomas Wiecki about PyMC3, a project for probabilistic programming in Python\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what probabilistic programming is?\nWhat is the PyMC3 project and how did you get involved with it?\nThe opening line for the project README is packed with a slew of terms that are rather opaque to the lay-person. Can you unpack that a bit and discuss some of the ways that PyMC3 is used in real-world projects?\nHow much knowledge of statistical modeling and Bayesian statistics is necessary to make effective use of PyMC3?\nCan you talk through an example use case for PyMC3 to illustrate how you would use it in a project?\n\nHow does it compare to the way that you would approach the same problem in a deterministic or frequentist modeling framework?\n\n\nCan you describe how PyMC3 is implemented?\nThere are a number of other projects that build on top of PyMC3, what are some that you find particularly interesting or noteworthy?\nWhat do you find to be the most useful features of PyMC3 and what are some areas that you would like to see it improved?\nWhat have been the most interesting/unexpected/challenging lessons that you have learned in the process of building and maintaining PyMC3?\nWhat is in store for the future of PyMC3?\n\nKeep In Touch\n\nPyMC\n\nGitHub\nDiscourse Forum\n\n\nThomas\n\ntwiecki on GitHub\n@twiecki on Twitter\nWebsite\n\n\n\nPicks\n\nTobias\n\nFantastic Beasts And Where To Find Them\nFantastic Beasts: The Crimes Of Grindelwald\n\n\nThomas\n\nHyperion by Dan Simmons\nThe Mind Illuminated\n\n\n\nLinks\n\nPyMC3\nQuantopian\nUniversity of Tubingen\nMatLab\nProbabilistic Modeling\nProbability Distribution\nA/B Testing\nBayesian Statistics\nBeta Distribution\nBernoulli Distribution\nP-Value\nHamiltonian Monte Carlo sampling algorithm\nMetropolis Hastings Inference Algorithm\nTheano\nBayesian Methods For Hackers by Cameron Davidson-Pilon\nBayesian Analysis With Python by Osvaldo Martin\nTensorflow\nMXNet deep learning framework\n\nPyTorch\n\n\nTensorflow Probability\nBAMBI package to build generalized linear models\nPMProphet PyMC3 implementation of Facebook’s Prophet for timeseries prediction\nExoplanet\nBEAT (Bayesian Earthquake Analysis Tool)\nPyMC3 in Google Summer of Code\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

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Most programming is deterministic, relying on concrete logic to determine the way that it operates. However, there are problems that require a way to work with uncertainty. PyMC3 is a library designed for building models to predict the likelihood of certain outcomes. In this episode Thomas Wiecki explains the use cases where Bayesian statistics are necessary, how PyMC3 is designed and implemented, and some great examples of how it is being used in real projects.

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Interview

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Keep In Touch

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Picks

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about Bayesian statistics, probabilistic modeling, and when to use them","date_published":"2019-04-28T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/5fcfc724-cb10-4014-a314-a43c697f7759.mp3","mime_type":"audio/mpeg","size_in_bytes":34332183,"duration_in_seconds":3289}]},{"id":"podlove-2019-04-22t18:07:08+00:00-c43a426adc4f4b1","title":"Exploring Indico: A Full Featured Event Management Platform","url":"https://www.pythonpodcast.com/indico-event-management-episode-208","content_text":"Summary\nManaging an event is rife with inherent complexity that scales as you move from scheduling a meeting to organizing a conference. Indico is a platform built at CERN to handle their efforts to organize events such as the Computing in High Energy Physics (CHEP) conference, and now it has grown to manage booking of meeting rooms. In this episode Adrian Mönnich, core developer on the Indico project, explains how it is architected to facilitate this use case, how it has evolved since its first incarnation two decades ago, and what he has learned while working on it. The Indico platform is definitely a feature rich and mature platform that is worth considering if you are responsible for organizing a conference or need a room booking system for your office.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nBots and automation are taking over whole categories of online interaction. Discover.bot is an online community designed to serve as a platform-agnostic digital space for bot developers and enthusiasts of all skill levels to learn from one another, share their stories, and move the conversation forward together. They regularly publish guides and resources to help you learn about topics such as bot development, using them for business, and the latest in chatbot news. For newcomers to the space they have the Beginners Guide To Bots that will teach you the basics of how bots work, what they can do, and where they are developed and published. To help you choose the right framework and avoid the confusion about which NLU features and platform APIs you will need they have compiled a list of the major options and how they compare. Go to pythonpodcast.com/discoverbot today to get started and thank them for their support of the show.\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Adrian Mönnich about Indico, the effortless open-source tool for event organisation, archival and collaboration\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what Indico is and how the project got started?\n\nWhat are some other projects which target a similar use case and what were they lacking that led to Indico being necessary?\n\n\nCan you talk through an example workflow for setting up and managing an event in Indico?\n\nHow does the lifecycle change when working with larger events, such as PyCon?\n\n\nCan you describe how Indico is architected and how its design has evolved since it was first built?\n\nWhat are some of the most complex or challenging portions of Indico to implement and maintain?\n\n\nThere are a lot of areas for exercising constraint resolution algorithms. Can you talk through some of the business logic of how that operates?\nMost of Indico is highly configurable and flexible. How do you approach managing sane defaults to prevent users getting overwhelmed when onboarding?\n\nWhat is your approach to testing given how complex the project is?\n\n\nWhat are some of the most interesting or unexpected ways that you have seen Indico used?\nWhat are some of the most interesting/unexpected lessons that you have learned in the process of building Indico?\nWhat do you have planned for the future of the project?\n\nKeep In Touch\n\nIndico\n\nWebsite\nGitHub\nIRC\n\n\nAdrian\n\nThiefMaster on GitHub\n\n\n\nPicks\n\nTobias\n\nMortal Engines movie\n\n\nAdrian\n\nVirtual Reality\nPortal VR\n\n\n\nLinks\n\nIndico\nTornado\n\nPodcast Interview\n\n\nCERN\nHigh Energy Physics\nCHEP (Computing in High Energy Physics) conference\nZODB\nPostgreSQL\n\nData Engineering Podcast Interview\n\n\nSQLAlchemy\nFlask\nWSGI == Web Server Gateway Interface\nMako Templates\nJinja\nReactJS\nStripe\nPaypal\nIndico Introduction Video\nReveal.js\nMod_Python\nZope\nDoodle\nLDAP == Lightweight Directory Access Protocol\nDaylight Saving Time\nIndico User Guide\nPy.Test\n\nPodcast Episode\n\n\nSelenium\nFlask Plugin Engine\nCERN Indico Plugins\nLinux Plumber’s Conference\nOpen SUSE\nF-Strings\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Managing an event is rife with inherent complexity that scales as you move from scheduling a meeting to organizing a conference. Indico is a platform built at CERN to handle their efforts to organize events such as the Computing in High Energy Physics (CHEP) conference, and now it has grown to manage booking of meeting rooms. In this episode Adrian Mönnich, core developer on the Indico project, explains how it is architected to facilitate this use case, how it has evolved since its first incarnation two decades ago, and what he has learned while working on it. The Indico platform is definitely a feature rich and mature platform that is worth considering if you are responsible for organizing a conference or need a room booking system for your office.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about the Indico platform built at CERN to organize conferences and archive presentation materials","date_published":"2019-04-22T14:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/c11ead04-ffcf-49f3-be68-3063d47a5b4e.mp3","mime_type":"audio/mpeg","size_in_bytes":30201343,"duration_in_seconds":3226}]},{"id":"podlove-2019-04-15t17:25:56+00:00-d27f8898460d48a","title":"Exploring Python's Internals By Rewriting Them In Rust","url":"https://www.pythonpodcast.com/rust-python-interpreter-episode-207","content_text":"Summary\nThe CPython interpreter has been the primary implementation of the Python runtime for over 20 years. In that time other options have been made available for different use cases. The most recent entry to that list is RustPython, written in the memory safe language Rust. One of the added benefits is the option to compile to WebAssembly, offering a browser-native Python runtime. In this episode core maintainers Windel Bouwman and Adam Kelly explain how the project got started, their experience working on it, and the plans for the future. Definitely worth a listen if you are curious about the inner workings of Python and how you can get involved in a relatively new project that is contributing to new options for running your code.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYour host is Tobias Macey and today I’m interviewing Adam Kelly and Windel Bouwman about RusPython, a project to implement a new Python interpreter in Rust\n\nInterview\n\nIntroduction\nHow did you get introduced to Python?\nCan you start by explaining what Rust is for anyone who isn’t familiar with it?\nHow did RustPython got started and what are your goals for the project?\nCan you discuss what is involved in implementing a fully compliant Python interpreter?\nWhat are some of the challenges that you face in replicating the capabilities of the CPython interpreter?\n\nAre you attempting to maintain bug parity?\nHow much of the stdlib needs to be reimplemented?\nCan you compare and contrast the benefits of Rust vs C?\nWill the end result be compatible with libraries that rely on C extensions such as NumPy?\n\n\nWhat is the current state of the project?\n\nWhat are some of the notable missing features?\n\n\nCan you talk through your vision of how the WebAssembly support will manifest and the types of applications that it will enable?\n\nHow much effort have you put into size optimization for the webassembly target to reduce client-side load time?\nAre there any existing options for minification of Python code so that it can be delivered to users with less bandwidth?\n\n\nWhat have been some of the most interesting/challenging/unexpected aspects of implementing a Python runtime?\nWhat do you have planned for the future of the project?\nWhat are the risks that you anticipate which could derail the project before it becomes production ready?\n\nContact Info\n\nWindel\n\nwindelbouwman on GitHub\nWebsite\n@windelbouwman on Twitter\n@windel@todon.nl on Mastodon\n\n\nAdam\n\ncthulahoops on GitHub\n@cthulahoops on Twitter\n\n\n\nPicks\n\nTobias\n\nOysterhead\n\n\nAdam\n\nFZF fuzzy finder\n\n\nWindel\n\nTQDM Python progress bar\n\n\n\nLinks\n\nRustPython\nWindel Presentation EuroPython\nRust\nC++\nRust Memory Safety\nMicroPython\n\nPodcast Episode\n\n\nPyPy\nOuroboros – Pure Python standard library\nWebAssembly\nlalrpop – Rust parser generator\nRust Crates\nPickItUp in-browser Python game engine\nQuickSilver Game Engine\nPEP 441\nJIT (Just-In-Time) Compilation\n\nThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

The CPython interpreter has been the primary implementation of the Python runtime for over 20 years. In that time other options have been made available for different use cases. The most recent entry to that list is RustPython, written in the memory safe language Rust. One of the added benefits is the option to compile to WebAssembly, offering a browser-native Python runtime. In this episode core maintainers Windel Bouwman and Adam Kelly explain how the project got started, their experience working on it, and the plans for the future. Definitely worth a listen if you are curious about the inner workings of Python and how you can get involved in a relatively new project that is contributing to new options for running your code.

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Announcements

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Interview

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Contact Info

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Picks

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Links

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The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with the core maintainers of the new RustPython interpreter about their experience rebuilding Python in Rust","date_published":"2019-04-15T13:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/e92dde52-9371-4315-846c-ed94e75ca1a5.mp3","mime_type":"audio/mpeg","size_in_bytes":29551762,"duration_in_seconds":2428}]},{"id":"podlove-2019-04-07t19:54:19+00:00-07e2952f3518a03","title":"Version Control For Your Machine Learning Projects","url":"https://www.pythonpodcast.com/data-version-control-episode-206","content_text":"Summary\nVersion control has become table stakes for any software team, but for machine learning projects there has been no good answer for tracking all of the data that goes into building and training models, and the output of the models themselves. To address that need Dmitry Petrov built the Data Version Control project known as DVC. In this episode he explains how it simplifies communication between data scientists, reduces duplicated effort, and simplifies concerns around reproducing and rebuilding models at different stages of the projects lifecycle. If you work as part of a team that is building machine learning models or other data intensive analysis then make sure to give this a listen and then start using DVC today.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nBots and automation are taking over whole categories of online interaction. Discover.bot is an online community designed to ​serve as a platform-agnostic digital space for bot developers and enthusiasts of all skill levels to learn from one another, share their stories, and move the conversation forward together. They regularly publish guides and resources to help you learn about topics such as bot development, using them for business, and the latest in chatbot news. For newcomers to the space they have the Beginners Guide To Bots that will teach you the basics of how bots work, what they can do, and where they are developed and published. To help you choose the right framework and avoid the confusion about which NLU features and platform APIs you will need they have compiled a list of the major options and how they compare. Go to pythonpodcast.com/discoverbot today to get started and thank them for their support of the show.\nYou listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Dmitry Petrov about DVC, an open source version control system for machine learning projects\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what DVC is and how it got started?\nHow do the needs of machine learning projects differ from other software applications in terms of version control?\nCan you walk through the workflow of a project that uses DVC?\n\nWhat are some of the main ways that it differs from your experience building machine learning projects without DVC?\n\n\nIn addition to the data that is used for training, the code that generates the model, and the end result there are other aspects such as the feature definitions and hyperparameters that are used. Can you discuss how those factor into the final model and any facilities in DVC to track the values used?\nIn addition to version control for software applications, there are a number of other pieces of tooling that are useful for building and maintaining healthy projects such as linting and unit tests. What are some of the adjacent concerns that should be considered when building machine learning projects?\nWhat types of metrics do you track in DVC and how are they collected?\n\nAre there specific problem domains or model types that require tracking different metric formats?\n\n\nIn the documentation it mentions that the data files live outside of git and can be managed in external storage systems. I’m wondering if there are any plans to integrate with systems such as Quilt or Pachyderm that provide versioning of data natively and what would be involved in adding that support?\nWhat was your motivation for implementing this system in Python?\n\nIf you were to start over today what would you do differently?\n\n\nBeing a venture backed startup that is producing open source products, what is the value equation that makes it worthwile for your investors?\nWhat have been some of the most interesting, challenging, or unexpected aspects of building DVC?\nWhat do you have planned for the future of DVC?\n\nKeep In Touch\n\ndmpetrov on GitHub\nBlog\n@fullstackml on Twitter\nLinkedIn\n\nPicks\n\nTobias\n\nOtter.ai\n\n\nDmitry\n\nGo outside and get some fresh air \n\n\n\nLinks\n\nDVC\nIterative.ai\nLinear Regression\nLogistic Regression\nC++\nPerl\nGit\nVersion Control System\nUber Michaelangelo\nDomino Data Lab\nGit LFS\nAUC == Area Under Curve metric for evaluating machine learning model performance\nWes McKinney Interview\nPyTorch\n\nPodcast Interview\n\n\nTensorflow\nTensorBoard\nMLFlow\nQuilt Data\n\nData Engineering Podcast Episode\n\n\nPachyderm\n\nData Engineering Podcast Episode\n\n\nApache Airflow\n\nPodcast Interview\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Version control has become table stakes for any software team, but for machine learning projects there has been no good answer for tracking all of the data that goes into building and training models, and the output of the models themselves. To address that need Dmitry Petrov built the Data Version Control project known as DVC. In this episode he explains how it simplifies communication between data scientists, reduces duplicated effort, and simplifies concerns around reproducing and rebuilding models at different stages of the projects lifecycle. If you work as part of a team that is building machine learning models or other data intensive analysis then make sure to give this a listen and then start using DVC today.

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Interview

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Keep In Touch

\n\n

Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with the creator of DVC about how it improves collaboration and reduces duplicate effort on data science teams","date_published":"2019-04-08T06:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/64dd0873-75c3-4fd7-9424-aa6b48f87dcb.mp3","mime_type":"audio/mpeg","size_in_bytes":29314589,"duration_in_seconds":2679}]},{"id":"podlove-2019-04-01t02:05:21+00:00-a54d266a9050228","title":"Building Scalable Ecommerce Sites On Saleor","url":"https://www.pythonpodcast.com/saleor-ecommerce-episode-205","content_text":"Summary\nEcommerce is an industry that has largely faded into the background due to its ubiquity in recent years. Despite that, there are new trends emerging and room for innovation, which is what the team at Mirumee focuses on. To support their efforts, they build and maintain the open source Saleor framework for Django as a way to make the core concerns of online sales easy and painless. In this episode Mirek Mencel and Patryk Zawadzki discuss the projects that they work on, the current state of the ecommerce industry, how Saleor fits with their technical and business strategy, and their predictions for the near future of digital sales.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nCheck out the Practical AI podcast from our friends at Changelog Media to learn and stay up to date with what’s happening in AI\nYou listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.\nYour host as usual is Tobias Macey and today I’m interviewing Mirek Mencel and Patryk Zawadzki about their work at Mirumee, building ecommerce applications in Python, based on their open source framework Saleor\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing the types of projects that you work on at Mirumee and how the company got started?\nThere are a number of libraries and frameworks that you build and maintain. What is your motivation for providing these components freely and how does that play into your overall business strategy?\nThe most substantial project that you maintain is Saleor. Can you describe what it is and the story behind its creation?\n\nHow does it compare to other ecommerce implementations in the Python space?\nIf someone is agnostic to language and web framework, what would make them choose Saleor over other options that would be available to them?\n\n\nWhat are some of the most challenging aspects of building a successful ecommerce platform?\n\nHow do the technical needs of an ecommerce site differ as it grows from small to medium and large scale?\n\n\nWhich components of an online store are often overlooked?\nOne of the common features of ecommerce sites that can drive substantial revenue is a well-built recommender system. What are some best practice strategies that you have discovered during your client work?\nWhat are some projects that you have seen built with Saleor that were particular interesting, innovative, or unexpected?\nWhat are your predictions for the future of the ecommerce industry?\nWhat do you have planned for the future of the Saleor framework and the Mirumee business?\n\nKeep In Touch\n\nMirumee\n\nWebsite\nGithub\n@mirumeelabs on Twitter\n\n\nMirek\n\n@mirekmencel on Twitter\nmirekm on GitHub\n\n\nPatryk\n\npatrys on GitHub\n@patrys on Twitter\nWebsite\n\n\n\nPicks\n\nTobias\n\nWreck It Ralph: Ralph Breaks The Internet\n\n\nMirek\n\nA Guide To The Good Life: The Ancient Art Of Stoic Joy by William B. Irvine\n\n\nPatryk\n\nRelease It: Design And Deploy Production Ready Software by Michael Nygard\n\n\n\nLinks\n\nMirumee\nSaleor\nDjango\nPHP\nPyramid web framework\nPylons\nMagento eCommerce platform\nEcommerce\nSatchmo\nSatchless\nPrices library for handling price data\nFrench National Assembly\nDjango Oscar\n\nPodcast Interview\n\n\nDavid Winterbottom\nEbay\nAmazon\nEtsy\nShopify\nAriadne GraphQL framework for Python\nGraphene GraphQL framework for Python\n\nPodcast Interview\n\n\nApollo JavaScript GraphQL framework\nPWA == Progressive Web Apps\nSKU == Stock Keeping Unit\nCollective Intelligence\nElasticsearch\n\nData Engineering Podcast Interview\n\n\nA/B Testing\nRoom Lab store built on Saleor\nAugmented Reality\nWebGL\nSaleor Cloud\nASGI\n\nPodcast Interview\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Ecommerce is an industry that has largely faded into the background due to its ubiquity in recent years. Despite that, there are new trends emerging and room for innovation, which is what the team at Mirumee focuses on. To support their efforts, they build and maintain the open source Saleor framework for Django as a way to make the core concerns of online sales easy and painless. In this episode Mirek Mencel and Patryk Zawadzki discuss the projects that they work on, the current state of the ecommerce industry, how Saleor fits with their technical and business strategy, and their predictions for the near future of digital sales.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview about the state of ecommerce and the Saleor framework for Python and Django","date_published":"2019-03-31T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/ac06616e-1597-400b-86c8-5cb4c12e02ec.mp3","mime_type":"audio/mpeg","size_in_bytes":43179109,"duration_in_seconds":3482}]},{"id":"podlove-2019-03-25t15:12:50+00:00-6597ec4c7c38bd5","title":"A Quick Python Check-in With Naomi Ceder","url":"https://www.pythonpodcast.com/naomi-ceder-quick-python-episode-20","content_text":"Summary\nNaomi Ceder was fortunate enough to learn Python from Guido himself. Since then she has contributed books, code, and mentorship to the community. Currently she serves as the chair of the board to the Python Software Foundation, leads an engineering team, and has recently completed a new draft of the Quick Python Book. In this episode she shares her story, including a discussion of her experience as a technical author and a detailed account of the role that the PSF plays in supporting and growing the community.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nCheck out the Practical AI podcast from our friends at Changelog Media to learn and stay up to date with what’s happening in AI\nYou listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.\nYour host as usual is Tobias Macey and today I’m interviewing Naomi Ceder about her career and contributions in the Python community\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nHow are you using Python in your current day-to-day?\nYou have been working with Python for a long time at this point, and you have become very involved in supporting and growing the community. What is your motivation for dedicating so much of your time and energy into work that isn’t directly related to paying the bills?\nYou have been the chair of the PSF for a few years now. What are your responsibilities in that position?\nWhat do you find to be the most under-rated, misunderstood, or overlooked activities of the PSF?\n\nHow much of the success of the Python language and its community can be attributed to the presence and support of the PSF?\n\n\nIn addition to the work you do with the PSF, other community activities, and your day job, you have also written the 2nd and 3rd editions of the Quick Python Book. Can you give a synopsis of what the book covers and the audience that it is intended for?\nIn the process of writing the book and updating it between revisions, what are some of the features of the language or standard library that you discovered or learned more about which you have been able to use in your work?\nWhat are some of the other language communities that you have been involved with and what lessons have you learned from them that you would like to see reflected in Python?\nWhat are some of the other projects that you have been involved with that you are most proud of, whether technical or otherwise?\nWhat are you most excited about in the near to medium future?\n\nKeep In Touch\n\n@NaomiCeder on Twitter\nWeb\n\nQuick Python Book\n\nGet 40% off everything at Manning with code podinit19 at checkout\nEnter to win a free copy\n\nPicks\n\nTobias\n\nInkscape vector graphics editor\n\n\nNaomi\n\nLa Casa de las Flores (House of Flowers) (Netflix)\n\n\n\nLinks\n\nThe Quick Python Book\nDick Blick Art Materials\nThe PSF\n@ThePSF on Twitter\nManning Publishers\nPEP8\nETL\nCollections Module\nTurtle Library\nPyCon Hatchery\nPyCon Charlas\n\nPodcast Episode\n\n\nThe GIL\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Naomi Ceder was fortunate enough to learn Python from Guido himself. Since then she has contributed books, code, and mentorship to the community. Currently she serves as the chair of the board to the Python Software Foundation, leads an engineering team, and has recently completed a new draft of the Quick Python Book. In this episode she shares her story, including a discussion of her experience as a technical author and a detailed account of the role that the PSF plays in supporting and growing the community.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Quick Python Book

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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\"\"

","summary":"An interview with Naomi Ceder about her career and contributions to the Python community","date_published":"2019-03-25T12:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/54f0710a-40ec-4ae9-b1e6-bad3e00d9ddc.mp3","mime_type":"audio/mpeg","size_in_bytes":23327308,"duration_in_seconds":2312}]},{"id":"podlove-2019-03-18t10:22:14+00:00-d4832c8c1550ebf","title":"Wes McKinney's Career In Python For Data Analysis","url":"https://www.pythonpodcast.com/wes-mckinney-python-for-data-analysis-episode-203","content_text":"Summary\nPython has become one of the dominant languages for data science and data analysis. Wes McKinney has been working for a decade to make tools that are easy and powerful, starting with the creation of Pandas, and eventually leading to his current work on Apache Arrow. In this episode he discusses his motivation for this work, what he sees as the current challenges to be overcome, and his hopes for the future of the industry.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nCheck out the Practical AI podcast from our friends at Changelog Media to learn and stay up to date with what’s happening in AI\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with O’Reilly Media for the Strata conference in San Francisco on March 25th and the Artificial Intelligence conference in NYC on April 15th. Here in Boston, starting on May 17th, you still have time to grab a ticket to the Enterprise Data World, and from April 30th to May 3rd is the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.\nYour host as usual is Tobias Macey and today I’m interviewing Wes McKinney about his contributions to the Python community and his current projects to make data analytics easier for everyone\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nYou have spent a large portion of your career on building tools for data science and analytics in the Python ecosystem. What is your motivation for focusing on this problem domain?\nHaving been an open source author and contributor for many years now, what are your current thoughts on paths to sustainability?\nWhat are some of the common challenges pertaining to data analysis that you have experienced in the various work environments and software projects that you have been involved in?\n\nWhat area(s) of data science and analytics do you find are not receiving the attention that they deserve?\n\n\nRecently there has been a lot of focus and excitement around the capabilities of neural networks and deep learning. In your experience, what are some of the shortcomings or blind spots to that class of approach that would be better served by other classes of solution?\nYour most recent work is focused on the Arrow project for improving interoperability across languages. What are some of the cases where a Python developer would want to incorporate capabilities from other runtimes?\n\nDo you think that we should be working to replicate some of those capabilities into the Python language and ecosystem, or is that wasted effort that would be better spent elsewhere?\n\n\nNow that Pandas has been in active use for over a decade and you have had the opportunity to get some space from it, what are your thoughts on its success?\n\nWith the perspective that you have gained in that time, what would you do differently if you were starting over today?\n\n\nYou are best known for being the creator of Pandas, but can you list some of the other achievements that you are most proud of?\nWhat projects are you most excited to be working on in the near to medium future?\nWhat are your grand ambitions for the future of the data science community, both in and outside of the Python ecosystem?\nDo you have any parting advice for active or aspiring data scientists, or resources that you would like to recommend?\n\nKeep In Touch\n\nwesm on GitHub\nWebsite\n@wesmckinn on Twitter\n\nPicks\n\nTobias\n\nRoald Dahl\n\n\nWes\n\nThe Soul Of A New Machine by Tracy Kidder\n\n\n\nLinks\n\nUrsa Labs\nPandas\n\nPodcast Interview with Jeff Reback\nPandas Extension Arrays Interview with Tom Augsburger\n\n\nAQR Capital Management\nDistributed Computing\nSQL\nExcel\nDuke University\nAppNexus\nChang She\nIbis\nOpen Source Governance\nApache Software Foundation\nPaul Graham\nSchlep Blindness\nBig Data File Formats\n\nAvro\nParquet\nORC\nData Engineering Podcast Episode\n\n\nApache Arrow\nHadoop\nSpark\n\nData Engineering Podcast Episode\n\n\nApache Impala\nR Language\nRuby\nRust\nPandas 2.0 Design Docs\nApache Arrow and the 10 Things I Hate About Pandas\nGeoPandas\nStatsmodels\nPython For Data Analysis by Wes McKinney\n2 Sigma\nR Studio\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Python has become one of the dominant languages for data science and data analysis. Wes McKinney has been working for a decade to make tools that are easy and powerful, starting with the creation of Pandas, and eventually leading to his current work on Apache Arrow. In this episode he discusses his motivation for this work, what he sees as the current challenges to be overcome, and his hopes for the future of the industry.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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\"\"

","summary":"An interview with Wes Mckinney about the path that led him from Pandas to Apache Arrow, and everything in between","date_published":"2019-03-18T06:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/9399fd34-a204-4ade-9577-3620fd406eb7.mp3","mime_type":"audio/mpeg","size_in_bytes":35413695,"duration_in_seconds":3104}]},{"id":"podlove-2019-03-10t21:41:36+00:00-4cae84363cc98f0","title":"The Past, Present, and Future of Deep Learning In PyTorch","url":"https://www.pythonpodcast.com/pytorch-deep-learning-epsiode-202","content_text":"Summary\nThe current buzz in data science and big data is around the promise of deep learning, especially when working with unstructured data. One of the most popular frameworks for building deep learning applications is PyTorch, in large part because of their focus on ease of use. In this episode Adam Paszke explains how he started the project, how it compares to other frameworks in the space such as Tensorflow and CNTK, and how it has evolved to support deploying models into production and on mobile devices.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nCheck out the Practical AI podcast from our friends at Changelog Media to learn and stay up to date with what’s happening in AI\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with O’Reilly Media for the Strata conference in San Francisco on March 25th and the Artificial Intelligence conference in NYC on April 15th. Here in Boston, starting on May 17th, you still have time to grab a ticket to the Enterprise Data World, and from April 30th to May 3rd is the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.\nYour host as usual is Tobias Macey and today I’m interviewing Adam Paszke about PyTorch, an open source deep learning platform that provides a seamless path from research prototyping to production deployment\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what deep learning is and how it relates to machine learning and artificial intelligence?\nCan you explain what PyTorch is and your motivation for creating it?\n\nWhy was it important for PyTorch to be open source?\n\n\nThere is currently a large and growing ecosystem of deep learning tools built for Python. Can you describe the current landscape and how PyTorch fits in relation to projects such as Tensorflow and CNTK?\n\nWhat are some of the ways that PyTorch is different from Tensorflow and CNTK, and what are the areas where these frameworks are converging?\n\n\nHow much knowledge of machine learning, artificial intelligence, or neural network topologies are necessary to make use of PyTorch?\n\nWhat are some of the foundational topics that are most useful to know when getting started with PyTorch?\n\n\nCan you describe how PyTorch is architected/implemented and how it has evolved since you first began working on it?\n\nYou recently reached the 1.0 milestone. Can you talk about the journey to that point and the goals that you set for the release?\n\n\nWhat are some of the other components of the Python ecosystem that are most commonly incorporated into projects based on PyTorch?\nWhat are some of the most novel, interesting, or unexpected uses of PyTorch that you have seen?\nWhat are some cases where PyTorch is the wrong choice for a problem?\nWhat is the process for incorporating these new techniques and discoveries into the PyTorch framework?\n\nWhat are the areas of active research that you are most excited about?\n\n\nWhat are some of the most interesting/useful/unexpected/challenging lessons that you have learned in the process of building and maintaining PyTorch?\nWhat do you have planned for the future of PyTorch?\n\nKeep In Touch\n\napaszke on GitHub\n@apaszke on Twitter\nLinkedIn\n\nPicks\n\nTobias\n\nUn Lun Dun by China Miéville\n\n\nAdam\n\nIn Praise Of Copying by Marcus Boon\n\n\n\nLinks\n\nPyTorch\nUniversity of Warsaw\nPoland\nPolish Olympiad In Informatics\nDeep Learning\nAutomatic Differentiation\nTorch 7\nLua\nTensorflow\nCNTK\nTensorflow 2\nCaffe2\nEPFL (Ecole polytechnique fédérale de Lausanne)\nFast.ai\nTorchScript\nONNX\nTransfer Learning\nC++\nReinforcement Learning\nNumPy\nSciPy\nMatPlotLib\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

The current buzz in data science and big data is around the promise of deep learning, especially when working with unstructured data. One of the most popular frameworks for building deep learning applications is PyTorch, in large part because of their focus on ease of use. In this episode Adam Paszke explains how he started the project, how it compares to other frameworks in the space such as Tensorflow and CNTK, and how it has evolved to support deploying models into production and on mobile devices.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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","summary":"An interview with the creator of the popular PyTorch deep learning framework","date_published":"2019-03-10T17:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/34e86dc8-0371-4297-9db2-0d67b5cf1c7e.mp3","mime_type":"audio/mpeg","size_in_bytes":36751012,"duration_in_seconds":2532}]},{"id":"podlove-2019-03-04t18:31:11+00:00-026503dc738075b","title":"How To Include Redis In Your Application Architecture","url":"https://www.pythonpodcast.com/redis-python-application-architecture-episode-201","content_text":"Summary\nThe Redis database recently celebrated its 10th birthday. In that time it has earned a well-earned reputation for speed, reliability, and ease of use. Python developers are fortunate to have a well-built client in the form of redis-py to leverage it in their projects. In this episode Andy McCurdy and Dr. Christoph Zimmerman explain the ways that Redis can be used in your application architecture, how the Python client is built and maintained, and how to use it in your projects.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nAnd to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with O’Reilly Media for the Strata conference in San Francisco on March 25th and the Artificial Intelligence conference in NYC on April 15th. Here in Boston, starting on May 17th, you still have time to grab a ticket to the Enterprise Data World, and from April 30th to May 3rd is the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.\nYour host as usual is Tobias Macey and today I’m interviewing Andy McCurdy and Christoph Zimmerman about the Redis database, and some of the various ways that it is used by Python developers\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what Redis is and how you got involved in the project?\nHow does the redis-py project relate to the Redis database and what motivated you to create the Python client?\nWhat are some of the main use cases that Redis enables?\nCan you describe how Redis-py is implemented and some of the primitives that it provides for building applications on top of?\n\nHow do the release cycles of redis-py and the Redis database relate to each other?\nHow closely does redis-py match the features of the Redis database?\nWhat are some of the convenience methods or features that you have added to make the client more Pythonic?\n\n\nRedis is often used as a key/value cache for web applications, in some cases replacing Memcached. What are the characteristics of Redis that lend themselves well to this purpose?\n\nWhat are some edge cases or gotchas that users should be aware of?\n\n\nWhat are some of the common points of confusion or difficulties when storing and retrieving values in Redis?\nWhat have been some of the most challenging aspects of building and maintaining the Redis Python client?\nWhat are some of the anti-patterns that you have seen around how developers build on top of Redis?\nWhat are some of the most interesting or unexpected ways that you have seen Redis used?\nWhat are some of the least used or most misunderstood features of Redis that you think developers should know about?\nWhat are some of the recent and near-future improvements or features in Redis that you are most excited by?\n\nKeep In Touch\n\nAndy\n\n@andymccurdy on Twitter\nandymccurdy on GitHub\n\n\nChristoph\n\nchrisAtRedis on GitHub\nLinkedIn\n\n\n\nPicks\n\nTobias\n\nRowan Atkinson\n\n\nAndy\n\nThe Food Lab: Better Home Cooking Through Science by J. Kenji Lopez-Alt\nDota 2 Auto Chess (Community Mod)\n\n\nChristoph\n\nIPA Infused With Grapefruit Juice\nredis-py\nSelenium Python client\nDaniel Suarez\n\nInflux\n\n\n\n\n\nLinks\n\nredis-py\nRedis DB\nRedis Labs\nPHP\nDjango\nReflective Operating System Architectures\nTCL\nPerl\nLinux\nMemcached\nNextCloud\nC programming language\nuWSGI\nFlask\nGevent\nPyPy\nre-json\nredis-graph\nRedis-search\nMongoDB\nBloom Filter\nhiredis\nRedis Sentinel HA plugin\nLua programming language\nOpenWRT\nLuCI\nMicroPython\n\nPodcast Episode\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

The Redis database recently celebrated its 10th birthday. In that time it has earned a well-earned reputation for speed, reliability, and ease of use. Python developers are fortunate to have a well-built client in the form of redis-py to leverage it in their projects. In this episode Andy McCurdy and Dr. Christoph Zimmerman explain the ways that Redis can be used in your application architecture, how the Python client is built and maintained, and how to use it in your projects.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview about the ways that Redis is used by Python developers","date_published":"2019-03-04T13:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/6768d963-3ecc-4996-9e55-949a8f284072.mp3","mime_type":"audio/mpeg","size_in_bytes":48783326,"duration_in_seconds":3670}]},{"id":"podlove-2019-02-25t02:21:06+00:00-83e264cf12fd17e","title":"Marshmallow Data Validation Library","url":"https://www.pythonpodcast.com/marshmallow-data-validation-episode-200","content_text":"Summary\nAny time that your program needs to interact with other systems it will have to deal with serializing and deserializing data. To prevent duplicate code and provide validation of the data structures that your application is consuming Steven Loria created the Marshmallow library. In this episode he explains how it is built, how to use it for rendering data objects to various serialization formats, and some of the interesting and unique ways that it is incorporated into other projects.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nAnd to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes and tell your friends and co-workers\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYou listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss the Strata conference in San Francisco on March 25th and the Artificial Intelligence conference in NYC on April 15th, both run by our friends at O’Reilly Media. Go to pythonpodcast.com/stratacon and pythonpodcast.com/aicon to register today and get 20% off\nYour host as usual is Tobias Macey and today I’m interviewing Steven Loria about Marshmallow, a Python serialization library that is agnostic to your framework and object mapper of choice\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what Marshmallow is and the history of the project?\n\nWhat are some of the capabilities that make it unique from other similar projects in the Python ecosystem?\n\n\nWhat are some of the main use cases for schematized serialization and deserialization?\nCan you walk through how a user would get started with Marshmallow, particularly for complex or nested schemas?\nCan you describe how Marshmallow is implemented?\n\nHow has that design evolved since you first began working on it?\nHow have the changes in the Python language and ecosystem impacted the requirements and use cases for Marshmallow?\n\n\nWhat are some of the most interesting or unexpected ways that you have seen Marshmallow used?\nWhat have been some of the most interesting, complex, or challenging aspects of building the Marshmallow project and community?\n\nWhat are lessons you’ve learned from maintaining marshmallow?\n\n\nWhat have been some of the benefits and drawbacks of keeping Marshmallow agnostic to any frameworks or object mappers?\nWhat are some of the edge cases that users of Marshmallow should be aware of?\nWhat are some of the little-known features of Marshmallow that you find most useful?\nWhat do you have planned for the future of Marshmallow?\n\nKeep In Touch\n\nEmail\nWebsite\n@sloria1 on Twitter\n\nPicks\n\nTobias\n\nSherlock BBC tv series\n\n\nSteven\n\nGreater Than Code podcast\n\n\n\nLinks\n\nMarshmallow\nButterfly Network\nBiology\nORM (Object Relational Mapper)\nODM (Object Document Mapper)\nWebargs\nAvro\nSwagger/OpenAPI\nREST (REpresentational State Transfer)\nJSON-Schema\nEnvirons\nDjango Rest Framework\nWTForms\nDynamoDB\nMongoDB\nEtsy’s boundary-layer for building Airflow DAGs from config files\n\nAirflow Podcast Episode\n\n\nToasted Marshmallow\n\nLyft Blog Post\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Any time that your program needs to interact with other systems it will have to deal with serializing and deserializing data. To prevent duplicate code and provide validation of the data structures that your application is consuming Steven Loria created the Marshmallow library. In this episode he explains how it is built, how to use it for rendering data objects to various serialization formats, and some of the interesting and unique ways that it is incorporated into other projects.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview about addressing the challenges of data serialization in Python","date_published":"2019-02-24T22:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/ed3fcb72-9fb6-45c1-ad3e-169f476edd02.mp3","mime_type":"audio/mpeg","size_in_bytes":23133399,"duration_in_seconds":2044}]},{"id":"podlove-2019-02-18t13:04:41+00:00-4d2f740245ef22c","title":"Unpacking The Python Toolkit For Chaos Engineering","url":"https://www.pythonpodcast.com/chaos-toolkit-chaos-engineering-episode-199","content_text":"Summary\nChaos engineering is the practice of injecting failures into your production systems in a controlled manner to identify weaknesses in your applications. In order to build, run, and report on chaos experiments Sylvain Hellegouarch created the Chaos Toolkit. In this episode he explains his motivation for creating the toolkit, how to use it for improving the resiliency of your systems, and his plans for the future. He also discusses best practices for building, running, and learning from your own experiments.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nAnd to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Sylvain Hellegouarch about Chaos Toolkit, a framework for building and automating chaos engineering experiments\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what Chaos Engineering is?\nWhat is the Chaos Toolkit and what motivated you to create it?\n\nHow does it compare to the Gremlin platform?\n\n\nWhat is the workflow for using Chos Toolkit to build and run an experiment?\n\nWhat are the best practices for building a useful experiment?\nOnce you have an experiment created, how often should it be executed?\n\n\nWhen running an experiment, what are some strategies for identifying points of failure, particularly if they are unexpected?\n\nWhat kinds of reporting and statistics are captured during a test run?\n\n\nCan you describe how Chaos Toolkit is implemented and how it has evolved since you began working on it?\nWhat are some of the most challenging aspects of ensuring that the experiments run via the Chaos Toolkit are safe and have a reliable rollback available?\nWhat have been some of the most interesting/useful/unexpected lessons that you have learned in the process of building and maintaining the Chaos Toolkit project and community?\nWhat do you have planned for the future of the project?\n\nKeep In Touch\n\nlawouach on GitHub\nBlog\n@lawouach on Twitter\nLinkedIn\n\nPicks\n\nTobias\n\nTime Trap\n\n\nSylvain\n\nPlaying Guitar\nStep away from the computer\n\n\n\nLinks\n\nChaos Toolkit\nChaos IQ\nGremlin chaos engineering service\nRuss Miles Chaos IQ co-founder\nZope\nCherryPy minimalist Python web framework\n\nCherrypy Essentials book\n\n\nChaos Engineering\nChaos Engineering Book\nDevOps\nSRE (Site Reliability Engineering)\nDark Debt\nNetflix Simian Army\nChaos Monkey\nTerraform\nKubecon\nIstio service mesh\nChaos Platform\nPyInstaller\nComposition vs Inheritance\nOpen Chaos Initiative\nCNCF\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Chaos engineering is the practice of injecting failures into your production systems in a controlled manner to identify weaknesses in your applications. In order to build, run, and report on chaos experiments Sylvain Hellegouarch created the Chaos Toolkit. In this episode he explains his motivation for creating the toolkit, how to use it for improving the resiliency of your systems, and his plans for the future. He also discusses best practices for building, running, and learning from your own experiments.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
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\"\"

","summary":"An interview about improving software reliability with chaos engineering experiments","date_published":"2019-02-18T08:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/0ed426e4-3af9-47d3-9e20-05ecb8fd8ba7.mp3","mime_type":"audio/mpeg","size_in_bytes":33645352,"duration_in_seconds":3579}]},{"id":"podlove-2019-02-11t19:08:48+00:00-a02525e00cdd8de","title":"Computational Musicology For Python Programmers","url":"https://www.pythonpodcast.com/music21-computational-musicology-episode-198","content_text":"Summary\nMusic is a part of every culture around the world and throughout history. Musicology is the study of that music from a structural and sociological perspective. Traditionally this research has been done in a manual and painstaking manner, but the advent of the computer age has enabled an increase of many orders of magnitude in the scope and scale of analysis that we can perform. The music21 project is a Python library for computer aided musicology that is written and used by MIT professor Michael Scott Cuthbert. In this episode he explains how the project was started, how he is using it personally, professionally, and in his lectures, as well as how you can use it for your own exploration of musical analysis.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nAnd to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Michael Cuthbert about music21, a toolkit for computer aided musicology\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what computational musicology is?\nWhat is music21 and what motivated you to create it?\n\nWhat are some of the use cases that music21 supports, and what are some common requests that you purposefully don’t support?\n\n\nHow much knowledge of musical notation, structure, and theory is necessary to be able to work with music21?\nCan you talk through a typical workflow for doing analysis of one or more pieces of existing music?\n\nWhat are some of the common challenges that users encounter when working with it (either on the side of Python or musicology/musical theory)?\nWhat about for doing exploration of new musical works?\n\n\nAs a professor at MIT, what are some of the ways that music21 has been incorporated into your classroom?\n\nWhat have they enjoyed most about it?\n\n\nHow is music21 implemented, and how has its structure evolved since you first started it?\n\nWhat have been the most challenging aspects of building and maintaining the music21 project and community?\n\n\nWhat are some of the most interesting, unusual, or unexpected ways that you have seen music21 used?\n\nWhat are some analyses that you have performed which yielded unexpected results?\n\n\nWhat do you have planned for the future of music21?\nBeyond computational analysis of musical theory, what are some of the other ways that you are using Python in your academic and professional pursuits?\n\nKeep In Touch\n\nmscuthbert on GitHub\n@mscuthbert on Twitter\n\nPicks\n\nTobias\n\nMozart’s Requiem performed by Berlin Philharmonik and conducted by Claudio Abbado\n\n\nMichael\n\nvon Karajan Institute – Karajan was a major conductor of the 60s — his Institute now sponsors research into new projects in music technology and are big advocates of using Python for their data analysis.\nRuth Crawford Seeger, String Quartet (1931) performed by The Playground Ensemble\n\n\n\nLinks\n\nmusic21\nStudies in Western Music History: Quantitative and Computational Approaches to Music History on MIT Open Courseware\nMIT\nPerl\nNational Bureau of Economic Research\nZen of Python\nMusicology\nMatplotlib\nOrange\n\nPodcast Episode\n\n\nscikit-learn\nAbjad Python Package\nSciPy\nnumpy\nPandas\n\nPodcast Episode\n\n\nPyLevenshtein\nLevenshtein Distance\nPyGame\nAVL Tree\nSubversion (SVN)\nBach Chorales\nArtusi.xyz Interactive Music Theory\nVexFlow\nMIT Digital Humanities\nNLTK\nFlask\nFortran\nDjango\nHumdrum\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Music is a part of every culture around the world and throughout history. Musicology is the study of that music from a structural and sociological perspective. Traditionally this research has been done in a manual and painstaking manner, but the advent of the computer age has enabled an increase of many orders of magnitude in the scope and scale of analysis that we can perform. The music21 project is a Python library for computer aided musicology that is written and used by MIT professor Michael Scott Cuthbert. In this episode he explains how the project was started, how he is using it personally, professionally, and in his lectures, as well as how you can use it for your own exploration of musical analysis.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"An interview about analyzing musical structure, history, and evolution in Python using music21","date_published":"2019-02-11T14:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/b90ef385-97ae-4e76-9129-9f5fcb2b1ba5.mp3","mime_type":"audio/mpeg","size_in_bytes":29328589,"duration_in_seconds":2868}]},{"id":"podlove-2019-02-04t01:45:30+00:00-51371d9bd8520d4","title":"Classic Computer Science For Pythonistas","url":"https://www.pythonpodcast.com/computer-science-in-python-episode-197","content_text":"Summary\nSoftware development is a career that attracts people from all backgrounds, and Python in particular helps to make it an approachable occupation. Because of the variety of paths that can be taken it is becoming increasingly common for practitioners to bypass the traditional computer science education. In this episode David Kopec discusses some of the classic problems that he has found most useful to understand in his work as a professor and practitioner of software engineering. He shares his motivation for writing the book \"Classic Computer Science Problems In Python\", the practical approach that he took, and an overview of how the contents can be used in your day-to-day work.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nAnd to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing David Kopec about his recent book \"Classic Computer Science Problems In Python\"\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by discussing your motivation for creating this book and the subject matter that it covers?\n\nHow do you define a \"classic\" computer science problem and what was your criteria for selecting the specific cases that you included in the book?\n\n\nWhat are your favorite features of the Python language, and which of them did you learn as part of the process of writing the examples for this book?\nWhich classes of problems have you found to be most difficult for your readers and students to master?\n\nWhich do you consider to be most relevant/useful to professional software engineers?\n\n\nI was pleasantly surprised to see introductory aspects of artificial intelligence included in the subject matter that you covered. How did you approach the challenge of making the underlying principles accessible to readers who don’t necessarily have a background in the related fields of mathematics?\nWhat are some of the most interesting or unexpected changes that you had to make in the process of adapting your examples from Swift to Python in order to make them appropriately idiomatic?\nBy aiming for an intermediate audience you free yourself of the need to incorporate fundamental aspects of programming, but there can be a wide variety of experiences at that level of experience. How did you approach the challenge of making the text accessible while still being accurate and engaging?\nWhat are some of the resources that you would recommend to readers who would like to continue learning about computer science after completing your book?\n\nKeep In Touch\n\n@davekopec on Twitter\nWebsite\n\nBook Discount And Giveaway\n\nUse code podinit19 to get 40% off all Manning products\n\nPicks\n\nTobias\n\nElementor\n\n\nDavid\n\nnesdev\nThe Curse of Oak Island\n\n\n\nLinks\n\nClassic Computer Science Problems in Python\nClassic Computer Science Problems in Swift\nDart For Absolute Beginners\nDart\nSwift\nManning Publications\nApress\nPython Data Classes\nPython Type Hints\nRecursion\nA* Search Algorithm\nNeural Network\nChamplain College\nBurlington, VT, USA\nHyperLoop\nData Structures And Algorithms In Python by Michael T. Goodrich, Roberto Tamassia, Michael H. Goldwasser\nMyPy\n\nPodcast Interview\n\n\nPyTorch\nMinimax\nDartmouth College\nBig O Notation\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

Software development is a career that attracts people from all backgrounds, and Python in particular helps to make it an approachable occupation. Because of the variety of paths that can be taken it is becoming increasingly common for practitioners to bypass the traditional computer science education. In this episode David Kopec discusses some of the classic problems that he has found most useful to understand in his work as a professor and practitioner of software engineering. He shares his motivation for writing the book "Classic Computer Science Problems In Python", the practical approach that he took, and an overview of how the contents can be used in your day-to-day work.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Book Discount And Giveaway

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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\"\"

","summary":"An interview with the author of "Classic Computer Science Problems In Python"","date_published":"2019-02-03T21:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/06d58b42-17db-4d3e-90c3-2c545726ea7d.mp3","mime_type":"audio/mpeg","size_in_bytes":38408992,"duration_in_seconds":2848}]},{"id":"podlove-2019-01-28t23:37:40+00:00-b266d89594a1472","title":"What You Need To Know About Open Source Licenses And Intellectual Property","url":"https://www.pythonpodcast.com/software-licenses-for-developers-episode-196","content_text":"Summary\nAs a developer and user of open source code, you interact with software and digital media every day. What is often overlooked are the rights and responsibilities conveyed by the intellectual property that is implicit in all creative works. Software licenses are a complicated legal domain in their own right, and they can often conflict with each other when you factor in the web of dependencies that your project relies on. In this episode Luis Villa, Co-Founder of Tidelift, explains the catagories of software licenses, how to select the right one for your project, and what to be aware of when you contribute to someone else’s code.\nAnnouncements\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nAnd to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Luis Villa about software licensing and intellectual property rules that developers need to know\n\nInterview\n\nIntroductions\nHow did you get started as a programmer?\nIntellectual property law and licensing of software, data, and media are complicated topics that are often poorly understood by developers. Can you start off by giving an overview of categories of intellectual property that we should be thinking of?\nMost of us who have created or used software, whether it is open or closed source, have at some point come across various licenses. What may not be immediately obvious is that there are degrees of compatibility between these licenses. What are some guiding principles for determining which licenses are in conflict?\n\nIn an organization, who is responsible for ensuring compliance with software and content licensing within a given project?\nWhen introducing new dependencies into a project or system what steps should be taken to evaluate license compatibility and compliance?\n\n\nWhen creating a new project, one of the steps in the process is to select a license. What are some useful guidelines or questions to determine which license to use?\nAnother aspect of software licensing that developers might run into is when contributing to an open source project where a contributor license agreement might be necessary. What should we be thinking about when deciding whether to sign such an agreement?\nIn addition to software libraries, developers might need to use content such as images, audio, or video in their projects which have their own copyright and licensing considerations. What are some of the things that we should be looking for in those situations?\nAnother component of our systems that has grown in its importance with the rise of advanced analytics is data. We may need to use open data sources, pay for access to data repositories, or provide access to data that is under our control. What are some common approaches to licensing or terms of use for these contexts?\n\nWhat should we be wary of when using or providing data in our applications?\n\n\nHow much of the work that you do at Tidelift is spent on educating developers and customers on the finer points of intellectual property management?\n\nWhat are some of the most common difficulties or points of confusion that you encounter?\n\n\nWhat are some useful resources that you would recommend to anyone who is interested in learning more about intellectual property and software licensing?\n\nKeep In Touch\n\nWebsite\n@luis_in_140 on Twitter\nLinkedIn\n\nPicks\n\nTobias\n\nSpider Man: Into The Spiderverse\n\n\nLuis\n\nThe Good Place\nTwitter and Teargas by Zeynep Tufecki\n\n\n\nLinks\n\nIntellectual Property and Open Source: A Practical Guide To Protecting Code by Van Lindberg\nTidelift\nBASIC\nApple //e\nCopyright\nTrademark\nPatent\nCopyleft\nOSI Approved Licenses\nPermissive Licenses\nStrong and Weak Copyleft\nSSPL (Server Side Public License)\nOSI (Open Source Initiative)\nContributor License Agreement\nFSF (Free Software Foundation)\nDCO (Developer Certificate of Origin)\nCreative Commons\nNoun Project\nFree Music Archive\nWikimedia Commons\nTL;DR Legal\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

As a developer and user of open source code, you interact with software and digital media every day. What is often overlooked are the rights and responsibilities conveyed by the intellectual property that is implicit in all creative works. Software licenses are a complicated legal domain in their own right, and they can often conflict with each other when you factor in the web of dependencies that your project relies on. In this episode Luis Villa, Co-Founder of Tidelift, explains the catagories of software licenses, how to select the right one for your project, and what to be aware of when you contribute to someone else’s code.

\n

Announcements

\n\n

Interview

\n\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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\"\"

","summary":"An Interview About Software Licenses And Intellectual Property For Developers","date_published":"2019-01-28T18:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/dcfef376-46f8-4f79-966d-f53a4b79820e.mp3","mime_type":"audio/mpeg","size_in_bytes":30251293,"duration_in_seconds":3778}]},{"id":"podlove-2019-01-21t12:12:35+00:00-d4f5ce2571a098a","title":"Counteracting Code Complexity With Wily","url":"https://www.pythonpodcast.com/wily-code-complexity-episode-195","content_text":"Summary\nAs we build software projects, complexity and technical debt are bound to creep into our code. To counteract these tendencies it is necessary to calculate and track metrics that highlight areas of improvement so that they can be acted on. To aid in identifying areas of your application that are breeding grounds for incidental complexity Anthony Shaw created Wily. In this episode he explains how Wily traverses the history of your repository and computes code complexity metrics over time and how you can use that information to guide your refactoring efforts.\nPreface\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nAnd to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Anthony Shaw about Wily, a command-line application for tracking and reporting on complexity of Python tests and applications\n\nInterview\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what Wily is and what motivated you to create it?\nWhat is software complexity and why should developers care about it?\n\nWhat are some methods for measuring complexity?\n\n\nI know that Python has the McCabe tool, but what other methods are there for determining complexity, both in Python and for other languages?\nWhat kinds of useful signals can you derive from evaluating historical trends of complexity in a codebase?\nWhat are some other useful metrics for tracking and maintaining the health of a software project?\nOnce you have established the points of complexity in your software, what are some strategies for remediating it?\nWhat are your favorite tools for refactoring?\nWhat are some of the aspects of developer-oriented tools that you have found to be most important in your own projects?\nWhat are your plans for the future of Wily, or any other tools that you have in mind to aid in producing healthy software?\n\nKeep In Touch\n\nanthonywritescode on GitHub\n@anthonypjshaw on Twitter\nWebsite\nMedium\n\nPicks\n\nTobias\n\nBaobab\nImpractical Jokers\n\n\nAnthony\n\nLine Of Duty\nFierce Girls\n\n\n\nLinks\n\nWily\nDimension Data\nPluralsight\nReal Python\nSeattle\nC#\nCyclomatic Complexity\nMcCabe\nGit\nC\nAssembly\nHalstead\nRadon\nThe Zen Of Python\nVocabulary Metric\nJava\nAnti Patterns\nGod Object\nPre-Commit\nCodeclimate\nGlom\nASQ\nPyCharm\nPyDocStyle\nPyLint\nBlack\nSunburst Chart\nVisual Studio Code\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n","content_html":"

Summary

\n

As we build software projects, complexity and technical debt are bound to creep into our code. To counteract these tendencies it is necessary to calculate and track metrics that highlight areas of improvement so that they can be acted on. To aid in identifying areas of your application that are breeding grounds for incidental complexity Anthony Shaw created Wily. In this episode he explains how Wily traverses the history of your repository and computes code complexity metrics over time and how you can use that information to guide your refactoring efforts.

\n

Preface

\n\n

Interview

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Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n
\n\n

\"\"

","summary":"A Command Line Tool To Monitor Code Complexity In Your Python Projects","date_published":"2019-01-21T08:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/41bb556d-4abe-4c16-be34-9ad305cfe9e8.mp3","mime_type":"audio/mpeg","size_in_bytes":25959164,"duration_in_seconds":2177}]},{"id":"podlove-2019-01-14t00:10:10+00:00-0155b41cc59da09","title":"Teaching Digital Archaeology With Jupyter Notebooks","url":"https://www.pythonpodcast.com/odate-digital-archaeology-textbook-episode-194","content_text":"Summary\n\nComputers have found their way into virtually every area of human endeavor, and archaeology is no exception. To aid his students in their exploration of digital archaeology Shawn Graham helped to create an online, digital textbook with accompanying interactive notebooks. In this episode he explains how computational practices are being applied to archaeological research, how the Online Digital Archaeology Textbook was created, and how you can use it to get involved in this fascinating area of research.\n\nIntroduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!\nAnd to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Shawn Graham about his work on the Online Digital Archaeology Textbook\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what digital archaeology is?\nTo facilitate your teaching you have collaborated on the O-DATE textbook and associated Jupyter notebooks. Can you describe what that resource covers and how the project got started?\nWhat have you found to be the most critical lessons for your students to help them be effective archaeologists?\n\nWhat are the most useful aspects of leveraging computational techniques in an archaeological context?\n\n\n\nCan you describe some of the sources and formats of data that would commonly be encountered by digital archaeologists?\nThe notebooks that accompany the text have a mixture of R and Python code. What are your personal guidelines for when to use each language?\nHow have the skills and tools of software engineering influenced your views and approach to research and education in the realm of archaeology?\nWhat are some of the most novel or engaging ways that you have seen computers applied to the field of archaeology?\nWhat are your goals and aspirations for the O-DATE project?\n\n\nKeep In Touch\n\n\nBlog\n@electricarchaeo on Twitter\n\n\nPicks\n\n\nTobias\n\nTaoTronics Noise Cancelling Earbuds\n\n\n\nShawn\n\n\nIan Rankin\nIn A House Of Lies\n\n\n\n\n\nLinks\n\n\nO-DATE Textbook\nCarleton University\nOttawa Canada\nSimulation Modeling\nAgent Based Modeling\nNetLogo\nComplexity Theory\nArchaeology\nDigital Archaeology\nThe Programming Historian\nUniversity of Western Ontario\nHistorical GIS\nArcGIS\nQGIS\nDigital Humanities\nProject Jupyter\n\nPodcast Episode\n\n\n\nBinder – Service for hosting Jupyter notebooks\nE-Campus Ontario\nGraph Databases\nSparQL\nOpenContext.org\nTDAR (The Digital Archaeology Record)\nR Language\nR OpenSci\nArrow\nPandas\n\n\nPodcast Episode\n\n\n\nNeural Networks\nGenerative Adversarial Networks\nComputer Vision\nArchaeogaming\nAlamagordo Atari Excavation\nLeiden University\nInteractive Pasts Conference\nPhotogrammetry\nLIDAR\nPalmyran Arch\nBen Marwick\nMatt Harris\nJolene Smith\nSara Perry\nRachel Opitz\nColleen Morgan\nPatrick Burns\nEthan Watrall\nAndrew Reinhard\nNeha Gupta\nKatherine Cook\nValue Foundation\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Computers have found their way into virtually every area of human endeavor, and archaeology is no exception. To aid his students in their exploration of digital archaeology Shawn Graham helped to create an online, digital textbook with accompanying interactive notebooks. In this episode he explains how computational practices are being applied to archaeological research, how the Online Digital Archaeology Textbook was created, and how you can use it to get involved in this fascinating area of research.

\n\n

Introduction

\n\n\n\n

Interview

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\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

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\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Using Jupyter Notebooks To Teach Computational Techniques To Archaeologists","date_published":"2019-01-13T19:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/a70734d0-bd41-49ab-87c8-c13cf56b5452.mp3","mime_type":"audio/mpeg","size_in_bytes":27681642,"duration_in_seconds":2975}]},{"id":"podlove-2019-01-07t02:37:55+00:00-568b47235d7372e","title":"Analyzing Satellite Image Data Using PyTroll","url":"https://www.pythonpodcast.com/pytroll-with-martin-raspaud-episode-193","content_text":"Summary\n\nEvery day there are satellites collecting sensor readings and imagery of our Earth. To help make sense of that information, developers at the meteorological institutes of Sweden and Denmark worked together to build a collection of Python packages that simplify the work of downloading and processing satellite image data. In this episode one of the core developers of PyTroll explains how the project got started, how that data is being used by the scientific community, and how citizen scientists like you are getting involved.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute.\nAnd to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Martin Raspaud about PyTroll, a suite of projects for processing earth observing satellite data\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what PyTroll is and how the overall project got started?\nWhat is the story behind the name?\nWhat are the main use cases for PyTroll? (e.g. types of analysis, research domains, etc.)\nWhat are the primary types of data that would be processed and analayzed with PyTroll? (e.g. images, sensor readings, etc.)\nWhen retrieving the data, are you communicating directly with the satellites, or are there facilities that fetch the information periodically which you can then interface with?\nHow do you locate and select which satellites you wish to retrieve data from?\nWhat are the main components of PyTroll and how do they fit together?\nFor someone processing satellite data with PyTroll, can you describe the workflow?\nWhat are some of the main data formats that are used by satellites?\nWhat tradeoffs are made between data density/expressiveness and bandwidth optimization?\nWhat are some of the common issues with data cleanliness or data integration challenges?\nOnce the data has been retrieved, what are some of the types of analysis that would be performed with PyTroll?\nAre there other tools that would commonly be used in conjunction with PyTroll?\nWhat are some of the unique challenges posed by working with satellite observation data?\nHow has the design and capability of the various PyTroll packages evolved since you first began working on it?\nWhat are some of the most interesting or unusual ways that you have seen PyTroll used?\nWhat are some of the lessons that you have learned while building PyTroll that you have found to be most useful or unexpected?\nWhat do you have planned for the future of PyTroll?\n\n\nKeep In Touch\n\n\nMartin\n\nmraspaud on GitHub\n@MartinRaspaud on Twitter\n\n\n\nPytroll\n\n\nWebsite\nSlack\nMailing List\n@PyTroll on Twitter\n\n\n\n\n\nPicks\n\n\nTobias\nTool\nA Perfect Circle\nMartin\nVulfpeck\n\n\nLinks\n\n\nPyTroll\nSwedish Meteorological and Hydrological Institute\nCommon Lisp\nDanish Meteorological Institute\nTrolls in Scandinavian Lore\nNumPy\nKISS (Keep It Simple Stupid)\nSpectroscopy\nRadiance\nPolar Orbiting Satellite\nGeostationary Satellite\nEUMETSAT\nSatPy\nPyResample\nCartographic Projection\nProj4\nGOES16\n[GOES17](https://en.wikipedia.org/wiki/GOES-17?utm_source=rss&utm_medium=rss\nDask\nData Engineering Podcast Episode\nNetCDF\nHDF5\nPySpectral\nPyCoast\nSupervisorD\nTrollCast\nEuropean Space Agency\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Every day there are satellites collecting sensor readings and imagery of our Earth. To help make sense of that information, developers at the meteorological institutes of Sweden and Denmark worked together to build a collection of Python packages that simplify the work of downloading and processing satellite image data. In this episode one of the core developers of PyTroll explains how the project got started, how that data is being used by the scientific community, and how citizen scientists like you are getting involved.

\n\n

Preface

\n\n\n\n

Interview

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Keep In Touch

\n\n

\n\n

Picks

\n\n\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Using Python To Process Satellite Image Data (Interview)","date_published":"2019-01-06T21:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/90b6b56a-f96b-485d-9dfc-b37629177606.mp3","mime_type":"audio/mpeg","size_in_bytes":32640007,"duration_in_seconds":2637}]},{"id":"podlove-2018-12-31t12:34:55+00:00-988fec6054e0adc","title":"Building GraphQL APIs in Python Using Graphene with Syrus Akbary","url":"https://www.pythonpodcast.com/graphene-with-syrus-akbary-episode-192","content_text":"Summary\n\nThe web has spawned numerous methods for communicating between applications, including protocols such as SOAP, XML-RPC, and REST. One of the newest entrants is GraphQL which promises a simplified approach to client development and reduced network requests. To make implementing these APIs in Python easier, Syrus Akbary created the Graphene project. In this episode he explains the origin story of Graphene, how GraphQL compares to REST, how you can start using it in your applications, and how he is working to make his efforts sustainable.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute.\nAnd to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Syrus Akbary about Graphene, a python library for building your APIs with GraphQL\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is GraphQL and what is the benefit vs a REST-based API?\n\nHow does it compare to specifications such as OpenAPI (formerly Swagger) or RAML?\n\n\n\nCan you explain what Graphene is and your motivation for building it?\n\n\nIn addition to the Python implementation there is also a JavaScript library. Is that primarily for use as a client or can it also be used in Node for serving APIs?\n\n\n\nWhat is involved in building a GraphQL API?\n\n\nWhat does Graphene do to simplify this process?\n\n\n\nHow is Graphene implemented and how has that evolved since you first started working on it?\n\n\nIs there a set of tests for verifying the compliance of Graphene or a specific API with the GraphQL specification?\n\n\n\nWhat are some of the most complex or confusing aspects of building a GraphQL API?\nWhat are some of the unique capabilities that are offered by building an application with GraphQL as the communication interface?\nWhile reading through documentation in preparation for our conversation I noticed the Quiver project. Can you explain what that is and how it fits with the other Graphene projects?\n\n\nWhat is it doing under the hood to optimize serving of the API?\n\n\n\nFor someone who is interested in adding a GraphQL interface to an existing application, what would be involved?\nThe documentation mentions creation of a schema, as well as defining queries. Is it possible for a client to craft queries that don’t match directly with those defined in the server layer?\nWhat are some of the most interesting or surprising uses of Graphene and GraphQL that you have seeen?\nWhat are some cases where it would be more practical to implement an API using REST instead of GraphQL?\nWhat are some references that you would recommend for anyone who wants to learn more about GraphQL and its ecosystem?\nWhat are your plans for the future of Graphene?\n\n\nKeep In Touch\n\n\nsyrusakbary on GitHub\nWebsite\n@syrusakbary on Twitter\n\n\nPicks\n\n\nTobias\n\nAudible\n\n\n\nSyrus\n\n\nWeb Assembly\n\n\n\n\n\nLinks\n\n\nGraphene\nGraphQL\nREST (REpresentational State Transfer\nOpenAPI\nRAML\nPHP\nFacebook Engineering\nGraphene-SQLAlchemy\nGraphene-Django\nGraphiQL\nPyJade\nDjango Rest Framework\nHow To GraphQL\nPython 3.7 Dataclasses\n\nGraphene GitHub Issue\n\n\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

The web has spawned numerous methods for communicating between applications, including protocols such as SOAP, XML-RPC, and REST. One of the newest entrants is GraphQL which promises a simplified approach to client development and reduced network requests. To make implementing these APIs in Python easier, Syrus Akbary created the Graphene project. In this episode he explains the origin story of Graphene, how GraphQL compares to REST, how you can start using it in your applications, and how he is working to make his efforts sustainable.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"GraphQL in Python Made Easy With Graphene (Interview)","date_published":"2018-12-31T07:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/5ecc4d12-b53f-441b-a543-8bcb27bd33de.mp3","mime_type":"audio/mpeg","size_in_bytes":38160250,"duration_in_seconds":3168}]},{"id":"podlove-2018-12-24t03:15:19+00:00-2cb357116c551cb","title":"AIORTC: An Asynchronous WebRTC Framework with Jeremy Lainé","url":"https://www.pythonpodcast.com/aiortc-with-jeremy-laine-episode-191","content_text":"Summary\n\nReal-time communication over the internet is an amazing feat of modern engineering. The protocol that powers a majority of video calling platforms is WebRTC. In this episode Jeremy Lainé explains why he wrote a Python implementation of this protocol in the form of AIORTC. He also discusses how it works, how you can use it in your own projects, and what he has planned for the future.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute.\nAnd to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Jeremy Lainé about AIORTC, an asynchronous implementation of the WebRTC and ObjectRTC protocols in Python\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what the WebRTC and ObjectRTC protocols are?\n\nWhat are some of the main use cases for these protocols?\n\n\n\nWhat is AIORTC and what was your motivation for creating it?\n\n\nHow does it compare to other implementations of the RTC protocols?\nWhy do you think there haven’t been any other Python implementations?\n\n\n\nWhat are some of the benefits of having a Python implementation of the RTC protocol?\nHow is AIORTC implemented?\n\n\nWhat have been some of the most difficult or challenging aspects of implementing a WebRTC compliant library?\nWhat are some of the most interesting or useful lessons that you have learned in the process?\n\n\n\nWhat is involved in building an application on top of AIORTC?\n\n\nWhat would be required to integrate AIORTC into an existing application built with something such as Flask or Django?\n\n\n\nWhat are some of the most interesting uses of AIORTC that you have seen?\nWhat are some of the projects that you would like to build with AIORTC?\nWhat are some cases where it would make more sense to use a different library or framework for your WebRTC projects?\nWhat are your plans for the future of AIORTC?\n\n\nKeep In Touch\n\n\njlaine on GitHub\nWebsite\n@JeremyLaine on Twitter\n\n\nPicks\n\n\nTobias\n\nTengger Cavalry\n\n\n\nJeremy\n\n\nPyAV\nMike Boers\n\n\n\n\n\nLinks\n\n\nAIORTC\nWebRTC\nElectrical Engineering\n[C](https://en.wikipedia.org/wiki/C_(programming_language)?utm_source=rss&utm_medium=rss\nC++\nPHP\nRuby\nSTUN (Session Traversal Utilities for NAT)\nTURN (Traversal Using Relays around NAT)\nICE (Internet Connectivity Establishment)\nTLS (Transport Layer Security)\nRTP (Real-time Transport Protocol)\nZencastr\nJitsi\nRawRTC\nAsyncIO\nAIOICE\nCryptography\n\nPodcast.init Episode\n\n\n\nOpenCV\nPyAV\nFFMPEG\nEdge Detection\nAsterisk\nRaspberry Pi\nDatagram Transport Security\nMozilla\nAugmented Reality\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Real-time communication over the internet is an amazing feat of modern engineering. The protocol that powers a majority of video calling platforms is WebRTC. In this episode Jeremy Lainé explains why he wrote a Python implementation of this protocol in the form of AIORTC. He also discusses how it works, how you can use it in your own projects, and what he has planned for the future.

\n\n

Preface

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Interview

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Building WebRTC Applications In Python Using AIORTC (Interview)","date_published":"2018-12-23T22:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/62a271da-6bde-42c5-b8ca-6dfd4d2e9e24.mp3","mime_type":"audio/mpeg","size_in_bytes":29168761,"duration_in_seconds":2450}]},{"id":"podlove-2018-12-17t03:06:59+00:00-c9ec6e048a2ce22","title":"Polyglot: Multi-Lingual Natural Language Processing with Rami Al-Rfou","url":"https://www.pythonpodcast.com/polyglot-with-rami-al-rfou-episode-190","content_text":"Summary\n\nUsing computers to analyze text can produce useful and inspirational insights. However, when working with multiple languages the capabilities of existing models are severely limited. In order to help overcome this limitation Rami Al-Rfou built Polyglot. In this episode he explains his motivation for creating a natural language processing library with support for a vast array of languages, how it works, and how you can start using it for your own projects. He also discusses current research on multi-lingual text analytics, how he plans to improve Polyglot in the future, and how it fits in the Python ecosystem.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute.\nAnd to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Rami Al-Rfou about Polyglot, a natural language pipeline with support for an impressive amount of languages\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what Polyglot is and your reasons for starting the project?\nWhat are the types of use cases that Polyglot enables which would be impractical with something such as NLTK or SpaCy?\nA majority of NLP libraries have a limited set of languages that they support. What is involved in adding support for a given language to a natural language tool?\n\nWhat is involved in adding a new language to Polyglot?\nWhich families of languages are the most challenging to support?\n\n\n\nWhat types of operations are supported and how consistently are they supported across languages?\nHow is Polyglot implemented?\nIs there any capacity for integrating Polyglot with other tools such as SpaCy or Gensim?\nHow much domain knowledge is required to be able to effectively use Polyglot within an application?\nWhat are some of the most interesting or unique uses of Polyglot that you have seen?\nWhat have been some of the most complex or challenging aspects of building Polyglot?\nWhat do you have planned for the future of Polyglot?\nWhat are some areas of NLP research that you are excited for?\n\n\nKeep In Touch\n\nPicks\n\n\nTobias\n\nDuolingo\n\n\n\nRami\n\n\nThe Wizard and the Prophet: Two Remarkable Scientists and Their Dueling Visions to Shape Tomorrow’s World by Charles C. Mann\n\n\n\n\n\nLinks\n\n\nPolyglot\nPolyglot-NER\nJordan\nNLP (Natural Language Processing)\nStony Brook University\nArabic\nSentiment Analysis\nAssembly Language\nC\n.NET\nStack Overflow\nDeep Learning\nWord Embedding\nWikipedia\nWord2Vec\nNLTK (Python Natural Language Toolkit)\nSpaCy\n\nPodcast Episode\n\n\n\nGensim\n\n\nPodcast Episode\n\n\n\nMorphology\nMorpheme\nTransfer Learning\nRead The Docs\nBERT (Bidirectional Encoder Representations from Transformers)\nFastText\ndata.world\n\n\nData Engineering Podcast Episode\n\n\n\nQuilt package management for data\n\n\nData Engineering Podcast Episode\n\n\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Using computers to analyze text can produce useful and inspirational insights. However, when working with multiple languages the capabilities of existing models are severely limited. In order to help overcome this limitation Rami Al-Rfou built Polyglot. In this episode he explains his motivation for creating a natural language processing library with support for a vast array of languages, how it works, and how you can start using it for your own projects. He also discusses current research on multi-lingual text analytics, how he plans to improve Polyglot in the future, and how it fits in the Python ecosystem.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n

Picks

\n\n

\n\n

Links

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Multi-Language Text Analytics In Python Using Polyglot (Interview)","date_published":"2018-12-16T22:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/327ba212-9059-4311-8115-6a816d22c248.mp3","mime_type":"audio/mpeg","size_in_bytes":39696428,"duration_in_seconds":2621}]},{"id":"podlove-2018-12-10t04:33:35+00:00-4b96649a4ad1327","title":"Gnocchi: A Scalable Time Series Database For Your Metrics with Julien Danjou","url":"https://www.pythonpodcast.com/gnocchi-with-julien-danjou-episode-189","content_text":"Summary\n\nDo you know what your servers are doing? If you have a metrics system in place then the answer should be “yes”. One critical aspect of that platform is the timeseries database that allows you to store, aggregate, analyze, and query the various signals generated by your software and hardware. As the size and complexity of your systems scale, so does the volume of data that you need to manage which can put a strain on your metrics stack. Julien Danjou built Gnocchi during his time on the OpenStack project to provide a time oriented data store that would scale horizontally and still provide fast queries. In this episode he explains how the project got started, how it works, how it compares to the other options on the market, and how you can start using it today to get better visibility into your operations.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute.\nAnd to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nJoin the community in the new Zulip chat workspace at pythonpodcast.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Julien Danjou about Gnocchi, an open source time series database built to handle large volumes of system metrics\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what Gnocchi is and how the project got started?\n\nWhat was the motivation for moving Gnocchi out of the Openstack organization and into its own top level project?\n\n\n\nThe space of time series databases and metrics as a service platforms are both fairly crowded. What are the unique features of Gnocchi that would lead someone to deploy it in place of other options?\n\n\nWhat are some of the tools and platforms that are popular today which hadn’t yet gained visibility when you first began working on Gnocchi?\n\n\n\nHow is Gnocchi architected?\n\n\nHow has the design changed since you first started working on it?\nWhat was the motivation for implementing it in Python and would you make the same choice today?\n\n\n\nOne of the interesting features of Gnocchi is its support of resource history. Can you describe how that operates and the types of use cases that it enables?\n\n\nDoes that factor into the multi-tenant architecture?\n\n\n\nWhat are some of the drawbacks of pre-aggregating metrics as they are being written into the storage layer (e.g. loss of fidelity)?\n\n\nIs it possible to maintain the raw measures after they are processed into aggregates?\n\n\n\nOne of the challenging aspects of building a scalable metrics platform is support for high-cardinality data. What sort of labelling and tagging of metrics and measures is available in Gnocchi?\nFor someone who wants to implement Gnocchi for their system metrics, what is involved in deploying, maintaining, and upgrading it?\n\n\nWhat are the available integration points for extending and customizing Gnocchi?\n\n\n\nOnce metrics have been stored, aggregated, and indexed, what are the options for querying and analyzing the collected data?\nWhen is Gnocchi the wrong choice?\nWhat do you have planned for the future of Gnocchi?\n\n\nKeep In Touch\n\n\njd on GitHub\nWebsite\n@juldanjou on Twitter\n\n\nPicks\n\n\nTobias\n\nMarketplace Podcast\n\n\n\nJulien\n\n\nMergify\n\n\n\n\n\nLinks\n\n\nGnocchi\nRedHat\nOpenStack\nObject Oriented Programming\nO’Reilly\nDebian\nCeilometer\nPrometheus\nTime Series\nMySQL\nGerrit\nZuul\n\nPodcast Episode\n\n\n\nGitHub\nGitLab\nGraphite\n\n\nPodcast Episode\n\n\n\nDataDog\nRabbitMQ\nInfluxDB\nCeph\n\n\nPodcast Episode\n\n\n\nS3\nOpenStack Swift\nCassandra\nHoneycomb Observability Service\n\n\nPodcast Episode\n\n\n\nAMQP\nRedis\nDSL (Domain Specific Language)\nGolang\nRBAC (Role-Based Access Control)\nCollectD\nStatsD\nGnocchi Client\nTelegraf\nGrafana\nTimescaleDB\n\n\nPodcast Episode\n\n\n\nOpenStack Heat\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Do you know what your servers are doing? If you have a metrics system in place then the answer should be “yes”. One critical aspect of that platform is the timeseries database that allows you to store, aggregate, analyze, and query the various signals generated by your software and hardware. As the size and complexity of your systems scale, so does the volume of data that you need to manage which can put a strain on your metrics stack. Julien Danjou built Gnocchi during his time on the OpenStack project to provide a time oriented data store that would scale horizontally and still provide fast queries. In this episode he explains how the project got started, how it works, how it compares to the other options on the market, and how you can start using it today to get better visibility into your operations.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Building A Timeseries Database Optimized For Scale and Read Speed (Interview)","date_published":"2018-12-10T00:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/30e146a1-ba04-4c49-8b64-fdc78900f1f2.mp3","mime_type":"audio/mpeg","size_in_bytes":25323427,"duration_in_seconds":2356}]},{"id":"podlove-2018-12-03t03:20:24+00:00-9fa9d68f52e7df9","title":"Keeping Up With The Python Community For Fun And Profit with Dan Bader","url":"https://www.pythonpodcast.com/python-community-content-with-dan-bader-episode-188","content_text":"Summary\n\nKeeping up with the work being done in the Python community can be a full time job, which is why Dan Bader has made it his! In this episode he discusses how he went from working as a software engineer, to offering training, to now managing both the Real Python and PyCoders properties. He also explains his strategies for tracking and curating the content that he produces and discovers, how he thinks about building products, and what he has learned in the process of running his businesses.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nJoin the community in the new Zulip chat workspace at podcastinit.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Dan Bader about finding, filtering, and creating resources for Python developers at Real Python, PyCoders, and his own trainings\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nLet’s start by discussing your primary job these days and how you got to where you are.\n\nIn the past year you have also taken over management of the Real Python site. How did that come about and what are your responsibilities?\nYou just recently took over management of the PyCoders newsletter and website. Can you describe the events that led to that outcome and the responsibilities that came along with it?\n\n\n\nWhat are the synergies that exist between your various roles and projects?\n\n\nWhat are the areas of conflict? (e.g. time constraints, conflicts of interest, etc.)\n\n\n\nBetween PyCoders, Real Python, your training materials, your Python tips newsletter, and your coaching you have a lot of incentive to keep up to date with everything happening in the Python ecosystem. What are your strategies for content discovery?\n\n\nWith the diversity in use cases, geography, and contributors to the landscape of Python how do you work to counteract any bias or blindspots in your work?\n\n\n\nThere is a constant stream of information about any number of topics and subtopics that involve the Python language and community. What is your process for filtering and curating the resources that are ultimately included in the various media properties that you oversee?\nIn my experience with the podcast one of the most difficult aspects of maintaining relevance as a content creator is obtaining feedback from your audience. What do you do to foster engagement and facilitate conversations around the work that you do?\nYou have also built a few different product offerings. Can you discuss the process involved in identifying the relevant opportunities and the creation and marketing of them?\nCreating, collecting, and curating content takes a significant investment of time and energy. What are your avenues for ensuring the sustainability of your various projects?\nWhat are your plans for the future growth and development of your media empire?\nAs someone who is so deeply involved in the conversations flowing through and around Python, what do you see as being the greatest threats and opportunities for the language and its community?\n\n\nKeep In Touch\n\n\n@dbader_org on Twitter\nWebsite\ndbader on GitHub\n\n\nPicks\n\n\nTobias\n\nData Engineering Podcast\n\n\n\nDan\n\n\nBlack code formatter\nŁukasz Langa\n\n\n\n\n\nLinks\n\n\nDan Bader\nNerd Lettering\nReal Python\nPyCoders\nComputer Science\nVancouver, BC\nDjango\nRaymond Hettinger\nData Science\nFlask\nPythonista Cafe\nPython Tricks\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

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Keeping up with the work being done in the Python community can be a full time job, which is why Dan Bader has made it his! In this episode he discusses how he went from working as a software engineer, to offering training, to now managing both the Real Python and PyCoders properties. He also explains his strategies for tracking and curating the content that he produces and discovers, how he thinks about building products, and what he has learned in the process of running his businesses.

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Preface

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Interview

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Keep In Touch

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Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Serving The Python Community Through Content Curation (Interview)","date_published":"2018-12-02T22:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/2796136a-4190-4a0b-8107-6239ff66eeb5.mp3","mime_type":"audio/mpeg","size_in_bytes":46341061,"duration_in_seconds":3476}]},{"id":"podlove-2018-11-26t02:01:23+00:00-6921d777cec3a7e","title":"Using Calibre To Keep Your Digital Library In Order with Kovid Goyal","url":"https://www.pythonpodcast.com/calibre-with-kovid-goyal-episode-187","content_text":"Summary\n\nDigital books are convenient and useful ways to have easy access to large volumes of information. Unfortunately, keeping track of them all can be difficult as you gain more books from different sources. Keeping your reading device synchronized with the material that you want to read is also challenging. In this episode Kovid Goyal explains how he created the Calibre digital library manager to solve these problems for himself, how it grew to be the most popular application for organizing ebooks, and how it works under the covers. Calibre is an incredibly useful piece of software with a lot of hidden complexity and a great story behind it.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nJoin the community in the new Zulip chat workspace at podcastinit.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Kovid Goyal about Calibre, the powerful and free ebook management tool\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what Calibre is and how the project got started?\nHow are you able to keep up to date with device support in Calibre, given the continual release of new devices and platforms that a user can read ebooks on?\nWhat are the main features of Calibre?\n\nWhat are some of the most interesting and most popular plugins that have been creatd for Calibre?\n\n\n\nCan you describe the software architecture for the project and how it has evolved since you first started working on it?\nYou have been maintaining and improving Calibre for a long time now. What is your motivation to keep working on it?\n\n\nHow has the focus of the project and the primary use cases changed over the years that you have been working on it?\n\n\n\nIn addition to its longevity, Calibre has also become a de-facto standard for ebook management. What is your opinion as to why it has gained and kept its popularity?\n\n\nWhat are some of the competing options and how does Calibre differentiate from them?\n\n\n\nIn addition to the myriad devices and platforms, there is a significant amount of complexity involved in supporting the different ebook formats. What have been the most challenging or complex aspects of managing and converting between the formats?\nOne of the challenges around maintaining a private library of electronic resources is the prevalence of DRM restricted content available through major publishers and retailers. What are your thoughts on the current state of digital book marketplaces?\nWhat was your motivation for implementing Calibre in Python?\n\n\nIf you were to start the project over today would you make the same choice?\nAre there any aspects of the project that you would implement differently if you were starting over?\n\n\n\nWhat are your plans for the future of Calibre?\n\n\nKeep In Touch\n\n\nkovidgoyal on GitHub\nWebsite\nPatreon\n\n\nPicks\n\n\nTobias\n\nAmerican Gods by Neil Gaiman\n\n\n\nKovid\n\n\nInto Thin Air by John Krakauer\nAbout how an expedition to climb Everest went wrong. Wonderful account of the difficulties of high altitude mountaineering and the determination it needs.\nThe Steerswoman’s Road by Rosemary Kirstein\nAbout the spirit of scientific enquiry in a fallen civilization on an alien planet with partial terraforming that is slowly failing.\n\n\n\n\n\nLinks\n\n\nCalibre\nKDE\nCaltech\nSony PRS500\nLinux\nKindle\nKobo\nePUB\nCalibre Recipes\nRapydscrypt NG\nGoodreads\nQt\nPyQt\nbuild-calibre\nKitty\nDRM (Digital Rights Management)\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

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Digital books are convenient and useful ways to have easy access to large volumes of information. Unfortunately, keeping track of them all can be difficult as you gain more books from different sources. Keeping your reading device synchronized with the material that you want to read is also challenging. In this episode Kovid Goyal explains how he created the Calibre digital library manager to solve these problems for himself, how it grew to be the most popular application for organizing ebooks, and how it works under the covers. Calibre is an incredibly useful piece of software with a lot of hidden complexity and a great story behind it.

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Preface

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Interview

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Keep In Touch

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Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Using Calibre To Keep Your Digital Library In Order (Interview)","date_published":"2018-11-25T21:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/c0fca032-3b76-4e08-b588-54f76ebef969.mp3","mime_type":"audio/mpeg","size_in_bytes":28342732,"duration_in_seconds":2605}]},{"id":"podlove-2018-11-19t00:22:44+00:00-27333dd7aacd498","title":"Entity Extraction, Document Processing, And Knowledge Graphs For Investigative Journalists with Friedrich Lindenberg","url":"https://www.pythonpodcast.com/aleph-with-friedrich-lindenberg-episode-186","content_text":"Summary\n\nInvestigative reporters have a challenging task of identifying complex networks of people, places, and events gleaned from a mixed collection of sources. Turning those various documents, electronic records, and research into a searchable and actionable collection of facts is an interesting and difficult technical challenge. Friedrich Lindenberg created the Aleph project to address this issue and in this episode he explains how it works, why he built it, and how it is being used. He also discusses his hopes for the future of the project and other ways that the system could be used.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode today to get a $20 credit and launch a new server in under a minute.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nJoin the community in the new Zulip chat workspace at podcastinit.com/chat\nRegistration for PyCon US, the largest annual gathering across the community, is open now. Don’t forget to get your ticket and I’ll see you there!\nYour host as usual is Tobias Macey and today I’m interviewing Friedrich Lindenberg about Aleph, a tool to perform entity extraction across documents and structured data\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what Aleph is and how the project got started?\nWhat is investigative journalism?\n\nHow does Aleph fit into their workflow?\nWhat are some other tools that would be used alongside Aleph?\nWhat are some ways that Aleph could be useful outside of investigative journalism?\n\n\n\nHow is Aleph architected and how has it evolved since you first started working on it?\nWhat are the major components of Aleph?\n\n\nWhat are the types of documents and data formats that Aleph supports?\n\n\n\nCan you describe the steps involved in entity extraction?\n\n\nWhat are the most challenging aspects of identifying and resolving entities in the documents stored in Aleph?\n\n\n\nCan you describe the flow of data through the system from a document being uploaded through to it being displayed as part of a search query?\nWhat is involved in deploying and managing an installation of Aleph?\nWhat have been some of the most interesting or unexpected aspects of building Aleph?\nAre there any particularly noteworthy uses of Aleph that you are aware of?\nWhat are your plans for the future of Aleph?\n\n\nKeep In Touch\n\n\nWebsite\n@pudo on Twitter\npudo on GitHub\n\n\nPicks\n\n\nTobias\n\nMechanical Soup\n\n\n\nFriedrich\n\n\nphonenumbers – because it’s useful\npyicu – super nerdy but amazing \nsqlalchemy – my all-time favorite python package\n\n\n\n\n\nLinks\n\n\nAleph\nOrganized Crime and Corruption Reporting Project\nOCR (Optical Character Recognition)\nJorge Luis Borges\nBuenos Aires\nInvestigative Journalism\nAzerbaijan\nSignal\nOpen Corporates\nOpen Refine\nMoney Laundering\nE-Discovery\nCSV\nSQL\nEntity Extraction (Named Entity Recognition)\nApache Tika\nPolyglot\nSpaCy\n\nPodcast.__init__ Episode\n\n\n\nLibreOffice\nTesseract\nfollowthemoney\nElasticsearch\nKnowledge Graph\nNeo4J\nGephi\nEdward Snowden\nDocument Cloud\nOverview Project\nVeracrypt\nQubes OS\nI2 Analyst Notebook\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Investigative reporters have a challenging task of identifying complex networks of people, places, and events gleaned from a mixed collection of sources. Turning those various documents, electronic records, and research into a searchable and actionable collection of facts is an interesting and difficult technical challenge. Friedrich Lindenberg created the Aleph project to address this issue and in this episode he explains how it works, why he built it, and how it is being used. He also discusses his hopes for the future of the project and other ways that the system could be used.

\n\n

Preface

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Interview

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Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Aleph: Extracting Knowledge Graphs From Unstructured Documents (Interview)","date_published":"2018-11-18T19:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/d7cc30c9-7902-4c4e-8842-d97e457ec224.mp3","mime_type":"audio/mpeg","size_in_bytes":33642766,"duration_in_seconds":2352}]},{"id":"podlove-2018-10-29t00:02:04+00:00-03d002c65a97f7c","title":"Bringing Python To The Spanish Language Community with Maricela Sanchez","url":"https://www.pythonpodcast.com/pymex-with-maricela-sanchez-episode-185","content_text":"Summary\n\nThe Python Community is large and growing, however a majority of articles, books, and presentations are still in English. To increase the accessibility for Spanish language speakers, Maricela Sanchez helped to create the Charlas track at PyCon US, and is an organizer for Python Day Mexico. In this episode she shares her motivations for getting involved in community building, her experiences working on Python Day Mexico and PyCon Charlas, and the lessons that she has learned in the process.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nJoin the community in the new Zulip chat workspace at podcastinit.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Maricela Sanchez Miranda about her work in organizing PyCon Charlas, the spanish language track at PyCon US, as well as Python Day Mexico\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you briefly describe PyCon Charlas and Python Day Mexico?\n\nWhat has been your motivation for getting involved with organizing these community events?\n\n\n\nWhat do you find to be the unique characteristics of the Python community in Mexico?\nWhat kind of feedback have you gotton from the Charlas track at PyCon?\nWhat are your goals for fostering these Spanish language events?\nWhat are some of the lessons that you have learned from PyCon Charlas that were useful in organizing Python Day Mexico?\nWhat have been the most challenging or complicated aspects of organizing Python Day Mexico?\n\n\nHow many attendees do you anticipate? How has that affected your planning and preparation?\n\n\n\nAre there any aspects of the geography, infrastructure, or culture of Mexico that you have found to be either beneficial or challenging for organizing a conference?\nDo you anticipate PyCon Charlas and Python Day Mexico becoming annual events?\nWhat is your advice for anyone who is interested in organizing a conference in their own region or language?\n\n\nKeep In Touch\n\n\nmayela on GitHub\n@mayela0x14 on Twitter\n\n\nPicks\n\n\nTobias\n\nCardLine Dinosaurs\n\n\n\nMaricela\n\n\nLinks\n\n\nPython Day Mexico\nPyCon Charlas\nPyCon Hatchery\nPyCon Latin America\nMexico City\nGuadalajara\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

The Python Community is large and growing, however a majority of articles, books, and presentations are still in English. To increase the accessibility for Spanish language speakers, Maricela Sanchez helped to create the Charlas track at PyCon US, and is an organizer for Python Day Mexico. In this episode she shares her motivations for getting involved in community building, her experiences working on Python Day Mexico and PyCon Charlas, and the lessons that she has learned in the process.

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Preface

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Interview

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Keep In Touch

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Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Bringing Python To The Spanish Language Community (Interview)","date_published":"2018-10-28T20:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/f2d79a29-bae9-4af8-b39d-d8cf384e5998.mp3","mime_type":"audio/mpeg","size_in_bytes":12860191,"duration_in_seconds":1168}]},{"id":"podlove-2018-10-22t01:58:29+00:00-8b1c92f4afc10e0","title":"Of Checklists, Ethics, and Data with Emily Miller and Peter Bull","url":"https://www.pythonpodcast.com/deon-with-emily-miller-and-peter-bull-episode-184","content_text":"Summary\n\nAs data science becomes more widespread and has a bigger impact on the lives of people, it is important that those projects and products are built with a conscious consideration of ethics. Keeping ethical principles in mind throughout the lifecycle of a data project helps to reduce the overall effort of preventing negative outcomes from the use of the final product. Emily Miller and Peter Bull of Driven Data have created Deon to improve the communication and conversation around ethics among and between data teams. It is a Python project that generates a checklist of common concerns for data oriented projects at the various stages of the lifecycle where they should be considered. In this episode they discuss their motivation for creating the project, the challenges and benefits of maintaining such a checklist, and how you can start using it today.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nJoin the community in the new Zulip chat workspace at podcastinit.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Emily Miller and Peter Bull about Deon, an ethics checklist for data projects\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what Deon is and your motivation for creating it?\nWhy a checklist, specifically? What’s the advantage of this over an oath, for example?\nWhat is unique to data science in terms of the ethical concerns, as compared to traditional software engineering?\nWhat is the typical workflow for a team that is using Deon in their projects?\nDeon ships with a default checklist but allows for customization. What are some common addendums that you have seen?\n\nHave you received pushback on any of the default items?\n\n\n\nHow does Deon simplify communication around ethics across team boundaries?\nWhat are some of the most often overlooked items?\nWhat are some of the most difficult ethical concerns to comply with for a typical data science project?\nHow has Deon helped you at Driven Data?\nWhat are the customer facing impacts of embedding a discussion of ethics in the product development process?\nSome of the items on the default checklist coincide with regulatory requirements. Are there any cases where regulation is in conflict with an ethical concern that you would like to see practiced?\nWhat are your hopes for the future of the Deon project?\n\n\nKeep In Touch\n\n\nEmily\n\nLinkedIn\nejm714 on GitHub\n\n\n\nPeter\n\n\nLinkedIn\n@pjbull on Twitter\npjbull on GitHub\n\n\n\nDriven Data\n\n\n@drivendataorg on Twitter\ndrivendataorg on GitHub\nWebsite\n\n\n\n\n\nPicks\n\n\nTobias\n\nRichard Bond Glass Art\n\n\n\nEmily\n\n\nTandem Coffee in Portland, Maine\n\n\n\nPeter\n\n\nThe Model Bakery in Saint Helena and Napa, California\n\n\n\n\n\nLinks\n\n\nDeon\nDriven Data\nInternational Development\nBrookings Institution\nStata\nEconometrics\nMetis Bootcamp\nPandas\n\nPodcast Episode\n\n\n\nC#\n.NET\nPodcast.__init__ Episode On Software Ethics\nJupyter Notebook\n\n\nPodcast Episode\n\n\n\nWord2Vec\ncookiecutter data science\nLogistic Regression\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

As data science becomes more widespread and has a bigger impact on the lives of people, it is important that those projects and products are built with a conscious consideration of ethics. Keeping ethical principles in mind throughout the lifecycle of a data project helps to reduce the overall effort of preventing negative outcomes from the use of the final product. Emily Miller and Peter Bull of Driven Data have created Deon to improve the communication and conversation around ethics among and between data teams. It is a Python project that generates a checklist of common concerns for data oriented projects at the various stages of the lifecycle where they should be considered. In this episode they discuss their motivation for creating the project, the challenges and benefits of maintaining such a checklist, and how you can start using it today.

\n\n

Preface

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Interview

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Keep In Touch

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Picks

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Links

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\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Of Checklists, Ethics, and Data (Interview)","date_published":"2018-10-21T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/97825b06-8812-4e78-ba10-6e72ac412bbc.mp3","mime_type":"audio/mpeg","size_in_bytes":32705081,"duration_in_seconds":2716}]},{"id":"podlove-2018-10-15t01:21:13+00:00-fea440e3ac7ebce","title":"How Python Is Used To Build A Startup At Wanderu with Chris Kirkos and Matt Warren","url":"https://www.pythonpodcast.com/wanderu-with-chris-kirkos-and-matt-warren-episode-183","content_text":"Summary\n\nThe breadth of use cases that Python supports, coupled with the level of productivity that it provides through its ease of use have contributed to the incredible popularity of the language. To explore the ways that it can contribute to the success of a young and growing startup two of the lead engineers at Wanderu discuss their experiences in this episode. Matt Warren, the technical operations lead, explains the ways that he is using Python to build and scale the infrastructure that Wanderu relies on, as well as the ways that he deploys and runs the various Python applications that power the business. Chris Kirkos, the lead software architect, describes how the original Django application has grown into a suite of microservices, where they have opted to use a different language and why, and how Python is still being used for critical business needs. This is a great conversation for understanding the business impact of the Python language and ecosystem.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nJoin the community in the new Zulip chat workspace at podcastinit.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Matt Warren and Chris Kirkos and about the ways that they are using Python at Wanderu\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what Wanderu does?\n\nHow is the platform architected?\n\n\n\nWhat are the broad categories of problems that you are addressing with Python?\nWhat are the areas where you chose to use a different language or service?\nWhat ratio of new projects and features are implemented using Python?\n\n\nHow much of that decision process is influenced by the fact that you already have so much pre-existing Python code?\nFor the projects where you don’t choose Python, what are the reasons for going elsewhere?\n\n\n\nWhat are some of the limitations of Python that you have encountered while working at Wanderu?\nWhat are some of the places that you were surprised to find Python in use at Wanderu?\nWhat have you enjoyed most about working with Python?\n\n\nWhat are some of the sharp edges that you would like to see smoothed over in future versions of the language?\n\n\n\nWhat is the most challenging bug that you have dealt with at Wanderu that was attributable in some sense to the fact that the code was written in Python?\nIf you were to start over today on any of the pieces of the Wanderu platform, are there any that you would write in a different language?\nWhich libraries have been the most useful for your work at Wanderu?\n\n\nWhich ones have caused you the most pain?\n\n\n\n\n\nKeep In Touch\n\n\nMatt\n\n@matthewwwarren on Twitter\nLinkedIn\n\n\n\nChris\n\n\nLinkedIn\n\n\n\n\n\nPicks\n\n\nTobias\n\nDataGrip\n\n\n\nMatt\n\n\nChacarero\n\n\n\nChris\n\n\nPDB\nIPDB\nPUDB\nVSCode\n\n\n\n\n\nLinks\n\n\nWanderu\nNortheastern University\nC++\nPerl\nMicroservices\nPostgreSQL\n\nData Engineering Podcast Episode\n\n\n\nMongoDB\nDjango\nNode.js\nGo-lang\nAWS\nETL (Extract, Transform, and Load)\nData Warehouse\nGraph Database\nTwisted\n\n\nPodcast Episode\n\n\n\nGevent\nScrapy\nVirtualenv\nRuby\nRbenv\nBoto3\nPyMongo\nAnsible\nPip\nTLS\nCryptography\n\n\nPodcast Episode\n\n\n\nSetuptools\nOpenstack\nRequests\nPyCountry\nSOAP (Simple Object Access Protocol)\nXML\nJinja\nOpenSSL\npytest\nBandit\n\n\nPodcast Episode\n\n\n\nGang of Four\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

The breadth of use cases that Python supports, coupled with the level of productivity that it provides through its ease of use have contributed to the incredible popularity of the language. To explore the ways that it can contribute to the success of a young and growing startup two of the lead engineers at Wanderu discuss their experiences in this episode. Matt Warren, the technical operations lead, explains the ways that he is using Python to build and scale the infrastructure that Wanderu relies on, as well as the ways that he deploys and runs the various Python applications that power the business. Chris Kirkos, the lead software architect, describes how the original Django application has grown into a suite of microservices, where they have opted to use a different language and why, and how Python is still being used for critical business needs. This is a great conversation for understanding the business impact of the Python language and ecosystem.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n

\n\n

Picks

\n\n

\n\n

Links

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"A Case Study Of Building A Startup On Python (Interview)","date_published":"2018-10-14T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/567b114c-1632-4fdf-9470-8f37033a038e.mp3","mime_type":"audio/mpeg","size_in_bytes":23706183,"duration_in_seconds":2062}]},{"id":"podlove-2018-10-09t02:01:56+00:00-6969b2a440f02c9","title":"Building A Game In Python At PyWeek with Daniel Pope","url":"https://www.pythonpodcast.com/pyweek-with-daniel-pope-episode-182","content_text":"Summary\n\nMany people learn to program because of their interest in building their own video games. Once the necessary skills have been acquired, it is often the case that the original idea of creating a game is forgotten in favor of solving the problems we confront at work. Game jams are a great way to get inspired and motivated to finally write a game from scratch. This week Daniel Pope discusses the origin and format for PyWeek, his experience as a participant, and the landscape of options for building a game in Python. He also explains how you can register and compete in the next competition.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nJoin the community in the new Zulip chat workspace at podcastinit.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Daniel Pope about PyWeek, a one week challenge to build a game in Python\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what PyWeek is and how the competition got started?\n\nWhat is your current role in relation to PyWeek and how did you get involved?\n\n\n\nWhat are the strengths of the Python lanaguage and ecosystem for developing a game?\nWhat are some of the common difficulties encountered by participants in the challenge?\nWhat are some of the most commonly used libraries and tools for creating and packaging the games?\nWhat are some shortcomings in the available tools or libraries for Python when it comes to game development?\nWhat are some examples of libraries or tools that were created and released as a result of a team’s efforts during PyWeek?\nHow often do games that get started during PyWeek continue to be developed and improved?\n\n\nHave there ever been games that went on to be commercially viable?\n\n\n\nWhat are some of the most interesting or unusual games that you have seen submitted to PyWeek?\nCan you describe your experience as a competitor in PyWeek?\n\n\nHow do you structure your time during the competition week to ensure that you can complete your game?\n\n\n\nWhat are the benefits and difficulties of the one week constraint for development?\nHow has PyWeek changed over the years that you have been involved with it?\nWhat are your hopes for the competition as it continues into the future?\n\n\nKeep In Touch\n\n\n@lordmauve on Twitter\nBlog\nlordmauve on GitHub\n\n\nPicks\n\n\nTobias\n\nThe Architecht Show\n\n\n\nDan\n\n\nRed Blob Games\nDesigning Virtual Worlds by Richard Bartle\n\n\n\n\n\nLinks\n\n\nPyWeek\nTwo Sigma\nGame Jam\nRichard Jones\nPyGame\nPyglet\nSDL\nPyGame Zero\nCocos 2D\nDoctor Corovich’s Flying Atomic Squid\nMortimer The Lepidopterist\nLudum Dare\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Many people learn to program because of their interest in building their own video games. Once the necessary skills have been acquired, it is often the case that the original idea of creating a game is forgotten in favor of solving the problems we confront at work. Game jams are a great way to get inspired and motivated to finally write a game from scratch. This week Daniel Pope discusses the origin and format for PyWeek, his experience as a participant, and the landscape of options for building a game in Python. He also explains how you can register and compete in the next competition.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"PyWeek: A One Week Competition To Build A Game In Python (Interview)","date_published":"2018-10-08T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/cd4972d0-d20d-4795-8713-effecbddf295.mp3","mime_type":"audio/mpeg","size_in_bytes":19220940,"duration_in_seconds":1806}]},{"id":"podlove-2018-10-02t02:01:45+00:00-1804d643094abef","title":"Managing Application Secrets with Brian Kelly","url":"https://www.pythonpodcast.com/managing-application-secrets-with-brian-kelly-episode-181","content_text":"Summary\n\nAny application that communicates with other systems or services will at some point require a credential or sensitive piece of information to operate properly. The question then becomes how best to securely store, transmit, and use that information. The world of software secrets management is vast and complicated, so in this episode Brian Kelly, engineering manager at Conjur, aims to help you make sense of it. He explains the main factors for protecting sensitive information in your software development and deployment, ways that information might be leaked, and how to get the whole team on the same page.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nJoin the community in the new Zulip chat workspace at podcastinit.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Brian Kelly about how to store, deploy, and use sensitive information in your applications\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nTo begin with, how do you define a secret in the context of an application?\nWhat are the broad categories for solutions to secrets management?\nWhat are the different aspects of secrets management in the lifecycle of developing, deploying, and maintaining an application?\nHow does the scale of a project or organization impact the strategies that are reasonable for secrets management?\nWhat are some of the most challenging aspects of secrets management at the different stages of usage?\n\nWhat are some of the common reasons that secrets management strategies fail?\nWhat are some of the vulnerabilities or attack vectors that development teams should be thinking about when working with credentials?\n\n\n\nWhat are your thoughts on versioning of secrets?\nBeyond storing and deploying sensitive information, what are some of the secondary concerns around secrets management that development teams should be thinking about?\nHow does the use of multiple environments (e.g. dev, QA, production, etc.) affect the strategies used for secrets management?\nWhat are some of the most useful resources that you have found for anyone looking to learn more about this subject?\n\n\nKeep In Touch\n\n\n@brikelly on Twitter\nBlog\nbrikelly on GitHub\n\n\nPicks\n\n\nTobias\n\nThe Inheritance Cycle\n\n\n\nBrian\n\n\nDonegal Ireland\n\n\n\n\n\nLinks\n\n\nConjur\nCyberArk\nDatawire\nTranspiler\nIDL\nCSRF (Cross-Site Request Forgery)\nHashicorp Vault\nContinuous Integration\nContinuous Delivery\nTLS (Transport Layer Security)\nRBAC (Role Based Access Control)\nTerraform\nSQL Injection\nSecretless\nMFA\nDuo Security\nKubernetes\nSummon\nOWASP Top 10\nConfiguration Management\nPuppet\nChef\nAnsible\nSaltStack\nImmutable Infrastructure\nConjur Blog\nKrebs On Security\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Any application that communicates with other systems or services will at some point require a credential or sensitive piece of information to operate properly. The question then becomes how best to securely store, transmit, and use that information. The world of software secrets management is vast and complicated, so in this episode Brian Kelly, engineering manager at Conjur, aims to help you make sense of it. He explains the main factors for protecting sensitive information in your software development and deployment, ways that information might be leaked, and how to get the whole team on the same page.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"The Basics Of Secrets Management For Software Engineers (Interview)","date_published":"2018-10-01T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/1b5c7939-4de9-4a6b-9d05-5a6edb77ca31.mp3","mime_type":"audio/mpeg","size_in_bytes":30262588,"duration_in_seconds":2343}]},{"id":"podlove-2018-09-24t02:10:27+00:00-bdb4c48dc61f949","title":"Django, Channels, And The Asynchronous Web with Andrew Godwin","url":"https://www.pythonpodcast.com/django-channels-and-the-asynchronous-web-with-andrew-godwin-episode-180","content_text":"Summary\n\nOnce upon a time the web was a simple place with one main protocol and a predictable sequence of request/response interactions with backend applications. This is the era when Django began, but in the intervening years there has been an explosion of complexity with new asynchronous protocols and single page Javascript applications. To help bridge the gap and bring the most popular Python web framework into the modern age Andrew Godwin created Channels. In this episode he explains how the first version of the asynchronous layer for Django applications was created, how it has changed in the jump to version 2, and where it will go in the future. Along the way he also discusses the challenges of async development, his work on designing ASGI as the spiritual successor to WSGI, and how you can start using all of this in your own projects today.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nJoin the community in the new Zulip chat workspace at podcastinit.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Andrew Godwin about Django Channels 2.x and the ASGI specification for modern, asynchronous web protocols\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you start with an overview of the problem that Channels is aiming to solve?\nAsynchronous frameworks have existed in Python for a long time. What are the tradeoffs in those frameworks that would lead someone to prefer the combination of Django and Channels?\nFor someone who is familiar with traditional Django or working on an existing application, what are the steps involved in integrating Channels?\nChannels is a project that you have been working on for a significant amount of time and which you recently re-architected. What were the shortcomings in the 1.x release that necessitated such a major rewrite?\n\nHow is the current system architected?\n\n\n\nWhat have you found to be the most challenging or confusing aspects of managing asynchronous web protocols both as an author of Channels/ASGI and someone building on top of them?\n\n\nWhile reading through the documentation there were mentions of the synchronous nature of the Django ORM. What are your thoughts on asynchronous database access and how important that is for future versions of Django and Channels?\n\n\n\nAs part of your implementation of Channels 2.x you introduced a new protocol for asynchronous web applications in Python in the form of ASGI. How does this differ from the WSGI standard and what was your process for developing this specification?\n\n\nWhat are your hopes for what the Python community will do with ASGI?\n\n\n\nWhat are your plans for the future of Channels?\nWhat are some of the most interesting or unexpected uses of Channels and/or ASGI?\n\n\nKeep In Touch\n\n\n@andrewgodwin on Twitter\nWebsite\nandrewgodwin on GitHub\n\n\nPicks\n\n\nTobias\n\nNobody Listens To Paula Poundstone\n\n\n\nAndrew\n\n\nLiterary Appreciation Of The Olson Timezones Database\n\n\n\n\n\nLinks\n\n\nChannels\nASGI\nDjango\nSouth\nDjango Migrations\nPHP\nTurbogears\nWSGI\nWebsockets\nEventlet\nHTTP\nWebRTC\nIPFS\nTwisted\nTornado\n\nPodcast Episode\n\n\n\nDaphne\nRedis\nUvicorn\nHeisenbugs\nDeadlock\nCherryPy\nFlask\nWSGI 2\n\n\nPodcast Episode\n\n\n\nStarlette\nDjango Rest Framework\nThom Christie\nPEP Process Episode\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Once upon a time the web was a simple place with one main protocol and a predictable sequence of request/response interactions with backend applications. This is the era when Django began, but in the intervening years there has been an explosion of complexity with new asynchronous protocols and single page Javascript applications. To help bridge the gap and bring the most popular Python web framework into the modern age Andrew Godwin created Channels. In this episode he explains how the first version of the asynchronous layer for Django applications was created, how it has changed in the jump to version 2, and where it will go in the future. Along the way he also discusses the challenges of async development, his work on designing ASGI as the spiritual successor to WSGI, and how you can start using all of this in your own projects today.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Bringing Django Into The Age Of The Asynchronous Web With Channels (Interview)","date_published":"2018-09-23T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/8cb82dfc-5383-4fcc-9ea4-ac2eeccc50d8.mp3","mime_type":"audio/mpeg","size_in_bytes":31170431,"duration_in_seconds":2506}]},{"id":"podlove-2018-09-16t23:02:59+00:00-a7b78ced4c6a2e8","title":"The Business Of Technical Authoring With William Vincent","url":"https://www.pythonpodcast.com/technical-authoring-with-william-vincent-episode-179","content_text":"Summary\n\nThere are many aspects of learning how to program and at least as many ways to go about it. This is multiplicative with the different problem domains and subject areas where software development is applied. In this episode William Vincent discusses his experiences learning how web development mid-career and then writing a series of books to make the learning curve for Django newcomers shallower. This includes his thoughts on the business aspects of technical writing and teaching, the challenges of keeping content up to date with the current state of software, and the ever-present lack of sufficient information for new programmers.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nJoin the community in the new Zulip chat workspace at podcastinit.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing William Vincent about his experience learning to code mid-career and then writing a series of books to bring you along on his journey from beginner to advanced Django developer\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nHow has your experience as someone who began working as a developer mid-career influenced your approach to software?\nHow do you compare Python options for web development (Django/Flask) to others such as Ruby on Rails or Node/Express in the JavaScript world?\nWhat was your motivation for writing a beginner guide to Django?\n\nWhat was the most difficult aspect of determining the appropriate level of depth for the content?\nAt what point did you decide to publish the tutorial you were compiling as a book?\n\n\n\nIn the posts that you wrote about your experience authoring the books you give a detailed description of the economics of being an author. Can you discuss your thoughts on that?\n\n\nFocusing on a library or framework, such as Django, increases the maintenance burden of a book, versus one that is written about fundamental principles of computing. What are your thoughts on the tradeoffs involved in selecting a topic for a technical book?\n\n\n\nChallenges of creating useful intermediate content (lots of beginner tutorials and deep dives, not much in the middle)\nAfter your initial foray into technical authoring you decided to follow it with two more books. What other topics are you covering with those?\n\n\nOnce you are finished with the third do you plan to continue writing, or will you shift your focus to something else?\n\n\n\nTranslating content to reach a larger audience\nWhat advice would you give to someone who is considering writing a book of their own?\n\n\nWhat alternative avenues do you think would be more valuable for themselves and their audience?\nAlternative avenues for providing useful training to developers\n\n\n\n\n\nKeep In Touch\n\n\nEmail\nwsvincent on GitHub\nWebsite\n\n\nPicks\n\n\nTobias\n\nPractical AI\n\n\n\nWilliam\n\n\nawesome-django\nThe Digital Doctor by Robert Wachter\n\n\n\n\n\nLinks\n\n\nQuizlet\nDjango\nLearn Python The Hard Way\nInvent Your Own Computer Games with Python\nRuby on Rails\nNode.js\nExpress\nLearnBoost\nDavid Heinemeier Hanson\nMeteor.js\nClass-Based Views\nRails Tutorial\nLeanpub\nGumroad\nStack Overflow\nEgghead.io\nFrontend Masters\nGatsby.js\nJekyll\nPachyderm\nData Engineering Podcast\n\nPachyderm Interview\n\n\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

There are many aspects of learning how to program and at least as many ways to go about it. This is multiplicative with the different problem domains and subject areas where software development is applied. In this episode William Vincent discusses his experiences learning how web development mid-career and then writing a series of books to make the learning curve for Django newcomers shallower. This includes his thoughts on the business aspects of technical writing and teaching, the challenges of keeping content up to date with the current state of software, and the ever-present lack of sufficient information for new programmers.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Technical Authoring For Fun But Not Much Profit (Interview)","date_published":"2018-09-16T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/fc0c80c4-3b0d-4b37-818b-d4ef7f8532ea.mp3","mime_type":"audio/mpeg","size_in_bytes":40127535,"duration_in_seconds":2978}]},{"id":"podlove-2018-09-10t01:54:37+00:00-669fa1be122efe4","title":"Keep Your Code Clean Using pre-commit with Anthony Sottile","url":"https://www.pythonpodcast.com/pre-commit-with-anthony-sottile-episode-178","content_text":"Summary\n\nMaintaining the health and well-being of your software is a never-ending responsibility. Automating away as much of it as possible makes that challenge more achievable. In this episode Anthony Sottile describes his work on the pre-commit framework to simplify the process of writing and distributing functions to make sure that you only commit code that meets your definition of clean. He explains how it supports tools and repositories written in multiple languages, enforces team standards, and how you can start using it today to ship better software.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nJoin the community in the new Zulip chat workspace at podcastinit.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Anthony Sottile about pre-commit, a framework for managing and maintaining hooks for multiple languages\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what a pre-commit hook is and some of the ways that they are useful for developers?\nWhat was you motivation for creating a framework to manage your pre-commit hooks?\n\nHow does it differ from other projects built to manage these hooks?\n\n\n\nWhat are the steps for getting someone started with pre-commit in a new project?\nWhich other event hooks would be most useful to implement for maintaining the health of a repository?\nWhat types of operations are most useful for ensuring the health of a project?\nWhat types of routines should be avoided as a pre-commit step?\nInstalling the hooks into a user’s local environment is a manual step, so how do you ensure that all of your developers are using the configured hooks?\n\n\nWhat factors have you found that lead to developers skipping or disabling hooks?\n\n\n\nHow is pre-commit implemented and how has that design evolved from when you first started?\n\n\nWhat have been the most difficult aspects of supporting multiple languages and package managers?\nWhat would you do differently if you started over today?\nWould you still use Python?\n\n\n\nFor someone who wants to write a plugin for pre-commit, what are the steps involved?\nWhat are some of the strangest or most unusual uses of pre-commit hooks that you have seen?\nWhat are your plans for the future of pre-commit?\n\n\nKeep In Touch\n\n\nasottile on GitHub\n@codewithanthony on Twitter\nanthonywritescode on twitch\nanthonywritescode on YouTube\n\n\nPicks\n\n\nTobias\n\nTag\n\n\n\nAnthony\n\n\nYes Theory\n\n\n\n\n\nLinks\n\n\npre-commit\n\nList of hooks\n\n\n\nLyft\n\n\nCareers\n\n\n\nGit\nGit hooks\n\n\nhttps://githooks.com/?utm_source=rss&utm_medium=rss\n\n\n\nFlake8\nMake\nTox\nType Annotations\nxargs\nBash\nshlex\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Maintaining the health and well-being of your software is a never-ending responsibility. Automating away as much of it as possible makes that challenge more achievable. In this episode Anthony Sottile describes his work on the pre-commit framework to simplify the process of writing and distributing functions to make sure that you only commit code that meets your definition of clean. He explains how it supports tools and repositories written in multiple languages, enforces team standards, and how you can start using it today to ship better software.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Multi-language pre-commit hooks for better software development (Interview)","date_published":"2018-09-09T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/4e667436-00d0-49a8-8fa9-80826d1e8784.mp3","mime_type":"audio/mpeg","size_in_bytes":17615238,"duration_in_seconds":1492}]},{"id":"podlove-2018-09-03t19:09:42+00:00-28e7aefa4b34b2e","title":"Infection Monkey Vulnerability Scanner with Daniel Goldberg","url":"https://www.pythonpodcast.com/infection-monkey-vulnerability-scanner-with-daniel-goldberg-episode-177","content_text":"Summary\n\nHow secure are your servers? The best way to be sure that your systems aren’t being compromised is to do it yourself. In this episode Daniel Goldberg explains how you can use his project Infection Monkey to run a scan of your infrastructure to find and fix the vulnerabilities that can be taken advantage of. He also discusses his reasons for building it in Python, how it compares to other security scanners, and how you can get involved to keep making it better.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nJoin the community in the new Zulip chat workspace at podcastinit.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Daniel Goldberg about Infection Monkey, an open source system breach simulation tool for evaluating the security of your network\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is infection monkey and what was the reason for building it?\n\nWhat was the reasoning for building it in Python?\nIf you were to start over today what would you do differently?\n\n\n\nPenetration testing is typically an endeavor that requires a significant amount of knowledge and experience of security practices. What have been some of the most difficult aspects of building an automated vulnerability testing system?\n\n\nHow does a deployed instance keep up to date with recent exploits and attack vectors?\n\n\n\nHow does Infection Monkey compare to other tools such as Nessus and Nexpose?\nWhat are some examples of the types of vulnerabilities that can be discovered by Infection Monkey?\nWhat kinds of information can Infection Monkey discover during a scan?\n\n\nHow does that information get reported to the user?\nHow much security experience is necessary to understand and address the findings in a given report generated from a scan?\n\n\n\nWhat techniques do you use to ensure that the simulated compromises can be safely reverted?\nWhat are some aspects of network security and system vulnerabilities that Infection Monkey is unable to detect and/or analyze?\nFor someone who is interested in using Infection Monkey what are the steps involved in getting it set up?\n\n\nWhat is the workflow for running a scan?\nIs Infection Monkey intended to be run continuously, or only with the interaction of an operator?\n\n\n\nWhat are your plans for the future of Infection Monkey?\n\n\nKeep In Touch\n\n\ndanielguardicore on GitHub\nGuardicore Blog\n\n\nPicks\n\n\nTobias\n\nDarkest Hour\n\n\n\nDaniel\n\n\nHow Complex Systems Fail\n\n\n\n\n\nLinks\n\n\nInfection Monkey\nGuardicore\nStack Overflow\nMetasploit\nAsyncIO\nReact\nNessus\nNexpose\nShellshock\nWannacry\nSimian Army\nChaos Engineering\nCapuchin Monkey\nGoogle Summer of Code\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

How secure are your servers? The best way to be sure that your systems aren’t being compromised is to do it yourself. In this episode Daniel Goldberg explains how you can use his project Infection Monkey to run a scan of your infrastructure to find and fix the vulnerabilities that can be taken advantage of. He also discusses his reasons for building it in Python, how it compares to other security scanners, and how you can get involved to keep making it better.

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Preface

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Interview

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Keep In Touch

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Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Secure Your Systems By Breaking Them With Infection Monkey (Interview)","date_published":"2018-09-03T19:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/fb48db43-d1ff-4fe6-bfbf-c80cbfa377bd.mp3","mime_type":"audio/mpeg","size_in_bytes":23498481,"duration_in_seconds":2064}]},{"id":"podlove-2018-08-27t00:34:21+00:00-10cba6d54bad124","title":"Fast Stream Processing In Python Using Faust with Ask Solem","url":"https://www.pythonpodcast.com/fast-stream-processing-in-python-using-faust-with-ask-solem-episode-176","content_text":"Summary\n\nThe need to process unbounded and continually streaming sources of data has become increasingly common. One of the popular platforms for implementing this is Kafka along with its streams API. Unfortunately, this requires all of your processing or microservice logic to be implemented in Java, so what’s a poor Python developer to do? If that developer is Ask Solem of Celery fame then the answer is, help to re-implement the streams API in Python. In this episode Ask describes how Faust got started, how it works under the covers, and how you can start using it today to process your fast moving data in easy to understand Python code. He also discusses ways in which Faust might be able to replace your Celery workers, and all of the pieces that you can replace with your own plugins.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nJoin the community in the new Zulip chat workspace at podcastinit.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Ask Solem about Faust, a library for building high performance, high throughput streaming systems in Python\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is Faust and what was your motivation for building it?\n\nWhat were the initial project requirements that led you to use Kafka as the primary infrastructure component for Faust?\n\n\n\nCan you describe the architecture for Faust and how it has changed from when you first started writing it?\n\n\nWhat mechanism does Faust use for managing consensus and failover among instances that are working on the same stream partition?\n\n\n\nWhat are some of the lessons that you learned while building Celery that were most useful to you when designing Faust?\nWhat have you found to be the most common areas of confusion for people who are just starting to build an application on top of Faust?\nWhat has been the most interesting/unexpected/difficult aspects of building and maintaining Faust?\nWhat have you found to be the most challenging aspects of building streaming applications?\nWhat was the reason for releasing Faust as an open source project rather than keeping it internal to Robinhood?\nWhat would be involved in adding support for alternate queue or stream implementations?\nWhat do you have planned for the future of Faust?\n\n\nKeep In Touch\n\n\n@asksol on Twitter\nask on GitHub\n\n\nPicks\n\n\nTobias\n\nSuper Troopers 2\n\n\n\nAsk\n\n\nMicrosound by Curtis Roads\n\n\n\n\n\nLinks\n\n\nFaust\nRobinHood\nKafka Streams\nRabbitMQ\nAsyncIO\nCelery\nKafka\nConfluent\nWrite-Ahead Log\nRocksDB\nRedis\nPulsar\nKSQL\nExactly Once Semantics\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

The need to process unbounded and continually streaming sources of data has become increasingly common. One of the popular platforms for implementing this is Kafka along with its streams API. Unfortunately, this requires all of your processing or microservice logic to be implemented in Java, so what’s a poor Python developer to do? If that developer is Ask Solem of Celery fame then the answer is, help to re-implement the streams API in Python. In this episode Ask describes how Faust got started, how it works under the covers, and how you can start using it today to process your fast moving data in easy to understand Python code. He also discusses ways in which Faust might be able to replace your Celery workers, and all of the pieces that you can replace with your own plugins.

\n\n

Preface

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Interview

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\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Fast Stream Processing On Kafka In Python With Faust (Interview)","date_published":"2018-08-26T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/087675a8-6b5c-4aad-9376-d61094c11334.mp3","mime_type":"audio/mpeg","size_in_bytes":18692586,"duration_in_seconds":1725}]},{"id":"podlove-2018-08-20t04:02:26+00:00-097bf367f1d54e9","title":"Don't Just Stand There, Get Programming! with Ana Bell","url":"https://www.pythonpodcast.com/getting-programming-with-ana-bell-episode-175","content_text":"Summary\n\nWriting a book is hard work, especially when you are trying to teach such a broad concept as programming. In this episode Ana Bell discusses her recent work in writing Get Programming: Learn To Code With Python, including her views on how to separate the principles from the implementation, making the book evergreen in its appeal, and how her experience as a lecturer at MIT has helped her maintain the perspectives of beginners. She also shares her views on the values of learning about programming, even when you have no intention of doing it as a career and ways to take the next steps if that is your goal.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nAs you know, Python has become one of the most popular programming languages in the world, due to the size, scope, and friendliness of the language and community. But, it can be tough learning it when you’re just starting out. Luckily, there’s an easy way to get involved. Written by MIT lecturer Ana Bell and published by Manning Publications, Get Programming: Learn to code with Python is the perfect way to get started working with Python. Ana’s experience as a teacher of Python really shines through, as you get hands-on with the language without being drowned in confusing jargon or theory. Filled with practical examples and step-by-step lessons to take on, Get Programming is perfect for people who just want to get stuck in with Python. Get your copy of the book with a special 40% discount for Podcast.__init__ listeners at podcastinit.com/get-programming using code: Bell40!\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nJoin the community in the new Zulip chat workspace at podcastinit.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Ana Bell about her book, Get Programming: Learn to code with Python, and her approach to teaching how to code\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing your motivation for writing a book about learning to program?\n\nWho is the target audience for this book?\nWhat level of competence do you want the reader to have when they have completed it?\n\n\n\nWhat were the most challenging aspects of writing a book for beginning programmers?\n\n\nWhat did you do to recapture the “beginner mind” while writing?\n\n\n\nThere are a large variety of books on learning to program and at least as many approaches. Can you describe the techniques that you use in your book to help readers grasp the concepts that you cover?\nOne of the problems of writing a book about technology is that there is no stationary target to aim for due to the constant advancement of the industry. How do you reconcile that reality with the need for a book to remain relevant for an extended period of time?\n\n\nHow do you decide what to include and what to leave out when writing about learning how to program?\n\n\n\nWhat advice do you have for people who have read your book and want to continue on to a career in development?\n\n\nKeep In Touch\n\n\nMIT Bio\n@anabellphd on Twitter\n\n\nPicks\n\n\nTobias\n\nNetdata\n\n\n\nAna\n\n\nStar Realms\nBetween Two Cities\n\n\n\n\n\nLinks\n\n\nGet Programming by Ana Bell\nedX\nMIT\nMachine Learning\nGithub\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Writing a book is hard work, especially when you are trying to teach such a broad concept as programming. In this episode Ana Bell discusses her recent work in writing Get Programming: Learn To Code With Python, including her views on how to separate the principles from the implementation, making the book evergreen in its appeal, and how her experience as a lecturer at MIT has helped her maintain the perspectives of beginners. She also shares her views on the values of learning about programming, even when you have no intention of doing it as a career and ways to take the next steps if that is your goal.

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Preface

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Interview

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Keep In Touch

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Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"The Challenges Of Writing A Book To Teach Beginners To Program (Interview)","date_published":"2018-08-20T00:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/2c076cc6-ed06-472f-b4a6-992735638152.mp3","mime_type":"audio/mpeg","size_in_bytes":24092309,"duration_in_seconds":2107}]},{"id":"podlove-2018-08-12t22:34:08+00:00-2211000410fae61","title":"The Masonite Web Framework With Joe Mancuso","url":"https://www.pythonpodcast.com/masonite-with-joe-mancuso-episode-174","content_text":"Summary\n\nMasonite is an ambitious new web framework that draws inspiration from many other successful projects in other languages. In this episode Joe Mancuso, the primary author and maintainer, explains his goal of unseating Django from its position of prominence in the Python community. He also discusses his motivation for building it, how it is architected, and how you can start using it for your own projects.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nJoin the community in the new Zulip chat workspace at podcastinit.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Joe Mancuso about Masonite, the modern and developer centric python web framework.\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is Masonite and what was the motivation for creating it?\n\nHow does it fit in the current landscape of Python web frameworks?\n\n\n\nWhy might someone choose to use Masonite over Python frameworks?\n\n\nIf someone isn’t already decided on using Python, what are some reasons that they might choose Masonite over frameworks in other languages?\n\n\n\nCan you describe the framework architecture and how it has evolved over the lifetime of the project?\nWhat are some examples of projects that have been built with Masonite and what aspects of the framework are they leveraging?\nFor someone who is starting a new project with Masonite what are some of the concepts that they should be familiar with?\n\n\nWhat is their workflow for starting their project?\nHow does that workflow change when working with an existing application?\n\n\n\nWhat are some of the plans that you have for the future of Masonite?\n\n\nKeep In Touch\n\n\nJoe\n\nBlog\n@masoniteproject on Twitter\njosephmancuso on GitHub\n\n\n\nMasonite\n\n\nMasoniteFramework on GitHub\nDocs\nSlack\n\n\n\n\n\nPicks\n\n\nTobias\n\nYeti Mugs\n\n\n\nJoe\n\n\nGitbook.io\nDev.to\n\n\n\n\n\nLinks\n\n\nMasonite on GitHub\nCodecademy\nPHP\nDjango\nLaravel\nDependency Injection\nInversion of Control\nWSGI\nGunicorn\nWaitress\nNexmo\nMasonite Slack\nMathias Johansson\nTrello\n@masoniteproject\nMasonite Repo\nMasonite Documentation\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Masonite is an ambitious new web framework that draws inspiration from many other successful projects in other languages. In this episode Joe Mancuso, the primary author and maintainer, explains his goal of unseating Django from its position of prominence in the Python community. He also discusses his motivation for building it, how it is architected, and how you can start using it for your own projects.

\n\n

Preface

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Interview

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Keep In Touch

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Picks

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Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Masonite: A Python Web Framework Trying To Compete With Django (Interview)","date_published":"2018-08-12T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/9638e1ce-762a-4cbd-83d0-fd10a5f46663.mp3","mime_type":"audio/mpeg","size_in_bytes":42988629,"duration_in_seconds":2600}]},{"id":"podlove-2018-08-06t02:02:20+00:00-f0f4bae5de0cc24","title":"Helping Teacher's Bring Python Into The Classroom With Nicholas Tollervey","url":"https://www.pythonpodcast.com/education-with-nicholas-tollervey-episode-173","content_text":"Summary\n\nThere are a number of resources available for teaching beginners to code in Python and many other languages, and numerous endeavors to introduce programming to educational environments. Sometimes those efforts yield success and others can simply lead to frustration on the part of the teacher and the student. In this episode Nicholas Tollervey discusses his work as a teacher and a programmer, his work on the micro:bit project and the PyCon UK education summit, as well as his thoughts on the place that Python holds in educational programs for teaching the next generation.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nJoin the community in the new Zulip chat workspace at podcastinit.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Nicholas Tollervey about his efforts to improve the accessibility of Python for educators\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nHow has your experience as a teacher influenced your work as a software engineer?\nWhat are some of the ways that practicing software engineers can be most effective in supporting the efforts teachers and students to become computationally literate?\n\nWhat are your views on the reasons that computational literacy is important for students?\n\n\n\nWhat are some of the most difficult barriers that need to be overcome for students to engage with Python?\n\n\nHow important is it, in your opinion, to expose students to text-based programming, as opposed to the block-based environment of tools such as Scratch?\nAt what age range do you think we should be trying to engage students with programming?\n\n\n\nWhen the teacher’s day was introduced as part of the education summit for PyCon UK what was the initial reception from the educators who attended?\n\n\nHow has the format for the teacher’s portion of the conference changed in the subsequent years?\nWhat have been some of the most useful or beneficial aspects for the teacher’s and how much engagement occurs between the conferences?\n\n\n\nWhat was your involvement in the initiative that brought the BBC micro:bit to UK classrooms?\n\n\nWhat kinds of feedback have you gotten from students who have had an opportunity to use them?\nWhat are some of the most interesting or unexpected uses of the micro:bit that you have seen?\n\n\n\n\n\nKeep In Touch\n\n\n@ntoll on Twitter\nntoll on GitHub\nWebsite\n\n\nPicks\n\n\nTobias\n\nThe Dark Materials Trilogy Audiobooks by Phillip Pullman\n\n\n\nNicholas\n\n\nMoon Dust by Andrew Smith\nTotally Wired by Andrew Smith\n\n\n\n\n\nLinks\n\n\nntoll.org\nTuba\nRoyal College of Music\nFry IT\nMicroPython\n\nPodcast Interview With Damien George\n\n\n\nMicroPython Book\nMu\nScratch\nJupyter\nJohn Pinner\nLondon Python Code Dojo\nAlan Turing\nTim Berners-Lee\nCharles Babbage\nREPL (Read-Eval-Print Loop\nDaniel Pope\nPyGame\nRaspberry Pi Foundation\nPyGame Zero\nNetwork Zero\nGPIO Zero\nComputing At School\nBBC\nPSF\nTouchDevelop\nTypeScript\nDamien George\nARM\nCode Kingdoms\nmicro:bit\nBarclay’s\nPyCon US Education Summit\nRaspberry Pi Foundation Code Club\nQumisha Goss Keynote\nAdafruit\nCircuitPython\nNeoPixel\nPyBoard\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

There are a number of resources available for teaching beginners to code in Python and many other languages, and numerous endeavors to introduce programming to educational environments. Sometimes those efforts yield success and others can simply lead to frustration on the part of the teacher and the student. In this episode Nicholas Tollervey discusses his work as a teacher and a programmer, his work on the micro:bit project and the PyCon UK education summit, as well as his thoughts on the place that Python holds in educational programs for teaching the next generation.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Helping Teacher's Bring Python Into The Classroom (Interview)","date_published":"2018-08-06T01:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/2c4ff85b-494b-42a5-a2a7-10565819376e.mp3","mime_type":"audio/mpeg","size_in_bytes":68125563,"duration_in_seconds":3559}]},{"id":"podlove-2018-07-30t01:15:11+00:00-c0a9d5f2132e3d3","title":"Continuous Delivery For Complex Systems Using Zuul with Monty Taylor","url":"https://www.pythonpodcast.com/zuul-with-monty-taylor-episode-172","content_text":"Summary\n\nContinuous integration systems are important for ensuring that you don’t release broken software. Some projects can benefit from simple, standardized platforms, but as you grow or factor in additional projects the complexity of checking your deployments grows. Zuul is a deployment automation and gating system that was built to power the complexities of OpenStack so it will grow and scale with you. In this episode Monty Taylor explains how he helped start Zuul, how it is designed for scale, and how you can start using it for your continuous delivery systems. He also discusses how Zuul has evolved and the directions it will take in the future.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nJoin the community in the new Zulip chat workspace at podcastinit.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Monty Taylor about Zuul, a platform that drives continuous integration, delivery, and deployment systems with a focus on project gating and interrelated projects.\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what Zuul is and how the project got started?\nHow do you view Zuul in the broader landscape of CI/CD systems (e.g. GoCD, Jenkins, Travis, etc.)?\nWhat is the workflow for someone who is defining a pipeline in Zuul?\n\nHow are the pipelines tested and promoted?\nOne of the problems that are often encountered in CI/CD systems is the difficulty of testing changes locally. What kind of support is available in Zuul for that?\n\n\n\nCan you describe the project architecture?\n\n\nWhat aspects of the architecture enable it to scale to large projects and teams?\n\n\n\nHow difficult would it be to swap the Ansible integration for another orchestration tool?\nWhat would be involved in adding support for additional version control systems?\nWhat are your plans for the future of the project?\n\n\nKeep In Touch\n\n\nemonty on GitHub\nWebsite\n@e_monty on Twitter\n\n\nPicks\n\n\nTobias\n\nHitchhiker’s Guide To The Galaxy\n\n\n\nMonty\n\n\nBojack Horseman\n\n\n\n\n\nLinks\n\n\nRed Hat\nZuul\nOpenStack\nJim Blair\nPerl\nSNPP\nRackspace\nNASA\nDrizzle\nSun Microsystems\nMySQL\nContinuous Integration\nContinuous Delivery\nLaunchpad\nBzr\nJenkins\nJess Frazelle\nGraphite\nStatsD\ngraphite.openstack.org\ngrafana.openstack.org\nsubunit\nAnsible\nHelm\nSoftware Factory\nGerrit\nGit\nPerforce\nSubversion\nZookeeper\nGearman\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Continuous integration systems are important for ensuring that you don’t release broken software. Some projects can benefit from simple, standardized platforms, but as you grow or factor in additional projects the complexity of checking your deployments grows. Zuul is a deployment automation and gating system that was built to power the complexities of OpenStack so it will grow and scale with you. In this episode Monty Taylor explains how he helped start Zuul, how it is designed for scale, and how you can start using it for your continuous delivery systems. He also discusses how Zuul has evolved and the directions it will take in the future.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Zuul: The Continuous Delivery Platform For Large Systems (Interview)","date_published":"2018-07-29T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/44051638-08be-47dc-9114-2b95718c8d85.mp3","mime_type":"audio/mpeg","size_in_bytes":75198559,"duration_in_seconds":4021}]},{"id":"podlove-2018-07-23t01:22:53+00:00-ce935e113ffcace","title":"Michael Foord On Testing, Mock, TDD, And The Python Community","url":"https://www.pythonpodcast.com/michael-foord-on-testing-mock-tdd-and-the-python-community-episode-171","content_text":"Summary\n\nMichael Foord has been working on building and testing software in Python for over a decade. One of his most notable and widely used contributions to the community is the Mock library, which has been incorporated into the standard library. In this episode he explains how he got involved in the community, why testing has been such a strong focus throughout his career, the uses and hazards of mocked objects, and how he is transitioning to freelancing full time.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nJoin the community in the new Zulip chat workspace at podcastinit.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Michael Foord mockingly, about his career in Python\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nOne of the main threads in your career appears to be software testing. What aspects of testing do you find so interesting and how did you first get exposed to that aspect of building software?\n\nHow has the language and ecosystem support for testing evolved over the course of your career?\nWhat are some of the areas that you find it to still be lacking?\n\n\n\nMock is one of your projects that has been widely adopted and ultimately incorporated into the standard library. What was your reason for starting it in the first place?\n\n\nMocking can be a controversial topic. What are your current thoughts on how and when to use mocks, stubs, and fixtures?\n\n\n\nHow do you view the state of the art for testing in Python as it compares to other languages that you have worked in?\nYou were fairly early in the move to supporting Python 2 and 3 in a single project with Mock. How has that overall experience changed in the intervening years since Python 2.4 and 3.2?\nWhat are some of the notable evolutions in Python and the software industry that you have experienced over your career?\nYou recently transitioned to acting as a software trainer and consultant full time. Where are you focusing your energy currently and what are your grand plans for the future?\n\n\nKeep In Touch\n\n\nEmail\nWebsite\nTwitter\n\n\nPicks\n\n\nTobias\n\n-Ology Books\n\n\n\nMichael\n\n\nImaginary Authors\nFalling Into The Sea\nCity On Fire\n\n\n\n\n\nLinks\n\n\nIronPython\nLondon\nIronPython in Action\nMock\nUnitTest\nPlay By Email\nSmalltalk\nRegular Expression\nDijkstra’s Algorithm\nurllib2\nResolver Systems\nTDD (Test-Driven Development)\nPyCon\nTrent Nelson\nFractals\nUnicode\nJoel Spolsky (Unicode)\nOOP (Object-Oriented Programming)\nEnd-to-end Testing\nUnit Testing\nCanonical\nSelenium\nAnsible\nAnsible Tower\n\nAWX (Open Source Tower Codebase)\n\n\n\nContinuous Integration\nContinuous Delivery\nAgile Software Development\nGitHub\nGitLab\nJenkins\nNightwatch.js\npy.test\nMartin Fowler\nMonkey Patching\nDecorator\nContext Manager\nautospec\nGolang\n2to3\nSix\nInstagram Keynote\nTrans-code\nDjango Girls\nPyLadies\nAgile Abstractions\nDavid Beazley\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Michael Foord has been working on building and testing software in Python for over a decade. One of his most notable and widely used contributions to the community is the Mock library, which has been incorporated into the standard library. In this episode he explains how he got involved in the community, why testing has been such a strong focus throughout his career, the uses and hazards of mocked objects, and how he is transitioning to freelancing full time.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Mocks, Stubs, and TDD With Michael Foord (Interview)","date_published":"2018-07-22T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/ce5422f2-ad39-4397-9f29-dbc9e0d49a40.mp3","mime_type":"audio/mpeg","size_in_bytes":37266001,"duration_in_seconds":3311}]},{"id":"podlove-2018-07-15t16:45:26+00:00-7b474c4a5b4f2fa","title":"The Past, Present, and Future of Twisted with Moshe Zadka","url":"https://www.pythonpodcast.com/twisted-with-moshe-zadka-episode-170","content_text":"Summary\n\nTwisted is one of the earliest frameworks for developing asynchronous applications in Python and it has yet to fulfill its original purpose. It can be used to build network servers that integrate a multitude of protocols, increase the performance of your I/O bound applications, serve as the full web stack for your WSGI projects, and anything else that needs a battle tested and performant foundation. In this episode long time maintainer Moshe Zadka discusses the history of Twisted, how it has evolved over the years, the transition to Python 3, some of its myriad use cases, and where it is headed in the future. Try it out today and then send some thanks to all of the people who have dedicated their time to building it.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nTo get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nJoin the community in the new Zulip chat workspace at podcastinit.com/chat\nYour host as usual is Tobias Macey and today I’m interviewing Moshe Zadka about Twisted, the original multi-function tool for asynchronous operations and network protocols in Python\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nFor anyone who isn’t familiar with Twisted can you share a brief overview of what it is?\n\nWhat was the original motivation for creating it?\nHow did you get involved with the project and what is your current role in the team?\n\n\n\nHow can people learn to use Twisted?\n\n\nWhat are some of the common difficulties that new users encounter?\n\n\n\nWhat did you learn working on Twisted?\nWho uses Twisted?\n\n\nWhen is Twisted the wrong choice?\nWhat are some examples of systems that aren’t using Twisted but should be?\n\n\n\nWhat are some of the ways that Twisted has evolved and changed over the years?\nWhat are some of the ways people can support Twisted?\nWhat are some of the plans for the future of Twisted?\n\n\nKeep In Touch\n\n\nMoshe Zadka\nTwisted\n\nMailing List\nIRC\n\n\n\n\n\nPicks\n\n\nTobias\n\nLeatherman Wave+\n\n\n\nMoshe\n\n\nUnsong Book\n\n\n\n\n\nLinks\n\n\nTwisted\nGlyph Lefkowitz\nIRC\nasync/await\nPyvideo\nPyCon 2017 Tutorial\nasyncio\nGTK\nSNMP\nGunicorn\nuWSGI\nWSGI\nNginx\nSupervisor\nasynchat\nasyncore\nNcolony\nThe Ultimate Quality Development System\n\nMoshe’s article on UQDS\n\n\n\nUnicode prefix\n2to3\nSix\nUnit Tests\nAutomat\nTLA+\n\n\nPyCon CA Presentation\n\n\n\nSans IO\n\n\nCory Benfield’s talk\n\n\n\nTubes\nHyper\nH2\nH11\nApple Calendar Server Github\nDuo Security using Cyclone\nMatrix — Used by French government\nAIOHTTP\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Twisted is one of the earliest frameworks for developing asynchronous applications in Python and it has yet to fulfill its original purpose. It can be used to build network servers that integrate a multitude of protocols, increase the performance of your I/O bound applications, serve as the full web stack for your WSGI projects, and anything else that needs a battle tested and performant foundation. In this episode long time maintainer Moshe Zadka discusses the history of Twisted, how it has evolved over the years, the transition to Python 3, some of its myriad use cases, and where it is headed in the future. Try it out today and then send some thanks to all of the people who have dedicated their time to building it.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n

\n\n

Picks

\n\n

\n\n

Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"The Twisted Framework For Asynchronous Network Servers In Python (Interview)","date_published":"2018-07-15T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/221cab11-33ab-45aa-871c-4d8a6a89668c.mp3","mime_type":"audio/mpeg","size_in_bytes":20031418,"duration_in_seconds":2082}]},{"id":"podlove-2018-07-08t20:58:26+00:00-804fe4a1214fdae","title":"Mike Driscoll And His Career In Python","url":"https://www.pythonpodcast.com/mike-driscoll-episode-169","content_text":"Summary\n\nMike Driscoll has been writing blogs and books for the Python community for years, including his popular series on the Python Module Of The Week. In his daily work he uses Python to test graphical interfaces written in C++ and QT for embedded platforms. In this episode he explains his work, how he got involved in writing as a regular exercise, and an overview of his recent books.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nTo get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Mike Driscoll about using Python to test QT UIs for embedded platforms, his experience running a popular Python blog, and being a self-published author\n\n\nTechnically, I am testing a C++ Qt app that is deployed to an embedded system\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing the way in which you are using Python for your work?\nWhat benefits does Python provide for writing and running tests for projects written in other languages?\n\nWhat are the drawbacks or limitations?\n\n\n\nWhat are some of the tools or techniques that you have found most useful for your work?\n\n\nHow much of that was hard-earned knowledge vs finding it in reference material or prior art?\n\n\n\nWhat are some of the most interesting and/or difficult aspects of testing graphical interfaces?\nWhat are some of the most surprising or unexpected aspects of the problem space that you have discovered through your work?\nWhat are some of the other ways in which you have worked with the Python language and community?\nWhat are you most interested in working toward in the future?\n\n\nKeep In Touch\n\n\nBlog\n@driscollis on Twitter\ndriscollis on GitHub\nBooks\n\n\nPicks\n\n\nTobias\n\nDraw.io\n\n\n\nMike\n\n\nQt for Python\nJupyter Notebook\n\n\n\n\n\nLinks\n\n\nMouse vs. Python\nC++\nQt\nAg Leader\nSquish\nCFFI\nCtypes\nTcl\nJavascript\nRuby\nFroglogic\nSelenium\nPillow\nOpenCV\nWxPython\nPSF\nPyCon\nBrett Cannon\nCarol Willing\nReportLab\nPDFRW\nBrett Cannon PyCon 2018 Keynote\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Mike Driscoll has been writing blogs and books for the Python community for years, including his popular series on the Python Module Of The Week. In his daily work he uses Python to test graphical interfaces written in C++ and QT for embedded platforms. In this episode he explains his work, how he got involved in writing as a regular exercise, and an overview of his recent books.

\n\n

Preface

\n\n\n\n

Technically, I am testing a C++ Qt app that is deployed to an embedded system

\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Mike Driscoll's Career In Python (Interview)","date_published":"2018-07-08T17:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/2af9d7d4-3316-41aa-9a8f-e9d2d9d68101.mp3","mime_type":"audio/mpeg","size_in_bytes":17085189,"duration_in_seconds":1411}]},{"id":"podlove-2018-07-02t03:04:34+00:00-bf917019e256a66","title":"The Pulp Artifact Repository with Bihan Zhang and Austin Macdonald","url":"https://www.pythonpodcast.com/pulp-with-bihan-zhang-and-austin-macdonald-episode-168","content_text":"Summary\n\nHosting your own artifact repositories can have a huge impact on the reliability of your production systems. It reduces your reliance on the availability of external services during deployments and ensures that you have access to a consistent set of dependencies with known versions. Many repositories only support one type of package, thereby requiring multiple systems to be maintained, but Pulp is a platform that handles multiple content types and is easily extendable to manage everything you need for running your applications. In this episode maintainers Bihan Zhang and Austin Macdonald explain how the Pulp project works, the exciting new changes coming in version 3, and how you can get it set up to use for your deployments today.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nTo get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Austin Macdonald and Bihan Zhang about Pulp, a platform for hosting and managing software package repositories\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is Pulp and how did the project get started?\nWhat are the use cases/benefits for hosting your own artifact repository?\nWhat is the high level architecture of the platform?\n\nPulp 3 appears to be a fairly substantial change in architecture and design. What will be involved in migrating an existing installation to the new version when it is released?\n\n\n\nWhat is involved in adding support for a new type of artifact/package?\nHow does Pulp compare to other artifact repositories?\nWhat are the major pieces of work that are required before releasing Pulp 3?\nWhat have been some of the most interesting/unexpected/challenging aspects of building and maintaining Pulp?\nWhat are your plans for the future of Pulp?\n\n\nKeep In Touch\n\n\nAustin\n\nasmacdo on GitHub\n@asmacdo on Twitter\n\n\n\nBihan\n\n\nLinkedIn\n\n\n\nPulp Project\n\n\nEmail\nGitHub\nWebsite\n#pulp on freenode\n\n\n\n\n\nPicks\n\n\nTobias\n\nSoonish\n\n\n\nAustin\n\n\nShostakovitch String Quartet #8\n\n\n\nBihan\n\n\nAOPA: Air Safety Institute YouTube Channel\n\n\n\n\n\nLinks\n\n\nPulp\nRedHat\nFrench Horn\nXKCD\nRPM\nDebian\nPyPI\nCenter For Open Science\nSciPy\nAnsible\nDjango Project\nDjango Storages\nArtifactory\nWarehouse\nOCI (Open Container Initiative)\nCrane\nDocker\nTwinehttps://github.com/pypa/twine?utm_source=rss&utm_medium=rss\nMaven\nRead-through Cache\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Hosting your own artifact repositories can have a huge impact on the reliability of your production systems. It reduces your reliance on the availability of external services during deployments and ensures that you have access to a consistent set of dependencies with known versions. Many repositories only support one type of package, thereby requiring multiple systems to be maintained, but Pulp is a platform that handles multiple content types and is easily extendable to manage everything you need for running your applications. In this episode maintainers Bihan Zhang and Austin Macdonald explain how the Pulp project works, the exciting new changes coming in version 3, and how you can get it set up to use for your deployments today.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n

\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Pulp: An Extensible Artifact Repository In Python (Interview)","date_published":"2018-07-02T00:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/d747ddb2-995b-43c5-9d92-b19f5e70c7a3.mp3","mime_type":"audio/mpeg","size_in_bytes":24245857,"duration_in_seconds":1843}]},{"id":"podlove-2018-06-25t01:24:58+00:00-d3998b6a5567731","title":"Bringing Africa Online At Ascoderu with Clemens Wolff","url":"https://www.pythonpodcast.com/lokole-with-clemens-wolff-episode-167","content_text":"Summary\n\nThe future is here, it’s just not evenly distributed. One of the places where this is especially true is in sub-Saharan Africa which is a vast region with little to no reliable internet connectivity. To help communities in this region leapfrog infrastructure challenges and gain access to opportunities for education and market information the Ascoderu non-profit has built Lokole. In this episode one of the lead engineers on the project, Clemens Wolff, explains what it is, how it is built, and how the venerable e-mail protocols can continue to provide access cheaply and reliably.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nTo get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Clemens Wolff about how Ascoderu is using Python to help communities in sub-Saharan Africa gain access to the digital age\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is the mission of Ascoderu and how did the organization get started?\n\nHow did you get involved?\n\n\n\nThe primary project that you build and maintain is Lokole. What is it and how does it help you in achieving the goals of the organization?\n\n\nWhat are the limitations of using e-mail as the only interface to the broader internet?\nWhat are some of the most interesting or unexpected uses of email in isolation have you seen?\n\n\n\nFrom the user perspective, can you describe the overall experience of interacting with Lokole?\n\n\nWhat is happening in the background?\nDid you consider using a binary message format such as Avro, protocol buffers, or msgpack in place of JSON?\n\n\n\nWhat kind of fault tolerance techniques are built into the overall information flow?\nWhat are the most challenging or unexpected aspects of building Lokole and interacting with the user communities?\nWhat projects do you have planned for the future?\n\n\nKeep In Touch\n\n\nEmail\nGitHub\nLinkedIn\n\n\nPicks\n\n\nTobias\n\nHubspot CRM\n\n\n\nClemens\n\n\nAli Farka Toure\n\n\n\n\n\nLinks\n\n\nAscoderu\nLokole\nNLTK\nHaskell\nDRC\nLokole client\nLokole server\nAli Express\nRaspberry Pi\nOrange Pi\nUganda\nTanzania\nJSON\nAvro\nmsgpack\ngzip\nGmail\nLingala\nwvdial\nUSB Modeswitch\nGnome SIM database\nBenin\nAgricultural Engineer\nOuternet\nInternet In A Box\nmkvvconf\nAzure for non-profits\nKubernetes\nConnexion\nZalando\nOpen API\nSendgrid\nAzure Service Bus\nAmbassador Container\nPillow\nUnited Nations Sustainable Development Goals\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

The future is here, it’s just not evenly distributed. One of the places where this is especially true is in sub-Saharan Africa which is a vast region with little to no reliable internet connectivity. To help communities in this region leapfrog infrastructure challenges and gain access to opportunities for education and market information the Ascoderu non-profit has built Lokole. In this episode one of the lead engineers on the project, Clemens Wolff, explains what it is, how it is built, and how the venerable e-mail protocols can continue to provide access cheaply and reliably.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Bringing Africa Online Using E-mail And Python (Interview)","date_published":"2018-06-24T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/86d88ac8-bbb8-4f8e-b682-0fd992c3f9f5.mp3","mime_type":"audio/mpeg","size_in_bytes":28374893,"duration_in_seconds":2553}]},{"id":"podlove-2018-06-17t13:44:16+00:00-892443a873af31a","title":"Understanding Machine Learning Through Visualizations with Benjamin Bengfort and Rebecca Bilbro","url":"https://www.pythonpodcast.com/yellowbrick-with-bejnamin-bengfort-and-rebecca-bilbro-episode-166","content_text":"Summary\n\nMachine learning models are often inscrutable and it can be difficult to know whether you are making progress. To improve feedback and speed up iteration cycles Benjamin Bengfort and Rebecca Bilbro built Yellowbrick to easily generate visualizations of model performance. In this episode they explain how to use Yellowbrick in the process of building a machine learning project, how it aids in understanding how different parameters impact the outcome, and the improved understanding among teammates that it creates. They also explain how it integrates with the scikit-learn API, the difficulty of producing effective visualizations, and future plans for improvement and new features.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nTo get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Rebecca Bilbro and Benjamin Bengfort about Yellowbrick, a scikit extension to use visualizations for assisting with model selection in your data science projects.\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe the use case for Yellowbrick and how the project got started?\nWhat is involved in visualizing scikit-learn models?\n\nWhat kinds of information do the visualizations convey?\nHow do they aid in understanding what is happening in the models?\n\n\n\nHow much direction does yellowbrick provide in terms of knowing which visualizations will be helpful in various circumstances?\nWhat does the workflow look like for someone using Yellowbrick while iterating on a data science project?\nWhat are some of the common points of confusion that your students encounter when learning data science and how has yellowbrick assisted in achieving understanding?\nHow is Yellowbrick iplemented and how has the design changed over the lifetime of the project?\nWhat would be required to integrate with other visualization libraries and what benefits (if any) might that provide?\n\n\nWhat about other ML frameworks?\n\n\n\nWhat are some of the most challenging or unexpected aspects of building and maintaining Yellowbrick?\nWhat are the limitations or edge cases for yellowbrick?\nWhat do you have planned for the future of yellowbrick?\nBeyond visualization, what are some of the other areas that you would like to see innovation in how data science is taught and/or conducted to make it more accessible?\n\n\nKeep In Touch\n\n\nRebecca Bilbro\n\nGithub\nTwitter\n\n\n\nBenjamin Bengfort\n\n\nGithub\nTwitter\n\n\n\n\n\nPicks\n\n\nTobias\n\nPoutine\n\n\n\nRebecca\n\n\nThe color yellow\n\n\n\nBenjamin\n\n\nALL CAPS\n\n\n\n\n\nLinks\n\n\nHadoop\nNatural Language Processing\nMachine Learning\nscikit-learn\nModel Selection Triple\nthe machine learning workflow\nscikit-yb\nYellowbrick\nVisualizer API\nVisual Tests\nJupyter\nMatplotlib\nTensorflow\nHyperparameter\nParallel Coordinates\nRadviz\nRank2D\nPrediction Error Plot\nResiduals Plot\nValidation Curves\nAlpha Selection\nFrequency Distribution Plot\nBayes Theorem\nSeaborn\nStop Words\nN-gram\nCraig – Bias and Fairness of Algorithms\nShiny\nBokeh\nKeras\nStatsModels\nTensorboard\nPyTorch\nNumPy\nVoxel\nWizard of Oz\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Machine learning models are often inscrutable and it can be difficult to know whether you are making progress. To improve feedback and speed up iteration cycles Benjamin Bengfort and Rebecca Bilbro built Yellowbrick to easily generate visualizations of model performance. In this episode they explain how to use Yellowbrick in the process of building a machine learning project, how it aids in understanding how different parameters impact the outcome, and the improved understanding among teammates that it creates. They also explain how it integrates with the scikit-learn API, the difficulty of producing effective visualizations, and future plans for improvement and new features.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n

\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Using Data Visualization To Improve Your Machine Learning Projects (Interview)","date_published":"2018-06-17T09:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/5d37970d-b257-45bb-bffe-8a5c8dd5f9f3.mp3","mime_type":"audio/mpeg","size_in_bytes":39947313,"duration_in_seconds":3313}]},{"id":"podlove-2018-06-11t02:28:00+00:00-6228332d5b6708d","title":"Modern Database Clients On The Command Line with Amjith Ramanujam","url":"https://www.pythonpodcast.com/dbcli-with-amjith-ramanujam-episode-165","content_text":"Summary\n\nThe command line is a powerful and resilient interface for getting work done, but the user experience is often lacking. This can be especially pronounced in database clients because of the amount of information being transferred and examined. To help improve the utility of these interfaces Amjith Ramanujam built PGCLI, quickly followed by MyCLI with the Prompt Toolkit library. In this episode he describes his motivation for building these projects, how their popularity led him to create even more clients, and how these tools can help you in your command line adventures.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nTo get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Amjith Ramanujam about DBCLI, an umbrella project for command line database clients with autocompletion and syntax highlighting.\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is the DBCLI project?\n\nWhich of the clients was the first to be created and what was your motivation for starting it?\n\n\n\nAt what point did you decide to create the DBCLI umbrella for the different projects and what benefits does it provide?\nHow much functionality is shared between the different clients?\nWhat additional functionality do the different clients provide over those that are distributed with their respective engines?\nHow do you optimize for cases where large volumes of data are returned from a query?\nWhat are some of the most interesting or surprising things that you have learned about database engines in the process of building client interfaces for them?\nWhat are the most challenging aspects of building the different database clients?\nWhat are some unexpected hardships that you encountered through this open source project?\nWhat are some unexpected pleasant surprises that you encountered through this project? \nWhy did you hand over the project leadership for pgcli and mycli to other devs? Was it a hard decision? \nWhy do you optimize on being nice over being right?\nHow did Microsoft get involved with dbcli? mssql-cli\nWhat’s been the reception for the projects? \nWhat are your plans for upcoming releases of the various clients?\nWhich database engines are you planning to target next?\n\n\nKeep In Touch\n\n\namjith on GitHub\n@amjithr on Twitter\nBlog\n\n\nPicks\n\n\nTobias\n\nDownsizing\n\n\n\nAmjith\n\n\nDosas\nSarasate\n\n\n\n\n\nLinks\n\n\nDBCLI\nHaskell\nLearn you as haskell\nList Comprehension\nPGCLI\nMyCLI\nMSSQL-CLI\nPrompt Toolkit\n\nPodcast.__init__ Interview\n\n\n\nBPython\nDjangoCon EU\nCLI Helpers\nPython Generators\nPGSpecial\nLongboarding\nIrina Truong\nThomas Roten(sp)\nPostGreSQL\nMySQL\nMicrosoft SQL Server\nSQLite\nOracle DB\nCassandra DB\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

The command line is a powerful and resilient interface for getting work done, but the user experience is often lacking. This can be especially pronounced in database clients because of the amount of information being transferred and examined. To help improve the utility of these interfaces Amjith Ramanujam built PGCLI, quickly followed by MyCLI with the Prompt Toolkit library. In this episode he describes his motivation for building these projects, how their popularity led him to create even more clients, and how these tools can help you in your command line adventures.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Modern Database Interfaces For The Command Line (Interview)","date_published":"2018-06-10T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/6cb15c17-f0bf-4611-afb3-a64cf24b11b0.mp3","mime_type":"audio/mpeg","size_in_bytes":21346223,"duration_in_seconds":1839}]},{"id":"podlove-2018-06-04t00:53:34+00:00-a7468a278fda71f","title":"Pandas Extension Arrays with Tom Augspurger","url":"https://www.pythonpodcast.com/pandas-extension-arrays-with-tom-augspurger-episode-164","content_text":"Summary\n\nPandas is a swiss army knife for data processing in Python but it has long been difficult to customize. In the latest release there is now an extension interface for adding custom data types with namespaced APIs. This allows for building and combining domain specific use cases and alternative storage mechanisms. In this episode Tom Augspurger describes how the new ExtensionArray works, how it came to be, and how you can start building your own extensions today.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nTo get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Tom Augspurger about the extension interface for Pandas data frames and the use cases that it enables\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nMost people are familiar with Pandas, but can you describe at a high level the new extension interface?\n\nWhat is the story behind the implementation of this functionality?\nPrior to this interface what was the option for anyone who wanted to extend Pandas?\n\n\n\nWhat are some of the new data types that are available as external packages?\n\n\nWhat are some of the unique use cases that they enable?\n\n\n\nHow is the new interface implemented within Pandas?\nWhat were the most challenging or difficult aspects of building this new functionality?\nWhat are some of the more interesting possibilities that you are aware of for new extension types?\nWhat are the limitations of the interface for libraries that add new array functionality?\nWhat is the next major change or improvement that you would like to add in Pandas?\n\n\nKeep In Touch\n\n\ntomaugspurger on GitHub\n@TomAugspurger on Twitter\n\n\nPicks\n\n\nTobias\n\nBlack Panther\n\n\n\nTom\n\n\nDask-ML\n\n\n\n\n\nLinks\n\n\nPandas\nExtensionArray\nOriginal IP Address proposal\nMid-implementation blog post\nDataframe\nNumpy\nCyberpandas\nGeopandas\nGIS\nArrow\nCuPy\nJQ\nWes McKinney\nArray ufunc\nMatplotlib\nAltair\nSeaborn\nBokeh\n\nPodcast.__init__ Interview\n\n\n\nDask\n\n\nData Engineering Interview\n\n\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Pandas is a swiss army knife for data processing in Python but it has long been difficult to customize. In the latest release there is now an extension interface for adding custom data types with namespaced APIs. This allows for building and combining domain specific use cases and alternative storage mechanisms. In this episode Tom Augspurger describes how the new ExtensionArray works, how it came to be, and how you can start building your own extensions today.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Adding New Tools To The Pandas Swiss Army Knife (Interview)","date_published":"2018-06-03T23:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/51a7df0f-02a5-4393-a608-7fef3549d8b5.mp3","mime_type":"audio/mpeg","size_in_bytes":23467111,"duration_in_seconds":2006}]},{"id":"podlove-2018-05-27t02:12:28+00:00-9a384dc1e43edde","title":"Making A Difference Through Software With Eric Schles","url":"https://www.pythonpodcast.com/making-a-difference-through-software-with-eric-schles-episode-163","content_text":"Summary\n\nSoftware development is a skill that can create value and reduce drudgery in a wide variety of contexts. Sometimes the causes that are most in need of software expertise are also the least able to pay for it. By volunteering our time and abilities to causes that we believe in, we can help make a tangible difference in the world. In this episode Eric Schles describes his experiences working on social justice initiatives and the types of work that proved to be the most helpful to the groups that he was working with.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nTo get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Eric Schles about how to get involved with social justice causes as an engineer\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat are some ways that engineers can create real-world impact with their skills?\nWhat are some of the common roadblocks to contribution that people should be aware of?\nWhat are some of the types of projects or tools that can provide the most value compared to the amount of effort?\nDo you have any advice for picking an organization or cause that will benefit the most from technical expertise?\nMany of the tools and systems that get built for public or non-profit organizations require some amount of data for them to be useful. Do you have any advice on methods for identifying, locating, or collecting the necessary information for feeding into these projects?\nWhat are some of the design factors that should be considered when building tools for these organizations to allow them to be maintainable and sustainable in the absense of an experienced engineer?\n\n\nKeep In Touch\n\n\nEricSchles on GitHub\n@EricSchles on Twitter\n\n\nPicks\n\n\nTobias\n\nShoes without laces\n\n\n\nEric\n\n\nCatboost\nPomegranate\n\n\n\n\n\nLinks\n\n\nUSDS\n18F\nOCW\n\nPython Course\n\n\n\nSAS\nR\nMachine Learning\nVersion Control\nGitHub\nAgile\nOCR (Optical Character Recognition)\nEric Schles Interview On Podcast.__init__\nExcel\nETL (Extract Transform Load)\nAutomate The Boring Stuff\nWeb Scraping\nThomas Levine\nElasticsearch\nTrello\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Software development is a skill that can create value and reduce drudgery in a wide variety of contexts. Sometimes the causes that are most in need of software expertise are also the least able to pay for it. By volunteering our time and abilities to causes that we believe in, we can help make a tangible difference in the world. In this episode Eric Schles describes his experiences working on social justice initiatives and the types of work that proved to be the most helpful to the groups that he was working with.

\n\n

Preface

\n\n\n\n

Interview

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Helping To Build A Better World Through Software (Interview)","date_published":"2018-05-26T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/80eaf0a0-174c-42fe-bb62-3c4bdc718f29.mp3","mime_type":"audio/mpeg","size_in_bytes":31291995,"duration_in_seconds":2593}]},{"id":"podlove-2018-05-21t00:15:22+00:00-3a45f63e76d7236","title":"Asking Questions From Data Using Active Learning with Tivadar Danka","url":"https://www.pythonpodcast.com/modal-with-tivadar-danka-episode-162","content_text":"Summary\n\nOne of the challenges of machine learning is obtaining large enough volumes of well labelled data. An approach to mitigate the effort required for labelling data sets is active learning, in which outliers are identified and labelled by domain experts. In this episode Tivadar Danka describes how he built modAL to bring active learning to bioinformatics. He is using it for doing human in the loop training of models to detect cell phenotypes with massive unlabelled datasets. He explains how the library works, how he designed it to be modular for a broad set of use cases, and how you can use it for training models of your own.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nTo get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Tivadar Danka about modAL, a modular active learning framework for Python3\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is active learning?\n\nHow does it differ from other approaches to machine learning?\n\n\n\nWhat is modAL and what was your motivation for starting the project?\nFor someone who is using modAL, what does a typical workflow look like to train their models?\nHow do you avoid oversampling and causing the human in the loop to become overwhelmed with labeling requirements?\nWhat are the most challenging aspects of building and using modAL?\nWhat do you have planned for the future of modAL?\n\n\nKeep In Touch\n\n\n@TivadarDanka on Twitter\ncosmic-cortex on GitHub\nhttps://www.tivadardanka.com?utm_source=rss&utm_medium=rss for anything else \n\n\nPicks\n\n\nTobias\n\nPeter Rabbit Movie\n\n\n\nTivadar\n\n\nUri Alon: An Introduction to Systems Biology – Design Principles of Biological Circuits, book and online lectures\n\n\n\n\n\nLinks\n\n\nmodAL homepage\nmodAL on GitHub\nmodAL paper\nBioinformatics\nHungary\nPhenotypes\nActive Learning\nSupervised Learning\nUnsupervised Learning\nSnorkel\nActive Feature-Value Acquisition\nscikit-learn\nEntropy\nPyTorch\nTensorflow\nKeras\nJupyter Notebooks\nBayesian Optimization\nHyperparameters\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

One of the challenges of machine learning is obtaining large enough volumes of well labelled data. An approach to mitigate the effort required for labelling data sets is active learning, in which outliers are identified and labelled by domain experts. In this episode Tivadar Danka describes how he built modAL to bring active learning to bioinformatics. He is using it for doing human in the loop training of models to detect cell phenotypes with massive unlabelled datasets. He explains how the library works, how he designed it to be modular for a broad set of use cases, and how you can use it for training models of your own.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Efficient Data Labeling With Active Learning (Interview)","date_published":"2018-05-20T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/e0283037-8cf1-4718-8997-ef0cb79517be.mp3","mime_type":"audio/mpeg","size_in_bytes":19618282,"duration_in_seconds":1671}]},{"id":"podlove-2018-05-13t10:29:58+00:00-48901b07789b687","title":"Great Expectations For Your Data Pipelines with Abe Gong and James Campbell","url":"https://www.pythonpodcast.com/great-expectations-with-abe-gong-and-james-campbell-episode-161","content_text":"Summary\n\nTesting is a critical activity in all software projects, but one that is often neglected in data pipelines. The complexities introduced by the inherent statefulness of the problem domain and the interdependencies between systems contribute to make pipeline testing difficult to manage. To make this endeavor more manageable Abe Gong and James Campbell have created Great Expectations. In this episode they discuss how you can use the project to create tests in the exploratory phase of building a pipeline and leverage those to monitor your systems in production. They also discussed how Great Expectations works, the difficulties associated with pipeline testing and managing associated technical debt, and their future plans for the project.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nFinding a bug in production is never a fun experience, especially when your users find it first. Airbrake error monitoring ensures that you will always be the first to know so you can deploy a fix before anyone is impacted. With open source agents for Python 2 and 3 it’s easy to get started, and the automatic aggregations, contextual information, and deployment tracking ensure that you don’t waste time pinpointing what went wrong. Go to podcastinit.com/airbrake today to sign up and get your first 30 days free, and 50% off 3 months of the Startup plan.\nTo get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com\nYour host as usual is Tobias Macey and today I’m interviewing James Campbell and Abe Gong about Great Expectations, a tool for testing the data in your analytics pipelines\n\n\nInterview\n\n\nIntroduction\nHow did you first get introduced to Python?\nWhat is Great Expectations and what was your motivation for starting it?\nWhat are some of the complexities associated with testing analytics pipelines?\n\nWhat types of tests can be executed to ensure data integrity and accuracy?\n\n\n\nWhat are some examples of the potential impact of pipeline debt?\nWhat is Great Expectations and how does it simplify the process of building and executing pipeline tests?\nWhat are some examples of the types of tests that can be built with Great Expectations?\nFor someone getting started with Great Expectations what does the workflow look like?\nWhat was your reason for using Python for building it?\n\n\nHow does the choice of language benefit or hinder the contexts in which Great Expectations can be used?\n\n\n\nWhat are some cases where Great Expectations would not be usable or useful?\nWhat have been some of the most challenging aspects of building and using Great Expectations?\nWhat are your hopes for Great Expectations going forward?\n\n\nContact Info\n\n\nJames\n\njpcampb2 on GitHub\n\n\n\nAbe\n\n\nabegong on GitHub\nWebsite\n@AbeGong on Twitter\n\n\n\n\n\nPicks\n\n\nTobias\n\nFitbit Versa\n\n\n\nJames\n\n\nUnplug and spend some time away from the computer\n\n\n\nAbe\n\n\nSuperconductive Health\nSlack: Getting Past Burnout, Busy Work, and the Myth of Total Efficiency\n\n\n\n\n\nLinks\n\n\nSuperconductive Health\nLaboratory for Analytical Sciences\nGreat Expectations\nMedium Post\nDAG (Directed Acyclic Graph)\nSLA (Service Level Agreement)\nIntegration Testing\nData Engineering\nHistogram\nPandas\nSQLAlchemy\nTutorial Videos\nJupyter Notebooks\nDataframe\nAirflow\nLuigi\nSpark\nOozie\nAzkaban\nJSON\nXML\n\n\nThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Testing is a critical activity in all software projects, but one that is often neglected in data pipelines. The complexities introduced by the inherent statefulness of the problem domain and the interdependencies between systems contribute to make pipeline testing difficult to manage. To make this endeavor more manageable Abe Gong and James Campbell have created Great Expectations. In this episode they discuss how you can use the project to create tests in the exploratory phase of building a pipeline and leverage those to monitor your systems in production. They also discussed how Great Expectations works, the difficulties associated with pipeline testing and managing associated technical debt, and their future plans for the project.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Contact Info

\n\n

\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Testing Data Pipelines With Great Expectations (Interview)","date_published":"2018-05-13T06:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/0ee2405d-9589-4635-a22f-3abab2000c60.mp3","mime_type":"audio/mpeg","size_in_bytes":39096871,"duration_in_seconds":3042}]},{"id":"podlove-2018-05-06t20:36:50+00:00-20aa12e9dd20a72","title":"Exploring Color Theory In Python With Thomas Mansencal","url":"https://www.pythonpodcast.com/colour-with-thomas-mansencal-episode-160","content_text":"Summary\n\nWe take it for granted every day, but creating and displaying vivid colors in our digital media is a complicated and often difficult process. There are different ways to represent color, the ways in which they are displayed can cause them to look different, and translating between systems can cause losses of information. To simplify the process of working with color information in code Thomas Mansencal wrote the Colour project. In this episode we discuss his motiviation for creating and sharing his library, how it works to translate and manage color representations, and how it can be used in your projects.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nFinding a bug in production is never a fun experience, especially when your users find it first. Airbrake error monitoring ensures that you will always be the first to know so you can deploy a fix before anyone is impacted. With open source agents for Python 2 and 3 it’s easy to get started, and the automatic aggregations, contextual information, and deployment tracking ensure that you don’t waste time pinpointing what went wrong. Go to podcastinit.com/airbrake today to sign up and get your first 30 days free, and 50% off 3 months of the Startup plan.\nTo get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nYour host as usual is Tobias Macey and today I’m interviewing Thomas Mansencal about Colour, a python library for working with algorithms and transformations to explore color theory\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is color theory?\n\nHow does Colour assist in the process of working with some of the practical applications of colour science?\n\n\n\nWhat was your motivation for creating Colour?\nWhat are some example use cases for colour?\nOne of the aspects of color in digital environments that is often confusing is the number of different ways that it can be represented. What are the relative benefits of things like RGB, HSV, CMYK, etc.?\nHow is the Colour library architected and how has that evolved over time?\n\n\nAre there new developments in the area of color theory that need to be periodically incorporated into the library?\n\n\n\nWhat have you found to be some of the most often misunderstood aspects of color?\nWhat have been some of the most difficult or frustrating aspects of building, maintaining, and promoting Colour?\nWhat are some of the most interesting or unexpected uses of Colour that you have seen?\nWhat are your plans for the future of Colour?\n\n\nKeep In Touch\n\n\nWebsite\n\n\nPicks\n\n\nTobias\n\nBeasts of Olympus by Lucy Coates\n\n\n\nThomas\n\n\nCoursera Mathematics Machine Learning Course\n\n\n\n\n\nLinks\n\n\nColour\nColor Theory\nColor Science\nWeta Digital\nWingnut AR\nVisual Effects Artist\nAllegro\nAutoDesk Maya\nPyQT\nIsaac Newton\nColor Wheel\nColorimetry\nCIE\nVY Canis Majoris (Red Hypergiant)\nRigel (Blue-White Supergiant)\nKelvin Temperature Scale\nBlack Body Radiation\nHDRI (High Dynamic Range Imaging)\nAdobe DNG SDK\nICC\nOpenColorIO\nMERCK Group\nColor Space\nRGB\nHSV\nCMYK\nCIE XYZ\nCIE RGB\nCIE Lab\nCIE Luv\nsRGB\nGamma Correction\nAdditive Color Space\nSubtractive Color Space\nColor Blindness\nGustavo Machado\nRods and Cones\nDichromacy\nColor Appearance Model\nUniform Color Spaces\nJOSS\nArXiv\nCIECAM02 Color Appearance Model\nCinematic Color\nJeremy Selan (Author of OpenColorIO)\nAcademy Color Encoding System\nColor Appearance Models by Mark D. Fairchild\nThe Reproduction of Colour by Dr. R.W.G. Hunt\nColor Science: Concepts and Methods, Quantitative Data and Formulae, 2nd Edition by Günther Wyszecki and W. S. Stiles\nKatherine Crowson\nGoogle Colab\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

We take it for granted every day, but creating and displaying vivid colors in our digital media is a complicated and often difficult process. There are different ways to represent color, the ways in which they are displayed can cause them to look different, and translating between systems can cause losses of information. To simplify the process of working with color information in code Thomas Mansencal wrote the Colour project. In this episode we discuss his motiviation for creating and sharing his library, how it works to translate and manage color representations, and how it can be used in your projects.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Managing Color For Graphics In Python With Colour (Interview)","date_published":"2018-05-06T16:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/cfe3f9be-05bc-42e5-a31b-76a2b1c38328.mp3","mime_type":"audio/mpeg","size_in_bytes":44828795,"duration_in_seconds":3460}]},{"id":"podlove-2018-04-30t00:41:08+00:00-8e14438c98d26b6","title":"Destroy All Software With Gary Bernhardt","url":"https://www.pythonpodcast.com/destroy-all-software-with-gary-bernhardt-episode-159","content_text":"Summary\n\nMany developers enter the market from backgrounds that don’t involve a computer science degree, which can lead to blind spots of how to approach certain types of problems. Gary Bernhardt produces screen casts and articles that aim to teach these principles with code to make them approachable and easy to understand. In this episode Gary discusses his views on the state of software education, both in academia and bootcamps, the theoretical concepts that he finds most useful in his work, and some thoughts on how to build better software.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nFinding a bug in production is never a fun experience, especially when your users find it first. Airbrake error monitoring ensures that you will always be the first to know so you can deploy a fix before anyone is impacted. With open source agents for Python 2 and 3 it’s easy to get started, and the automatic aggregations, contextual information, and deployment tracking ensure that you don’t waste time pinpointing what went wrong. Go to podcastinit.com/airbrake today to sign up and get your first 30 days free, and 50% off 3 months of the Startup plan.\nTo get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nYour host as usual is Tobias Macey and today I’m interviewing Gary Bernhardt about teaching and learning Python in the current software landscape\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nAs someone who makes a living from teaching aspects of programming what is your view on the state of software education?\n\nWhat are some of the ways that we as an industry can improve the experience of new developers?\nWhat are we doing right?\n\n\n\nYou spend a lot of time exploring some of the fundamental aspects of programming and computation. What are some of the lessons that you have learned which transcend software languages?\n\n\nUtility of graphs in understanding software\nMechanical sympathy\n\n\n\nWhat are the benefits of ‘from scratch’ tutorials that explore the steps involved in building simple versions of complex topics such as compilers or web frameworks?\n\n\nKeep In Touch\n\n\n@garybernhardt on Twitter\ngarybernhardt on GitHub\n\n\nPicks\n\n\nTobias\n\nTerry Pratchett\n\n\n\nGary\n\n\nDestroy All Software\nDeconstruct Conference\nOut Of The Tarpit\nAlgorithms + Data Structures = Programs by Niklaus Wirth\nDan Grossman Programming Languages Course (click the “Videos” links under “course materials”)\nU of W\nJohn Carmack post reconsidering some earlier positions\n\n\n\n\n\nLinks\n\n\nWat\nBirth and Death of Javascript\nDestroy All Software\nDeconstruct\nData Structures\nComputer Science\nCompilers\nProgramming Bootcamps\nGraph Theory\nJulia Evans\n\n@b0rk on Twitter\n\n\n\nAllen Downey\nJupyter Notebook\nHalting Problem\nIdris\nVisual Basic 3.0\nSet Theory\nML Family of Languages\nSML, a simple dialect of ML\nSML/NJ, a compiler for SML\nOCamL, a more modern dialect of ML\nF#, an even newer dialect of ML\nClojure, a modern Lisp-like language\nLua Grammar (scroll to the very bottom for the full grammar)\nJohn Carmack\nTwitter Thread Explaining Episode Context\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Many developers enter the market from backgrounds that don’t involve a computer science degree, which can lead to blind spots of how to approach certain types of problems. Gary Bernhardt produces screen casts and articles that aim to teach these principles with code to make them approachable and easy to understand. In this episode Gary discusses his views on the state of software education, both in academia and bootcamps, the theoretical concepts that he finds most useful in his work, and some thoughts on how to build better software.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Useful Computer Science Principals For Software Engineers (Interview)","date_published":"2018-04-29T20:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/72b6077d-fbf2-4c6f-bbc8-f1e087af6504.mp3","mime_type":"audio/mpeg","size_in_bytes":43573528,"duration_in_seconds":3126}]},{"id":"podlove-2018-04-22t12:02:29+00:00-77b50dca4ed5972","title":"Scaling Deep Learning Using Polyaxon with Mourad Mourafiq","url":"https://www.pythonpodcast.com/polyaxon-with-mourad-mourafiq-episode-158","content_text":"Summary\n\nWith libraries such as Tensorflow, PyTorch, scikit-learn, and MXNet being released it is easier than ever to start a deep learning project. Unfortunately, it is still difficult to manage scaling and reproduction of training for these projects. Mourad Mourafiq built Polyaxon on top of Kubernetes to address this shortcoming. In this episode he shares his reasons for starting the project, how it works, and how you can start using it today.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nFinding a bug in production is never a fun experience, especially when your users find it first. Airbrake error monitoring ensures that you will always be the first to know so you can deploy a fix before anyone is impacted. With open source agents for Python 2 and 3 it’s easy to get started, and the automatic aggregations, contextual information, and deployment tracking ensure that you don’t waste time pinpointing what went wrong. Go to podcastinit.com/airbrake today to sign up and get your first 30 days free, and 50% off 3 months of the Startup plan.\nTo get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nYour host as usual is Tobias Macey and today I’m interviewing Mourad Mourafiq about Polyaxon, a platform for building, training and monitoring large scale deep learning applications.\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you give a quick overview of what Polyaxon is and your motivation for creating it?\nWhat is a typical workflow for building and testing a deep learning application?\nHow is Polyaxon implemented?\n\nHow has the internal architecture evolved since you first started working on it?\nWhat is unique to deep learning workloads that makes it necessary to have a dedicated tool for deploying them?\nWhat does Polyaxon add on top of the existing functionality in Kubernetes?\n\n\n\nIt can be difficult to build a docker container that holds all of the necessary components for a complex application. What are some tips or best practices for creating containers to be used with Polyaxon?\nWhat are the relative tradeoffs of the various deep learning frameworks that you support?\nFor someone who is getting started with Polyaxon what does the workflow look like?\n\n\nWhat is involved in migrating existing projects to run on Polyaxon?\n\n\n\nWhat have been the most challenging aspects of building Polyaxon?\nWhat are your plans for the future of Polyaxon?\n\n\nKeep In Touch\n\n\nWebsite\n@mmourafiq on Twitter\nmouradmourafiq on GitHub\n\n\nPicks\n\n\nTobias\n\nKubernetes\nKubernetes Up And Running\nKelsey Hightower\nFood Fight Show With Kelsey Hightower\n\n\n\nMourad\n\n\nSchopenhauer\n\n\n\n\n\nLinks\n\n\nPolyaxon\nInvestment Banking\nLuxembourg\nMatlab\nText Mining\nTensorflow\nDocker\nKubernetes\nDeep Learning\n\nFree Deep Learning Textbook\n\n\n\nMachine Learning Engineer\nHyperparameters\nContinuous Integration\nPyTorch\nMXNet\nScikit-Learn\nHelm\nMesos\nSpark\nSparkML\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

With libraries such as Tensorflow, PyTorch, scikit-learn, and MXNet being released it is easier than ever to start a deep learning project. Unfortunately, it is still difficult to manage scaling and reproduction of training for these projects. Mourad Mourafiq built Polyaxon on top of Kubernetes to address this shortcoming. In this episode he shares his reasons for starting the project, how it works, and how you can start using it today.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Scalable Deep Learning on Kubernetes with Polyaxon (Interview)","date_published":"2018-04-22T20:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/7787a24a-e6dc-4394-8909-2ed7a980af0a.mp3","mime_type":"audio/mpeg","size_in_bytes":25276995,"duration_in_seconds":2159}]},{"id":"podlove-2018-04-15t00:27:54+00:00-0a17e9aae7d56aa","title":"Electricity Map: Real Time Visibility of Power Generation with Olivier Corradi","url":"https://www.pythonpodcast.com/electricity-map-with-olivier-corradi-episode-157","content_text":"Summary\n\nOne of the biggest issues facing us is the availability of sustainable energy sources. As individuals and energy consumers it is often difficult to understand how we can make informed choices about energy use to reduce our impact on the environment. Electricity Map is a project that provides up to date and historical information about the balance of how the energy we are using is being produced. In this episode Olivier Corradi discusses his motivation for creating Electricity Map, how it is built, and his goals for the project and his other work at Tomorrow Co.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nFinding a bug in production is never a fun experience, especially when your users find it first. Airbrake error monitoring ensures that you will always be the first to know so you can deploy a fix before anyone is impacted. With open source agents for Python 2 and 3 it’s easy to get started, and the automatic aggregations, contextual information, and deployment tracking ensure that you don’t waste time pinpointing what went wrong. Go to podcastinit.com/airbrake today to sign up and get your first 30 days free, and 50% off 3 months of the Startup plan.\nTo get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nYour host as usual is Tobias Macey and today I’m interviewing Olivier Corradi about Electricity Map and using Python to analyze data of global power generation\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat was your motivation for creating Electricity Map?\n\nHow can an average person use or benefit from the information that is available in the map?\n\n\n\nWhat sources are you using to gather the information about how electricity is generated and distributed in various geographic regions?\n\n\nIs there any standard format in which this data is produced?\nWhat are the biggest difficulties associated with collecting and consuming this data?\nHow much confidence do you have in the accuracy of the data sources?\nIs there any penalty for misrepresenting the fuel consumption or waste generation for a given plant?\n\n\n\nCan you describe the architecture of the system and how it has evolved?\nWhat are some of the most interesting uses of the data in your database and API that you are aware of?\n\n\nHow do you measure the impact or effectiveness of the information that you provide through the different interfaces to the data that you have aggregated?\n\n\n\nHow have you built a community around the project?\n\n\nHow has the community helped in building and growing Electricity Map? \n\n\n\nWhat are some of the most unexpected things that you have learned in the process of building Electricity Map?\nWhat are your plans for the future of Electricity Map?\n\n\nKeep In Touch\n\n\n@corradio on Twitter\nLinkedIn\ncorradio on GitHub\n\n\nPicks\n\n\nTobias\n\nRollerblading\n\n\n\nOlivier\n\n\nDeep Mind AlphaGo Documentary\nConsumer’s Guide To Climate Change Impact \n\n\n\n\n\nLinks\n\n\nElectricity Map\nMachine Learning\nYoutube\nClimate Change\nFossil Fuels\nCarbon Intensity\nGreenhouse Gas Equivalencies Calculations\nOpen Data\nElectricity Map Project Source\nLignite\nMarginal Carbon Intensity\nElectricity Map Forecast API\nIPCC (Intergovernmental Panel on Climate Change\nRedis\nD3.js\nSpark\nTensorflow\nSpatiotemporal Data\nMongoDB\nMatrix Inversion\nPyGRIB\nTomorrow Co.\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

One of the biggest issues facing us is the availability of sustainable energy sources. As individuals and energy consumers it is often difficult to understand how we can make informed choices about energy use to reduce our impact on the environment. Electricity Map is a project that provides up to date and historical information about the balance of how the energy we are using is being produced. In this episode Olivier Corradi discusses his motivation for creating Electricity Map, how it is built, and his goals for the project and his other work at Tomorrow Co.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Using Data To Help Fight Climate Change (Interview)","date_published":"2018-04-14T20:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/7df04359-b54c-47d0-9502-a639f89f1c86.mp3","mime_type":"audio/mpeg","size_in_bytes":38108551,"duration_in_seconds":2873}]},{"id":"podlove-2018-04-08t21:35:58+00:00-89e21802f5c4c7d","title":"Building And Growing Nylas with Christine Spang","url":"https://www.pythonpodcast.com/nylas-with-christine-spang-episode-156","content_text":"Summary\n\nEmail is one of the oldest methods of communication that is still in use on the internet today. Despite many attempts at building a replacement and predictions of its demise we are sending more email now than ever. Recognizing that the venerable inbox is still an important repository of information, Christine Spang co-founded Nylas to integrate your mail with the rest of your tools, rather than just replacing it. In this episode Christine discusses how Nylas is built, how it is being used, and how she has helped to grow a successful business with a strong focus on diversity and inclusion.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nFinding a bug in production is never a fun experience, especially when your users find it first. Airbrake error monitoring ensures that you will always be the first to know so you can deploy a fix before anyone is impacted. With open source agents for Python 2 and 3 it’s easy to get started, and the automatic aggregations, contextual information, and deployment tracking ensure that you don’t waste time pinpointing what went wrong. Go to podcastinit.com/airbrake today to sign up and get your first 30 days free, and 50% off 3 months of the Startup plan.\nTo get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Christine Spang about Nylas and the modern era of email\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you explain what Nylas is and some of its history?\nWhat do you think it is about email as a protocol and a means of communication that has made it so resilient in the face of technological evolution?\nWhat lessons did you learn from your initial offering of the N1 mail client and how has that informed your current focus?\nNylas as a company appears to have a strong focus on diversity and inclusion. Can you speak to how you encourage that type of environment and how it manifests at work?\nWhat are some of the ways that Python is used at Nylas?\nCan you share some examples of services that you have written in other languages and why you felt that Python was not the right choice?\nWhat are some of the use cases that Nylas enables?\nWhat are some of the most interesting or innovative uses of the Nylas platform that you have seen?\nHow do you manage privacy and security in your sync service given the sensitivity of the data that you are handling?\nWhat are some of the biggest challenges that you are currently facing at Nylas?\nWhat do you think will be the future of email?\n\n\nKeep In Touch\n\n\nLinkedIn\n@spang on Twitter\nWebsite\nGitHub\n\n\nPicks\n\n\nTobias\nTrello\nChristine\nFounders For Change\n\n\nLinks\n\n\nNylas\nMIT\nKSplice\nDebian\nLisp\nREST\nEmail\nN1 Mail Client\nMailspring\nNylas Employee Handbook\nHackbright Academy\nCode2040\nTextIO\nKey Values\nIMAP\nOAuth\nMySQL\nGevent\nReact\nCRM (Customer Relationship Management)\nSendGrid\nMailGun\nMailChimp\nGDPR (General Data Protection Regulation)\nSOC2\nOWASP Top 10\nPrinciple of Least Privilege\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Email is one of the oldest methods of communication that is still in use on the internet today. Despite many attempts at building a replacement and predictions of its demise we are sending more email now than ever. Recognizing that the venerable inbox is still an important repository of information, Christine Spang co-founded Nylas to integrate your mail with the rest of your tools, rather than just replacing it. In this episode Christine discusses how Nylas is built, how it is being used, and how she has helped to grow a successful business with a strong focus on diversity and inclusion.

\n\n

Preface

\n\n\n\n

Interview

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Integrating Email With The Modern Web At Nylas (Interview)","date_published":"2018-04-08T19:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/a5c3ae5c-87c1-40f5-a205-b26b7a303b75.mp3","mime_type":"audio/mpeg","size_in_bytes":25489793,"duration_in_seconds":2609}]},{"id":"podlove-2018-04-01t21:35:18+00:00-c9e8a5dc4f64100","title":"Synthetic Data Generation Using Mimesis with Nikita Sobolev","url":"https://www.pythonpodcast.com/mimesis-with-nikita-sobolev-episode-155","content_text":"Summary\n\nMost applications require data to operate on in order to function, but sometimes that data is hard to come by, so why not just make it up? Mimesis is a library for randomly generating data of different types, such as names, addresses, and credit card numbers, so that you can use it for testing, anonymizing real data, or for placeholders. This week Nikita Sobolev discusses how the project got started, the challenges that it has posed, and how you can use it in your applications.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nTo get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nYour host as usual is Tobias Macey and today I’m interviewing Nikita Sobolev about Mimesis, a library for quickly generating synthetic data\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is mimesis and how does it compare to other projects such as faker and factory_boy?\n\nWhat was the motivation for creating it?\n\n\n\nOne of the features that is advertised is the speed of Mimesis. What techniques are used to ensure that the data is generated quickly?\nWhat are the built in mechanisms for generating data?\n\n\nWhat options do users have for customizing the types of data that can get generated?\n\n\n\nWhat are some of the most complicated providers to write and maintain?\nWhat are some of the use cases outside of unit or integration tests where Mimesis could be beneficial?\n\n\nHow would you use Mimesis to anonymize data from a production environment to be used for testing?\n\n\n\nWhat are the most challenging aspects of maintaining the Mimesis project?\nWhat are some of the plans that you have for the future of Mimesis?\n\n\nKeep In Touch\n\n\nsobolevn on GitHub\n@sobolevn on Twitter\nEmail\n\n\nPicks\n\n\nTobias\n\nCoco\n\n\n\nNikita\n\n\nI Am A Mediocre Developer\n\n\n\n\n\nLinks\n\n\nMimesis\nDjango\nFaker\nFactory Boy\nInternationalization (I18N)\nUnicode\nEnum\nPipfile\nGeoJSON\nMimesis Cloud\nSanic\nGraphQL\nImpostor Syndrome\nImposter Syndrome Disclaimer: Add this to all of your projects!\nJacob Kaplan-Moss PyCon Keynote\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Most applications require data to operate on in order to function, but sometimes that data is hard to come by, so why not just make it up? Mimesis is a library for randomly generating data of different types, such as names, addresses, and credit card numbers, so that you can use it for testing, anonymizing real data, or for placeholders. This week Nikita Sobolev discusses how the project got started, the challenges that it has posed, and how you can use it in your applications.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Fast, Multilingual, Configurable Data Generation In Python With Mimesis (Interview)","date_published":"2018-04-01T17:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/caf29cd7-54d4-44f3-b2e3-e389be15c39a.mp3","mime_type":"audio/mpeg","size_in_bytes":24077673,"duration_in_seconds":1957}]},{"id":"podlove-2018-03-25t02:41:58+00:00-9197c42bca95016","title":"Luminoth: AI Powered Computer Vision for Python with Joaquin Alori","url":"https://www.pythonpodcast.com/luminoth-with-joaquin-alori-episode-154","content_text":"Summary\n\nMaking computers identify and understand what they are looking at in digital images is an ongoing challenge. Recent years have seen notable increases in the accuracy and speed of object detection due to deep learning and new applications of neural networks. In order to make it easier for developers to take advantage of these techniques Tryo Labs built Luminoth. In this interview Joaquín Alori explains how how Luminoth works, how it can be used in your projects, and how it compares to API oriented services for computer vision.\n\nIntroduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nFor complete visibility into your application stack, deployment tracking, and powerful alerting, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix bugs in no time. Go to podcastinit.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt.\nTo get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nYour host as usual is Tobias Macey and today I’m interviewing Joaquín Alori about Luminoth, a deep learning toolkit for computer vision in Python\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is Luminoth and what was your motivation for creating it?\nComputer vision has been a focus of AI research for decades. How do current approaches with deep learning compare to previous generations of tooling?\nWhat are some of the most difficult problems in visual processing that still need to be solved?\nWhat are the limitations of Luminoth for building a computer vision application and how do they differ from the capabilities of something built with a prior generation of tooling such as OpenCV?\nFor someone who is interested in using Luminoth in their project what is the current workflow?\nHow do the capabilities of Luminoth compare with some of the various service based options such as Rekognition for Amazon or the Cloud Vision API from Google?\n\nWhat are some of the motivations for using Luminoth in place of these services?\n\n\n\nWhat are some of the highest priority features that you are focusing on implementing in Luminoth?\nWhen is Luminoth the wrong choice for a computer vision application and what are some of the strongest alternatives at the moment?\n\n\nKeep In Touch\n\n\n@JoaquinAlori on Twitter\nLinkedIn\n\n\nPicks\n\n\nTobias\n\nPyCon US\n\n\n\nJoaquin\n\n\n3Blue1Brown\n\n\n\n\n\nLinks\n\n\nLuminoth\nLuminoth Release Announcement\nTryo Labs\nUruguay\nIndustrial Engineering\nManufacturing Engineering\nElon Musk\nArtificial Intelligence\nDeep Learning\nNeural Networks\nObject Detection\nImage Segmentation\nConvolutional Neural Network\nRecurrent Neural Network\nBack Propagation\nGeoff Hinton\nCapsule Networks\nGenerative Adversarial Networks\nSVM (Support Vector Machine)\nHaar Classifiers\nOpenCV\nDrones\nGPU (Graphics Processing Unit)\nRekognition\nCloud Vision API\nTensorFlow Object Detection API\nSonnet\nDeepMind\nCaffe\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Making computers identify and understand what they are looking at in digital images is an ongoing challenge. Recent years have seen notable increases in the accuracy and speed of object detection due to deep learning and new applications of neural networks. In order to make it easier for developers to take advantage of these techniques Tryo Labs built Luminoth. In this interview Joaquín Alori explains how how Luminoth works, how it can be used in your projects, and how it compares to API oriented services for computer vision.

\n\n

Introduction

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Easy AI Computer Vision For Python With Luminoth (Interview)","date_published":"2018-03-24T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/5be999f4-9176-4184-8463-7e6a882020ba.mp3","mime_type":"audio/mpeg","size_in_bytes":15234918,"duration_in_seconds":1287}]},{"id":"podlove-2018-03-18t12:27:52+00:00-ccee615cf8abe93","title":"Thonny: The IDE For Beginning Programmers with Aivar Annamaa","url":"https://www.pythonpodcast.com/thonny-with-aivar-annamaa-episode-153","content_text":"Summary\n\nLearning to program is a rewarding pursuit, but is often challenging. One of the roadblocks on the way to proficiency is getting a development environment installed and configured. In order to simplify that process Aivar Annamaa built Thonny, a Python IDE designed for beginning programmers. In this episode he discusses his initial motivations for starting Thonny and how it helps newcomers to Python learn and understand how to write software.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nWhen you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.\nFor complete visibility into your application stack, deployment tracking, and powerful alerting, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix bugs in no time. Go to podcastinit.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt.\nTo get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.\nVisit podcastinit.com to subscribe to the show, sign up for the newsletter, and read the show notes.\nYour host as usual is Tobias Macey and today I’m interviewing Aivar Annamaa about Thonny, a Python IDE for beginning programmers\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat was your motivation for building an IDE focused on beginning programmers?\nWhat are the features of Thonny that make it easier for users to understand what is happening in their programs?\nWhat have you found to be the types of issues that users most frequently struggle with and how does Thonny help overcome those gaps in understanding?\nWhat kinds of tutorials or supporting material have you found to be the most useful for teaching students the principles that they need to be able to take advantage of the environment that Thonny provides?\nHow is Thonny built and what have been the most challenging aspects of writing an IDE in Python?\nWhat are some of the interface design choices that you have made to avoid confusing or overwhelming beginning users?\nOnce a user becomes more proficient in Python is there a point where it no longer makes sense to continue using Thonny for development?\nI noticed that Thonny has an plugin architecture and there is an extension for interacting with the BBC micro:bit. What are some of the other types of extensions that you would like to see built for Thonny?\n\n\nKeep In Touch\n\n\nAivar\n\n@aivarannamaa on Twitter\naivarannamaa on GitHub\nGoogle Scholar Page\n\n\n\nThonny\n\n\nWebsite\nForum\n@thonnyide on Twitter\nSource repository and wiki\n\n\n\n\n\nPicks\n\n\nTobias\n\nData Engineering Podcast\nKubo and the Two Strings\n\n\n\nAivar\n\n\nMicroPython\nPodcast.__init__ Interview\nHow to Talk So Kids Will Listen & Listen So Kids Will Talk\n\n\n\n\n\nLinks\n\n\nThonny\nUniversity of Tartu\nEstonia\nRecursion\nTKinter\nAivar Estonian Textbook\nPascal\nMyPy\n\nPodcast.__init__ Interview\n\n\n\nBBC Micro:bit\nVersion Control\nGitHub\nGitLab\nElm Compiler Messages\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Learning to program is a rewarding pursuit, but is often challenging. One of the roadblocks on the way to proficiency is getting a development environment installed and configured. In order to simplify that process Aivar Annamaa built Thonny, a Python IDE designed for beginning programmers. In this episode he discusses his initial motivations for starting Thonny and how it helps newcomers to Python learn and understand how to write software.

\n\n

Preface

\n\n\n\n

Interview

\n\n\n\n

Keep In Touch

\n\n

\n\n

Picks

\n\n

\n\n

Links

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"How An IDE Can Help New Programmers Learn Python (Interview)","date_published":"2018-03-18T11:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/34483a63-93f8-4937-8a2f-20ae8958dd7f.mp3","mime_type":"audio/mpeg","size_in_bytes":22628157,"duration_in_seconds":1790}]},{"id":"podlove-2018-03-12t04:38:00+00:00-95bc89e4ea6d9a9","title":"Keeping The Beets with Adrian Sampson","url":"https://www.pythonpodcast.com/beets-with-adrian-sampson-episode-152","content_text":"Summary\n\nMaintaining a consistent taxonomy for your music library is a challenging and time consuming endeavor. Eventually you end up with a mess of folders and files with inconsistent names and missing metadata. Beets is built to solve this problem by programmatically managing the tags and directory structure for all of your music files and providing a fast lookup when you are trying to find that perfect song to play. Adrian Sampson began the project because he was trying to clean up his own music collection and in this episode he discusses how the project was built, how streaming media is affecting our relationship to digital music, and how he envisions Beets position in the ecosystem in the future.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.\nIf you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Adrian Sampson about Beets, the swiss army knife for managing your music library.\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is Beets and what was your reason for creating it?\n\nWhat was your reason for using Python and if you were to start over today would you make the same choice?\n\n\n\nIf I have a directory with inconsistent naming conventions, poor organization, and some random folders full of mixed MP3 files how can Beets help me and what does the workflow look like?\nHow is Beets architected to allow for interactively processing a large volume of media files and how has the design evolved over the time that you have been working on it?\nWhat are your thoughts on the current trend toward streaming music services replacing local media files?\nWhat have been some of the most challenging aspects of building Beets?\nWhat are some of the most interesting uses for Beets that you have seen?\nWhat are some of the other projects for managing a music library and how does Beets compare to them?\nAre there any features that you have planned for the future of Beets, or any new functionality that you would like to see contributed?\n\n\nKeep In Touch\n\n\nsampsyo on GitHub\nWebsite\n@samps on Twitter\n\n\nPicks\n\n\nTobias\n\nMozart’s Requiem\nWikipedia\nYouTube\nGov’t Mule\nDarkest Hour\n\n\n\nAdrian\n\n\nSpiralizer\nSpiralized Beats With Pesto\n\n\n\n\n\nLinks\n\n\nBeets\nSQLite\nMutagen\nID3 Tags\nMusicbrainz\nBandcamp\nFree Music Archive\nCornell\nAcoustID\nChromaprint\nMusicbrainz Picard\niTunes\nSpotify\nAmazon Music\nDLNA\nUPnP\nAURA\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Maintaining a consistent taxonomy for your music library is a challenging and time consuming endeavor. Eventually you end up with a mess of folders and files with inconsistent names and missing metadata. Beets is built to solve this problem by programmatically managing the tags and directory structure for all of your music files and providing a fast lookup when you are trying to find that perfect song to play. Adrian Sampson began the project because he was trying to clean up his own music collection and in this episode he discusses how the project was built, how streaming media is affecting our relationship to digital music, and how he envisions Beets position in the ecosystem in the future.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Organize Your Music Library with Beets (Interview)","date_published":"2018-03-12T00:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/6db2576d-b6d4-4983-a032-b6367f842c0a.mp3","mime_type":"audio/mpeg","size_in_bytes":27257813,"duration_in_seconds":2363}]},{"id":"podlove-2018-03-04t17:32:01+00:00-e93fe653c373f19","title":"Salabim: Logistics Simulation with Ruud van der Ham","url":"https://www.pythonpodcast.com/salabim-with-ruud-van-der-ham-episode-151","content_text":"Summary\n\nDetermining the best way to manage the capacity and flow of goods through a system is a complicated issue and can be exceedingly expensive to get wrong. Rather than experimenting with the physical objects to determine the optimal algorithm for managing the logistics of everything from global shipping lanes to your local bank, it is better to do that analysis in a simulation. Ruud van der Ham has been working in this area for the majority of his professional life at the Dutch port of Rotterdam. Using his acquired domain knowledge he wrote Salabim as a library to assist others in writing detailed simulations of their own and make logistical analysis of real world systems accessible to anyone with a Python interpreter.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.\nIf you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Ruud van der Ham about Salabim, a Python library for conducting discrete event simulations\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what Discrete Event Simulation is and how Salabim helps with that?\n\nCan you explain how you chose the name?\n\n\n\nWhat was your motivation for creating Salabim and how does it compare to other tools for discrete event simulation?\nHow does discrete event simulation compare with state machines?\nHow is Salabim implemented and how has the design evolved over the time that you have been working on it?\nI understand that you have done a majority of Salabim was written on an iPad. Can you speak about why you have chosen that as your development environment and your experience working in that manner?\nWhat are some examples of the types of models that you can model with Salabim?\n\n\nWhat would an implementation of one of these models look like for someone using Salabim?\n\n\n\nWhat options does a user have to verify the accuracy of a simulation created with Salabim?\nOne of the nice aspects of Salabim is the fact that it provides a visual output as a simulation runs. Can you describe the workflow for someone who wants to use Salabim for modeling and visualizing a system?\nAt what point does a system become too complex to encapsulate in a simulation and what techniques can you use to modularize it to make a simulation useful?\nWhen is Salabim not the right tool to use and what would you suggest for people who find themselves in that situation?\nWhat have been some of the most complicated or difficult aspects of building and maintaining Salabim?\nWhat are some of the new features or improvements that you have planned for the future of Salabim?\n\n\nKeep In Touch\n\n\nEmail\n\n\nPicks\n\n\nTobias\n\nCuisinart Burr Mill Coffee Grinder\n\n\n\nRuud\n\n\nPythonista (Python for iOS)\nPython Notes for Professionals\nFluent Python by Luciano Ramalho\n\n\n\n\n\nLinks\n\n\nSalabim\n\nGitHub\n\n\n\nDining Philosophers Animation\nElevator Animation\nRotterdam\nDiscrete Event Simulation\nContainer Terminal Automation\nBasic\nAlgol\nPascal\nOperations Research\nContinuous Simulation\nSimula\nCoroutines\nSymPy\nAnother DES in Python: SimPy\nDES in Julia: SimJulia\nDES in R: Simmer\nDES in Delphi/Pascal: Tomas\nPillow\nPyPy\nDelphi\nPyGame\nPyQT\nTkInter\nInspect Module\nOpenCV\nBlender\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Determining the best way to manage the capacity and flow of goods through a system is a complicated issue and can be exceedingly expensive to get wrong. Rather than experimenting with the physical objects to determine the optimal algorithm for managing the logistics of everything from global shipping lanes to your local bank, it is better to do that analysis in a simulation. Ruud van der Ham has been working in this area for the majority of his professional life at the Dutch port of Rotterdam. Using his acquired domain knowledge he wrote Salabim as a library to assist others in writing detailed simulations of their own and make logistical analysis of real world systems accessible to anyone with a Python interpreter.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Logistics Simulation and Analysis in Python with Salabim (Interview)","date_published":"2018-03-04T12:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/156a45e4-453e-4d02-9cff-dee105fbb14b.mp3","mime_type":"audio/mpeg","size_in_bytes":38362915,"duration_in_seconds":3098}]},{"id":"podlove-2018-02-26t03:13:06+00:00-c8cfcbf085f05ff","title":"Laboratory: Safer Refactoring with Joe Alcorn","url":"https://www.pythonpodcast.com/laboratory-with-joe-alcorn-episode-150","content_text":"Summary\n\nEvery piece of software that has been around long enough ends up with some piece of it that needs to be redesigned and refactored. Often the code that needs to be updated is part of the critical path through the system, increasing the risks associated with any change. One way around this problem is to compare the results of the new code against the existing logic to ensure that you aren’t introducing regressions. This week Joe Alcorn shares his work on Laboratory, how the engineers at GitHub inspired him to create it as an analog to the Scientist gem, and how he is using it for his day job.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.\nIf you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nA brief announcement before we start the show:\n\nIf you work with data or want to learn more about how the projects you have heard about on the show get used in the real world then join me at the Open Data Science Conference in Boston from May 1st through the 4th. It has become one of the largest events for data scientists, data engineers, and data driven businesses to get together and learn how to be more effective. To save 60% off your tickets go to podcastinit.com/odsc-east-2018 and register.\n\n\n\nYour host as usual is Tobias Macey and today I’m interviewing Joe Alcorn about using Laboratory as a safety net for your refactoring.\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you start be explaining what Laboratory is and what motivated you to start the project?\nHow much of the design and implementation were directly inspired by the Scientist project from GitHub and how much of it did you have to figure out from scratch due to differences in the target languages?\nWhat have been some of the most challenging aspects of building and maintaining Laboratory, and have you had any opportunities to use it on itself?\nFor someone who would like to use Laboratory in their project, what does the workflow look like and what potential pitfalls should they watch out for?\nIn the documentation you mention that portions of code that perform I/O and create side effects should be avoided. Have you found any strategies to allow for stubbing out the external interactions while still executing the rest of the logic?\nHow do you keep track of the results for active experiments and what sort of reporting is available?\nWhat are some examples of the types of routines that would be good candidates for conducting an experiment?\nWhat are some of the most complicated or difficult pieces of code that you have refactored with the help of Laboratory?\nGiven the fact that Laboratory is intended to be run in production and provide a certain measure of safety, what methods do you use to ensure that users of the library will not suffer from a drastic increase in overhead or unintended aberrations in the functionality of their software?\nAre there any new features or improvements that you have planned for future releases of Laboratory?\n\n\nKeep In Touch\n\n\njoealcorn on GitHub\nWebsite\n\n\nPicks\n\n\nTobias\n\nChronicles of Narnia\n\n\n\nJoe\n\n\nWhy We Sleep: Unlocking The Power of Sleep and Dreams by Matthew Walker, PhD\n\n\n\n\n\nLinks\n\n\nMarvel App\nGitHub: Move Fast and Fix Things\nGitHub Scientist: Measure Twice, Cut Over Once\nScientist\nLaboratory\nSure Footed Refactoring\nGraphite\nStatsD\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Every piece of software that has been around long enough ends up with some piece of it that needs to be redesigned and refactored. Often the code that needs to be updated is part of the critical path through the system, increasing the risks associated with any change. One way around this problem is to compare the results of the new code against the existing logic to ensure that you aren’t introducing regressions. This week Joe Alcorn shares his work on Laboratory, how the engineers at GitHub inspired him to create it as an analog to the Scientist gem, and how he is using it for his day job.

\n\n

Preface

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\n\n

Interview

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Safely Refactor Your Python Projects With Laboratory (Interview)","date_published":"2018-02-25T22:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/3f170218-d9ac-43f0-a14d-acc03a4f4f20.mp3","mime_type":"audio/mpeg","size_in_bytes":13006618,"duration_in_seconds":1313}]},{"id":"podlove-2018-02-18t02:38:11+00:00-725333bcbd68289","title":"Software Architecture For Developers with Neal Ford","url":"https://www.pythonpodcast.com/software-architecture-with-neal-ford-episode-149","content_text":"Summary\n\nWhether it is intentional or accidental, every piece of software has an existing architecture. In this episode Neal Ford discusses the role of a software architect, methods for improving the design of your projects, pitfalls to avoid, and provides some resources for continuing to learn about how to design and build successful systems.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.\nIf you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nA few announcements before we start the show:\n\nThere is still time to register for the O’Reilly Software Architecture Conference in New York. Use the link podcastinit.com/sacon-new-york to register and save 20%\nIf you work with data or want to learn more about how the projects you have heard about on the show get used in the real world then join me at the Open Data Science Conference in Boston from May 1st through the 4th. It has become one of the largest events for data scientists, data engineers, and data driven businesses to get together and learn how to be more effective. To save 60% off your tickets go to podcastinit.com/odsc-east-2018 and register.\n\n\n\nWith many thanks to O’Reilly Media, I have two items to give away. To sign up you just need to subscribe to the mailing list at podcastinit.com and you will have the chance to win either a copy of Neal’s book, Building Evolutionary Architectures, or a Bronze ticket to the O’Reilly Software Architecture Conference in New York. I will be picking the winners on February 21st. \nYour host as usual is Tobias Macey and today I’m interviewing Neal Ford about principles of software architecture for developers\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nA majority of your work has been focused on software architectures and how that can be used to facilitate delivery of working systems. Can you start by giving a high level description of what software architecture is and how it fits into the overall development process?\nOne of the difficulties that arise in long-lived projects is that technical debt accrues to the point that forward progress stagnates due to fear that any changes will cause the system to stop functioning. What are some methods that developers can use to either guard against that eventuality, or address it when it happens?\nWhat are some of the broad categories of architectural patterns that developers should be aware of?\nAre there aspects of the language that a system or application is being implemented in which influence the style of architecture that is commonly used?\nWhat are some architectural anti-patterns that you have found to be the most commonly occurring?\nSoftware is useless if there is no way to deliver it to the end user. What are some of the challenges that are most often overlooked by engineering teams and how do you solve for them?\nBeyond the purely technological aspects, what other elements of software production and delivery are necessary for a successful architecture?\nWhat resources can you recommend for someone who is interested in learning more about software architecture, whether as an individual contributor or in a full time architect role?\n\n\nKeep In Touch\n\n\nWebsite\n@neal4d on Twitter\n\n\nPicks\n\n\nTobias\n\nJumanji: Welcome to the Jungle\nLost City of Z\n\n\n\nNeal\n\n\nDeveloperToArchitect.com\nEvolutionaryArchitecture.com\nBroken Earth Series\n\n\n\n\n\nLinks\n\n\nThoughtworks\nNeal’s Blog\nLisp\nThoughtworks Technology Radar\nMartin Fowler: Who Needs an Architect?\nO’Reilly Software Architecture Conference\nSoft Skills\nMicroservices\nBuilding Evolutionary Architectures\nGithub: Move Fast and Fix Things\nContinuous Delivery\nGithub Scientist\nLaboratory (Scientist in Python)\nAgile Development\nThe Accidental Architect\nSystem Quality Attributes\nPipes and Filters\nMapReduce\nHadoop\nService Oriented Architecture\nLinux\nDevOps\nConfiguration Management\nReact\nAlibaba Open Source\nBaidu Open Source\nPragmatic Programmer\nTrunk Based Development\nPlantUML\nVisio\nMermaid Diagrams\nGraphviz\nEvernote\nSoftware Architecture Fundamentals\nEnterprise Integration Patterns\nArchitectural Katas\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Whether it is intentional or accidental, every piece of software has an existing architecture. In this episode Neal Ford discusses the role of a software architect, methods for improving the design of your projects, pitfalls to avoid, and provides some resources for continuing to learn about how to design and build successful systems.

\n\n

Preface

\n\n

\n\n

Interview

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Principles of Software Architecture For Developers (Interview)","date_published":"2018-02-17T21:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/c089788e-dabd-4bee-93d5-bc37959f2954.mp3","mime_type":"audio/mpeg","size_in_bytes":37632755,"duration_in_seconds":3028}]},{"id":"podlove-2018-02-10t23:16:38+00:00-568573e6cb09a3d","title":"ZimboPy","url":"https://www.pythonpodcast.com/zimbopy-episode-148","content_text":"Summary\n\nLearning to code is one of the most effective ways to be successful in the modern economy. To that end, Marlene Mhangami and Ronald Maravanyika created the ZimboPy organization to teach women and girls in Zimbabwe how to program in Python. In this episode they are joined by Mike Place to discuss how ZimboPy got started, the projects that their students have worked on, and how the community can get involved.\n\nPreface\n\nmu- Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\n– I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\n– When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.\n– If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.\n– Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\n– To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\n– Your host as usual is Tobias Macey and today I’m interviewing Marlene Mhangami, Mike Place, and Ronald Maravanyika about ZimboPy, an organization that teaches women and girls in Zimbabwe how to program using Python\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what the mission of ZimboPy is and how it got started?\nWhich languages did you consider using for your lessons and what was your reason for choosing Python?\nWhat subject matter do you cover in addition to pure programming concepts?\nWhat are some of the types of projects that the students have completed as part of their work with ZimboPy?\nWhat have been the most challenging aspects of running ZimboPy?\nHow is ZimboPy supported and what are your plans to ensure future sustainability?\nCan you share some success stories for the women and girls that you have worked with?\nFor anyone who is interested in replicating your work for other communities what advice do you have?\n\n\nKeep In Touch\n\n\nMike\n\ncachedout on GitHub\n@cachedout on Twitter\ncachedout on Keybase\n\n\n\nRonald\n\n\nRmaravanyika on GitHub\n@Rmaravanyika on Twitter\n\n\n\nMarlene\n\n\n@marlene_zw on Twitter\nLinkedIn\n\n\n\n\n\nPicks\n\n\nTobias\n\nClick\n\n\n\nRonald\n\n\nOdoo formerly OpenERP\n\n\n\n\n\nLinks\n\n\nZimboPy\nUnilever\nDjango Girls\nThomas Hatch\nSaltStack\nZimbabwe\nMechatronics\nRaspberry Pi\nOpenCV\nZimboPy Curriculum\nZimboPy Storefront\nOxfam\nOpen Collective\nZimboPy Mentorship Registration\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Learning to code is one of the most effective ways to be successful in the modern economy. To that end, Marlene Mhangami and Ronald Maravanyika created the ZimboPy organization to teach women and girls in Zimbabwe how to program in Python. In this episode they are joined by Mike Place to discuss how ZimboPy got started, the projects that their students have worked on, and how the community can get involved.

\n\n

Preface

\n\n

mu- Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
\n– I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
\n– When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.
\n– If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.
\n– Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)
\n– To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
\n– Your host as usual is Tobias Macey and today I’m interviewing Marlene Mhangami, Mike Place, and Ronald Maravanyika about ZimboPy, an organization that teaches women and girls in Zimbabwe how to program using Python

\n\n

Interview

\n\n\n\n

Keep In Touch

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\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"ZimboPy: Using Python To Empower Women In Zimbabwe (Interview)","date_published":"2018-02-10T21:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/e59247e1-b5ef-433c-9f2f-01c7f77b74e6.mp3","mime_type":"audio/mpeg","size_in_bytes":19452215,"duration_in_seconds":1760}]},{"id":"podlove-2018-02-05t01:14:28+00:00-50e3d13487ee6e0","title":"PyRay: Pure Python 3D Rendering with Rohit Pandey","url":"https://www.pythonpodcast.com/pyray-pure-python-3d-rendering-with-rohit-pandey-episode-147","content_text":"Summary\n\nUsing a rendering library can be a difficult task due to dependency issues and complicated APIs. Rohit Pandey wrote PyRay to address these issues in a pure Python library. In this episode he explains how he uses it to gain a more thorough understanding of mathematical models, how it compares to other options, and how you can use it for creating your own videos and GIFs.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.\nIf you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nA few announcements before we start the show:\n\nThere’s still time to get your tickets for PyCon Colombia, happening February 9th and 10th. Go to pycon.co to learn more and register.\nThere is also still time to register for the O’Reilly Software Architecture Conference in New York. Use the link podcastinit.com/sacon-new-york to register and save 20%\nIf you work with data or want to learn more about how the projects you have heard about on the show get used in the real world then join me at the Open Data Science Conference in Boston from May 1st through the 4th. It has become one of the largest events for data scientists, data engineers, and data driven businesses to get together and learn how to be more effective. To save 60% off your tickets go to podcastinit.com/odsc-east-2018 and register.\n\n\n\nYour host as usual is Tobias Macey and today I’m interviewing Rohit Pandey about PyRay, a 3d rendering library written completely in python\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what PyRay is and what motivated you to create it?\n[rohit] PyRay is an open source library written completely in Python that let’s you render three and higher dimensional objects and scenes. Development on it has been ongoing and new features have so far come about from videos for my Youtube channel. \nWhat does the internal architecture of PyRay look like and how has that design evolved since you first started working on it?\nWhat capabilities are unlocked by having a pure Python rendering library which would otherwise be impractical or impossible for Python developers to do with existing options?\n[rohit] Having a pure Python library makes it accessible with minimal fixed cost to Python users. The tradeoff is you lose on speed, but for many applications that isn’t an issue. I haven’t seen a library coded completely in Python that let’s you manipulate 3d and higher dimensional objects. The core usecase right now is Mathematical artwork. Google geometric gifs and you’ll see some fascinating, mesmerizing results. But those are created for the most part using tools that are not Python. Which is a pity since Python has a very extensive library of Mathematical functions. \nWhat have been some of the most challenging aspects of building and maintaining PyRay?\n[rohit] 3d objects – getting mesh plots. I have to develop routines from scratch for almost everything – shading objects, etc. Animated routines for characters.\n\n\nWhat are some of the most interesting or unexpected uses of PyRay that you are aware of?\n[rohit] Physical simulations. Ex: Testing if a solid is a fair die, getting lower bounds for space packing efficiencies of solids. Creating interactive demos where a user can draw to provide input.\n\nFor someone who wanted to contribute to PyRay are there any particular skills or experience that would be most helpful?\nBasic linear algebra and python\nWhat are some of the features or improvements that you have planned for the future of PyRay?\n\n\nKeep In Touch\n\npyray repo – https://github.com/ryu577/pyray?utm_source=rss&utm_medium=rss\n– Email\n– GitHub\n– LinkedIn\n\nPicks\n\n\nTobias\n\nBerserker Series by Fred Saberhagen\n\n\n\nRohit\n\n\nSamurai Math Youtube Channel\n3 Blue 1 Brown Youtube Channel\nIsaac Arthur Youtube Channel\n\n\n\n\n\nLinks\n\n\nPyRay\nPyRay Youtube Videos\nMicrosoft Azure\nData Science\nColumbia University\nR\nNielsen\n3Blue1Brown – Music and Measure Theory\nManim\nPython Subreddit\nMaya\nBlender\nPanda3D\nPOVRay\nPillow\nNumPy\nSciPy\nSupport Vector Machine\nLogistic Regression\nGeometric GIFs\nVapory\nRGB vs HSL Color Scales\nFFMPEG\nQuaternions\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Using a rendering library can be a difficult task due to dependency issues and complicated APIs. Rohit Pandey wrote PyRay to address these issues in a pure Python library. In this episode he explains how he uses it to gain a more thorough understanding of mathematical models, how it compares to other options, and how you can use it for creating your own videos and GIFs.

\n\n

Preface

\n\n

\n\n

Interview

\n\n\n\n

Keep In Touch

\n\n

pyray repo – https://github.com/ryu577/pyray?utm_source=rss&utm_medium=rss
\n– Email
\n– GitHub
\n– LinkedIn

\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Pure Python 3D Rendering with PyRay (Interview)","date_published":"2018-02-04T20:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/db5e4111-18e9-4b85-a998-3121281ced8f.mp3","mime_type":"audio/mpeg","size_in_bytes":33361622,"duration_in_seconds":2561}]},{"id":"podlove-2018-01-28t03:22:21+00:00-590b59e97dee42f","title":"MonkeyType with Carl Meyer and Matt Page","url":"https://www.pythonpodcast.com/monkeytype-with-carl-meyer-and-matt-page-episode-146","content_text":"Summary\n\nOne of the draws of Python is how dynamic and flexible the language can be. Sometimes, that flexibility can be problematic if the format of variables at various parts of your program is unclear or the descriptions are inaccurate. The growing middle ground is to use type annotations as a way of providing some verification of the format of data as it flows through your application and enforcing gradual typing. To make it simpler to get started with type hinting, Carl Meyer and Matt Page, along with other engineers at Instagram, created MonkeyType to analyze your code as it runs and generate the type annotations. In this episode they explain how that process works, how it has helped them reduce bugs in their code, and how you can start using it today.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.\nIf you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nA few announcements before we start the show:\n\nThere’s still time to get your tickets for PyCon Colombia, happening February 9th and 10th. Go to pycon.co to learn more and register.\nThere is also still time to register for the O’Reilly Software Architecture Conference in New York Feb 25-28. Use the link podcastinit.com/sacon-new-york to register and save 20%\nIf you work with data or want to learn more about how the projects you have heard about on the show get used in the real world then join me at the Open Data Science Conference in Boston from May 1st through the 4th. It has become one of the largest events for data scientists, data engineers, and data driven businesses to get together and learn how to be more effective. To save 60% off your tickets go to podcastinit.com/odsc-east-2018 and register.\n\n\n\nYour host as usual is Tobias Macey and today I’m interviewing Carl Meyer and Matt Page about MonkeyType, a system to collect type information at runtime for your Python 3 code\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is MonkeyType and how did the project get started?\nHow much overhead does the MonkeyType tracing add to the running system, and what techniques have you used to minimize the impact on production systems?\nGiven that the type information is collected from call traces at runtime, and some functions may accept multiple different types for the same arguments (e.g. add), do you have any logic that will allow for combining that information into a higher-order type that gets set as the annotation?\nHow does MonkeyType function internally and how has the implementation evolved over the time that you have been working on it?\nOnce the type annotations are present in your code base, what other tooling are you using to take advantage of that information?\nIt seems as though using MonkeyType to trace your running production systems could be a way to inadvertantly identify dead sections of code that aren’t being executed. Have you investigated ways to use the collected type information perform that analysis?\nWhat have been some of the most challenging aspects of building, using, and maintaining MonkeyType?\nWhat have been some of the most interesting or noteworthy things that you have learned in the process of working on and with MonkeyType?\nWhat have you found to be the most useful and most problematic aspects of the typing capabilities provided in recent versions of Python?\nFor someone who wants to start using MonkeyType today, what is involved in getting it set up and using it in a new or existing codebase?\nWhat features or improvements do you have planned for future releases of MonkeyType?\n\n\nKeep In Touch\n\n\nCarl\n\nEmail\n@carljm on Twitter\n\n\n\nMatt\n\n\nEmail\n@void_star on Twitter\n\n\n\n\n\nPicks\n\n\nTobias\n\nLyxPro HAS-30 Headphones\n\n\n\nCarl\n\n\nBroadchurch\nHappy Valley\n\n\n\nMatt\n\n\nAnova Sous Vide\n\n\n\n\n\nLinks\n\n\nMonkeyType\nInstagram\nDive Into Python\nPython 3 Typing Module\nMyPy\n\nProject Page\nPodcast.init Interview\n\n\n\nMike Krieger\nPyAnnotate\nType Annotations\nType Stubs\nPEP 523 frame evaluation api\nScuba\nHaskell\nRust\nPEP 563 Postponed Evaluation of Annotations\nGary Bernhardt – Ideology\ncoverage.py\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

One of the draws of Python is how dynamic and flexible the language can be. Sometimes, that flexibility can be problematic if the format of variables at various parts of your program is unclear or the descriptions are inaccurate. The growing middle ground is to use type annotations as a way of providing some verification of the format of data as it flows through your application and enforcing gradual typing. To make it simpler to get started with type hinting, Carl Meyer and Matt Page, along with other engineers at Instagram, created MonkeyType to analyze your code as it runs and generate the type annotations. In this episode they explain how that process works, how it has helped them reduce bugs in their code, and how you can start using it today.

\n\n

Preface

\n\n

\n\n

Interview

\n\n\n\n

Keep In Touch

\n\n

\n\n

Picks

\n\n

\n\n

Links

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"MonkeyType: Automatically Type Hint Your Python Code (Interview)","date_published":"2018-01-27T22:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/4d7d46b5-e888-462c-ba1a-0e70f06ada01.mp3","mime_type":"audio/mpeg","size_in_bytes":32951741,"duration_in_seconds":2905}]},{"id":"podlove-2018-01-15t20:17:59+00:00-338b3fef0f3d98c","title":"Learn Leap Fly: Using Python To Promote Global Literacy with Kjell Wooding","url":"https://www.pythonpodcast.com/learn-leap-fly-with-kjell-wooding-episode-145","content_text":"Summary\n\nLearning how to read is one of the most important steps in empowering someone to build a successful future. In developing nations, access to teachers and classrooms is not universally available so the Global Learning XPRIZE serves to incentivize the creation of technology that provides children with the tools necessary to teach themselves literacy. Kjell Wooding helped create Learn Leap Fly in order to participate in the competition and used Python and Kivy to build a platform for children to develop their reading skills in a fun and engaging environment. In this episode he discusses his experience participating in the XPRIZE competition, how he and his team built what is now Kasuku Stories, and how Python and its ecosystem helped make it possible.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.\nIf you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Kjell Wooding about Learn Leap Fly, a startup using Python on mobile devices to facilitate global learning\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what Learn Leap Fly does and how the company got started?\nWhat was your motivation for using Kivy as the primary technology for your mobile applications as opposed to the platform native toolkits or other multi-platform frameworks?\nWhat are some of the pedagogical techniques that you have incorporated into the technological aspects of your mobile application and are there any that you were unable to translate to a purely technical implementation.\nHow do you measure the effectiveness of the work that you are doing?\nHow has the framework of the XPRIZE influenced the way in which you have approached the design and development of your work?\nWhat have been some of the biggest challenges that you faced in the process of developing and deploying your submission for the XPRIZE?\nWhat are some of the features that you have planned for future releases of your platform?\n\n\nKeep In Touch\n\n\nLearn Leap Fly\n\nWebsite\n@learnleapfly on Twitter\n\n\n\nKjell\n\n\nllfkj on GitHub\n@pdokj on Twitter\n\n\n\n\n\nPicks\n\n\nTobias\n\nYamaha YHT-4930UBL Home Theater System\n\n\n\nKjell\n\n\nInstant Pot\nAnova Sous Vide\nModernist Cooking at Home\n\n\n\n\n\nLinks\n\n\nProgramming Python (O’Reilly)\nLearn Leap Fly\nTim Ferriss\nPeter Diamandis\nGlobal Learning XPRIZE\nKasuku Beta Program\nXPRIZE Foundation\nKivy\nKivy Flappy Bird\nPodcast.init Kivy Interview\nDeliberate Practice\nGoogle Pixel C\nBayesian Learning\nSciPy\nNumPy\nKeras\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Learning how to read is one of the most important steps in empowering someone to build a successful future. In developing nations, access to teachers and classrooms is not universally available so the Global Learning XPRIZE serves to incentivize the creation of technology that provides children with the tools necessary to teach themselves literacy. Kjell Wooding helped create Learn Leap Fly in order to participate in the competition and used Python and Kivy to build a platform for children to develop their reading skills in a fun and engaging environment. In this episode he discusses his experience participating in the XPRIZE competition, how he and his team built what is now Kasuku Stories, and how Python and its ecosystem helped make it possible.

\n\n

Preface

\n\n\n\n

Interview

\n\n\n\n

Keep In Touch

\n\n

\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Teaching The World To Read With Python (Interview)","date_published":"2018-01-21T01:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/965e0fa0-0f1b-4abc-8ecc-cb7a8ae50c79.mp3","mime_type":"audio/mpeg","size_in_bytes":30655017,"duration_in_seconds":2587}]},{"id":"podlove-2018-01-13t15:23:16+00:00-c7bb2ef1c8a5549","title":"Healthchecks.io: Open Source Alerting For Your Cron Jobs with Pēteris Caune","url":"https://www.pythonpodcast.com/healthchecks-with-peteris-caune-episode-144","content_text":"Summary\n\nYour backups are running every day, right? Are you sure? What about that daily report job? We all have scripts that need to be run on a periodic basis and it is easy to forget about them, assuming that they are working properly. Sometimes they fail and in order to know when that happens you need a tool that will let you know so that you can find and fix the problem. Pēteris Caune wrote Healthchecks to be that tool and made it available both as an open source project and a hosted version. In this episode he discusses his motivation for starting the project, the lessons he has learned while managing the hosting for it, and how you can start using it today.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.\nIf you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Pēteris Caune about Healthchecks, a Django app which serves as a watchdog for your cron tasks\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what Healthchecks is and what motivated you to build it?\nHow does Healthchecks compare with other cron monitoring projects such as Cronitor or Dead Man’s Snitch?\nYour pricing on the hosted service for Healthchecks.io is quite generous so I’m curious how you arrived at that cost structure and whether it has proven to be profitable for you?\nHow is Healthchecks functionality implemented and how has the design evolved since you began working on and using it?\nWhat have been some of the most challenging aspects of working on Healthchecks and managing the hosted version?\nFor someone who wants to run their own instance of the service what are the steps and services involved?\nWhat are some of the most interesting or unusual uses of Healtchecks that you are aware of?\nGiven that Healthchecks is intended to be used as part of an operations management and alerting system, what are the considerations that users should be aware of when deploying it in a highly available configuration?\nWhat improvements or features do you have planned for the future of Healthchecks?\n\n\nKeep In Touch\n\n\ncuu508 on GitHub\nBlog\n@cuu508 on Twitter\n\n\nPicks\n\n\nTobias\n\nLG 55UJ6300\n\n\n\nPēteris\n\n\nZwift\nTrainerRoad\n\n\n\n\n\nLinks\n\n\nHealthchecks.io\nGitHub\nRiga\nLatvia\nCross Country Cycling\nSemantic Web\nDjango\nFlask\nCron\nCronitor.io\nDead Man’s Snitch\nIPv6\nLoad Balancing\nPostGreSQL\nMySQL\nFabric\nAnsible\nDokku\nKubernetes\nHetzner\nCloudFlare\nPGPool II\nStreaming Replication\nCitus Data\n\nWebsite\nData Engineering Podcast Interview\n\n\n\nHeroku Fork\nthe Evolution of healthchecks.io Hosting Setup\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Your backups are running every day, right? Are you sure? What about that daily report job? We all have scripts that need to be run on a periodic basis and it is easy to forget about them, assuming that they are working properly. Sometimes they fail and in order to know when that happens you need a tool that will let you know so that you can find and fix the problem. Pēteris Caune wrote Healthchecks to be that tool and made it available both as an open source project and a hosted version. In this episode he discusses his motivation for starting the project, the lessons he has learned while managing the hosting for it, and how you can start using it today.

\n\n

Preface

\n\n\n\n

Interview

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Open Source Alerting For Your Cron Jobs (Interview)","date_published":"2018-01-13T20:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/296afcca-60a6-40ab-8518-7585f2c4e974.mp3","mime_type":"audio/mpeg","size_in_bytes":23612080,"duration_in_seconds":1644}]},{"id":"podlove-2018-01-07t02:54:52+00:00-73346872e66bec7","title":"Bonobo: Lightweight ETL Toolkit for Python 3 with Romain Dorgueil","url":"https://www.pythonpodcast.com/bonobo-with-romain-dorgueil-episode-143","content_text":"Summary\n\nA majority of the work that we do as programmers involves data manipulation in some manner. This can range from large scale collection, aggregation, and statistical analysis across distrbuted systems, or it can be as simple as making a graph in a spreadsheet. In the middle of that range is the general task of ETL (Extract, Transform, and Load) which has its own range of scale. In this episode Romain Dorgueil discusses his experiences building ETL systems and the problems that he routinely encountered that led him to creating Bonobo, a lightweight, easy to use toolkit for data processing in Python 3. He also explains how the system works under the hood, how you can use it for your projects, and what he has planned for the future.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.\nIf you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Romain Dorgueil about Bonobo, a data processing toolkit for modern Python\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is Bonobo and what was your motivation for creating it?\n\nWhat is the story behind the name?\n\n\n\nHow does Bonobo differ from projects such as Luigi or Airflow?\n[RD] After I explain why that’s totally different things, maybe a good follow up would be to ask about differences from other data streaming solutions, like Apache Beam or Spark.\nHow is Bonobo implemented and how has its architecture evolved since you began working on it?\nWhat have been some of the most challenging aspects of building and maintaining Bonobo?\nWhat are some extensions that you would like to have but don’t have the time to implement?\nWhat are some of the most interesting or creative uses of Bonobo that you are aware of?\nWhat do you have planned for the future of Bonobo?\n\n\nKeep In Touch\n\n\nBonobo Project\n\nBonobo ETL\nSlack\nGitHub\n\n\n\nRomain\n\n\nWebsite\n@rdorgueil on Twitter\nhartym on GitHub\n\n\n\n\n\nPicks\n\n\nTobias\n\nData Skeptic: Quantum Computing\n\n\n\nRomain\n\n\nMedikit, or how to manage hundreds of projects at the same time, still being able to sleep at night.\nRocker, a better builder for docker images.\n\n\n\n\n\nLinks\n\n\nBonobo\nRedHat\nAnaconda Installer\nETL\nPentaho\nRDC.ETL\nDAG (Directed Acyclic Graph)\nLuigi\nAirflow\nNamedTuple\nJupyter\nOAuth\nGraphviz\nDask\nData Engineering Podcast\nDask Interview\nSelenium\nZapier\nIFTTT (If This Then That)\nFPGA\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

A majority of the work that we do as programmers involves data manipulation in some manner. This can range from large scale collection, aggregation, and statistical analysis across distrbuted systems, or it can be as simple as making a graph in a spreadsheet. In the middle of that range is the general task of ETL (Extract, Transform, and Load) which has its own range of scale. In this episode Romain Dorgueil discusses his experiences building ETL systems and the problems that he routinely encountered that led him to creating Bonobo, a lightweight, easy to use toolkit for data processing in Python 3. He also explains how the system works under the hood, how you can use it for your projects, and what he has planned for the future.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n

\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Bonobo: Lightweight ETL Toolkit for Python 3 (Interview)","date_published":"2018-01-06T22:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/fbb65d97-00cc-47ef-afe1-e34c5876f033.mp3","mime_type":"audio/mpeg","size_in_bytes":47854941,"duration_in_seconds":3237}]},{"id":"podlove-2017-12-31t02:06:18+00:00-93ed67f625d5e5b","title":"Orange: Visual Data Mining Toolkit with Janez Demšar and Blaž Zupan","url":"https://www.pythonpodcast.com/orange-with-janez-demsar-and-blaz-zupan-episode-142","content_text":"Summary\n\nData mining and visualization are important skills to have in the modern era, regardless of your job responsibilities. In order to make it easier to learn and use these techniques and technologies Blaž Zupan and Janez Demšar, along with many others, have created Orange. In this episode they explain how they built a visual programming interface for creating data analysis and machine learning workflows to simplify the work of gaining insights from the myriad data sources that are available. They discuss the history of the project, how it is built, the challenges that they have faced, and how they plan on growing and improving it in the future.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.\nIf you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Blaž Zupan and Janez Demsar about Orange, a toolbox for interactive machine learning and data visualization in Python\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is Orange and what was your motivation for building it?\nWho is the target audience for this project?\nHow is the graphical interface implemented and what kinds of workflows can be implemented with the visual components?\nWhat are some of the most notable or interesting widgets that are available in the catalog?\nWhat are the limitations of the graphical interface and what options do user have when they reach those limits?\nWhat have been some of the most challenging aspects of building and maintaining Orange?\nWhat are some of the most common difficulties that you have seen when users are just getting started with data analysis and machine learning, and how does Orange help overcome those gaps in understanding?\nWhat are some of the most interesting or innovative uses of Orange that you are aware of?\nWhat are some of the projects or technologies that you consider to be your competition?\nUnder what circumstances would you advise against using Orange?\nWhat are some widgets that you would like to see in future versions?\nWhat do you have planned for future releases of Orange?\n\n\nKeep In Touch\n\n\nBlaž\n\nUniversity Bio\n@bzupan on Twitter\nBlazZupan on GitHub\nGoogle Scholar\n\n\n\nJanez\n\n\nUniversity Bio\n@jademsar on Twitter\njanezd on GitHub\nGoogle Scholar\n\n\n\n\n\nPicks\n\n\nTobias\n\nData Stories: What’s Going On In This Graph?\n\n\n\nBlaž\n\n\nHow I Built This\n\n\n\nJanez\n\n\nAdvent of Code\n\n\n\n\n\nLinks\n\n\nUniversity of Ljubljani\nData Explorer\nSilicon Graphics\nVisual Programming\nPyQT\nLinear Regression\nt-SNE\nK-Means\nTCL/TK\nNumpy\nScikit-Learn\nSciPy\nTextable.io\nRapidMiner\nSingle Cell Genomics\nTransfer Learning\nOrange Video Tutorials\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Data mining and visualization are important skills to have in the modern era, regardless of your job responsibilities. In order to make it easier to learn and use these techniques and technologies Blaž Zupan and Janez Demšar, along with many others, have created Orange. In this episode they explain how they built a visual programming interface for creating data analysis and machine learning workflows to simplify the work of gaining insights from the myriad data sources that are available. They discuss the history of the project, how it is built, the challenges that they have faced, and how they plan on growing and improving it in the future.

\n\n

Preface

\n\n\n\n

Interview

\n\n\n\n

Keep In Touch

\n\n

\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Orange: Graphical Toolkit For Data Mining and Visualization in Python (Interview)","date_published":"2017-12-30T21:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/e314deb1-9865-41c1-ae27-a1ba34109fc8.mp3","mime_type":"audio/mpeg","size_in_bytes":34757295,"duration_in_seconds":2945}]},{"id":"podlove-2017-12-24t01:59:00+00:00-3fe9bae837ff527","title":"Dramatiq: Distributed Task Queue For Python 3 with Bogdan Popa","url":"https://www.pythonpodcast.com/dramatiq-with-bogdan-popa-episode-141","content_text":"Summary\n\nA majority of projects will eventually need some way of managing periodic or long-running tasks outside of the context of the main application. This is where a distributed task queue becomes useful. For many in the Python community the standard option is Celery, though there are other projects to choose from. This week Bogdan Popa explains why he was dissatisfied with the current landscape of task queues and the features that he decided to focus on while building Dramatiq, a new, opinionated distributed task queue for Python 3. He also describes how it is designed, how you can start using it, and what he has planned for the future.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.\nIf you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Bogdan Popa about Dramatiq, a distributed task processing library for Python with a focus on simplicity, reliability and performance\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is Dramatiq and what was your motivation for creating it?\nHow does Dramatiq compare to other task queues in Python such as Celery or RQ?\nHow is Dramatiq implemented and how has the internal architecture evolved?\nWhat have been some of the most difficult aspects of building Dramatiq?\nWhat are some of the features that you are most proud of?\nFor someone who is interested in integrating Dramatiq into an application, can you describe the steps involved and the API?\nDo you provide any form of migration path or compatibility layer for people who are currently using Celery or RQ?\nCan you describe the licensing structure for the project and your reasoning?\n\nHow did you determine the price point for commercial licenses?\nHave you been successful in selling licenses for commercial use?\n\n\n\nWhat are some of the features that you have planned for future releases?\n\n\nKeep In Touch\n\n\nProject Website\nPersonal Website\nBogdanp on GitHub\n@Bogdanp on Twitter\n\n\nPicks\n\n\nTobias\n\nThe Anybodies by N.E. Bode\n\n\n\nBogdan\n\n\nPipenv\n\n\n\n\n\nLinks\n\n\nDramatiq\nLeadPages\nLisp\nCelery\nRQ\nBilliard\nKombu\nGoogle App Engine\nGAE Task Queue\nRabbitMQ\nAPScheduler\nRedis\nMemcached\nLRU (Least Recently Used)\nMiddleware\nGevent\nPika\nSQS (Amazon Simple Queue Service)\nGoogle Cloud PubSub\nDjango\nAPI*\nBundler\nCargo\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

A majority of projects will eventually need some way of managing periodic or long-running tasks outside of the context of the main application. This is where a distributed task queue becomes useful. For many in the Python community the standard option is Celery, though there are other projects to choose from. This week Bogdan Popa explains why he was dissatisfied with the current landscape of task queues and the features that he decided to focus on while building Dramatiq, a new, opinionated distributed task queue for Python 3. He also describes how it is designed, how you can start using it, and what he has planned for the future.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Dramatiq: Fast, Reliable, and Simple Distributed Task Queue for Python 3 (Interview)","date_published":"2017-12-23T21:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/3e99318a-f547-4b87-8d04-a805fba920e2.mp3","mime_type":"audio/mpeg","size_in_bytes":29321732,"duration_in_seconds":2293}]},{"id":"podlove-2017-12-17t00:54:52+00:00-e56465618d11b7a","title":"Jake Vanderplas: Data Science For Academic Research","url":"https://www.pythonpodcast.com/jake-vanderplas-episode-140","content_text":"Summary\n\nJake Vanderplas is an astronomer by training and a prolific contributor to the Python data science ecosystem. His current role is using Python to teach principles of data analysis and data visualization to students and researchers at the University of Washington. In this episode he discusses how he got started with Python, the challenges of teaching best practices for software engineering and reproducible analysis, and how easy to use tools for data visualization can help democratize access to, and understanding of, data.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.\nIf you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Jake Vanderplas about data science best practices, and applying them to academic sciences\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nHow has your astronomy background informed and influenced your current work?\nIn your work at the University of Washington, what are some of the most common difficulties that students face when learning data science?\n\nHow does that list differ for professional scientists who are learning how to apply data science to their work?\n\n\n\nWhere is the tooling still lacking in terms of enabling consistent and repeatable workflows?\nOne of the projects that you are spending time on now is Altair, which is a library for generating visualizations from Pandas dataframes. How does that work factor into your teaching?\nWhat are some of the most novel applications of data science that you have been involved with?\nWhat are some of the trends in data analysis that you are most excited for?\n\n\nKeep In Touch\n\n\nWebsite\n@jakevdp\njakevdp on GitHub\n\n\nPicks\n\n\nTobias\n\nThe Redwall Cookbook\n\n\n\nJake\n\n\nKevin M. Kruse\nWhite Flight by Kevin Kruse\n\n\n\n\n\nLinks\n\n\nUW eScience Institute\nNumPy\nSciPy\nSciPy Conference\nPyCon\nPandas\nSloan Digital Sky Survey\nSpectroscopy\nSoftware Carpentry\nData Carpentry\nGit\nMercurial\nMatplotlib\nAltair\nConda\nXonsh\nJupyter\nJupyter Lab\nVega\nVega-lite\nInteractive Data Lab\nD3\nMike Bostock\nBrian Granger\nBokeh\nGrammar of Graphics\nggplot2\nHoloviews\nWikimedia\nAstroPy\n\nPodcast.__init__ Interview About AstroPy\n\n\n\nLIGO\nWes McKinney\nFeather\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Jake Vanderplas is an astronomer by training and a prolific contributor to the Python data science ecosystem. His current role is using Python to teach principles of data analysis and data visualization to students and researchers at the University of Washington. In this episode he discusses how he got started with Python, the challenges of teaching best practices for software engineering and reproducible analysis, and how easy to use tools for data visualization can help democratize access to, and understanding of, data.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Jake Vanderplas: Python Data Science Tools And Best Practices For Academic Research","date_published":"2017-12-16T19:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/c20bfdbb-20e3-4f84-b1f5-cda74c1cb6ae.mp3","mime_type":"audio/mpeg","size_in_bytes":39293381,"duration_in_seconds":2967}]},{"id":"podlove-2017-12-10t12:09:35+00:00-9fa97598cb211b8","title":"Kenneth Reitz","url":"https://www.pythonpodcast.com/kenneth-reitz-episode-139","content_text":"Summary\n\nKenneth Reitz has contributed many things to the Python community, including projects such as Requests, Pipenv, and Maya. He also started the community written Hitchhiker’s Guide to Python, and serves on the board of the Python Software Foundation. This week he talks about his career in the Python community and digs into some of his current work.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.\nIf you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Kenneth Reitz about his career in Python\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nAn overarching theme of your open source projects is the idea of making them “For Humans”. Can you elaborate on how that came to be a focus for you and how that informs the way that you design and write your code?\n\n\nWhat are the projects that you are most proud of and which do you think have had the biggest impact on the Python community?\nA: Requests, Hitchhiker’s Guide to Python, and Pipenv (yet to come to full fruition).\n\n\nWhich projects have you authored which are relatively unknown but you think people would benefit from using more often?\nA: Maya: Datetime for Humans, and Records: SQL for Humans.\n\n\nOutside of the code that you write, what are some of your personal missions for the software industry in general and the Python community in particular?\nA: I consider myself a “spiritual alchemist”, which means “transformation of dark into light”. I seek to do “the great work”, in however in manifests, outside of the programming world, as well as within it.\n\n\nWhat do you think is the biggest gap in the tool chest for Python developers?\nA: I seek to fill all the voids that I see, and I’ve done my best to do that to the best of my ability. I think we have a lot of work to do in the area of single-file executable builds (a-la Go).\n\n\nWhat are your ambitions for future projects?\nA: At the moment, I have no current plans for future projects, but I’m sure something will come along at some point \n\n\nIf you weren’t working with Python what would you be doing instead?\nA: I’d have a lot less money and I’d be a lot less fufilled.\n\n\n\nKeep In Touch\n\n\nWebsite\n@kennethreitz on Twitter\nkennethreitz on GitHub\n\n\nPicks\n\n\nTobias\n\nAlgorithms to Live By\n\n\n\nKenneth\n\n\nThe Linux Programming Interface\n\n\n\n\n\nLinks\n\n\nHeroku\nSalesforce\nPSF Board of Directors\nCaldera Linux\nC\nPascal\nBasic\nGroovy\nJava\nPHP\nRuby\nThe Design of Everyday Things\nRequests\nHitchhiker’s Guide\nPipenv\nPipfile\nThe Update Framework\nFalsehoods Programmer’s Believe About Time\nPEP20\nPy2EXE\nCxfreeze\nBriefcase\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Kenneth Reitz has contributed many things to the Python community, including projects such as Requests, Pipenv, and Maya. He also started the community written Hitchhiker’s Guide to Python, and serves on the board of the Python Software Foundation. This week he talks about his career in the Python community and digs into some of his current work.

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Preface

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Interview

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Keep In Touch

\n\n\n\n

Picks

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\n\n

Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Interview With Kenneth Reitz On His Career With Python","date_published":"2017-12-10T07:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/121a87b3-c4d4-409f-a822-5a327641cf7d.mp3","mime_type":"audio/mpeg","size_in_bytes":32121085,"duration_in_seconds":2569}]},{"id":"podlove-2017-12-03t03:28:06+00:00-89827bc547cf309","title":"Asphalt: A Framework For Asynchronous Network Applications with Alex Grönholm","url":"https://www.pythonpodcast.com/asphalt-framework-with-alex-gronholm-episode-138","content_text":"Summary\n\nAs we rely more on small, distributed processes for building our applications, being able to take advantage of asynchronous I/O is increasingly important for performance. This week Alex Grönholm explains how the Asphalt Framework was created to make it easier to build these network oriented software stacks and the technical challenges that he faced in the process.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.\nIf you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Alex Grönholm about the Asphalt Framework, a Python microframework for network oriented applications\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is Asphalt and what was your reason for building it?\nHow does Asphalt compare to Twisted?\nWhat are the most challenging parts of writing asynchronous and event-based applications and how does Asphalt help simplify that process?\nWhen building an Asphalt application it can be easy to accidentally block an async loop by pulling in third party libraries that don’t support asynchronous execution. What are some of the techniques for identifying and resolving blocking portions of your application?\nWhat does the internal architecture of Asphalt look like and how has that evolved from when you first started working on it?\nWhat have been some of the most difficult aspects of building and evolving Asphalt?\nWhat are some of the most interesting or unexpected uses of Asphalt that you have seen?\nWhat are some of the new features or improvements that you have planned for the future of Asphalt?\n\n\nKeep In Touch\n\n\nGitter\nIRC\nGitHub\nagronholm on GitHub\n@agronholm on Twitter\n\n\nPicks\n\n\nTobias\n\nThor: Ragnarok\n\n\n\nAlex\n\n\nTwo Steps From Hell\n\n\n\n\n\nLinks\n\n\nAsphalt\nERP\nAsyncio\nTornado\nTwisted\nSQLAlchemy\nPEP 550\nSanic\nWAMP\nPodcast.init Interview About Crossbar\nTee\nFlexGet\nAPScheduler\nBitTorrent\nuvloop\nTokio\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

As we rely more on small, distributed processes for building our applications, being able to take advantage of asynchronous I/O is increasingly important for performance. This week Alex Grönholm explains how the Asphalt Framework was created to make it easier to build these network oriented software stacks and the technical challenges that he faced in the process.

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Preface

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Interview

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Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Asphalt: The Framework For Asynchronous Microservices in Python (Interview)","date_published":"2017-12-02T22:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/b5e1873f-b9c6-4615-b813-cae0f0943797.mp3","mime_type":"audio/mpeg","size_in_bytes":24911419,"duration_in_seconds":2084}]},{"id":"podlove-2017-11-25t19:44:22+00:00-281bd6907e3cfc2","title":"Golem: End-To-End Test Automation Framework with Luciano Renzi","url":"https://www.pythonpodcast.com/golem-framework-with-luciano-puccio-episode-137","content_text":"Summary\n\nThe importance of testing your software is widely talked about and well understood. What is not as often discussed is the different types of testing, and how end-to-end tests can benefit your team to ensure proper functioning of your application when it gets released to production. This week Luciano Renzi shares the work that he has done on Golem, a framework for building and executing an automation suite to exercise the entire system from the perspective of the user. He discusses his reasons for creating the project, how he things about testing, and where he plans on taking Golem in the future. Give it a listen and then take it for a test drive.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.\nIf you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Luciano Renzi about Golem, a framework and automation tool for end-to-end testing in Python\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is golem and what motivated you to create it?\n\nWhat was your inspiration for the name?\n\n\n\nWhy did you choose to use Python for Golem and if you were to start over today would you make the same choice?\nFor someone who is unfamiliar with the concept, can you describe what end-to-end testing is and the reasons for making it part of their development process?\nWhat is the main goal of Golem\nWhat does the internal architecture and implementation of Golem look like and how has that evolved from when you first started the project?\nHow does Golem compare to other Python libraries for automated browser testing and what was lacking in the existing solutions when you created it?\nWhat are the differences between golem and robot framework?\nWhat about projects written in other languages such as protractor?\nOne of the intriguing features of Golem is the web interface for constructing tests. What are the benefits of codeless automation & record-playback functionality?\nWhat are some of the most challenging aspects of building and maintaining Golem?\nIt seems that every browser automation library is ultimately a wrapper around Selenium. Why is a wrapper necessary and why haven’t any strong alternatives been created?\nWhat are the advantages of making Golem a framework for test automation, rather than a library?\nWhat are some of the most interesting or unexpected uses for Golem that you have seen?\nWhat do you have planned for the future of Golem?\nWhat is the current state of end to end automation and how do you see it evolving in the future?\nHow do you think machine learning and AI will be used in test automation?\n\n\nKeep In Touch\n\n\nluciano-renzi on GitHub\n@lucianorenzi_ on Twitter\n\n\nPicks\n\n\nTobias\n\nWeapons of Math Destruction\n\n\n\n\n\nLinks\n\n\nGolem\nElementum\nPascal\nWatir\nJUnit\nSelenium\nPage Object Pattern\nSelenium Grid\nSauce Labs\npy.test\n\nPodcast.init Interview About Py.Test\n\n\n\nRobot Framework\nMechanize\nAcceptance Tests\nProtractor\nWebdriver.io\nAppium \n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

The importance of testing your software is widely talked about and well understood. What is not as often discussed is the different types of testing, and how end-to-end tests can benefit your team to ensure proper functioning of your application when it gets released to production. This week Luciano Renzi shares the work that he has done on Golem, a framework for building and executing an automation suite to exercise the entire system from the perspective of the user. He discusses his reasons for creating the project, how he things about testing, and where he plans on taking Golem in the future. Give it a listen and then take it for a test drive.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Golem: Full Featured End-To-End Test Automation Framework in Python (Interview)","date_published":"2017-11-25T14:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/c761f7cf-64c3-4af7-8676-5c68d5ddb34a.mp3","mime_type":"audio/mpeg","size_in_bytes":40647946,"duration_in_seconds":3243}]},{"id":"podlove-2017-11-19t01:24:11+00:00-16ce59c5750d106","title":"Graphite Metrics Stack with Jason Dixon and Dan Cech","url":"https://www.pythonpodcast.com/graphite-metrics-with-jason-dixon-and-dan-cech-episode-136","content_text":"Summary\n\nDo you know what is happening in your production systems right now? If you have a comprehensive metrics platform then the answer is yes. If your answer is no, then this episode is for you. Jason Dixon and Dan Cech, core maintainers of the Graphite project, talk about how graphite is architected to capture your time series data and give you the ability to use it for answering questions. They cover the challenges that have been faced in evolving the project, the strengths that have let it stand the tests of time, and the features that will be coming in future releases.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.\nIf you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nNow is a good time to start planning your conference schedule for 2018. To help you out with that, guest Jason Dixon is offering a $100 discount for Monitorama in Portland, OR on June 4th – 6th and guest Dan Cech is offering a €50 discount to Grafanacon in Amsterdam, Netherlands March 1st and 2nd. There is also still time to get your tickets to PyCascades in Vancouver, BC Canada January 22nd and 23rd. All of the details are in the show notes\nYour host as usual is Tobias Macey and today I’m interviewing Jason Dixon and Dan Cech about Graphite\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is Graphite and how did you each get involved in the project?\nWhy should developers be thinking about collecting and reporting on metrics from their software and systems?\nHow do you think the Graphite project has contributed to or influenced the overall state of the art in systems monitoring?\nThere are a number of different projects that comprise a fully working Graphite deployment. Can you list each of them and describe how they fit together?\nWhat are some of the early design choices that have proven to be problematic while trying to evolve the project?\nWhat are some of the challenges that you have been faced with while maintaining and improving the various Graphite projects?\nWhat will be involved in porting Graphite to run on Python 3?\nIf you were to start the project over would you still use Python?\nWhat are the options for scaling Graphite and making it highly available?\nGiven the level of importance to a companies visibility into their systems, what development practices do you use to ensure that Graphite can operate reliably and fail gracefully?\nWhat are some of the biggest competitors to Graphite?\nWhen is Graphite not the right choice for tracking your system metrics?\nWhat are some of the most interesting or unusual uses of Graphite that you are aware of?\nWhat are some of the new features and enhancements that are planned for the future of Graphite?\n\n\nKeep In Touch\n\n\nJason\n\n@obfuscurity on Twitter\nWebsite\nobfuscurity on GitHub\n\n\n\nDan\n\n\n@dancech on Twitter\nWebsite\nDanCech on GitHub\n\n\n\n\n\nPicks\n\n\nTobias\n\nArchery\n\n\n\nJason\n\n\nRocket League\nMonitorama $100 Discount (Limited Quantity)\n\n\n\nDan\n\n\nHome Assistant\n\nPodcast.__init__ Interview\n\n\n\nGrafanaCon €50 discount with PODCASTINIT2018\n\n\n\n\nLinks\n\n\nGraphite\nSensu\nMonitorama\nRainTank\nGrafana Labs\nLibrato\nGitHub\nDyn\nTelemetry\nPerl\nPHP\nReact\nO’Reilly Graphite Book\nTime Series\nRRDTool\nInfluxDB\nAdrian Cockcroft\nNVMe\nPrometheus\nCNCF\nASAP Smoothing\nPyCascades\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Do you know what is happening in your production systems right now? If you have a comprehensive metrics platform then the answer is yes. If your answer is no, then this episode is for you. Jason Dixon and Dan Cech, core maintainers of the Graphite project, talk about how graphite is architected to capture your time series data and give you the ability to use it for answering questions. They cover the challenges that have been faced in evolving the project, the strengths that have let it stand the tests of time, and the features that will be coming in future releases.

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Preface

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Interview

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Keep In Touch

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Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Capture, Store, and Analyze Your System Metrics with Python (Interview)","date_published":"2017-11-18T20:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/d3e5020c-5e98-4dda-ae06-18421b1d8223.mp3","mime_type":"audio/mpeg","size_in_bytes":50784650,"duration_in_seconds":4457}]},{"id":"podlove-2017-11-11t20:12:28+00:00-b0ba1eff88de4cc","title":"Surprise! Recommendation Algorithms with Nicolas Hug","url":"https://www.pythonpodcast.com/surprise-with-nicolas-hug-episode-135","content_text":"Summary\n\nA relevant and timely recommendation can be a pleasant surprise that will delight your users. Unfortunately it can be difficult to build a system that will produce useful suggestions, which is why this week’s guest, Nicolas Hug, built a library to help with developing and testing collaborative recommendation algorithms. He explains how he took the code he wrote for his PhD thesis and cleaned it up to release as an open source library and his plans for future development on it.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.\nIf you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Nicolas Hug about Surprise, a scikit library for building recommender systems\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is Surprise and what was your motivation for creating it?\nWhat are the most challenging aspects of building a recommender system and how does Surprise help simplify that process?\nWhat are some of the ways that a user or company can bootstrap a recommender system while they accrue data to use a collaborative algorithm?\nWhat are some of the ways that a recommender system can be used, outside of the typical ecommerce example?\nOnce an algorithm has been deployed how can a user test the accuracy of the suggestions?\nHow is Surprise implemented and how has it evolved since you first started working on it?\nWhat have been the most difficult aspects of building and maintaining Surprise?\ncompetitors?\nWhat are the attributes of the system that can be modified to improve the relevance of the recommendations that are provided?\nFor someone who wants to use Surprise in their application, what are the steps involved?\nWhat are some of the new features or improvements that you have planned for the future of Surprise?\n\n\nKeep In Touch\n\n\nWebsite\n@hug_nicolas on Twitter\nnicolashug on GitHub\n\n\nPicks\n\n\nTobias\n\nSilk profiler for Django\n\n\n\n\n\nLinks\n\n\nSurprise\nGridsearch\nCold Start Problem\nContent-Based Recommendation\nEnsemble Learning\nSpotlight\nLightfm\nPandas\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

A relevant and timely recommendation can be a pleasant surprise that will delight your users. Unfortunately it can be difficult to build a system that will produce useful suggestions, which is why this week’s guest, Nicolas Hug, built a library to help with developing and testing collaborative recommendation algorithms. He explains how he took the code he wrote for his PhD thesis and cleaned it up to release as an open source library and his plans for future development on it.

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Preface

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Interview

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Keep In Touch

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Picks

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Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2017-11-11T15:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/1d20d505-d773-4169-9650-d8d76ea59712.mp3","mime_type":"audio/mpeg","size_in_bytes":26016366,"duration_in_seconds":1822}]},{"id":"podlove-2017-11-04t12:16:34+00:00-f2a8acbf333d08e","title":"Rasa: Build Your Own AI Chatbot with Joey Faulkner","url":"https://www.pythonpodcast.com/rasa-ai-chatbots-with-joey-faulkner-episode-134","content_text":"Summary\n\nWith the proliferation of messaging applications, there has been a growing demand for bots that can understand our wishes and perform our bidding. The rise of artificial intelligence has brought the capacity for understanding human language. Combining these two trends gives us chatbots that can be used as a new interface to the software and services that we depend on. This week Joey Faulkner shares his work with Rasa Technologies and their open sourced libraries for understanding natural language and how to conduct a conversation. We talked about how the Rasa Core and Rasa NLU libraries work and how you can use them to replace your dependence on API services and own your data.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.\nIf you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Joey Faulkner about Rasa Core and Rasa NLU for adding conversational AI to your projects.\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining the goals of Rasa as a company and highlighting the projects that you have open sourced?\nWhat are the differences between the Rasa Core and Rasa NLU libraries and how do they relate to each other?\nHow does the interaction model change when going from state machine driven bots to those which use Rasa Core and what capabilities does it unlock?\nHow is Rasa NLU implemented and how has the design evolved?\nWhat are the motivations for someone to use Rasa core or NLU as a library instead of available API services such as wit.ai, LUIS, or Dialogflow?\nWhat are some of the biggest challenges in gathering and curating useful training data?\nWhat is involved in supporting multiple languages for an application using Rasa?\nWhat are the biggest challenges that you face, past, present, and future, building and growing the tools and platform for Rasa?\nWhat would be involved for projects such as OpsDroid, Kalliope, or Mycroft to take advantage of Rasa and what benefit would that provide?\nOn the comparison page for the hosted Rasa platform it mentions a feature of collaborative model training, can you describe how that works and why someone might want to take advantage of it?\nWhat are some of the most interesting or unexpected uses of the Rasa tools that you have seen?\nWhat do you have planned for the future of Rasa?\n\n\nKeep In Touch\n\n\nGitter\nTwitter\n\n@joeymfaulkner\n@Rasa_HQ\n\n\n\nEmail\nGitHub\n\n\nPicks\n\n\nTobias\n\nInformation Architecture\n\n\n\nJoey\n\n\nDog Spotting\nRasa NLU Trainer\n\n\n\n\n\nLinks\n\n\nRasa Technologies\nRasa NLU\nRasa Core\nSpaCy\n\nPodcast.__init__ Interview with SpaCy Creator\n\n\n\nyt-project\n\n\nPodcast.__init__ Interview with yt-project\n\n\n\nChatbot\nWord2Vec\nState Machine\n\n\nPodcast.__init__ Episode About Automat with Glyph\n\n\n\nRecursive Neural Network\nMITIE\nSupport Vector Machine\nScikit Learn\nwit.ai\nLUIS\nDialogflow\nKeras\nReinforcement Learning\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

With the proliferation of messaging applications, there has been a growing demand for bots that can understand our wishes and perform our bidding. The rise of artificial intelligence has brought the capacity for understanding human language. Combining these two trends gives us chatbots that can be used as a new interface to the software and services that we depend on. This week Joey Faulkner shares his work with Rasa Technologies and their open sourced libraries for understanding natural language and how to conduct a conversation. We talked about how the Rasa Core and Rasa NLU libraries work and how you can use them to replace your dependence on API services and own your data.

\n\n

Preface

\n\n\n\n

Interview

\n\n\n\n

Keep In Touch

\n\n

\n\n

Picks

\n\n

\n\n

Links

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2017-11-04T08:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/657b8188-a844-4ad7-950d-daee8ec78e22.mp3","mime_type":"audio/mpeg","size_in_bytes":34994014,"duration_in_seconds":2940}]},{"id":"podlove-2017-10-29t00:58:58+00:00-6f2ab2ab3b5320b","title":"Eliot: Effective Logging with Itamar Turner-Trauring","url":"https://www.pythonpodcast.com/eliot-logging-with-itamar-turner-trauring-episode-133","content_text":"Summary\n\nUnderstanding what is happening in a software system can be difficult, especially when you have inconsistent log messages. Itamar Turner-Trauring created Eliot to make it possible for your project to tell you a story about how transactions flow through your program. In this week’s episode we go deep on proper logging practices, anti patterns, and how to improve your ability to debug your software with log messages.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.\nIf you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Itamar Turner-Trauring about Eliot, a library for managing complex logs across multiple processes.\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is Eliot and what problem were you trying to solve by creating it?\nHow is Eliot implemented and how has the design evolved since you first started working on it?\nWhy is it so important to have a standardized format for your application logs?\nWhat are some of the anti-patterns that you consider to be the most harmful when developers are setting up logging in their projects?\nWhat have been the most challenging aspects of building and maintaining Eliot?\nHow does Eliot compare to some of the other third party logging libraries available such as structlog or logbook?\nWhat are some of the improvements or additional features that you have planned for the future of Eliot?\n\n\nKeep In Touch\n\n\nWebsite\n@itamarst on Twitter\n\n\nPicks\n\n\nTobias\n\nMoonshot Podcast\n\n\n\nItamar\n\n\nMiddlemarch by George Eliot\n\n\n\n\n\nLinks\n\n\nEliot\nZope\nPHP\nOpenTracing\nZipkin\nCarl De Marcken\nSentry\nElasticsearch\nLogstash\nKibana\nEliot-Tree\nDaniel Lebrero\nFlocker\nContext Local Variables PEP (PEP 550)\nFlamegraph\nBrendan Gregg\nDAG\nStructlog\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Understanding what is happening in a software system can be difficult, especially when you have inconsistent log messages. Itamar Turner-Trauring created Eliot to make it possible for your project to tell you a story about how transactions flow through your program. In this week’s episode we go deep on proper logging practices, anti patterns, and how to improve your ability to debug your software with log messages.

\n\n

Preface

\n\n\n\n

Interview

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2017-10-28T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/5bf5e682-d69a-4277-a647-c46493b83fcf.mp3","mime_type":"audio/mpeg","size_in_bytes":30493305,"duration_in_seconds":2988}]},{"id":"podlove-2017-10-21t00:32:02+00:00-ecc95974a113893","title":"Donkey: Building Self Driving Cars with Will Roscoe","url":"https://www.pythonpodcast.com/donkey-with-will-roscoe-episode-132","content_text":"Summary\n\nDo you wish that you had a self-driving car of your own? With Donkey you can make that dream a reality. This week Will Roscoe shares the story of how he got involved in the arena of self-driving car hobbyists and ended up building a Python library to act as his pilot. We talked about the hardware involved, how he has evolved the code to meet unexpected challenges, and how he plans to improve it in the future. So go build your own self driving car and take it for a spin!\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.\nIf you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com)\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Will Roscoe about Donkey, a python library for building DIY self driving cars.\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is Donkey and what was your reason for creating it?\n\nWhat is the story behind the name?\n\n\n\nWhat was your reason for choosing Python as the language for implementing Donkey and if you were to start over today would you make the same choice?\nHow is Donkey implemented and how has its software architecture evolved?\nIs the library built in a way that you can process inputs from additional sensor types, such as proximity detectors or LIDAR?\nFor training the autopilot what are the input features that the model is testing against for the input data, and is it possible to change the features that it will try to detect?\nDo you have plans to incorporate any negative reinforcement techniques for training the pilot models so that errors in data collection can be identified as undesirable outcomes?\nWhat have been some of the most interesting or humorous successes and failures while testing your cars?\nWhat are some of the challenges involved with getting such a sophisticated stack of software running on a Raspberry Pi?\nWhat are some of the improvements or new features that you have planned for the future of Donkey?\n\n\nMedia\n\nDonkey Car Photos\n\nKeep In Touch\n\n\nDonkey Slack Channel\nWills Twitter – @dataduce\n#donkeycar on social\n\n\nPicks\n\n\nTobias\n\nOrgzly\nOrg Mode for Sublime\nOrg Mode for VSCode\nOrg Mode for Vim \n\n\n\nWill\n\n\nAlgorithms to Live By\nThe Structure of Scientific Revolutions\nA song I can’t stop nodding my head to\n\n\n\n\n\nLinks\n\n\nDonkey Car\nDIY Robocars\nTornado\n[Tornado on Podcast.init](https://www.pythonpodcast.com/episode-40-ben-darnell-on-tornado/?utm_source=rss&utm_medium=rss\nRaspberry Pi\nTensorFlow\nConvolutional Neural Network\nAdafruit\nLIDAR\nROS (Robot Operating System)\nUnity\nUdacity self driving car nano-degree\nSparkFun\nBeagleboard\nAdam Conway\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Do you wish that you had a self-driving car of your own? With Donkey you can make that dream a reality. This week Will Roscoe shares the story of how he got involved in the arena of self-driving car hobbyists and ended up building a Python library to act as his pilot. We talked about the hardware involved, how he has evolved the code to meet unexpected challenges, and how he plans to improve it in the future. So go build your own self driving car and take it for a spin!

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Media

\n\n

Donkey Car Photos

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2017-10-21T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/6eee78e6-5703-4c0b-b195-b52f6a46c7e1.mp3","mime_type":"audio/mpeg","size_in_bytes":21822704,"duration_in_seconds":2029}]},{"id":"podlove-2017-10-15t06:57:08+00:00-abcf9c703ad5359","title":"Event Sourcing with John Bywater","url":"https://www.pythonpodcast.com/event-sourcing-with-john-bywater-episode-131","content_text":"Summary\n\nThe way that your application handles data and the way that it is represented in your database don’t always match, leading to a lot of brittle abstractions to reconcile the two. In order to reduce that friction, instead of overwriting the state of your application on every change you can log all of the events that take place and then render the current state from that sequence of events. John Bywater joins me this week to discuss his work on the Event Sourcing library, why you might want to use it in your applications, and how it can change the way that you think about your data.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports the show on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.\nIf you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.\nVisit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email hosts@podcastinit.com\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing John Bywater about event sourcing, an architectural approach to make your data layer easier to scale and maintain.\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing the concept of event sourcing and the benefits that it provides?\nWhat is the event sourcing library and what was your reason for starting it?\nWhat are some of the reasons that someone might not want to implement an event sourcing approach in their persistence layer?\nGiven that you are storing a record for each event that occurs on a domain object, how does that affect the amount of storage necessary to support an event sourced application?\nWhat is the impact on performance and latency from an end user perspective when the application is using event sourcing to render the current state of the system?\nWhat does the internal architecture and design of your library look like and how has that evolved over time?\nIn the case where events are delivered out of order, how can you ensure that the present view of an object is reflected accurately?\nFor someone who wants to incorporate an event sourcing design into an existing application, how would they do that?\nHow do you manage schema changes in your domain model when you need to reconstruct present state from the beginning of an objects event sequence?\nWhat are some of the most interesting uses of event sourcing that you have seen?\nWhat are some of the features or improvements that you have planned for the future of you event sourcing library?\n\n\nKeep In Touch\n\n\nJohn\n\njohnbywater on GitHub\n@johnbywater on Twitter\n\n\n\n\n\nPicks\n\n\nTobias\n\nHeresy In The Church Of Docker\n\n\n\nJohn\n\n\nQuantDSL\n\n\n\n\n\nLinks\n\n\nCKAN\nData.gov\nPatterns of Enterprise Application Architecture\nObject Relational Impedance Mismatch\nEvent Sourcing (Pattern)\nEvent Sourcing (Library)\nN-Tiered Architecture\nDomain Driven Design\nEvent Storming\nORM, The Vietnam of Computer Science\nVaughn Vernon, Implementing Domain Driven Design\nActive Record Pattern\nOptimistic Concurrency Control\nPaxos\nDynamoDB\nMartin Fowler\nEric Evans\nThe Dark Side of Event Sourcing\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

The way that your application handles data and the way that it is represented in your database don’t always match, leading to a lot of brittle abstractions to reconcile the two. In order to reduce that friction, instead of overwriting the state of your application on every change you can log all of the events that take place and then render the current state from that sequence of events. John Bywater joins me this week to discuss his work on the Event Sourcing library, why you might want to use it in your applications, and how it can change the way that you think about your data.

\n\n

Preface

\n\n\n\n

Interview

\n\n\n\n

Keep In Touch

\n\n

\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2017-10-15T03:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/4fab3678-8caf-44f8-b040-327ce8508ed3.mp3","mime_type":"audio/mpeg","size_in_bytes":51607516,"duration_in_seconds":4106}]},{"id":"podlove-2017-10-07t10:55:15+00:00-340d66222d8e69f","title":"Kalliope with Nicolas Marcq and Thibaud Buffet","url":"https://www.pythonpodcast.com/kalliope-with-nicolas-marq-and-thibaud-buffet-episode-130","content_text":"Summary\n\nWouldn’t it be nice to have a personal assistant to answer your questions, help you remember important tasks, and control your environment? Meet Kalliope, a Python powered, modular, voice controlled automation platform. This week Nicolas Marcq and Thibaud Buffet explain how they started the project, what makes it stand out from other open source and commercial options, and how you can start using it today.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nNeed to learn more about how to scale your apps or learn new techniques for building them? Pluralsight has the training and mentoring you need to level up your skills. Go to www.pythonpodcast.com/pluralsight?utm_source=rss&utm_medium=rss to start your free trial today.\nVisit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nIf you work with data for your job or want to learn more about how open source is powering the latest innovations in data science then make your way to the Open Data Science Conference, happening in London in October and San Francisco in November. Follow the links in the show notes to register and help support the show in the process.\nYour host as usual is Tobias Macey and today I’m interviewing Nicolas Marcq and Thibaud Buffet about Kalliope, a modular always-on voice controlled personal assistant designed for home automation.\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is the Kalliope project and how did it get started?\nHow does Kalliope compare to commercial options such as Amazon Alexa and Google Home, as well as other open source projects such as Mycroft or Jasper?\nThe majority of voice assistant projects that I have seen default to interacting in English, whereas Kalliope is multi-lingual. What led you to that design choice and how is that implemented?\nOne of the perennial questions around voice assistants is privacy, so how does Kalliope work to mitigate the issues associated with having an always on device listening in people’s homes?\nHow is Kalliope architected internally and how has the design evolved over time?\nWhat are some of the most difficult or challenging aspects of building Kalliope and its associated projects?\nWhat are some of the most interesting uses of Kalliope that you are aware of?\nWhat are some of the most notable features or improvements that you have planned for the future of Kalliope?\nHow has the choice of Python as the implementation worked for you, and if you were to start over today do you think you would make the same decision?\n\n\nKeep In Touch\n\n\nNicolas\n\n@Sispheor on Twitter\nSispheor on GitHub\nWebsite\n\n\n\nThibaud\n\n\n@Tib_Tac on Twitter\nLaMonF on GitHub\n\n\n\n\n\nPicks\n\n\nTobias\n\nKiwi Crate\n\n\n\nNicolas\n\n\nRaspberry Pi Speaker\n\n\n\nThibaud\n\n\nReactiveX in Python\n\n\n\n\n\nLinks\n\n\nSnowboy\nMycroft\nMycroft Interview\nAmazon Alexa\nGoogle Home\nJasper\nKalliope\nTTS\nSTT\nCMU Sphinx\nAbstract Base Class\nMQTT\nRxPy Interview\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Wouldn’t it be nice to have a personal assistant to answer your questions, help you remember important tasks, and control your environment? Meet Kalliope, a Python powered, modular, voice controlled automation platform. This week Nicolas Marcq and Thibaud Buffet explain how they started the project, what makes it stand out from other open source and commercial options, and how you can start using it today.

\n\n

Preface

\n\n\n\n

Interview

\n\n\n\n

Keep In Touch

\n\n

\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2017-10-07T20:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/ae1fd791-19f2-4b20-b154-4138fd6928e1.mp3","mime_type":"audio/mpeg","size_in_bytes":21081580,"duration_in_seconds":1953}]},{"id":"podlove-2017-10-01t03:20:02+00:00-5937e9cbd758105","title":"Modoboa with Antoine Nguyen","url":"https://www.pythonpodcast.com/modoboa-with-antoine-nguyen-episode-129","content_text":"Summary\n\nEmail has long been the most commonly used means of communication on the internet. This week Antoine Nguyen talks about his work on the Modoboa project to make hosting your own mail server easier to manage. He discusses how the project got started, the tools that it ties together, and how he used Django to build a webmail and admin interface to make it more approachable.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nNeed to learn more about how to scale your apps or learn new techniques for building them? Pluralsight has the training and mentoring you need to level up your skills. Go to www.pythonpodcast.com/pluralsight?utm_source=rss&utm_medium=rss to start your free trial today.\nVisit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nIf you work with data for your job or want to learn more about how open source is powering the latest innovations in data science then make your way to the Open Data Science Conference, happening in London in October and San Francisco in November. Follow the links in the show notes to register and help support the show in the process.\nYour host as usual is Tobias Macey and today I’m interviewing Antoine Nguyen about Modoboa, a project to make mail hosting simple.\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is Modoboa and what is the problem that you were trying to solve when you started it?\n\nWhere does the name come from?\n\n\n\nSelf-hosting an email server was a common activity during the early stages of the internet, what are some of the reasons that someone should consider running their own mail server now that there are so many options for third-party hosting such as Gmail and Outlook?\nEmail hosting has become more complicated in recent years with the need to jump through a lot of hoops to maintain a sufficient reputation to keep your messages from being flagged as spam. Are there any utilities in Modoboa to assist with that process?\nThere are a lot of components that you have brought together for running an email server. Can you describe how the different pieces fit together and what layers you have built on top to help make the overall system more manageable?\nWhat does the scaling strategy look like for Modoboa?\nWhat is the most challenging aspect of building and maintaining Modoboa?\nWhat are some of the features that you have planned for the future of Modoboa?\n\n\nKeep In Touch\n\n\nEmail\n@antngu on Twitter\n\n\nPicks\n\n\nTobias\n\nDropbox Paper\n\n\n\nAntoine\n\n\nCapoeira\n\n\n\n\n\nLinks\n\n\nPyTk\nPostfix\nDovecot\nNextcloud\nOwncloud\nSPF Records\nDKIM\nDMARC\nSMTP\nIMAP\nApache Libcloud\nAmavis\nMail Transfer Agent\nRadicale\nAnsible\nDocker\nGentoo\nPacker\nSynology\nDrobo\nProsody\nLua\nXMPP\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Email has long been the most commonly used means of communication on the internet. This week Antoine Nguyen talks about his work on the Modoboa project to make hosting your own mail server easier to manage. He discusses how the project got started, the tools that it ties together, and how he used Django to build a webmail and admin interface to make it more approachable.

\n\n

Preface

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Interview

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Keep In Touch

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Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Mail hosting made simple with Python","date_published":"2017-09-30T23:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/08e2d203-737a-4046-a5bf-763cd4c8afd1.mp3","mime_type":"audio/mpeg","size_in_bytes":21945093,"duration_in_seconds":1998}]},{"id":"podlove-2017-09-24t02:23:51+00:00-0d3eab7493bcf90","title":"QuTiP with Paul Nation","url":"https://www.pythonpodcast.com/modoboa-with-antoine-nguyen-episode-129qutip-with-paul-nation-episode-128","content_text":"Summary\n\nThe future of computation and our understanding of the world around us is driven by the quantum world. This week Paul Nation explains how the Quantum Toolbox in Python (QuTiP) is being used in research projects that are expanding our knowledge of the physical universe.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nNeed to learn more about how to scale your apps or learn new techniques for building them? Pluralsight has the training and mentoring you need to level up your skills. Go to www.pythonpodcast.com/pluralsight?utm_source=rss&utm_medium=rss to start your free trial today.\nVisit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nIf you work with data for your job or want to learn more about how open source is powering the latest innovations in data science then make your way to the Open Data Science Conference, happening in London in October and San Francisco in November. Follow the links in the show notes to register and help support the show in the process.\nYour host as usual is Tobias Macey and today I’m interviewing Paul Nation about QuTIP, the quantum toolbox in Python.\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nBefore we start talking about QuTiP, can you provide us with a baseline definition of what quantum mechanics is?\nWhat is QuTIP and how did the project get started?\nIs QuTiP used purely in academics, or are there other users?\nWhat are some of the practical innovations that have been created as a result of research into different areas of quantum optics?\nHow do you foresee the advent of practical quantum computers impacting the state of quantum mechanical research?\nGiven the inherent complexity of the subject matter that you are dealing with, how do you approach the challenge of trying to present a usable API to users of QuTiP while not inhibiting their ability to operate at a low level when necessary?\nWhat is the process for incorporating new understandings of quantum mechanical theory into the QuTiP package?\nWhat are some of the most difficult aspects of simulating quantum systems in a standard computational environment?\nWhat is the most enjoyable aspect of working on QuTiP, what is the least enjoyable?\nWhat are some of the most notable research results that you are aware of which used QuTiP as part of their studies?\nWhat are some resources that you can recommend for anyone who wants to learn more about quantum mechanics?\n\n\nKeep In Touch\n\n\nQuTiP\nQuSTaR\n\n\nPicks\n\n\nTobias\n\nedx.org\n\n\n\nPaul\n\n\nCython\nMatplotlib\nCheyenne Mountain Zoo\n\n\n\n\n\nLinks\n\n\nQuantum Optics\n2 Level System\nComplex Numbers\nQubit\nQuantum Computing\nHarmonic Oscillator\nNature Scientific Journal\nIBM Quantum Experience\nD-Wave\nRigetti Quantum Computing\nQuantum Supremacy\nHamiltonian\nSparse Matrix\nRichard Feynman\nDask\nProject Q\nQuantum State Transfer via Noisy Photonic and Phononic Waveguides paper by Peter Zoller\nExtending the lifetime of a quantum bit with error correction in superconducting circuits paper by Rob Shoelkopf (Yale)\nQuTiP Documentation\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

The future of computation and our understanding of the world around us is driven by the quantum world. This week Paul Nation explains how the Quantum Toolbox in Python (QuTiP) is being used in research projects that are expanding our knowledge of the physical universe.

\n\n

Preface

\n\n\n\n

Interview

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Quantum Computation in Python","date_published":"2017-09-23T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/8f7755cf-3bbf-45b8-966c-adf39ce9799b.mp3","mime_type":"audio/mpeg","size_in_bytes":24920991,"duration_in_seconds":2191}]},{"id":"podlove-2017-09-17t03:58:54+00:00-88c86aabdb0570b","title":"Lego Robotics with David Lechner and Denis Demidov","url":"https://www.pythonpodcast.com/lego-robotics-with-david-lechner-and-denis-demidov-episode-127","content_text":"Summary\n\nDo you like Legos, robots, and Python? This week I am joined by David Lechner and Denis Demidov to talk about the ev3dev project and how you can program your Lego Mindstorms with Python!\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nNeed to learn more about how to scale your apps or learn new techniques for building them? Pluralsight has the training and mentoring you need to level up your skills. Go to www.pythonpodcast.com/pluralsight?utm_source=rss&utm_medium=rss to start your free trial today.\nVisit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nIf you work with data for your job or want to learn more about how open source is powering the latest innovations in data science then make your way to the Open Data Science Conference, happening in London in October and San Francisco in November. Follow the links in the show notes to register and help support the show in the process.\nYour host as usual is Tobias Macey and today I’m interviewing David Lechner and Denis Demidov about using Python with the ev3dev platform for programming LEGO robots\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you explain what the ev3dev project is and some of the story about how and why it got started?\nWhat is LEGO’s opinion of the ev3dev project?\nFor anyone who isn’t familiar with the MINDSTORMS EV3 product from LEGO, can you give a brief overview of the hardware that they come with?\nOther than allowing users to program in environments other than the block-based editor that LEGO provides, what capabilities does the ev3dev project add to the MINDSTORMS EV3 platform?\nHow are the language bindings generated and how do the different implementations compare to each other?\nWhat are the most challenging aspects of building and maintaining the ev3dev distribution and various language bindings?\nOne of the things that my son is curious about is the possibility for integrating his MINDSTORMS with projects such as Kalliope or Mycroft to allow for voice controlled robots. Are you aware of anyone having done so or how you would approach something like that?\nWhat are some of the most interesting or innovative projects that you have seen people make with the MINDSTORMS platform running ev3dev?\nWhy would someone want to use MINDSTORMS instead of any of the other robotics platforms that are available?\nFor someone who is interested in learning more about intermediate and advanced robotics, what are some resources that you would recommend?\n\n\nKeep In Touch\n\n\nDenis\n\n@denis_demidov on Twitter\nddemidov on Github\n\n\n\nDavid\n\n\ndlech on Github\nWebsite\n\n\n\n\n\n\nPicks\n\n\nTobias\n\nRaspberry Pi\nKalliope\n\n\n\nDenis\n\n\npybind11\n\n\n\nDavid\n\n\nLocal food\nLocalHarvest\n\n\n\n\n\nLinks\n\n\nev3dev\nLego MINDSTORMS\nBeagleBone\nLego Mindstorms Community\nC++\nJupyter Notebooks\nRalph Hempel\nForth\nRCX\nNXT\nEV3\nARMv5\nDebian\nPiStorms\nBrickPi\nEVB\nUART\nEV3 Schematics Look for “EV3 Hardware Developer Kit” in “Advanced Users” section.\nI2C\nRPyC\nLaurens Valk\nLiquid Templates\nDelta Robot\nQuest For Space\nLego Technic\nMindsensors.com\nCool robots built with ev3dev\nMicropython\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Do you like Legos, robots, and Python? This week I am joined by David Lechner and Denis Demidov to talk about the ev3dev project and how you can program your Lego Mindstorms with Python!

\n\n

Preface

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Interview

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Keep In Touch

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Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2017-09-17T00:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/7347afce-a704-4074-ac7c-57bf111d89eb.mp3","mime_type":"audio/mpeg","size_in_bytes":32009185,"duration_in_seconds":2641}]},{"id":"podlove-2017-09-10t12:27:38+00:00-176ad002e97923a","title":"Cloud-Init with Scott Moser","url":"https://www.pythonpodcast.com/cloud-init-with-scott-moser-episode-126","content_text":"Summary\n\nServer administration is a complex endeavor, but there are some tools that can make life easier. If you are running your workload in a cloud environment then cloud-init is here to help. This week Scott Moser explains what cloud-init is, how it works, and how it became the de-facto tool for configuring your Linux servers at boot.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nNeed to learn more about how to scale your apps or learn new techniques for building them? Pluralsight has the training and mentoring you need to level up your skills. Go to www.pythonpodcast.com/pluralsight?utm_source=rss&utm_medium=rss to start your free trial today.\nVisit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nIf you work with data for your job or want to learn more about how open source is powering the latest innovations in data science then make your way to the Open Data Science Conference, happening in London in October and San Francisco in November. Follow the links in the show notes to register and help support the show in the process.\nYour host as usual is Tobias Macey and today I’m interviewing Scott Moser about cloud-init, a set of python scripts and utilities to make your cloud images be all they can be!\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is cloud-init and how did the project get started?\nWhy was Python chosen as the language for implementing cloud-init?\nHow has cloud-init come to be the de-facto utility for managing cloud instances across vendors and distributions?\nAre there any viable competitors to cloud-init? coreos-cloudinit, some others.\nHow much overlap is there between cloud-init and configuration management tools such as SaltStack, Ansible, Chef, etc.\nHow have you architected cloud-init to allow for compatibility across operating system distributions?\nWhat is the most difficult or complex aspect of building and maintaining cloud-init? [os integration, networking, goal of “do stuff without reboot”]\nGiven that it is used as a critical component of the production deployment mechanics for a large number of people, how do you ensure an appropriate level of stability and security while developing cloud-init?\nHow do you think the status of cloud-init as a Canonical project has affected the level of contributions that you receive?\nHow much of the support and roadmap is contributed by individual vs corporate users such as AWS and Azure?\nWhat are some of the most unexpected or creative uses of cloud-init that you have seen? [https://wiki.ubuntu.com/OpenCompute?utm_source=rss&utm_medium=rss “disposable use os”]\nIn your experience, what has been the biggest stumbling block for new users of cloud-init?\nDo you have any notable features or improvements planned for the future of cloud-init, or do you feel that it has reached a state of feature-completeness? \n\n\nKeep In Touch\n\n\nsmoser on GitHub\n\n\nPicks\n\n\nTobias\n\nmu4e\nisync\n\n\n\nScott\n\n\nLXD\n\n\n\n\n\nLinks\n\n\nIBM – Linux Technology Center\nCloud-Init\nUbuntu\nCanonical\nCoreOS\nEC2\nOpenStack\nCentOS\nRHEL\ncoreos-cloudinit\nJuJu\nPuppet\nSystemV\nUpstart\nSystemD\nJoyent SmartOS\nDigital Ocean\nIPv4\nIPv6\nCanonical MaaS+\nJSON-Schema\nLXD\nLaunchpad\nBzr\nGit\nSUSE\nFreeBSD\nKVM\nGo-lang\nPretty Table\nRAID\nZFS\nLVM\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

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Server administration is a complex endeavor, but there are some tools that can make life easier. If you are running your workload in a cloud environment then cloud-init is here to help. This week Scott Moser explains what cloud-init is, how it works, and how it became the de-facto tool for configuring your Linux servers at boot.

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Preface

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Interview

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Keep In Touch

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Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2017-09-10T08:30:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/3025ac63-6d8e-4fd5-a98d-16b374d7bb05.mp3","mime_type":"audio/mpeg","size_in_bytes":34827406,"duration_in_seconds":2990}]},{"id":"podlove-2017-09-03t00:27:58+00:00-2c572cbef4c77ba","title":"Biopython with Peter Cock, Wibowo Arindrarto, and Tiago Antão","url":"https://www.pythonpodcast.com/biopython-with-peter-cock-wibowo-andrarto-and-tiago-antao-episode-125","content_text":"Summary\n\nAdvances in the techniques used for genome sequencing are providing us with more information to unlock the secrets of biology. But how does that data get processed and analyzed? With Python of course! This week I am joined by some of the core maintainers of Biopython to discuss what bioinformatics is, how Python is used to help power the research in the field, and how Biopython helps to tie everything together.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nNeed to learn more about how to scale your apps or learn new techniques for building them? Pluralsight has the training and mentoring you need to level up your skills. Go to www.pythonpodcast.com/pluralsight?utm_source=rss&utm_medium=rss to start your free trial today.\nVisit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nIf you work with data for your job or want to learn more about how open source is powering the latest innovations in data science then make your way to the Open Data Science Conference, happening in London in October and San Francisco in November. Follow the links in the show notes to register and help support the show in the process.\nYour host as usual is Tobias Macey and today I’m interviewing Peter Cock, Wibowo Arindrarto, and Tiago Antão about biopython, a suite of python tools for computational molecular biology.\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what bioinformatics is and highlight some of the different areas of research?\nWhat is biopython and how did it get started?\nBiopython has a long history behind it. How has the project evolved over that time to meet the changing needs in terms of both research amd computation?\nHow does Biopython compare to the sibling Bio* projects in other programming languages?\nWhat does a common workflow look like for someone who is working with biological data?\nWhat are some of the most interesting or innovative uses of Biopython that you are aware of?\nWhat are some of the most challenging aspects of developing and supporting Biopython?\nWhat are some of the most exciting developments in bioinformatics, either recently or coming up?\nHow much domain knowledge is necessary for someone who wants to contribute to the project?\nWhat are some of the most problematic limitations of Biopython and how do you work around them?\n\n\nKeep In Touch\n\n\nPeter\n\nWebsite\n\n\n\nWibowo\n\n\nWebsite\n@_bow_ on Twitter\n\n\n\nTiago\n\n\nWebsite\n@tiagoantao on Twitter\n\n\n\nBiopython\n\n\nGitHub\n\n\n\n\n\nPicks\n\n\nTobias\n\nKeep it Low Conf\n\n\n\nPeter\n\n\nJupyter Notebooks (formerly IPython) for producing notebooks combining code, graphical output and descriptive code. Can be seen as a modern take on Donald Knuth’s Literate programming?\n\n\n\nWibowo\n\n\nConda for installing software, including BioConda for community packaged software in bioinformatics.\n\n\n\nTiago\n\n\nBrython project for writing Python 3 in your browser using JavaScript\nGlacier National Park in North West Montana\n\n\n\n\n\nLinks\n\n\nBioJava\nBioRuby\nBioPerl\nBioJS\nOpen Bioinformatics Foundation\nSoftware In The Public Interest\nOxford Nanopore Technology (for sequencing in the field etc)\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

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Advances in the techniques used for genome sequencing are providing us with more information to unlock the secrets of biology. But how does that data get processed and analyzed? With Python of course! This week I am joined by some of the core maintainers of Biopython to discuss what bioinformatics is, how Python is used to help power the research in the field, and how Biopython helps to tie everything together.

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Preface

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Interview

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Keep In Touch

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Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Unlocking the secrets of life with Python","date_published":"2017-09-02T20:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/db1f64c3-5d2d-4c92-8c3e-0523fbd3a815.mp3","mime_type":"audio/mpeg","size_in_bytes":32284745,"duration_in_seconds":2729}]},{"id":"podlove-2017-08-26t11:47:19+00:00-e3427f401017b37","title":"opsdroid with Jacob Tomlinson","url":"https://www.pythonpodcast.com/opsdroid-with-jacob-tomlinson-episode-124","content_text":"Summary\n\nServer administration is an activity that often happens in an isolated context in a terminal. ChatOps is a way of bringing that work into a shared environment and unlocking more collaboration. This week Jacob Tomlinson talks about the work he has done on opsdroid, a new bot framework targeted at tying together the various services and environments that modern production systems rely on.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nNeed to learn more about how to scale your apps or learn new techniques for building them? Pluralsight has the training and mentoring you need to level up your skills. Go to www.pythonpodcast.com/pluralsight?utm_source=rss&utm_medium=rss to start your free trial today.\nVisit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nIf you work with data for your job or want to learn more about how open source is powering the latest innovations in data science then make your way to the Open Data Science Conference, happening in London in October and San Francisco in November. Follow the links in the show notes to register and help support the show in the process.\nYour host as usual is Tobias Macey and today I’m interviewing Jacob Tomlinson about opsdroid\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is opsdroid and what was the problem that you were trying to solve when you started the project?\nWhat led you to choose Python as the language for implementing opsdroid?\nWhat did you find lacking in the multitude of other chat bots that necessitated starting a new project? (e.g. Hubot, Errbot, Lita)\nOne of the main features that you list in the documentation is the ease of installation. Why is that such an important aspect of the project and how is that implemented?\nWhat has been the most interesting and the most challenging aspect of implementing opsdroid?\nOn the opsdroid organisation on GitHub there are many repositories for plugin modules. Do you see this being a management issue in the long term?\nHow is opsdroid architected and what were the system requirements that led to the current system design?\nHow do you manage authorization and authentication for performing commands against your production infrastructure in a group chat environment?\n\nWhat are some of the other security implications that users should be aware of when deploying a bot for interfacing with their deployment environment?\n\n\n\nHow does a chat-oriented bot framework differ from those that are being created for voice-oriented interaction?\nWhat do you have planned for the future of opsdroid?\n\n\nKeep In Touch\n\n\nWebsite\n@_JacobTomlinson on Twitter\njacobtomlinson on GitHub\n\n\nPicks\n\n\nTobias\n\nRough Translation Podcast\n\n\n\nJacob\n\n\nHome Assistant Podcast\n\n\n\n\n\nLinks\n\n\nIron Man Movie\nPuppet\nHubot\nChatOps\nasyncio\nHome Assistant\n\nPodcast.init Interview\n\n\n\napi.ai\nLuis\nLex\nSlack\nMycroft\nKalliope\nAmazon Alexa\nopsdroid audio\nSnowboy\nGoogle Home\nWit.ai\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

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Server administration is an activity that often happens in an isolated context in a terminal. ChatOps is a way of bringing that work into a shared environment and unlocking more collaboration. This week Jacob Tomlinson talks about the work he has done on opsdroid, a new bot framework targeted at tying together the various services and environments that modern production systems rely on.

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Preface

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Interview

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Keep In Touch

\n\n\n\n

Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2017-08-26T07:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/b1410874-c293-41ee-8db9-4a07af4ec3c1.mp3","mime_type":"audio/mpeg","size_in_bytes":64767930,"duration_in_seconds":2740}]},{"id":"podlove-2017-08-20t01:37:09+00:00-cd9d10f895b7966","title":"Ergonomica with Liam Schumm","url":"https://www.pythonpodcast.com/ergonomica-with-liam-schumm-episode-123","content_text":"Summary\n\nAs developers we spend a lot of our work day in a terminal window, using shells that were designed 30 years ago. This week Liam Schumm joins me to explain why he decided to write a new, more ergonomic shell environment to simplify his workflow.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nNeed to learn more about how to scale your apps or learn new techniques for building them? Pluralsight has the training and mentoring you need to level up your skills. Go to www.pythonpodcast.com/pluralsight?utm_source=rss&utm_medium=rss to start your free trial today.\nVisit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nIf you work with data for your job or want to learn more about how open source is powering the latest innovations in data science then make your way to the Open Data Science Conference, happening in London in October and San Francisco in November. Follow the links in the show notes to register and help support the show in the process.\nYour host as usual is Tobias Macey and today I’m interviewing Liam Schumm about Ergonomica\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is Ergonomica and what was your reason for creating it?\nWhat are some of the most difficult aspects of the project that you have experienced?\nHow is Ergonomica implemented?\nWhat was your reason for using a dialect of Lisp as the interface for a terminal environment as opposed to iterating on the idioms in shells such as Bash?\nHow does Ergonomica’s implementation differ from traditional shells such as Bash, Csh, and Powershell?\nHow does Ergonomica’s implementation differ from other alternative shells such as Xonsh, ZSH, and Fish?\nWhy did you choose to implement Ergonomica in Python?\nWhat’s your target group for Ergonomica?\nWhat do you have planned for the future of Ergonomica?\nReading through your website you are fairly well accomplished. How does your age factor into the kinds of projects that you are engaged in?\n\n\nKeep In Touch\n\n\nLiam’s GitHub\nEmail\n@liamschumm on Twitter\n\n\nPicks\n\n\nTobias\n\nMagic The Gathering: Arena of the Planeswalkers\n\n\n\nLiam\n\n\nGitLab CE\nPython-prompt-toolkit\nThriftbooks – 15% off your first order\n\n\n\n\n\nLinks\n\n\nPyGame\nMinecraft\nBeeware\nChiPy\nChi Hack Night\nXKCD Tar Comic\nPOSIX\nColorama\nPLY\nPeter Norvig\nHow to write a lisp interpreter in python\nZSH\nFish\nXonsh\nPyVim\nsh\nHomebrew\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

As developers we spend a lot of our work day in a terminal window, using shells that were designed 30 years ago. This week Liam Schumm joins me to explain why he decided to write a new, more ergonomic shell environment to simplify his workflow.

\n\n

Preface

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Interview

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2017-08-19T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/609b0857-8d25-465b-98e1-58d20c0b76d3.mp3","mime_type":"audio/mpeg","size_in_bytes":33289069,"duration_in_seconds":2524}]},{"id":"podlove-2017-08-12t23:56:40+00:00-87643f37c8721bd","title":"Data Retriever with Henry Senyondo","url":"https://www.pythonpodcast.com/data-retriever-with-henry-senyondo-episode-122","content_text":"Summary\n\nAnalyzing and interpreting data is a large portion of the work involved in scientific research. Getting to that point can be a lot of work on its own because of all of the steps required to download, clean, and organize the data prior to analysis. This week Henry Senyondo talks about the work he is doing with Data Retriever to make data preparation as easy as retriever install.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nVisit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Henry Senyondo about Data Retriever, the package manager for public data sets.\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you explain what data retriever is and the problem that it was built to solve?\nAre there limitations as to the types of data that can be managed by data retriever?\nWhat kinds of data sets are currently available and who are the target users?\nWhat is involved in preparing a new dataset to be available for installation?\nHow much of the logic for installing the data is shared between the R and Python implementations of Data Retriever and how do you ensure that the two packages evolve in parallel?\nHow is the project designed and what are some of the most difficult technical aspects of building it?\nWhat is in store for the future of data retriever?\n\n\nKeep In Touch\n\n\nGithub\n@henrykironde on Twitter\n\n\nPicks\n\n\nTobias\n\nOtium Bluetooth Receiver\nPanasonic Ergofit Headphones\nNitize Adhesive Pocket Clip\n\n\n\nHenry\n\n\nThe Three Idiots\n\n\n\n\n\nLinks\n\n\nWeecology Lab\nUniversity of Florida\nData Retriever\nLG\nR\nJulia\nOpen Knowledge Foundation\nFrictionless Data Format\nData Weaver\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Analyzing and interpreting data is a large portion of the work involved in scientific research. Getting to that point can be a lot of work on its own because of all of the steps required to download, clean, and organize the data prior to analysis. This week Henry Senyondo talks about the work he is doing with Data Retriever to make data preparation as easy as retriever install.

\n\n

Preface

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Interview

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Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2017-08-12T19:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/15adab11-e963-4c6c-903b-e585d9529a08.mp3","mime_type":"audio/mpeg","size_in_bytes":25697696,"duration_in_seconds":1075}]},{"id":"podlove-2017-08-05t10:48:52+00:00-482887cee3abc3f","title":"Coverage.py with Ned Batchelder","url":"https://www.pythonpodcast.com/coverage-py-with-ned-batchelder-episode-121","content_text":"Summary\n\nWe write tests to make sure that our code is correct, but how do you make sure the tests are correct? This week Ned Batchelder explains how coverage.py fills that need, how he became the maintainer, and how it works under the hood.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nVisit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Ned Batchelder about coverage.py, the ubiquitous tool for measuring your test coverage.\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is coverage.py and how did you get involved with the project?\nThe coverage project has become the de facto standard for measuring test coverage in Python. Why do you think that is?\nWhat is the utility of measuring test coverage?\nWhat are the downsides to measuring test coverage?\nOne of the notable capabilities that was introduced recently was the plugin for measuring coverage of Django templates. Why is that an important capability and how did you manage to make that work?\nHow does coverage conduct its measurements and how has that algorithm evolved since you first started work on it?\nWhat are the most challenging aspects of building and maintaining coverage.py?\nWhile I was looking at the bug tracker I was struck by the vast array of contexts in which coverage is used. Do you find it overwhelming trying to support so many operating systems and Python implementations?\nWhat might be added to coverage in the future?\n\n\nKeep In Touch\n\n\n@nedbat on Twitter\nWebsite\n\n\nPicks\n\n\nTobias\n\nOrg-Journal\n\n\n\nNed\n\n\nHypothesis\nThe Infinite Monkey Cage\n\n\n\n\n\nLinks\n\n\nedX\nLotus Notes\nZope\nCoverage.py\nGareth Rees\nTrace in stdlib\nFig Leaf\nState Machines\nCodeCov\nCoveralls\nCobertura\nTuring Completeness\nDjango Templates\nJinja2\nMako\nHy-lang\nGCov\nJython\nCode Triage Service\nWho Tests What\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

We write tests to make sure that our code is correct, but how do you make sure the tests are correct? This week Ned Batchelder explains how coverage.py fills that need, how he became the maintainer, and how it works under the hood.

\n\n

Preface

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Interview

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Keep In Touch

\n\n\n\n

Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2017-08-05T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/08c1a128-1236-4f28-9194-bae700ac9253.mp3","mime_type":"audio/mpeg","size_in_bytes":73021059,"duration_in_seconds":3114}]},{"id":"podlove-2017-07-30t02:23:17+00:00-48f34fd617d69cf","title":"Yosai with Darin Gordon","url":"https://www.pythonpodcast.com/yosai-with-darin-gordon-episode-120","content_text":"Summary\n\nFor any program that is used by more than one person you need a way to control identity and permissions. There are myriad solutions to that problem, but most of them are tied to a specific framework. Yosai is a flexible, general purpose framework for managing role-based access to your applications that has been decoupled from the underlying platform. This week the author of Yosai, Darin Gordon, joins us to talk about why he started it, his experience porting it from Java, and where he hopes to take it in the future.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nVisit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing\nDarin Gordon about Yosai, a security framework for Python applications\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is Yosai and what is the problem that you were trying to solve when you started it?\nHow does Yosai compare to existing libraries for web frameworks such as Flask-Security or Django Guardian and why might someone choose Yosai instead?\nIn the documentation it mentions that Yosai is a port of the Apache Shiro framework from Java to Python. What was most difficult about exposing a Pythonic interface while maintaining the core principles of the original?\nAuthentication and authorization are difficult problem domains and can cause significant issues if they are not implemented in a secure fashion. How do you ensure an appropriate level of quality in Yosai to be confident having people use it?\nTo start can you describe how the framework is architected and what is involved in integrating it with a project?\nOutside of the context of web applications, what are some situations where someone should consider integrating authentication and authorization into their project?\nWhat have been some of the most challenging aspects of building the Yosai project?\nTell us about the Rust extension you wrote earlier this year\nWhat do you have planned for the future of Yosai?\n\n\nKeep In Touch\n\n\nWebsite\nGitHub\n@darin_gordon on Twitter\n\n\nPicks\n\n\nTobias\n\nBrains On! podcast\n\n\n\nDarin\n\n\nThe Asphalt Framework. Asphalt is an asyncio-based microframework for network oriented applications.\n\n\n\n\n\nLinks\n\n\nYosai Project Web Page\nGithub Repo\nRBAC\nApache Shiro\nTOTP\nPyramid\nSOLID\nBuilder Pattern\nPOJO\nCorey Benfield\nHyper HTTP/2 Library\nPasslib\nHugo\nMKDocs\nYAML\nMiddleware\nIoT\nAuthz in Rust\nPyO3\nSnaek\nPyCon Canada\nPyCascades\nJSON Web Tokens\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

For any program that is used by more than one person you need a way to control identity and permissions. There are myriad solutions to that problem, but most of them are tied to a specific framework. Yosai is a flexible, general purpose framework for managing role-based access to your applications that has been decoupled from the underlying platform. This week the author of Yosai, Darin Gordon, joins us to talk about why he started it, his experience porting it from Java, and where he hopes to take it in the future.

\n\n

Preface

\n\n\n\n

Interview

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Keep In Touch

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Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2017-07-29T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/f21a74c6-50ea-4eab-b62b-3fbf06272753.mp3","mime_type":"audio/mpeg","size_in_bytes":51299843,"duration_in_seconds":2519}]},{"id":"podlove-2017-07-22t23:53:02+00:00-604457ccff17096","title":"Moving to MongoDB with Michael Kennedy","url":"https://www.pythonpodcast.com/moving-to-mongodb-with-michael-kennedy-episode-119","content_text":"Summary\n\nThere are dozens of decisions that need to be made when building an application. Sometimes this can lead to analysis paralysis and prevent you from making progress, so don’t let the perfect be the enemy of the good. This week Michael Kennedy shares his experience with evolving his application architecture when his business needs outgrew his initial designs.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nVisit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Mike Kennedy about his work scaling his apps and his business\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nIn some of your recent episodes you have mentioned the work that you did to migrate your applications to run on MongoDB. Can you start by describing the business case for these applications and how you arrived at the initial design?\nWhat was the limiting factor that led you to consider such a drastic shift in how you store and manage your data and what benefits did you gain when the work was complete?\n\nIf the issue was with scaling, how did you identify the choke points?\nWhy go from relational (SQLite) to document (Mongo) instead of what would seem a more obvious choice of a production grade relational engine such as PostGreSQL or MySQL?\n\n\n\nAre there any particular synergies that arise from using a document as opposed to a relational store when working with Python and what are some of the main considerations when deciding between them?\nWhat was happening in your business that precipitated the need for this work?\nHow are you talking to MongoDB from Python? Directly (via pymongo) or ORM-style?\n\n\nWhy did you make that choice?\nHow well is that working out? Advantages / drawbacks?\n\n\n\nIn addition to podcasting you have also been working to create a number of successful courses to teach people how to use Python. Is there anything specific to the language that translates into how you design the material?\nFor anyone who wants to learn more about the benefits and tradeoffs of using a document store with their Python applications, what are some resources that you recommend?\n\n\nKeep In Touch\n\n\nMichael\n\n@mkennedy on Twitter\nWebsites\n\nTalk Python\nPython Bytes\n\n\n\n\n\n\n\nPicks\n\n\nTobias\n\nOrg Mode\nLevar Burton Reads\n\n\n\nMike\n\n\nNewspaper\nRobomongo (now Robo 3T)\nThe Dark Secret at the Heart of AI\nHaibike SDURO Cross 4.0\n\n\n\n\n\nLinks\n\n\nSQLAlchemy\nSQLIte\nMySQL\nPostGreSQL\nNoSQL\nMongoDB\nDatabase Normalization\nForeign Keys\nDocument Database\nRollbar\nMongoEngine\nMongo Security Checkup\nMLab\nMongoDB Atlas\nMongoDB World\nO’Reilly Python Mongo Book\nMongoDB For Python Developers\nMomentum Dash\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

There are dozens of decisions that need to be made when building an application. Sometimes this can lead to analysis paralysis and prevent you from making progress, so don’t let the perfect be the enemy of the good. This week Michael Kennedy shares his experience with evolving his application architecture when his business needs outgrew his initial designs.

\n\n

Preface

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Interview

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Keep In Touch

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Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2017-07-22T19:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/c0b922b6-81bb-402a-99d5-3b6cc565f017.mp3","mime_type":"audio/mpeg","size_in_bytes":76026056,"duration_in_seconds":2878}]},{"id":"podlove-2017-07-16t01:39:25+00:00-799bbd2e958c04d","title":"Zulip Chat with Tim Abbott","url":"https://www.pythonpodcast.com/zulip-chat-with-tim-abbott-episode-118","content_text":"Summary\n\nIn modern work environments the email is being edged out by group chat as the preferred method of communication. The majority of the platforms used are commercial and closed source, but there is one project that is working to change that. Zulip is a project that aims to redefine how effective teams communicate and it is already gaining ground. This week Tim Abbott shares the story of how Zulip got started, how it is built, and why you might want to start using it.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nVisit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Tim Abbott about Zulip, a powerful open source group chat platform\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is Zulip and what was the initial inspiration for creating it?\n\nWhere does the name come from?\n\n\n\nMy understanding is that the project was initally intended to be a commercial product. Can you share some of the history of the acquisition by Dropbox and the journey to open sourcing it?\nHow has your experience at Dropbox influenced the evolution and implementation of the Zulip project?\nThere are a large number of group chat platforms available, both commercial and open source. How does Zulip differentiate itself from other options such as Slack or Mattermost?\nTypically real-time communication is difficult to achieve in a WSGI application. How is Zulip architected to allow for interactive communication?\nWhat have been the most challenging aspects of building and maintaining the Zulip project?\nWhat is involved in installing and running a Zulip server?\n\n\nFor a large installation, what are the options for scaling it out to handle greater load?\n\n\n\nThere is a large and healthy community that has built up around the Zulip project. What are some of the methods that you and others have used to foster that growth and engagement?\nWhat has been the most unexpected aspect of working on Zulip, whether technically or in terms of the community around it?\nWhat do you have planned for the future of Zulip?\n\n\nKeep In Touch\n\n\nZulip\n\nChat\n@zuliposs on Twitter\n\n\n\nTim\n\n\n@tabbott3 on Twitter\nWebsite\n\n\n\n\n\nPicks\n\n\nTobias\n\nLego Mindstorms EV3\n\n\n\nTim\n\n\nChecklist Manifesto\nTim’s Recipe Wiki\n\n\n\n\n\nLinks\n\n\nZulip\nKsplice\nElectron\nReact Native\nIFTTT\nZapier\nZephyr\nBarn Owl\nMyPy\nTornado\nDjango\nZulip Tornado Documentation\nMySQL\nPostGreSQL\nElasticSearch\nCode Triage\nEmoji\nPodcast.__init__ Zulip Chat\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

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In modern work environments the email is being edged out by group chat as the preferred method of communication. The majority of the platforms used are commercial and closed source, but there is one project that is working to change that. Zulip is a project that aims to redefine how effective teams communicate and it is already gaining ground. This week Tim Abbott shares the story of how Zulip got started, how it is built, and why you might want to start using it.

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Preface

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Interview

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Keep In Touch

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Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2017-07-15T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/550d5fe1-2a47-4a25-9ba4-a135be204bde.mp3","mime_type":"audio/mpeg","size_in_bytes":99522610,"duration_in_seconds":3639}]},{"id":"podlove-2017-07-09t02:33:48+00:00-15e985815b18f09","title":"NAPALM with David Barroso and Mircea Ulinic","url":"https://www.pythonpodcast.com/napalm-with-david-barosso-and-mircea-ulinic-episode-117","content_text":"Summary\n\nRouters and switches are the stitches in the invisible fabric of the internet which we all rely on. Managing that hardware has traditionally been a very manual process, but the NAPALM (Network Automation and Programmability Abstraction Layer with Multivendor support) is helping to change that. This week David Barroso and Mircea Ulinic explain how Python is being used to make sure that you can watch those cat videos.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nVisit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing David Barroso and Mircea Ulinic about NAPALM (Network Automation and Programmability Abstraction Layer with Multivendor support), the library for managing programmable network devices\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\n\n[david] 2012 trying to use django 1.4 to store data I had on confluence.\n[mircea] August 2008, when I bought the Learning Python, Mark Lutz, 2nd edition\n\n\n\nCan you start by explaining what NAPALM is and the problem that you were solving when you started working on it?\n\n\n[david] trying to remove all the if vendor_a do this, elif vendor_b do this other thing instead\n[mircea] only if I will feel there’s anything to add\n\n\n\nWhat led you to choose Python as the language for implementing it?\n\n\n[david] it’s what I knew best and vendors were starting to provide libraries to interact with their platforms so python seemed like a natural evolution as we could just provide an abstraction on top of those libraries that already existed.\n[mircea] I didn’t implement NAPALM, I was fistly a user then contributor, now I’m one of the maintainers.\n\n\n\nWhen working with network equipment it is easy to apply the wrong settings and bring down a large number of systems or lock yourself out entirely. Are there any tools in NAPALM to help prevent this from happening?\n\n\n[david] We provide mechanisms to ensure proper peer reviewing; we let operators propose a configuration and get a diff. We have a rollback mechanism so if you detect an issue you can immediately rollback and we also added support to the autorollback feature some vendors have.\n\n\n\nHow have you architected the library to allow for easy integration of new classes of network devices?\n\n\n[david] very simple architecture. Trying to avoid complex features like abstract classes, metaprogramming or decorators. Main reason is that I figured my main user base wasn’t going to be very python savvy so I wanted something simple. What I ended doing was simulating interfaces with with a base class that described the supported methods and how they were supposed to behave and an extensive testing framework that ensure the method signatures and the behaviors matched the expectations.\n\n\n\nDesigning and building a consistent API for such a wide variety of hardware and software platforms is a daunting task. How do you determine the lowest common set of functionality that you are going to expose as part of the core library vs delegating to the underlying dependencies?\n\n\n[david] We don’t necessarily go with the lowest common denominator. Sometimes we try to emulate features. For example, if a platform doesn’t support atomic changes we might simulate it by trying to send the configuration as a block and rollback immediately. Obviously a feature likes this is clearly documented so people is aware that this might happen. What we try to avoid though is implementing things that are very specific to a single vendor. In any case the way it has worked so far falls into two categories:\n\n\n\nconfiguration management. These are primitives like loading a candidate configuration for merging or replacing into the device, getting a diff back, commiting, discarding or rolling back configuration. These primitives were designed at the very begining of the project based on the netconf protocol and they have changed very little since then. When a primitive is not natively supported by a device we try to emulate it as with the atomicity example I gave before or we don’t implement it at all if it’s not possible.\nThe second category is what we call getters which are methods that retrieve information from the devices. Things like interface counters, bgp neighbors, etc. These are basically community driven. Someone opens an issue on github explaining the data that he or she needs, we discuss it, we define a model and then we work on it. Not all getters are supported on all platforms. People mostly implements them as they need.\nNow there is a third category though. It is actually funny but I presented napalm for the first time a couple of years ago at NANOG64. It turns out the day after, at the same venue, Google was presenting Openconfig. Openconfig is an effort to design a common set of models to operate the network. So, for example, they have models for BGP neighbors, for interfaces, vlans, etc… Those models try to be vendor agnostic and you should, in theory, be able to use them to configure or to retrieve consistently information from any device. Problem is that, of course, vendors are slow implementing them, they don’t even have plans for all of them or for all the platforms, etc… So the sad truth is that two years later support for Openconfig is extremely limited. However, in the last few months I have been working on integrating napalm with opencofig so now we have a beta version of napalm where you can use python bindings that can translate native data from a device into an Openconfig object and viceversa. That has two direct implications:\n\nNow we are not only operating all vendors with the same tool but we are also operating them with the same data structures. This means that I can get the configuration of a cisco device and translate it directly to junos configuration.\nIt also means that because now we are dealing with objects, I can do smart things like having an object that represents the candidate configuration, anotther object that represents a certain running state and simulate merges myself without having to rely on the device itself. I can even generate the exact commands to do the merge without having to rely on them doing the actual merge. I can also simulate the changes offline, I don’t even need access to the device anymore, I could be builting the objects from a backup or from the resulting configuration after merging different branches on github.\n\n\n\n\n\nI have seen a few posts recently discussing the use of NAPALM in conjunction with configuration management platforms such as SaltStack and Ansible. What are the tradeoffs of using the library directly vs integrated with these other tools?\n\n\n[david] napalm is a library in the strict sense. There is no business logic, no workflows, very little tooling embedded. Instead we try to implement as many primitives and be as flexible as possible so other tools can leverage on napalm to implement their workflows. What this means is that using napalm directly is great if you are writing a script to do backups or to solve a specific issue but if you want to build a whole framework for event driven automation or a configuration management system you are probably better off leveraging on napalm integration with salt/ansible/st2.\n\n\n\nI noticed in the documentation that merging configuration is supported. How do you manage conflicts and priority of nested data structures?\n\n\n[david] we try to make changes atomic. So if you make a change and trigger a conflict or you are missing some datastructure or some configuration is invalid configuration won’t be applied and the user will get an error. For platforms where changes can’t be atomic we try to apply the configuration changes in bulk and revert immediately if there is an error.\n\n\n\nHow does declarative modeling of network devices differ from general purpose operating systems and what unique challenges do they pose?\n\n\n[david] lack of tooling like sed/awk/etc. Lots of state. Configuration is state itself and in most cases you can’t even reload it. Which means you have to type the exact commands to go from state a to state b. Like trying to configure the network stack of linux with only the iproute2 tooling available.\n\n\n\nWhat are the most technically challenging aspects of managing different network hardware programmatically?\n\n\n[david] Inconsistencies and buggy code. Not even inconsistencies across different platforms but across minor revisions of the same platforms. Small API changes that are not backwards compatible, small differences on output commands that break regular expressions and APIs that break every second call.\n\n\n\nWhat are some of the most interesting or unusual uses of NAPALM that you have seen?\n\n\n[david]\n\n\n\nI have seen people replacing their SNMP based monitoring system with napalm.\nI have built myself what you could call “immutable infrastructure for the network”. So for example, when you have to do a configuration change you don’t apply that configuration change. What you do instead is compile a full configuration for the device and fully reload the state of the device. That ensures you are always into a known state. So if a user would connect to the device and do a change outside the change control system because you are fully deploying state you can be certain that the manual change will be wipeout. So there is no way out of the automation.\nWe also have this validate functionality integrated into napalm. With this functionality you can define a desired state, for example certain BGP neighbors have to be up and I must be receiving N prefixes from them. Napalm can then read those rules, figure out which data to retrieve and validate the data retrieved complies so I know some people using this state validation instead of using the traditional times series type of monitoring where you keep retrieving data constantly and alerting when you reach certain thresholds. I guess you could call this test driven monitoring?\n\n\n\n[mircea]\n\n\n\nSNMP thing\n\n\n\nFor someone who is interested in learning more about network management, what resources do you recommend?\n\n\n[david]\n\n\n\nnetworktocode.com has some resources, labs, the slack community behing the organization is very active as well.\nipspace.com has some good resources as well.\npynet.twb-tech.com is also another great place to check for courses\no’reilly has a book on Network Programmability and Automation which I haven’t read but I know the authors are very good so I am confident the content will be of high quality.\n\n\n\n[mircea]\n\n\n\nI blog about NAPALM & generally networking and network automation on my personal space: mirceaulinic.net\npacketpushers.net\n\n\n\n\n\nKeep In Touch\n\n\nDavid\n\n@dbarrosop on LinkedIn, GitHub and Twitter\nBlog\n\n\n\nMircea\n\n\nBlog\n@mirceaulinic on LinkedIn, GitHub and Twitter\n\n\n\nNAPALM\n\n\n@napalm_auto on Twitter\nDocumentation\n\n\n\n\n\nPicks\n\n\nTobias\n\nThe Twelve Networking Truths\nFalsehoods Programmers Believe About Networking\n\n\n\nDavid\n\n\nThe fear saga\nVR\n\n\n\nMircea\n\n\nDaily Zen\n\n\n\n\n\nLinks\n\n\nJuniper\nArista\nParamiko\nnetmiko\nCisco IOS\nVagrant\nNetconf Protocol\nBGP\nOSPF\nSNMP\nTCP\nIP\nZTP (Zero Touch Provisioning)\nPXE (Preboot eXecution Environment) Boot\nSaltStack\nAnsible\nStackStorm\nTrigger\nNAPALM Logs\nOpenConfig\nNANOG\nYANG\nData Plane\nNTP(network time protocol)\nSSH\nNetworking Resources\n\nPacketPushers.net\nO’Reilly – Network Programmability and Automation\nnetworktocode.com\nipspace.net\npynet.twb-tech.com\n\n\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Routers and switches are the stitches in the invisible fabric of the internet which we all rely on. Managing that hardware has traditionally been a very manual process, but the NAPALM (Network Automation and Programmability Abstraction Layer with Multivendor support) is helping to change that. This week David Barroso and Mircea Ulinic explain how Python is being used to make sure that you can watch those cat videos.

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Preface

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Interview

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Keep In Touch

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Picks

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Network automation with Python","date_published":"2017-07-08T23:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/3a5b190e-696d-4f7f-ac30-8b407b9eb620.mp3","mime_type":"audio/mpeg","size_in_bytes":92320205,"duration_in_seconds":3489}]},{"id":"podlove-2017-07-02t02:47:46+00:00-dde955e4f451d3e","title":"Automat State Machines with Glyph Lefkowitz","url":"https://www.pythonpodcast.com/automat-state-machines-with-glyph-lefkowitz-episode-116","content_text":"Summary\n\nThe venerable ‘if’ statement is a cornerstone of program flow and busines logic, but sometimes it can grow unwieldy and lead to unmaintainable software. One alternative that can result in cleaner and easier to understand code is a state machine. This week Glyph explains how Automat was created and how it has been used to upgrade portions of the Twisted project.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nVisit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Glyph about Automat, a library that provides self-service finite-state machines for the programmer on the go.\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is a state machine and when might you want to use one?\nThere are a number of libraries available on PyPI that facilitate the creation of state machines. Why did you feel the need to build a new option and how does it differ from what was already available?\nWhy do you think developers fall into the trap of complicated conditional structures rather than reaching for a state machine?\nFor someone who wants to integrate Automat into their project how would they go about that and what are some of the gotchas that they should keep in mind?\nWhat do the internals of Automat look like and how did you approach the overall design of the project?\nWhat are some of the more difficult aspects of designing and implementing state machines properly?\nWhat are some of the technical hurdles that you have been faced with in the process of building a library for implementing state machines?\nWhat do you have planned for the future of Automat?\nWhat are some of the most interesting use cases of Automat that you have seen?\n\n\nKeep In Touch\n\n\nEmail\n@glyph on Twitter\nGlyph on GitHub\n\n\nPicks\n\n\nTobias\n\nCommercial Electric color changing LED puck lights\n\n\n\nGlyph\n\n\nOmniFocus\nGTD\n\n\n\n\n\nLinks\n\n\nAutomat\nGlyph Interview About Software Ethics\nFinite State Automaton\nYacc\nBison\nFlex Parser Generator\nPyPI State Machine\nPure Mealy Machine\nMoore Machine\nMealy vs. Moore Machines\nLeaky Abstraction\n\nThe Law of Leaky Abstraction\n\n\n\nTwisted\nPython Descriptor\nGraphViz\nHypothesis\nPyCon Talk – TLS State Machine\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

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The venerable ‘if’ statement is a cornerstone of program flow and busines logic, but sometimes it can grow unwieldy and lead to unmaintainable software. One alternative that can result in cleaner and easier to understand code is a state machine. This week Glyph explains how Automat was created and how it has been used to upgrade portions of the Twisted project.

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Preface

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Interview

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Keep In Touch

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Picks

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2017-07-01T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/388c4e91-941c-4b6f-b85d-32c2d3c506fa.mp3","mime_type":"audio/mpeg","size_in_bytes":66953877,"duration_in_seconds":2967}]},{"id":"podlove-2017-06-24t14:25:15+00:00-d9cd81e1821f20c","title":"Nuclear Engineering with Dr. Katy Huff","url":"https://www.pythonpodcast.com/episode-115-nuclear-engineering-with-katy-huff","content_text":"Summary\n\nAccess to affordable and consistent electricity is one of the big challenges facing our modern society. Nuclear energy is one answer because of its reliable output and carbon-free operation. To make this energy accessible to a larger portion of the global population further reasearch and innovation in reactor design and fuel sources is necessary, and that is where Python can help. This week Dr. Katy Huff talks about the research that she is doing, the problems facing the nuclear industry, and how she uses Python to make it happen.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nVisit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Dr. Katy Huff about using Python for nuclear engineering\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what nuclear engineering is and give some examples of current research in the field?\nThe most widely used and recognized form of nuclear plant is the light water reactor, which, to my understanding, is also the most susceptible to melt-downs and release of radioactive material carried by escaped steam. What are some of the reactor types that are currently being researched to improve safety and efficiency?\nOne of the major policy and logistics issues regarding nuclear power plants is the problem of how to handle spent fuel rods. What are some of the methods that are being researched to solve this problem?\nIn your PyCon presentation you mentioned the Cyclus and PyNE projects as tools that you use in your research. Can you provide a brief overview of each and explain how you use them?\nWhat are some of the most pressing issues in nuclear engineering and how are you leveraging Python to help with addressing them?\nHow does open source software relate to open science, and how do they impact the impact the ways that research is performed?\nWhat are some of the current or future developments in nuclear engineering that you are most excited about?\n\n\nKeep In Touch\n\n\nWebsite\nTwitter\nResearch\n\n\nPicks\n\n\nTobias\n\nRyobi Tools\n\n\n\nKaty\n\n\nAtomic Awakening\nAtomic Accidents\nAtomic Adventures\n\n\n\n\n\nLinks\n\n\nPlasma\nNuclear Energy\nThorium\nUranium\nMolten Salt Reactor\nSpent fuel rods\nYucca Mountain\nNuclear Fuel Reprocessing\nSodium Cooled Fast Reactor\nPyCon Keynote\nPyNE\nCyclus\nAnthony Scopatz\nMoose Framework\nPartial Differential Equations\nREPL (Read Eval Print Loop)\nStellarator\nToroidal Fusion Device\nJournal of Open Source Software (JOSS)\nAmerican Nuclear Society\nNEI\nIAEA\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

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Access to affordable and consistent electricity is one of the big challenges facing our modern society. Nuclear energy is one answer because of its reliable output and carbon-free operation. To make this energy accessible to a larger portion of the global population further reasearch and innovation in reactor design and fuel sources is necessary, and that is where Python can help. This week Dr. Katy Huff talks about the research that she is doing, the problems facing the nuclear industry, and how she uses Python to make it happen.

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Preface

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Interview

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Keep In Touch

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Picks

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Using Python to power the world","date_published":"2017-06-24T10:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/de136635-e1c8-4501-9769-9980f3b93142.mp3","mime_type":"audio/mpeg","size_in_bytes":54392458,"duration_in_seconds":2295}]},{"id":"podlove-2017-06-17t11:15:23+00:00-cf7e0e6b6b83582","title":"Industrial Automation with Jonas Neubert","url":"https://www.pythonpodcast.com/episode-114-industrial-automation-with-jonas-neubert","content_text":"Summary\n\nWe all use items that are produced in factories, but do you ever stop to think about the code that powers that production? This week Jonas Neubert takes us behind the scenes and talks about the systems and software that power modern facilities, the development workflows, and how Python gets used to tie everything together.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nVisit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Jonas Neubert about using Python for industrial automation\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nHow did you get involved in factory automation?\nWhat are some of the technical challenges that are unique to a factory environment and the physical computing needs associated with it?\nWhen developing new capabilities for your factory, how do you manage proper testing of your software given the need to interoperate with the hardware?\nWhich languages are most frequently used for command and control of industrial systems and how does Python interface with them?\nHow do you manage the problem of interfacing with the various different protocols and data formats that are presented by the different hardware instruments?\nIn your PyCon presentation you commented on the fact that security in industrial automation systems is lacking. What are some of the most common issues that you have seen?\n\nWhy is it that security is such an issue in industrial systems?\n\n\n\nHow are production releases of your software managed and how does it differ from other types of products such as web applications?\nAside from manufacturing facilities, what are some other types of environments or industries that require similar levels of hardware automation?\nWhat are some of the most interesting or challenging projects that you have worked on?\nWhat are some of the packages on PyPI that you find most useful in your day-to-day work?\nFor someone who wants to get involved in industrial automation what kind of experience should they have and what are some of the resources that you recommend?\nWhat are some of the innovations in industrial automation that you are most excited about?\n\n\nKeep In Touch\n\n\n@jonemo on Twitter\nWebsite\nJobs at Tempo Automation\n\n\nPicks\n\n\nTobias\n\nOpeth\n\n\n\nJonas\n\n\nPycon 2017 Talks\nEric Evenchick – Hacking Cars with Python\nBuilding a wireless speedometer with MicroPython\nPython from space by Katherine Scott\nŁukasz Langa – Unicode what is the big deal\nMorgan Wahl – Text is More Complicated Than You Think Comparing and Sorting Unicode\nThe Prepared Newsletter by Spencer Wright\nLong Distance Amtrak rides!\n\n\n\n\n\nLinks\n\n\nTempo Automation\nPalm webOS\nInfinion Technologies\nDRAM\nService Oriented Architecture\nSingleton\nLight Curtain\nFactory Acceptance Testing\nSite Acceptance Testing\nTesting Pyramid\nProtocol Analyzer\nMultimeter\nGCode\nIEC-61131\nPascal\nLadder Logic\nOPC Standards\nOPC DA\nC#\nFactory Control Systems\nStuxnet\nIndustroyer\nIEC 61850\nIndustrial Internet of Things\nCounsyl\nPySerial\nFactoryBoy\nParameterized\nFreezegun\nStruct\nXMLRPC\nFactory Tours\nHow It’s Made\nMcMaster.com\nMass Customization\nLife Sciences\nCRISPR\nPyCon – Reprogramming the human genome\nTranscriptic\nAutodesk Life Sciences\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

We all use items that are produced in factories, but do you ever stop to think about the code that powers that production? This week Jonas Neubert takes us behind the scenes and talks about the systems and software that power modern facilities, the development workflows, and how Python gets used to tie everything together.

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Interview

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Keep In Touch

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Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Powering Factories with Python","date_published":"2017-06-17T23:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/b529784f-bf31-4c5c-ad12-cb89675d36ff.mp3","mime_type":"audio/mpeg","size_in_bytes":77980882,"duration_in_seconds":3726}]},{"id":"podlove-2017-06-11t10:49:45+00:00-955a9a2a6cd2a8f","title":"Jedi Code Completion with David Halter","url":"https://www.pythonpodcast.com/episode-113-jedi-code-completion-with-david-halter","content_text":"Summary\n\nWhen you’re writing python code and your editor offers some suggestions, where does that suggestion come from? The most likely answer is Jedi! This week David Halter explains the history of how the Jedi auto completion library was created, how it works under the hood, and where he plans on taking it.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nVisit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing David Halter about Jedi, an awesome autocompletion and static analysis library for Python\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you explain what Jedi is and what problem you were trying to solve when you created it?\n\nWhat is the story behind the name?\n\n\n\nWhile reading through the documentation I noticed that there is alpha support for linting with Jedi. Can you compare the linting approach and capabilities with those found in other tools such as pylint and flake8?\nWhat does the internal architecture and design look like?\nFrom the research that I did for the show it seems that, rather than use the AST to determine the structure of the code being completed you built your own parser and recursive evaluation of the other methods that you use for determining accurate completion?\n\n\nWhat was lacking in existing parsers that led you to build your own?\nWhat are some of the difficulties that you have encountered building and maintaining the grammar definitions and higher level API for parsing multiple versions of Python, including the 2 vs 3 split?\n\n\n\nWhat are some of the biggest challenges associated with introspecting user code?\nWhat are some of the ways that Jedi can be confounded by a user’s project?\nWhat are some of the most difficult technical hurdles that you have been faced with while building Jedi?\nWhat are some unusual or unexpected uses of Jedi that you have seen?\nWhat do you have planned for the future of Jedi?\n\n\nKeep In Touch\n\n\ndavidhalter on GitHub\n@jedidjah_ch on Twitter\n\n\nPicks\n\n\nTobias\n\nPatch utility\n\n\n\nDavid\n\n\nBears Den\nSoccer\nSinging\nDancing\nDocOpt\nOpenStack\n\n\n\n\n\nLinks\n\n\nCloudscale.ch\nVim\nYoucompleteme\nNeocomplete\npyflakes\npycodestyle\npylint\nParser Generator\nParser Error Recovery\nlib2to3\nPython grammar file\nFinite state automata\nType inference\nyapf\nAST module\nMyPy\nIPython\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

When you’re writing python code and your editor offers some suggestions, where does that suggestion come from? The most likely answer is Jedi! This week David Halter explains the history of how the Jedi auto completion library was created, how it works under the hood, and where he plans on taking it.

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Preface

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Interview

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Keep In Touch

\n\n\n\n

Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2017-06-10T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/175ef487-b66a-4542-8c21-77a4f91cc32c.mp3","mime_type":"audio/mpeg","size_in_bytes":62475219,"duration_in_seconds":2575}]},{"id":"podlove-2017-06-04t00:57:09+00:00-22e20b331e9eb14","title":"Coconut with Evan Hubinger","url":"https://www.pythonpodcast.com/episode-112-coconut-with-evan-hubinger","content_text":"Summary\n\nFunctional programming is gaining in popularity as we move to an increasingly parallel world. Sometimes you want access to purely functional syntax and capabilities but you don’t want to have to learn an entirely new language. Coconut is here to help! This week Evan Hubinger explains how Coconut is a functional language that compiles to Python and can be mixed and matched with the rest of your program.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nVisit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Evan Hubinger about Coconut, a functional language implemented as a superset of Python\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what Coconut is and what problem you were trying to solve when you created it?\n\nWhere did the name come from?\n\n\n\nHow is Coconut implemented and what does the compilation process for Coconut code look like?\nHow will I be able to debug my Python if I’m not the one writing it?\nThe documentation mentions that Coconut itself is compatible with both Python 2 and 3, are there any caveats to be aware of in terms of mixing in standard Python syntax?\nAre there any performance optimizations that you have had to perform in order to make things like recursion and pattern matching work at reasonable speeds in the Python VM?\nWhich functional languages have you taken inspiration from during the creation of Coconut?\nWhat are some of the most interesting or unexpected uses of Coconut that you have seen?\nWhat are some resources that you recommend for people who are interested in learning more about functional programming?\n\n\nKeep In Touch\n\n\nCoconut\n\nWebsite\nGitHub\nTutorial\nDocumentation\nFAQ\nChat room\n\n\n\nEvan\n\n\nGitHub\nLinkedIn\n\n\n\n\n\nPicks\n\n\nTobias\n\nElementTree\n\n\n\nEvan\n\n\npyparsing is an awesome PyPI package you should check out\n\n\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Functional programming is gaining in popularity as we move to an increasingly parallel world. Sometimes you want access to purely functional syntax and capabilities but you don’t want to have to learn an entirely new language. Coconut is here to help! This week Evan Hubinger explains how Coconut is a functional language that compiles to Python and can be mixed and matched with the rest of your program.

\n\n

Preface

\n\n\n\n

Interview

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\n\n

Keep In Touch

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Picks

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Purely functional Python","date_published":"2017-06-03T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/303d96d7-aed0-4902-9a4a-a4edcd72cb45.mp3","mime_type":"audio/mpeg","size_in_bytes":43122358,"duration_in_seconds":2011}]},{"id":"podlove-2017-05-28t02:55:34+00:00-2da68c56cf5beb8","title":"Cauldron with Scott Ernst","url":"https://www.pythonpodcast.com/episode-111-cauldron-notebook-with-scott-ernst","content_text":"Summary\n\nThe notebook format that has been exemplified by the IPython/Jupyter project has gained in popularity among data scientists. While the existing formats have proven their value, they are still susceptible with difficulties in collaboration and maintainability. Scott Ernst created the Cauldron notebook to be testable, production ready, and friendly to version control. This week we explore the capabilities, use cases, and architecture of Cauldron and how you can start using it today!\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nVisit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Scott Ernst about Cauldron, a new notebook format built with software engineering best practices in mind.\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining what Cauldron is and what problem you were trying to solve when you created it?\nIn the documentation it mentions that you can use any editor for creating the content of the notebook. Can you describe a typical workflow of authoring the various files and cells and viewing the output?\nHow does Cauldron compare to the Jupyter notebook format and what factors would lead someone to choose one over the other?\nDoes Cauldron support running languages other than Python? If not then what would be involved in adding that capability?\nCauldron notebooks support unit tests of individual cells. How does that process work and what are the limitations?\nThe option for running the notebook in the context of a task workflow tool appears to be a powerful capability. What are some of the considerations that are necessary when writing a notebook to be run in that manner?\nWhat are some of the most interesting or unexpected projects that you have seen people using Cauldron for?\nWhat do you have planned for the future of Cauldron?\n\n\nKeep In Touch\n\n\n@swernst on Twitter\nWebsite\n\n\nPicks\n\n\nTobias\n\nTiffany Aching Adventures\n\n\n\nScott\n\n\nApache Big Data Conference\n\n\n\n\n\nLinks\n\n\nWhen I Work\nIPython Interview\nSpark\nR2Py\nBokeh\n\nWebsite\nPodcast.init Interview\n\n\n\nLuigi\nAirflow\n\n\nWebsite\nPodcast.init Interview\n\n\n\nDigital Paleontology\nA16 Project\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

The notebook format that has been exemplified by the IPython/Jupyter project has gained in popularity among data scientists. While the existing formats have proven their value, they are still susceptible with difficulties in collaboration and maintainability. Scott Ernst created the Cauldron notebook to be testable, production ready, and friendly to version control. This week we explore the capabilities, use cases, and architecture of Cauldron and how you can start using it today!

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Preface

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Interview

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Keep In Touch

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Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"The notebook for software engineers","date_published":"2017-05-27T23:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/b3777c24-b394-4f45-be49-02a774319129.mp3","mime_type":"audio/mpeg","size_in_bytes":44243059,"duration_in_seconds":2271}]},{"id":"podlove-2017-05-19t14:10:10+00:00-e873705e6424d5a","title":"Tech Debt and Refactoring at Yelp! with Andrew Mason","url":"https://www.pythonpodcast.com/tech-debt-and-refactoring-at-yelp-with-andrew-mason-episode-110","content_text":"Summary\n\nHealthy code makes for happy coders, and there are many ways to measure the health of a project. This week Andrew Mason talks about the Undebt project from Yelp!, as well as some of the other tools and practices that have been developed to make sure that the balance on their technical debt card stays low. Give it a listen to learn how and why to measure and address the painful parts of your software.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nVisit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Andrew Mason about technical debt and refactoring with Undebt.\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nHow do you define technical debt and why is it an important aspect of a project to keep track of?\nHow would you characterize refactoring in general and when you might want to do it?\nWhat is Undebt and what was the problem that you were facing at Yelp when it was created?\nFor someone who wants to get started with using Undebt what does that process look like and how does it work under the covers?\nWhat are some of the other tools and techniques available for refactoring Python code and how do they differ from what is possible in Undebt?\nWhat are some of the other tools and methods that you use to maintain the overall health of your codebase?\nWhat are some of the limitations and edge cases that you have experiemced working with Undebt?\nIt is often a difficult balancing act when working in a team to determine how much time to spend paying down technical debt and building tools that will act as force multipliers vs doing feature work that will be visible to end-users. In your experience, what are some ways to manage that tension?\n\n\nKeep In Touch\n\n\nAndrew\n\nGitHub\nWebsite\n@andrew_mason1 on Twitter\n\n\n\n\n\nPicks\n\n\nTobias\n\nContinuous Delivery by Jez Humble and David Farley\n\n\n\nAndrew\n\n\nXI Editor\nThe Circle by David Eggers\n\n\n\n\n\nLinks\n\n\nMartin Fowler\n“Uncle” Bob Martin\ngit-code-debt\nUndebt\nPyParsing\nPodcast.init Episode About Parsing\nRope\nPre-Commit\nPyLint\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Healthy code makes for happy coders, and there are many ways to measure the health of a project. This week Andrew Mason talks about the Undebt project from Yelp!, as well as some of the other tools and practices that have been developed to make sure that the balance on their technical debt card stays low. Give it a listen to learn how and why to measure and address the painful parts of your software.

\n\n

Preface

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Interview

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Keep In Touch

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Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2017-05-20T08:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/f5bd8f98-bb9a-4463-b931-2a6a47c7b384.mp3","mime_type":"audio/mpeg","size_in_bytes":24991198,"duration_in_seconds":2066}]},{"id":"podlove-2017-05-14t03:59:39+00:00-d34b517c0f021d2","title":"LBRY with Jeremy Kauffman","url":"https://www.pythonpodcast.com/episode-109-lbry-with-jeremy-kauffman","content_text":"Summary\n\nContent discovery and delivery and how it works in the digital realm is one of the most critical pieces of our modern economy. The blockchain is one of the most disruptive and transformative technologies to arrive in recent years. This week Jeremy Kauffman explains how the company and platform of LBRY are combining the two in an attempt to redefine how content creators and consumers interact by creating a new distributed marketplace for all kinds of media.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nVisit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Jeremy Kaufman about LBRY, a new marketplace for media built on peer to peer storage and blockchain technologies.\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is LBRY and how did the idea for it get started?\nWhat, if any, mechanisms are there for content owners to address piracy?\nIs the LBRY blockchain purpose built for the protocol and application or is it using something like Ethereum under the covers?\nIn order to support a large scale distributed marketplace, the crypto coin that you are using will need to be able to support large transaction volumes so how have you architected it in order to achieve that capability?\nWhat technologies are you leveraging to facilitate the content distribution mechanism?\nOne of the current problems with Bitcoin mining is that as the complexity of the proofs has increased and dedicated operations have moved to ASICs it has become less feasible for an individual to take part. Is there any provision for that situation built into the LBRY blockchain or does it not matter due to the capabilities for individual users to earn coins by participating as part of the storage network?\nWhat led to the decision to use Python for the initial implementation?\nFor people who are participating in the LBRY network, what is the mechanism for them to convert their earned LBC into fiat currency?\nHow much of the overall LBRY stack is using Python and what other languages are you taking advantage of?\nWhat is the business plan for LBRY the company and what do you have planned for the future of LBRY?\n\n\nKeep In Touch\n\n\nJeremy\n\n@jeremykauffman on Twitter\nEmail\n\n\n\nLBRY\n\n\nWebsite\n@LBRYio on Twitter\n\n\n\n\n\nPicks\n\n\nTobias\n\nNeurotribes\n\n\n\nJeremy\n\n\nCrystals and Mud in Property Law\n\n\n\n\n\nLinks\n\n\nLBRY\nBitTorrent\nBitCoin\nBlockchain\nDistributed Hash Tables\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Content discovery and delivery and how it works in the digital realm is one of the most critical pieces of our modern economy. The blockchain is one of the most disruptive and transformative technologies to arrive in recent years. This week Jeremy Kauffman explains how the company and platform of LBRY are combining the two in an attempt to redefine how content creators and consumers interact by creating a new distributed marketplace for all kinds of media.

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Preface

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Interview

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Keep In Touch

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Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2017-05-14T00:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/fe0af7f5-7529-4afe-8cd5-1b771c0a12e2.mp3","mime_type":"audio/mpeg","size_in_bytes":29260221,"duration_in_seconds":2379}]},{"id":"podlove-2017-05-05t09:42:43+00:00-4e4c5af9532fb8d","title":"Python Goes To The Movies with Dhruv Govil","url":"https://www.pythonpodcast.com/episode-108-python-goes-to-the-movies-with-dhruv-govil","content_text":"Summary\n\nMovies are magic, and Python is part of what makes that magic possible. We go behind the curtain this week with Dhruv Govil to learn about how Python gets used to bring a movie from concept to completion. He shares the story of how he got started in film, the tools that he uses day to day, and some resources for further learning.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nVisit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and this week I am joined by Dhruv Govil to talk about how Python is used for making movies.\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nHow did you get started in the film-making business?\nWhat are some of the ways that Python is used in the process of bringing a movie to completion?\nHow much of the overall pipeline processing happens in Python vs just being used as a means of wiring together other programs.\nHow much of the code that gets written is reusable between different projects?\nWhat is involved in testing data assets when they are submitted to the pipeline for the open format conversion process?\nWhat are some of the libraries that you have found to be most useful in your day-to-day work?\nWhy do you think that Python is so widely used in the film industry and are there any other languages that you see being used in a similar manner?\nWhat are some of the areas where Python is used that you were most surprised by?\nAre there any portions of the process where you would like to be able to use Python but are unable due to performance or platform constraints?\nWhat are some of the most interesting projects that you have worked on and which are you most proud of?\nHow does the work that is done by developers and technical contributors get reflected in the final credits?\nFor anyone who is interested in working in the film industry as a technical contributor what advice do you have?\n\n\nKeep In Touch\n\n\nDhruv\n\nWebsite\n@DhruvGovil on Twitter\ndgovil on GitHub\n\n\n\n\n\nPicks\n\n\nTobias\n\nFirefox on Android\n\n\n\nDhruv\n\n\nGoogle Earth VR\n\n\n\n\n\nLinks\n\n\nUdemy: Python for MayaUdemy\nVancouver Film School\nGuardians of the Galaxy\nCloudy w/ chance meatballs 2\nBlog Post: Python For Feature Film\nPyQT\nPySide\nAutodesk Maya\nKatana\nNuke\nCython\nRez\nAlembic Geometry Storage Format\nPixar Universal Scene Description\nPyblish\nOpen Color IO\nEdge of Tomorrow\nPyOpenGL\nKraken\nFabric Engine\nSIGGRAPH Convention\nRay Tracing In A Weekend\nMathematics for Computer Graphics\nBlender\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Movies are magic, and Python is part of what makes that magic possible. We go behind the curtain this week with Dhruv Govil to learn about how Python gets used to bring a movie from concept to completion. He shares the story of how he got started in film, the tools that he uses day to day, and some resources for further learning.

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Preface

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Interview

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Keep In Touch

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Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2017-05-06T09:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/3ff9be18-0c62-4951-a947-4ea53e0ce594.mp3","mime_type":"audio/mpeg","size_in_bytes":33240221,"duration_in_seconds":2501}]},{"id":"podlove-2017-04-29t00:05:58+00:00-47ae71c5a8ed77a","title":"Scapy with Guillaume Valadon","url":"https://www.pythonpodcast.com/episode-107-scapy-with-guillaume-valadon","content_text":"Summary\n\nNetwork protocols are often inscrutable, but if you have an effective way to experiment with them then they expose a lot of power. This week Guillaume Valadon explains how Scapy can be used to inspect your network traffic, test the security of your systems, and develop brand new protocols, all in Python!\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nVisit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nGet a shirt and support the show! Go to https://teespring.com/podcastinit?utm_source=rss&utm_medium=rss and get a mug to go with it.\nYour host as usual is Tobias Macey and today I am interviewing Guillaume Valadon about Scapy, the swiss army knife for packet manipulation in Python\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you explain what Scapy is and what problem it was created to solve?\nHow has the decision to build Scapy in Python benefited the project?\nHow has the 10 year history of the project affected your ability to maintain and evolve the code?\nHow has the project evolved from the initial prototypes by Philippe Biondi through to its current incarnation as Scapy 2?\nI understand that the project was originally hosted on Bitbucket and then moved to Github. What prompted that decision and how has it played out?\nWho is the target audience and what are some of the primary intended use cases for Scapy?\nHow is the implementation of packet layering architected in order to allow for such flexibility and composability?\nWhat are some of the most interesting and unexpected ways that you have seen Scapy used?\nWhat protocols have been the most problematic to implement and maintain?\nWhat have been some of the most challenging aspects of developing Scapy?\nWhat do you have planned for the future of Scapy?\n\n\nContact Info\n\n\nGuillaume\n\nWebsite\nEmail\n@guedou on Twitter\n\n\n\n\n\nPicks\n\n\nTobias\n\nBuckethead\n\n\n\nGuillaume\n\n\nRust\n\n\n\n\n\nLinks\n\n\nSix\nUTScapy\nCodeCov\nAppveyor\nJython\nOpenBSD\nMicroPython\nNSA\nExtra Bacon\nSNMP\nASN.1\nX509\nTLS\nIPSec\nDNS\nHTTP2\nPEP8\nScapy 3\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Network protocols are often inscrutable, but if you have an effective way to experiment with them then they expose a lot of power. This week Guillaume Valadon explains how Scapy can be used to inspect your network traffic, test the security of your systems, and develop brand new protocols, all in Python!

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Preface

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Interview

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Contact Info

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Picks

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"The Swiss Army Knife of Python Networking","date_published":"2017-04-29T11:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/d923b5a9-1a30-4d60-ab13-60915a28e619.mp3","mime_type":"audio/mpeg","size_in_bytes":23704270,"duration_in_seconds":1918}]},{"id":"podlove-2017-04-22t09:56:15+00:00-26dd0d30e4dfc4a","title":"yt-project with Nathan Goldbaum and John Zuhone","url":"https://www.pythonpodcast.com/episode-106-yt-project-with-nathan-goldbaum-and-john-zuhone","content_text":"Summary\n\nAstrophysics and cosmology are fields that require working with complex multidimensional data to simulate the workings of our universe. The yt project was created to make working with this data and providing useful visualizations easy and fun. This week Nathan Goldbaum and John Zuhone share the story of how yt got started, how it works, and how it is being used right now.\n\nAnnouncements\n\n\nThe Open Data Science Conference is coming to Boston May 3rd-5th. Get your ticket now so you don’t miss out on your chance to learn more about the state of the art for data science and data engineering.\nNow you can get T-shirts, sweatshirts, mugs, and a tote bag to let the world know about Podcast.init, and you can support the show at the same time! Go to teespring.com/podcastinit and load up!\n\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nVisit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I’m interviewing Nathan Goldbaum and John Zuhone about the YT project for multi-dimensional data analysis.\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is yt and how did it get started?\nWhere does the name come from?\nHow does yt compare to other projects such as AstroPy for astronomical data analysis?\nWhat are the domains in which yt is most widely used?\nOne of the main use cases of yt is for visualizing multidimensional data. What are some of the design challenges in trying to represent such complicated domains via a visual model?\nSome of the sample datasets for the examples are rather large. What are some of the biggest challenges associated with running analyses on such substantial amounts of information?\nHow has the project evolved and what are some of the biggest challenges that it is facing going forward?\n\n\nContact\n\n\nJohn\n\n@njgoldbaum on Twitter\n\n\n\nNathan\n\n\n@astrojaz on Twitter\n\n\n\n\n\nPicks\n\n\nTobias\n\nScout2\n\n\n\nNathan\n\n\nThe Expanse Novels\n\n\n\nJohn\n\n\nVisual Studio Code\n\n\n\n\n\nLinks\n\n\nHDF5Py\nMatt Turk\nSeismodome\nComputational Fluid Dynamics\nAstroPy\n\nWebsite\nPodcast Interview\n\n\n\nSymPy\n\n\nWebsite\nPodcast Interview\n\n\n\nMagnetohydrodynamics\nNumerical Relativistic Hydrodynamics\nMPI4Py\nMatplotlib\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Astrophysics and cosmology are fields that require working with complex multidimensional data to simulate the workings of our universe. The yt project was created to make working with this data and providing useful visualizations easy and fun. This week Nathan Goldbaum and John Zuhone share the story of how yt got started, how it works, and how it is being used right now.

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Announcements

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Preface

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Interview

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Contact

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Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2017-04-22T19:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/9371db33-26d8-4e7e-9351-30442eefe6b6.mp3","mime_type":"audio/mpeg","size_in_bytes":48595210,"duration_in_seconds":2289}]},{"id":"podlove-2017-04-17t10:53:38+00:00-140a0771416c119","title":"Scikit-Image with Stefan van der Walt and Juan Nunez-Iglesias","url":"https://www.pythonpodcast.com/episode-105-scikit-image-with-stefan-van-der-walt-and-juan-nunez-iglesias","content_text":"Summary\n\nComputer vision is a complex field that spans industries with varying needs and implementations. Scikit-Image is a library that provides tools and techniques for people working in the sciences to process the visual data that is critical to their research. This week Stefan Van der Walt and Juan Nunez-Iglesias, co-authors of Elegant SciPy, talk about how the project got started, how it works, and how they are using it to power their experiments.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nVisit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.\nTo help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.\nYour host as usual is Tobias Macey and today I am interviewing Stefan van der Walt and Juan Nunez-Iglesias, co-authors of Elegant SciPy, about scikit-image\n\n\nInterview\n\n\nIntroduction\nHow did you get introduced to Python?\nWhat is scikit-image and how did the project get started?\nHow does its focus differ from projects like SimpleCV/OpenCV or Pillow?\nWhat are some of the common use cases for which the scikit-image package is typically employed?\nWhat are some of the ways in which images can exhibit higher dimensionality and what are some of the kinds of operations that scikit-image can perform in those situations?\nHow is scikit designed and what are some of the biggest challenges associated with its development, whether in the past, present, or future?\nWhat are some of the most interesting use cases for scikit-image that you have seen?\nWhat do you have planned for the future of scikit-image?\n\n\nContact Information\n\n\nStefan\n\nEmail\n@stefanvdwalt on Twitter\nWebsite\n\n\n\nJuan\n\n\nEmail\n@jnuneziglesias on Twitter\nWebsite\njni on GitHub\n\n\n\n\n\nPicks\n\n\nTobias\n\nSet\n\n\n\nStefan\n\n\nMonkey Island\nThimbleweed Park\nAqua Notes\n\n\n\nJuan\n\n\nMatilda the Musical\nWater Rower Rowing Machine\nBored Elon Musk OMG: “News app that connects to a blood pressure monitor and adjusts your feed accordingly.”\n\n\n\n\n\nLinks\n\n\nscikits.appspot.com\nSphinx Gallery\nSciPy Conference\nMinimum Cost Paths\nImage Stitching Tutorial\nElegant SciPy\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Computer vision is a complex field that spans industries with varying needs and implementations. Scikit-Image is a library that provides tools and techniques for people working in the sciences to process the visual data that is critical to their research. This week Stefan Van der Walt and Juan Nunez-Iglesias, co-authors of Elegant SciPy, talk about how the project got started, how it works, and how they are using it to power their experiments.

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Preface

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Interview

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Contact Information

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Picks

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2017-04-15T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/2a030bee-7afd-4e73-a97b-e2299bf30785.mp3","mime_type":"audio/mpeg","size_in_bytes":28799432,"duration_in_seconds":2513}]},{"id":"podlove-2017-04-08t10:11:08+00:00-1eb9bdc2abf184e","title":"Oscar Ecommerce with David Winterbottom and Michael van Tellingen","url":"https://www.pythonpodcast.com/episode-104-oscar-ecommerce-with-david-winterbottom-and-michael-van-tellingen","content_text":"Summary\n\nIf you have a product to sell, whether it is a physical good or a subscription service, then you need a way to manage your transactions. The Oscar ecommerce framework for Django is a flexible, extensible, and well built way for you to add that functionality to your website. This week David Winterbottom and Michael van Tellingen talk about how the project got started, how it works under the covers, and how you can start using it today.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nVisit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nBefore we start the show I have a couple of announcements\n\nI started a new Slack channel for guests and listeners of the show. Go to www.pythonpodcast.com/slack?utm_source=rss&utm_medium=rss to join in the conversation!\nIf you are interested in how open source powers innovations in data then you should check out the Open Source Data Science conference. It is coming to Boston, Massachusetts on March 3rd through the 5th so don’t miss out on your chance to level up and meet some new friends!\n\n\n\nYour host as usual is Tobias Macey and today I’m interviewing David Winterbottom and Michael van Tellingen about the Oscar framework for building ecommerce applications in Django.\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is Oscar and what problem were you trying to solve when you created it?\nAt face value ecommerce seems like a fairly straightforward problem domain but there is a lot of incidental complexity involved. What are some of the most challenging aspects of building and managing a web store?\nThe documentation states in a number of places that Oscar takes a ‘domain driven’ approach to building ecommerce applications. Can you explain what you mean by that and how it manifests in the project?\nWhat does the internal design of Oscar look like and how would someone get started with building a site with it?\nThere can be a benefit to having an opinionated approach when building a framework because it simplifies the implemenation for the user. What is the reasoning for choosing to expose and allow for complexity in Oscar?\nWhat are some of the most interesting and unexpected projects that you have seen built with Oscar?\nHow has ecommerce changed in the time since Oscar was first created, and how has that impacted its evolution?\nWhat is in store for the future of Oscar?\n\n\nContact\n\n\nDavid\n\nWebsite\n@codeinthehole on Twitter\nGitHub\n\n\n\nMichael\n\n\nWebsite\n\n\n\n\n\nPicks\n\n\nDavid\n\nDestroy All Software by Gary Bernhardt\n\n\n\nMichael\n\n\nPyCharm\nZeep (SOAP Library)\n\n\n\n\n\nLinks\n\n\nShopify\nTangent\nDomain Driven Design by Eric Evans (book)\nEntity, Attribute, Value Pattern\nHome Assistant Interview\nSpree Commerce\nMagento\nSaleor\nWagtail\nWagtail Interview\nDjango CMS\nKivy Garden\nAwesome Wagtail\nSaltStack Formulas\nPelican Plugins\nDjangoPackages.org\nDjango Treebeard\nTDD (Test Driven Development)\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

If you have a product to sell, whether it is a physical good or a subscription service, then you need a way to manage your transactions. The Oscar ecommerce framework for Django is a flexible, extensible, and well built way for you to add that functionality to your website. This week David Winterbottom and Michael van Tellingen talk about how the project got started, how it works under the covers, and how you can start using it today.

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Preface

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Interview

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Contact

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Picks

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2017-04-08T14:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/74671d2c-4e28-45b3-bac5-e8ededf95879.mp3","mime_type":"audio/mpeg","size_in_bytes":33653243,"duration_in_seconds":3217}]},{"id":"podlove-2017-04-01t23:46:04+00:00-cfced27847b9048","title":"Duplicity with Kenneth Loafman","url":"https://www.pythonpodcast.com/episode-103-duplicity-with-kenneth-loafman","content_text":"Summary\n\nEveryone who uses a computer on a regular basis knows the importance of backups. Duplicity is one of the most widely used backup technologies, and it’s written in Python! This week Kenneth Loafman shares how Duplicity got started, how it works, and why you should be using it every day.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nYour host as usual is Tobias Macey and today I’m interviewing Kenneth Loafman about Duplicity, the Python based backup tool\n\n\nInterview\n\n\nIntroduction\nHow did you get introduced to Python?\nCan you share some of the history of Duplicity?\nWhat is duplicity and how does it differ from other available backup tools?\nMany backup solutions are written in Java or lower level languages such as C, what is the motivation for using Python as the language for implementing Duplicity?\nAt face value backing up files seems like a straightforward task but there is a lot of incidental complexity. Can you describe the architecture and internals of Duplicity that allow for it to handle a wide variety of use cases?\nIt has been shown in a number of contexts that people will generally use the default settings, so by forcing people to opt out of encrypting their backups you are promoting security best practices in Duplicity. Why is it so important to have the archive encrypted, even if the storage medium is fully under the control of the person doing the backup?\nGiven that backups need to be highly reliable what are the steps that you take during the development process to ensure that there are no regressions?\nWhat mechanisms are built into duplicity to prevent data corruption?\nWhat are some of the most difficult or complex aspects of the problem space that Duplicity is dealing with?\nI noticed that you have a proposal for a new archive format to replace Tar. Can you describe the motivation for that and the design choices that have been made?\n\n\nContact\n\n\nKenneth Loafman\n\nEmail\n@FirstPrime on Twitter\n\n\n\n\n\nPicks\n\n\nTobias\n\nPassengers\n\n\n\nKenneth\n\n\nNCIS\nPlan 9 From Outer Space\n\n\n\n\n\nLinks\n\n\nrsync\nlibrsync\ndeja-dup\nduply\nECC\nduplicity\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Everyone who uses a computer on a regular basis knows the importance of backups. Duplicity is one of the most widely used backup technologies, and it’s written in Python! This week Kenneth Loafman shares how Duplicity got started, how it works, and why you should be using it every day.

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Preface

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Interview

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2017-04-01T19:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/1ea366a4-6072-49e8-9a3f-4d9c10d5fae1.mp3","mime_type":"audio/mpeg","size_in_bytes":47952393,"duration_in_seconds":2116}]},{"id":"podlove-2017-03-25t21:31:36+00:00-e79792e6651b890","title":"Digital Identity, Privacy, and Security with Brian Warner","url":"https://www.pythonpodcast.com/episode-102-brian-warner","content_text":"Summary\n\nAs the internet and digital technologies continue to infiltrate our way of life, we are forced to consider how our concepts of identity and security are reflected in these spaces. Brian Warner joins me this week to discuss his work on privacy focused projects that he has worked on, including the Tahoe LAFS, Firefox Sync, and Magic Wormhole. He also has some intriguing ideas about how we can replace passwords and what it means to have an online identity.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nYour host as usual is Tobias Macey and today I’m interviewing Brian Warner about digital identity, privacy, and security\n\n\nInterview\n\n\nPlease introduce yourself\nHow did you get introduced to Python?\nHow did you get involved in the area of cryptography and digital privacy?\nYou have created or made significant contributions to a number of projects that are focused on making secure communications and storage more accessible, including Tahoe LAFS (Least Authority File System), Magic Wormhole, and Petmail. Can you provide a brief overview of these projects and any others that you would like to mention?\nWhat problem were you trying to solve when you created or began contributing to each of them and how satisfied are you with their current state?\nWhat have you found to be the biggest barriers to adoption for these projects?\nHow do Tahoe and Magic Wormhole benefit an average user and what are your plans for their future development?\nOne of the most ubiquitous issues with our modern security infrastructure leading to compromise is the humble password. What are some technologies that you foresee replacing the need for passwords?\nAs technologists we are fairly well aware of the weaknesses in the systems that we use day-to-day. How can we make digital privacy and security more accessible?\n\n\nContact Info\n\nwarner on GitHub\n@lotharrr on Twitter\n\nPicks\n\n\nBrian\n\nRa on Things of Interest\nThe Golden Age by John C. Wright\n\n\n\n\n\nLinks\n\n\nTor\nPetmail (v1, ca 2003)\nPetmail (new)\nMojo Nation\nTahoe-LAFS\nMagic-Wormhole\nErasure Coding\nFirefox Sync\nJPAKE\nSPAKE2\nPyCon 2016 Presentation on Magic Wormhole (video)\n(slides)\nVersioneer\nKeybase File System\nLeast Authority Enterprises\nFoolscap\nSpiderOak\nObject Capability Pattern\nShamir’s Secret Sharing\nAutoCrypt\nSignal\nWhatsApp\nSimply Secure\n\n\n","content_html":"

Summary

\n\n

As the internet and digital technologies continue to infiltrate our way of life, we are forced to consider how our concepts of identity and security are reflected in these spaces. Brian Warner joins me this week to discuss his work on privacy focused projects that he has worked on, including the Tahoe LAFS, Firefox Sync, and Magic Wormhole. He also has some intriguing ideas about how we can replace passwords and what it means to have an online identity.

\n\n

Preface

\n\n\n\n

Interview

\n\n\n\n

Contact Info

\n\n

warner on GitHub
\n@lotharrr on Twitter

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Picks

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Links

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\"\"

","summary":"","date_published":"2017-03-25T17:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/3462417b-8388-4079-a3d6-9fb79de7b7dc.mp3","mime_type":"audio/mpeg","size_in_bytes":65979056,"duration_in_seconds":2803}]},{"id":"podlove-2017-03-18t14:19:39+00:00-8b13f6675e2ca9d","title":"Crossbar.io with Tobias Oberstein and Alexander Gödde","url":"https://www.pythonpodcast.com/episode-101-crossbar-io-with-tobias-oberstein-and-alexander-goedde","content_text":"Summary\n\nAs our system architectures and the Internet of Things continue to push us towards distributed logic we need a way to route the traffic between those various components. Crossbar.io is the original implementation of the Web Application Messaging Protocol (WAMP) which combines Remote Procedure Calls (RPC) with Publish/Subscribe (PubSub) communication patterns into a single communication layer. In this episode Tobias Oberstein describes the use cases and design patterns that become possible when you have event-based RPC in a high-throughput and low-latency system.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nYour host as usual is Tobias Macey and today I’m interviewing Tobias Oberstein and Alexander Gödde about Crossbar.io, a high throughput asynchronous router for the WAMP protocol\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is Crossbar and what is the problem that you were trying to solve when you created it?\nWhat is the status of the IETF WAMP protocol proposal?\nWhy have an open protocol – and how do you see the ecosystem?\nPython isn’t typically considered to be a high-performance language so what led you to use it for building Crossbar?\nHow is Crossbar architected for proxying requests from a highly distributed set of clients with low latency and high throughput?\nHow do you handle authorization between the various clients of the router so that potentially sensitive messages don’t get published to the wrong component?\nDoes Crossbar encapsulate any business logic or is that all pushed to the edges of the system?\nWhat are some of the typical kinds of applications that Crossbar is designed for?\nWhat are some common design paradigms that would be better suited for a WAMP implementation?\nWhat are some of the most interesting or surprising uses of Crossbar that you have seen?\nWhat do you have planned for the future of Crossbar?\n\n\nKeep In Touch\n\n\nMailing Lists\n\nhttps://groups.google.com/forum/#!forum/autobahnws?utm_source=rss&utm_medium=rss\nhttps://groups.google.com/forum/#!forum/wampws?utm_source=rss&utm_medium=rss\nhttps://groups.google.com/forum/#!forum/crossbario?utm_source=rss&utm_medium=rss\n\n\n\n#autobahn on IRC\n\n\nPicks\n\n\nTobias\n\nLogan\n\n\n\nAlex\n\n\nPivotal Tracker\n\n\n\nTobias\n\n\nPyPy\nBrian Warner\nClick\nprompt-toolkit\n\n\n\n\n\nLinks\n\n\nAutobahn\nWAMP\nPyPy\nAPI Gateway\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

As our system architectures and the Internet of Things continue to push us towards distributed logic we need a way to route the traffic between those various components. Crossbar.io is the original implementation of the Web Application Messaging Protocol (WAMP) which combines Remote Procedure Calls (RPC) with Publish/Subscribe (PubSub) communication patterns into a single communication layer. In this episode Tobias Oberstein describes the use cases and design patterns that become possible when you have event-based RPC in a high-throughput and low-latency system.

\n\n

Preface

\n\n\n\n

Interview

\n\n\n\n

Keep In Touch

\n\n

\n\n

Picks

\n\n

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Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2017-03-18T17:15:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/ab612be6-7b92-4112-86d3-6917906984ab.mp3","mime_type":"audio/mpeg","size_in_bytes":72269179,"duration_in_seconds":3167}]},{"id":"podlove-2017-03-10t11:23:29+00:00-f782611833184da","title":"MetPy: Taming The Weather With Python","url":"https://www.pythonpodcast.com/episode-100-metpy-with-ryan-may-sean-arms-and-john-leeman","content_text":"Summary\n\nWhat’s the weather tomorrow? That’s the question that meteorologists are always trying to get better at answering. This week the developers of MetPy discuss how their project is used in that quest and the challenges that are inherent in atmospheric and weather research. It is a fascinating look at dealing with uncertainty and using messy, multidimensional data to model a massively complex system.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nYour host as usual is Tobias Macey and today I’m interviewing Ryan May, Sean Arms, and John Leeman about MetPy, a collection of tools and notebooks for analyzing meteorological data in Python.\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is MetPy and what is the problem that prompted you to create it?\nCan you explain the problem domain for Meteorology and how it compares to other domains such as the physical sciences? \nHow do you deal with the inherent uncertainty of atmospheric and weather data?\nWhat are some of the data sources and data formats that a meteorologist works with?\nTo what degree is machine learning or artificial intelligence employed when modelling climate and local weather patterns?\nThe MetPy documentation has a number of examples of how to use the library and a number of them produce some fairly complex plots and graphs. How prevalent is the need to interact with meteorological data visually to properly understand what it is trying to tell you?\nI read through your developer guide and watched your SciPy talk about development automation in MetPy. My understanding is that individuals with a pure science background tend to eschew formal code styles and software engineering practices so I’m curious what your experience has been when interacting with your user community.\nWhat are some of the interesting innovations in weather science that you are looking forward to?\n\n\nKeep In Touch\n\n\nMetPy\n\n@MetPy on Twitter\nDocumentation\nGitHub\n\n\n\nRyan\n\n\n@dopplershift on Twitter\ndopplershift on GitHub\n\n\n\n\n\nPicks\n\n\nTobias\n\nDrill To Detail Podcast\nData Capital Episode\n\n\n\nRyan\n\n\npytest-mpl\n\n\n\nSean\n\n\nTrolls\n\n\n\nJohn\n\n\nEmbedded.fm\n\n\n\n\n\nLinks\n\n\nUnidata\nUniversity of Oklahoma – College of Atmospheric and Geographic Sciences\nUniversity Corporation for Atmospheric Research\nNetCDF\nGEMPACK\nXArray\nThe Climate Corporation\nGOES-16\nLDM\nGoes16 on Twitter\nDon’t Panic Geocast\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

What’s the weather tomorrow? That’s the question that meteorologists are always trying to get better at answering. This week the developers of MetPy discuss how their project is used in that quest and the challenges that are inherent in atmospheric and weather research. It is a fascinating look at dealing with uncertainty and using messy, multidimensional data to model a massively complex system.

\n\n

Preface

\n\n\n\n

Interview

\n\n\n\n

Keep In Touch

\n\n

\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2017-03-11T15:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/db8e4689-5e00-45f8-9cf1-1a4e9e82fed1.mp3","mime_type":"audio/mpeg","size_in_bytes":58259326,"duration_in_seconds":3142}]},{"id":"podlove-2017-03-03t11:26:11+00:00-ffdae6e5ff581c5","title":"The Update Framework: Securing Your Software Updates with Justin Cappos","url":"https://www.pythonpodcast.com/episode-99-the-update-framework-with-justin-cappos","content_text":"Summary\n\nIf you write software then there’s a good probability that you have had to deal with installing dependencies, but did you stop to ask whether you’re installing what you think you are? My guest this week is Professor Justin Cappos from the Secure Systems Lab at New York University and he joined me to discuss his work on The Update Framework which was built to guarantee that you never install a compromised package in your systems.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nYour host as usual is Tobias Macey and today I’m interviewing Justin Cappos about The Update Framework, an open spec and reference implementation for mitigating attacks on software update systems.\n\n\nInterview\n\n\nIntroduction\nHow did you first get introduced to Python?\nPlease start by explaining what The Update Framework (TUF) is and the problem that you were trying to solve when you created it.\nHow is TUF architected and what led you to choose Python for the reference implementation?\nTUF addresses the problem of ensuring that the packages that get installed are created by the right developers, but how do you properly establish trust in the first place?\nWhy are consistent and auditable dependencies important for the security of a system and how does TUF help with that goal?\nWhat are some of the known attack vectors for a software update system and how do Python and other systems attempt to mitigate these vulnerabilities? \nOne of the perennial problems with any dependency management system is that of transitive dependencies. How does TUF handle this extra complexity of ensuring that all of the secondary, tertiary, etc. dependencies are also properly pinned and trusted?\nFor someone who wants to start using TUF what are the steps to get it set up with pip?\nHow would a project that wants to use TUF, do so?\nWho is using TUF and when will it be used with PyPI?\n\n\nKeep In Touch\n\n\nhttps://ssl.engineering.nyu.edu/?utm_source=rss&utm_medium=rss\nhttps://ssl.engineering.nyu.edu/personalpages/jcappos/?utm_source=rss&utm_medium=rss\n\n\nPicks\n\n\nTobias\n\nThe Enchanted Forest Chronicles\n\n\n\nJustin\n\n\nHand Pulled Noodles\nLam Zhou\n\n\n\n\n\nLinks\n\n\nWhen the Going Gets Tough, Get TUF Going – PyCon 2016\nRPM\nApt\nStork Package Manager\nYubikey\nDistribution Packages Considered Insecure\nNotary\nFlynn\nUptane\nin-toto\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

If you write software then there’s a good probability that you have had to deal with installing dependencies, but did you stop to ask whether you’re installing what you think you are? My guest this week is Professor Justin Cappos from the Secure Systems Lab at New York University and he joined me to discuss his work on The Update Framework which was built to guarantee that you never install a compromised package in your systems.

\n\n

Preface

\n\n\n\n

Interview

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2017-03-04T14:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/72ee967f-25ec-49aa-82b3-67a042596c72.mp3","mime_type":"audio/mpeg","size_in_bytes":46041926,"duration_in_seconds":2241}]},{"id":"podlove-2017-02-26t03:05:21+00:00-83c1ebea8c89a22","title":"Pandas with Jeff Reback","url":"https://www.pythonpodcast.com/episode-98-pandas-with-jeff-reback","content_text":"Summary\n\nPandas is one of the most versatile and widely used tools for data manipulation and analysis in the Python ecosystem. This week Jeff Reback explains why that is, how you can use it to make your life easier, and what you can look forward to in the months to come.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nWhen you’re writing Python you need a powerful editor to automate routine tasks, maintain effective development practices, and simplify challenging things like refactoring. Our sponsor JetBrains delivers the perfect solution for you in the form of PyCharm, providing a complete set of tools for productive Python, Web, Data Analysis and Scientific development, available in 2 editions. The free and open-source PyCharm Community Edition is perfect for pure Python coding. PyCharm Professional Edition is a full-fledged tool, designed for professional Python, Web and Data Analysis developers. Today JetBrains is offering a 3-month free PyCharm Professional Edition individual subscription. Don’t miss this chance to use the best-in-class tool with intelligent code completion, automated testing, and integration with modern tools like Docker – go to <www.pythonpodcast.com/pycharm?utm_source=rss&utm_medium=rss> and use the promo code podcastinit during checkout.\nVisit the site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nYour host as usual is Tobias Macey and today I’m interviewing Jeff Reback about Pandas, the swiss army knife of data analysis in Python.\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nTo start off, what is Pandas and what is its origin story?\n\nHow did you get involved in the project’s development?\n\n\n\nFor someone who is just getting started with Pandas what are the fundamental ideas and abstractions in the library that are necessary to understand how to use it for working with data?\nPandas has quite an extensive API and I noticed that the most recent release includes a nice cheat sheet. How do you balance the power and flexibility of such an expressive API with the usability issues that can be introduced by having so many options of how to manipulate the data?\nThere is a strong focus for use in science and data analytics, but there are a number of other areas where Pandas is useful as well. What are some of the most interesting or unexpected uses that you have seen or heard of?\nWhat are some of the biggest challenges that you have encountered while working on Pandas?\nDo you find the constraint of only supporting two dimensional arrays to be limiting, or has it proven to be beneficial for the success of pandas?\nWhat’s coming for pandas? Pandas 2.0!\n\n\nKeep In Touch\n\n\n@jreback on Twitter\njreback on GitHub\n\n\nPicks\n\n\nTobias\n\nhttp://standards.mousepawgames.com/index.html?utm_source=rss&utm_medium=rss\n\n\n\nJeff\n\n\nTravis CI\nAppveyor\nCircle CI\n\n\n\n\n\nLinks\n\n\nContinuum Analytics\nMyths Programmers Believe About Time\nJupyter Notebook\nXArray\nDask\n\nWebsite\nInterview\n\n\n\nNumFocus\nPyLint Interview\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Pandas is one of the most versatile and widely used tools for data manipulation and analysis in the Python ecosystem. This week Jeff Reback explains why that is, how you can use it to make your life easier, and what you can look forward to in the months to come.

\n\n

Preface

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"The data swiss army knife","date_published":"2017-02-25T22:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/e2125732-93f3-421b-8706-c545204673e3.mp3","mime_type":"audio/mpeg","size_in_bytes":68960492,"duration_in_seconds":2962}]},{"id":"podlove-2017-02-18t11:53:08+00:00-d80533d8b2d8979","title":"PyTables with Francesc Alted","url":"https://www.pythonpodcast.com/episode-97-pytables-with-francesc-alted","content_text":"Summary\n\nHDF5 is a file format that supports fast and space efficient analysis of large datasets. PyTables is a project that wraps and expands on the capabilities of HDF5 to make it easy to integrate with the larger Python data ecosystem. Francesc Alted explains how the project got started, how it works, and how it can be used for creating sharable and archivable data sets.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. Linode will has announced new plans, including 1GB for $5 plan, high memory plans starting at 16GB for $60/mo and an upgrade in storage from 24GB to 30GB on our 2GB for $10 plan. \nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nYour host as usual is Tobias Macey and today I’m interviewing Francesc Alted about PyTables\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nTo start with, what is HDF5 and what was the problem that motivated you to wrap Python around it to create PyTables?\nWhich are the most relevant contributors for PyTables? How you interacted?\nHow is the project architected and what are some of the design decisions that you are most proud of?\nWhat are some of the typical use cases for PyTables and how does it tie into the broader Python data ecosystem?\nHow common is it to use an HDF5 file as a data interchange format to be shared between researchers or between languages?\nGiven the ability to create custom node types, does that inhibit the ability to interact with the stored data using other libraries?\nWhat are some of the capabilities of HDF5 and PyTables that can’t be reasonably replicated in other data storage systems?\nOne of the more intriguing capabilities that I noticed while reading the documentation is the ability to perform undo and redo operations on the data. How might that be leveraged in a real-world use case?\nWhat are some of the most interesting or unexpected uses of PyTables that you are aware of?\n\n\nKeep In Touch\n\n\n@FrancescAlted on Twitter\nFrancescAlted on GitHub\n\n\nPicks\n\n\nTobias\n\nThe Accountant\n\n\n\nFrancesc\n\n\nBlosc a high speed compressor, specially meant for binary data\nThe Lego Batman Movie\n\n\n\n\n\nLinks\n\n\nPyTables\nPyTables – Optimization\nPresentations and Videos about PyTables\nPart of the story behind PyTables\nHDF5\nPandas\nSIMD\nNumFOCUS\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

HDF5 is a file format that supports fast and space efficient analysis of large datasets. PyTables is a project that wraps and expands on the capabilities of HDF5 to make it easy to integrate with the larger Python data ecosystem. Francesc Alted explains how the project got started, how it works, and how it can be used for creating sharable and archivable data sets.

\n\n

Preface

\n\n\n\n

Interview

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Fast Big Data on your Laptop","date_published":"2017-02-18T13:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/28a17fd1-efee-40d7-8a09-271f7198187d.mp3","mime_type":"audio/mpeg","size_in_bytes":48214238,"duration_in_seconds":2955}]},{"id":"podlove-2017-02-11t11:38:46+00:00-e51f75aff687b21","title":"SKIDL with Dave Vandenbout","url":"https://www.pythonpodcast.com/episode-96-skidl-with-dave-vandenbout","content_text":"Summary\n\nAs circuits and electronic components become more complex, visual circuit building tools are more difficult to use effectively. If you wish that you could just write your circuits in Python then you’re in luck! Dave Vandenbout created a library called SKIDL that brings the power and flexibility of Python to the realm of Electrical Engineering and he tells us all about it in this weeks show.\n\nPreamble\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour host as usual is Tobias Macey and today I’m interviewing Dave Vandenbout about SKIDL, a library for designing and validating circuit layouts.\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you describe what SKIDL is and the problem that you were trying to solve when you first started it?\nMost of my experience designing circuits has been done using a graphical tool. If you are using Python for the entire layout does it become difficult to understand the overall circuit without the visual representation?\n\nIs there a way to generate a circuit diagram from the SKIDL code for a visual reference?\n\n\n\nIt seems that there is a substantial amount of electrical knowledge required to be able to design and build schematics in code. For someone who is more of a hobbyist or is just starting to work with circuit design are there any facilities of SKIDL to assist with that understanding?\nWhat does the testing and validation process of a generated circuit look like?\nWhat does the internal architecture of SKIDL look like and what are some of the biggest challenges that you have faced while building it?\nFor the generated netlist does SKIDL take into account voltage losses due to the lengths of the traces in the final PCB and does it have any facilities to optimize the overall layout for space and efficiency?\nSometimes a circuit board is meant to be accessible for maintenance or even display purposes. Is it possible to specify the arrangement of components to make them more aesthetically pleasing or to space them so that they are easier to access physical interface ports (e.g. GPIO pins or I2C buses)?\nWhat are some of the most interesting or surprising uses of SKIDL that you have seen?\n\n\nKeep In Touch\n\n\nWebsite\nDocumentation\n\n\nPicks\n\n\nTobias\n\nSamsonite Tectonic Backpack\n\n\n\nDave\n\n\nBall 4 by Jim Bouton\n\n\n\n\n\nLinks\n\n\nKiCad\nGerber Files\nASIC\nFPGA\nPHDL\nMyHDL\nVHDL\nSPICE Simulator\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

As circuits and electronic components become more complex, visual circuit building tools are more difficult to use effectively. If you wish that you could just write your circuits in Python then you’re in luck! Dave Vandenbout created a library called SKIDL that brings the power and flexibility of Python to the realm of Electrical Engineering and he tells us all about it in this weeks show.

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Preamble

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Interview

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Keep In Touch

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Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Building PCBs with Python","date_published":"2017-02-11T16:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/40ba8d27-6604-4615-be79-c4e544f74483.mp3","mime_type":"audio/mpeg","size_in_bytes":61423256,"duration_in_seconds":2449}]},{"id":"podlove-2017-02-04t11:51:12+00:00-f9ef01f069a0daf","title":"Parsing and Parsers with Dave Beazley and Erik Rose","url":"https://www.pythonpodcast.com/episode-95-parsing-and-parsers-with-dave-beazley-and-erik-rose","content_text":"Summary\n\nIf you have ever found yourself frustrated by a complicated regular expression or wondered how you can build your own dialect of Python then you need a parser. Dave Beazley and Erik Rose talk about what parsers are, how some of them work, and what you can do with them in this episode.\n\nPreface\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour host as usual is Tobias Macey and today I’m interviewing Erik Rose and Dave Beazley about what parsing is, why you might want to use it, and how their respective libraries Parsimonious and PLY make it easy.\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you each start by talking a bit about your respective libraries and what problem you were trying to solve when they were first created?\nIn what ways does a full-fledged parser differ from what a regular expression engine is capable of?\nWhat are some of the different high-level approaches to building a parser and when might you want to choose one over the others?\nI’m sure that when most people hear the term parsing they associate it with reading in a data interchange format such as JSON or CSV. What are some of the more interesting or broadly applicable uses of parsing that might not be as obvious?\nOne term that kept coming up while I was doing research for this interview was “Grammars”. How would you explain that concept for someone who is unfamiliar with it?\nOnce an input has been parsed, what does the resulting data look like and how would a developer interact with it to do something useful?\nFor someone who wants to build their own domain specific language (DSL) what are some of the considerations that they should be aware of to create the grammar?\nWhat are some of the most interesting or innovative uses of parsers that you have seen?\n\n\nKeep In Touch\n\n\nDave Beazley\n\n@dabeaz on Twitter\nWebsite\n\n\n\nErik Rose\n\n\n@ErikRose on Twitter\nWebsite\n\n\n\n\n\nPicks\n\n\nTobias\n\nTerminix\n\n\n\nErik\n\n\nRiven\nScummVM\n\n\n\nDave\n\n\niTerm2\nKerbal Space Program\n\n\n\n\n\nLinks\n\n\nPython Cookbook\nPython Essential Reference\nFathom\nSWIG\nWindows Scripting Host\nPEG (Brian Foord)\nParsing Techniques by Grune and Jacobs\nThe Dragon Book\nStack Overflow HTML regex parsing\nEarley parsing\nSPARK\nHy-lang\n\nDocs\nInterview\n\n\n\nTrampolining\nLisp\nNLTK\nSLY\nDXR\nLLVM\nNumba\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

If you have ever found yourself frustrated by a complicated regular expression or wondered how you can build your own dialect of Python then you need a parser. Dave Beazley and Erik Rose talk about what parsers are, how some of them work, and what you can do with them in this episode.

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Preface

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Interview

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Keep In Touch

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Picks

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Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"When a regular expression isn't enough","date_published":"2017-02-04T15:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/6f6cc41e-fb90-4be5-b4d9-e8b477eba8db.mp3","mime_type":"audio/mpeg","size_in_bytes":64841114,"duration_in_seconds":3000}]},{"id":"podlove-2017-01-28t12:31:13+00:00-3d1970ffdf55137","title":"Home Assistant with Paulus Schoutsen","url":"https://www.pythonpodcast.com/episode-94-home-assistant-with-paulus-schoutsen","content_text":"Summary\n\nDon’t you wish you could make all of your devices talk to each other? Check out Home Assistant, the Python 3 platform for unified automation. Paulus Schoutsen shares the story of how the project got started, what makes it tick, and how you can use it today!\n\nIntroduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour host as usual is Tobias Macey and today I’m interviewing Paulus Schoutsen about Home Assistant, the Python 3 platform for unifying your home automation.\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is Home Assistant and what was the initial frustration that inspired you to create it?\nHow useful would home assistant be for someone who doesn’t have a lot of the so-called ‘smart home’ technology?\nGiven the fact that the intended context for Home Assistant is in the user’s house or apartment, how do you ensure that their data and privacy are safe?\nReading through the documenation for installing and configuring Home Assitant, it seems prohibitively complex for someone who is not technically inclined. Has any work been done to try to package the project in a way that is more friendly to a casual user?\nWhat are some of the most difficult challenges that you have faced while building Home Assistant?\nWhy did you choose Python 3 as the technology for building this platform?\nThe list of supported services and integrations is quite impressive. How does the current architecture allow for that kind of growth?\nHow has the architecture of Home Assistant evolved from when you first started it?\nWhat are some of the products or platforms that you consider to be competitors of Home Assistant and how do you differentiate yourself?\nWhat are some of the most interesting or unexpected uses of Home Assistant that you have seen?\nWhat do you see as some of the most promising and the most troubling trends in the future of home automation?\n\n\nKeep In Touch\n\n\nGitter Chatroom\nForum\n\n\nPicks\n\n\nTobias\n\nMiss Peregrine’s Home for Peculiar Children\n\n\n\nPaulus\n\n\nRead a Newspaper\n\n\n\n\n\nLinks\n\n\nMycroft\n\nInterview\nProject Homepage\n\n\n\nLet’s Encrypt\nVoluptuous\nJSON-Schema\nHome Assistant PyCon Presentation\nasyncio\nOpen HAB\nMerai Botnet\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Don’t you wish you could make all of your devices talk to each other? Check out Home Assistant, the Python 3 platform for unified automation. Paulus Schoutsen shares the story of how the project got started, what makes it tick, and how you can use it today!

\n\n

Introduction

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Interview

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Keep In Touch

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Picks

\n\n

\n\n

Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2017-01-28T14:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/4edaa1a1-ad38-48d9-ba56-514270e812ba.mp3","mime_type":"audio/mpeg","size_in_bytes":34017324,"duration_in_seconds":2506}]},{"id":"podlove-2017-01-19t03:19:08+00:00-10f30d130563feb","title":"Cryptography with Paul Kehrer","url":"https://www.pythonpodcast.com/episode-93-cryptography-with-paul-kehrer","content_text":"Summary\n\nSooner or later you will need to encrypt or hash some data. Thankfully we have the Cryptography library, along with the other projects maintained by the Python Cryptographic Authority, to make sure that your crypto is done right. In this episode Paul Kehrer talks about how the PyCA got started, the projects that they maintain, and how you can start using cryptography in your programs today.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your app or experimenting with something you hear about in this episode.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour host as usual is Tobias Macey and today I’m interviewing Paul Kehrer about cryptography and encryption in Python\n\n\nInterview with Paul Kehrer\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you share a bit of the background behind the Python Cryptographic Authority and how you got involved?\nThere is an adage that you should never roll your own crypto because if there are bugs or exploits in your implementation then it can have potentially serious side effects. What problem was the Cryptography library created to solve that was important enough to proceed despite that risk?\nGiven the sensitive nature of the libraries that you are working on, what development practices are you relying on to prevent the introduction of vulnerabilities?\nWhile reading through the documentation I noticed that Cryptography links against OpenSSL. Is it possible to swap that out for alternative implementations such as LibreSSL or S2N?\nWhat are some of the testing techniques that you use to ensure the accuracy of the algorithms that you are using?\nWhat are some of the factors that a developer should keep in mind when selecting which cryptographic library to use in their projects?\nWhen might someone want to use the capabilities found in the cryptography library what do they need to be aware of while writing their application?\nFor someone who wants to incorporate the cryptography library into their project what are some of the potential pitfalls that they should be aware of and how much knowledge of encryption should they possess?\nIn what ways does the security landscape in Python differ from that of other languages that you are familiar with and what unique challenges do we face?\nWhat are some of the fundamental aspects of encryption and cryptography that you feel every developer should at least be aware of?\nIf anyone wants to learn more about security and encryption, what resources do you recommend?\n\n\nKeep In Touch\n\n\nTwitter – @reaperhulk\n\n\nPicks\n\n\nTobias\n\nMigadu\nCastle Panic\n\n\n\nPaul\n\n\nFrinkiac.com\nMorbotron\n\n\n\n\n\nLinks\n\n\nS2N\nLibreSSL\nCryptography 101\nGeneral Number Field Sieve\nLattice Based Crypto\nGoogle New Hope Cryptography\nHypothesis\nMersenne Twister\nCryptoPals Crypto Challenges\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Sooner or later you will need to encrypt or hash some data. Thankfully we have the Cryptography library, along with the other projects maintained by the Python Cryptographic Authority, to make sure that your crypto is done right. In this episode Paul Kehrer talks about how the PyCA got started, the projects that they maintain, and how you can start using cryptography in your programs today.

\n\n

Brief Introduction

\n\n\n\n

Interview with Paul Kehrer

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2017-01-21T08:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/e4db138d-e857-49ee-837a-ac2bbe5dbfc5.mp3","mime_type":"audio/mpeg","size_in_bytes":40329702,"duration_in_seconds":2520}]},{"id":"podlove-2017-01-14t18:32:38+00:00-9bfe49c1f635969","title":"Translate House with Dwayne Bailey and Ryan Northey","url":"https://www.pythonpodcast.com/episode-92-translate-house-with-dwayne-bailey-and-ryan-northey","content_text":"Summary\n\nWhat is internationalization, when should you add it to your program, and how do you get started? This week Dwayne Bailey and Ryan Northey tell us about their work with Translate House and the different projects that they have built to make translating your software easier.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour host as usual is Tobias Macey and today I’m interviewing Dwayne Bailey and Ryan Northey about Translate House and the process of internationalization and localization for software projects.\n\n\nInterview with Dwayne Bailey and Ryan Northey\n\n\nIntroductions\nHow did you get introduced to Python?\nWhy did you get involved in localisation, what got you started?\nHow would you describe the difference between internationalization and localization? Are there cases where it makes sense to only do one of those things?\nWhy should people localise software into other languages?\nTranslate House is an organization focused on localizing and internationalizing software projects. To that end there are a collection of projects that you develop and maintain. Can you briefly introduce each of them and describe their purpose?\nWhat was the first project that was created in that list and how did it lead to the creation of the other tools?\nAt what point did you decide that creating an organization to own and support the tools that you were building was the right choice to make?\nYou run a distributed organisation, how do you manage that?\nI was recently speaking with Michal Čihař about the Weblate project and he mentioned that he uses the Translate Toolkit for handling the low level aspects of managing the translation files. What are some of the architectural and design challenges that arise from needing to support so many different systems for managing source text and translations?\nHow do Pootle and Virtaal compare to other tools for web or desktop based translation? Are they primarily used for translating software or do they get used for other sources of text as well?\nGiven that Virtaal is intended for use on desktop systems by people who aren’t necessarily technically adept how have you approached the packaging and deployment aspects of it? What are some of the challenges that you have had to overcome?\nGiven the fact that multi-lingual translation requires interacting with a large quantity of text in numerous alphabets, what kind of impact has the unicode handling in Python 3 had on your projects?\nWhat do you have planned for the future of your projects?\n\n\nKeep In Touch\n\n\nGithub\nGitter\nRyan\n\nGithub\n\n\n\n\n\nPicks\n\n\nTobias\n\nGoogle Chromecast\n\n\n\nDwayne\n\n\nJitsi Meet\n\n\n\nRyan\n\n\nGitter\n\n\n\n\n\nLinks\n\n\nXLIFF\nGettext PO Format\nCLDR (Common Locale Data Repository)\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

What is internationalization, when should you add it to your program, and how do you get started? This week Dwayne Bailey and Ryan Northey tell us about their work with Translate House and the different projects that they have built to make translating your software easier.

\n\n

Brief Introduction

\n\n\n\n

Interview with Dwayne Bailey and Ryan Northey

\n\n\n\n

Keep In Touch

\n\n

\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2017-01-14T13:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/92011ef7-bfb4-4578-8e4a-71707a0d5f80.mp3","mime_type":"audio/mpeg","size_in_bytes":65697731,"duration_in_seconds":3532}]},{"id":"podlove-2017-01-19t03:18:48+00:00-49e3f4cfbb27c10","title":"Morepath with Martijn Faassen","url":"https://www.pythonpodcast.com/episode-91-morepath-with-martijn-faassen","content_text":"Summary\n\nPython has a wide and growing variety of web frameworks to choose from, but if you want one with super powers then you need Morepath. This week Martijn Faassen shares the story of how Morepath was created, how it differentiates itself from the other available options, and how you can use it to power your next project.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nYou’ll want to make sure that your users don’t have to put up with bugs, so you should use Rollbar for tracking and aggregating your application errors to find and fix the bugs in your application before your users notice they exist. Use the link rollbar.com/podcastinit to get 90 days and 300,000 errors for free on their bootstrap plan.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour host as usual is Tobias Macey and today I’m interviewing Martijn Faassen about the Morepath web framework.\n\n\nInterview with Martijn Faassen\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is Morepath and what problem were you trying to solve when you created it?\nThe tag line for the Morepath project is that it’s a web microframework with superpowers. What is special or different about it that sets it apart from the other options in the Python ecosystem?\nIt can be difficult to convince someone to migrate to a new framework, particularly if there is a lack of supporting ecosystem. What are some of the motivating factors for a developer to switch to Morepath if they already have experience with one of the more widely used frameworks?\nWhat does the internal architecture for Morepath look like and what are some of the challenges that you have faced while building it?\nOne of the features is the automatic link generation for ensuring that you don’t end up with dead links. Is there any support for permalinks or redirects so that if you refactor your site people won’t end up at a path that no longer exists?\nIn the documentation you make a number of references to the fact that Morepath is a routing based framework. Can you explain what you mean by that and how it differs from a traversal based framework?\nPart of the core elements of Morepath are your libraries Reg and Dectate. Can you describe each of them and explain some of how they came to be created?\nMorepath has a different conception of models than most frameworks that I’ve dealt with in that they aren’t necessarily associated with any form of database. Can you explain why that is and some of the patterns that it allows for?\nThe method for extending and reusing applications built in Morepath is through subclassing the objects and overriding specific methods. What is it about this approach that you found to be more flexible than the alternatives exhibited by other frameworks?\nWhat are some of the most interesting or unexpected uses of Morepath that you have seen?\nWhat do you have planned for the future of Morepath?\n\n\nKeep In Touch\n\n\nBlog\nTwitter\nGitHub\nEmail\n\n\nPicks\n\n\nTobias\n\nIMDB\nGyroscopes\n\n\n\nMartijn\n\n\nKen And Robin Talk About Stuff\nViili\n\n\n\n\n\nLinks\n\n\n13th age\nJSON API\nJSON-LD\nHydra (REST standard)\nGraphQL\nFalcor\naiohttp\nZope\nPyramid\nGrok\nOneGov\nMartijn – My Exit From Zope\nLXML\nElementree\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Python has a wide and growing variety of web frameworks to choose from, but if you want one with super powers then you need Morepath. This week Martijn Faassen shares the story of how Morepath was created, how it differentiates itself from the other available options, and how you can use it to power your next project.

\n\n

Brief Introduction

\n\n\n\n

Interview with Martijn Faassen

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2017-01-07T12:45:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/9a6160a3-b39f-4924-993c-36d2a4c0b187.mp3","mime_type":"audio/mpeg","size_in_bytes":92772541,"duration_in_seconds":3950}]},{"id":"podlove-2016-12-30t13:51:02+00:00-4ff7683bc43e164","title":"ERPNext with Rushabh Mehta","url":"https://www.pythonpodcast.com/episode-90-erpnext-with-rushabh-mehta","content_text":"Summary\n\nIf you need to track all of the pieces of a business and don’t want to use 15 different tools then you should probably be looking at an ERP (Enterprise Resource Planning) system. Unfortunately, a lot of them are big, clunky, and difficult to manage, so Rushabh Mehta decided to build one that isn’t. ERPNext is an open-source, web-based, easy to use ERP platform built with Python.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nYou’ll want to make sure that your users don’t have to put up with bugs, so you should use Rollbar for tracking and aggregating your application errors to find and fix the bugs in your application before your users notice they exist. Use the link rollbar.com/podcastinit to get 90 days and 300,000 errors for free on their bootstrap plan.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour host as usual is Tobias Macey and today I’m interviewing Rushabh Mehta about ERPNext\n\n\nInterview with Rushabh Mehta\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat does ERP stand for and what kinds of busineesses require that kind of software?\nWhat problem were you trying to solve when you created ERPNext and what factors led to the decision to write it in Python?\nHow is ERPNext architected and what are some of the biggest challenges that were faced during its creation?\nWhile researching the project I noticed that you created your own framework which is used for building ERPNext. What was lacking in the existing options that made building a new framework appealing?\nWhat are some of the projects that you consider to be your competitors and what are the features that would convince a user to choose ERPNext?\nFor someone who wants to self-host ERPNext what are the system requirements and what does the scaling strategy look like?\nOn the marketing site for ERPNext it is advertised as being for small and medium businesses. What are the characteristics of larger businesses that might not make them a good fit for the features or structure of ERPNext?\nWhat are some of the most interesting or unexpected ways that you have seen ERPNext put to use?\nAre there any interesting projects of features that you are working on for release in the near future?\n\n\nKeep In Touch\n\n\nRushabh\n\nTwitter\n\n\n\nERPNext\n\n\nForum\nGitHub\nWebsite\n\n\n\n\n\nPicks\n\n\nTobias\n\nWordPress\n\n\n\nRushabh\n\n\nReady Player One\n\n\n\n\n\nLinks\n\n\n8088 PC XT\nOdoo\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

If you need to track all of the pieces of a business and don’t want to use 15 different tools then you should probably be looking at an ERP (Enterprise Resource Planning) system. Unfortunately, a lot of them are big, clunky, and difficult to manage, so Rushabh Mehta decided to build one that isn’t. ERPNext is an open-source, web-based, easy to use ERP platform built with Python.

\n\n

Brief Introduction

\n\n\n\n

Interview with Rushabh Mehta

\n\n\n\n

Keep In Touch

\n\n

\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Run your business with Python on ERPNext","date_published":"2016-12-31T08:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/47562660-f2ef-4115-b6ca-994c00e1e221.mp3","mime_type":"audio/mpeg","size_in_bytes":43538534,"duration_in_seconds":1833}]},{"id":"podlove-2016-12-24t01:07:12+00:00-759181712aa7f82","title":"Jackie Kazil","url":"https://www.pythonpodcast.com/episode-89-jackie-kazil","content_text":"Summary\n\nJackie Kazil has led a distinguished and varied career with a strong focus on providing information and tools that empower others. This includes her work in data journalism, as a presidential innovation fellow, co-founding 18F, co-authoring a book, and being elected to the board of the Python Software Foundation. In this episode she shares these stories and more with us and how Python has helped her along the way.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your application.\nYou’ll want to make sure that your users don’t have to put up with bugs, so you should use Rollbar for tracking and aggregating your application errors to find and fix the bugs before your users notice they exist. Use the link rollbar.com/podcastinit to get 90 days and 300,000 errors for free on their bootstrap plan.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com to join other listeners of the show and share ideas for how to make it better.\nYour host as usual is Tobias Macey and today I’m interviewing Jackie Kazil about her work with 18F, writing Data Wrangling with Python, and her career with Python.\n\n\nInterview with Jackie Kazil\n\n\nIntroductions\nHow did you get introduced to Python?\nLooking at your background it shows that you got your start in Journalism and that you are now working on an additional degree in Computational Social Science. Can you share a bit about that journey and what set you on that path?\nWhat is computational social science and what has your particular focus been within that field?\nHow has your work in news media prepared you for your current role?\nOne of your many notable achievements is co-founding 18F. Can you start by explaining what that organization is and how you got involved in the efforts to build it?\nWhat are some of the notable uses of Python at 18F?\nIn what ways did your experience working with 18F differ from the work you have done at companies outside of government?\nYou recently co-wrote and published Data Wrangling with Python through O’Reilly Media. What kind of subject matter do you cover in the book and who is the target audience?\nThere are a number of resources available to learn the various tools for working with data in Python. What is the gap that this book is aiming to fill and how did you get started with it?\nWhat are some of the most interesting things that you learned while working on the book?\n\n\nKeep In Touch\n\n\nTwitter\nEmail\n\n\nPicks\n\n\nTobias\n\nJason Bourne Movies\n\n\n\nJackie\n\n\nCzech Dumpling Dough\n\n\n\n\n\nLinks\n\n\nByteback\nNetworkX\nProject Mesa\nGeoQ\nopenFOIA\nOpenFEC API\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Jackie Kazil has led a distinguished and varied career with a strong focus on providing information and tools that empower others. This includes her work in data journalism, as a presidential innovation fellow, co-founding 18F, co-authoring a book, and being elected to the board of the Python Software Foundation. In this episode she shares these stories and more with us and how Python has helped her along the way.

\n\n

Brief Introduction

\n\n\n\n

Interview with Jackie Kazil

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-12-24T08:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/ef9758c6-9d14-46c2-b385-f03815853235.mp3","mime_type":"audio/mpeg","size_in_bytes":56313907,"duration_in_seconds":2387}]},{"id":"podlove-2016-12-17t12:08:17+00:00-58f525634c1842f","title":"Weblate with Michal Čihař","url":"https://www.pythonpodcast.com/episode-88-weblate-with-michal-cihar","content_text":"Summary\n\nAdding translations to our projects makes them usable in more places by more people which, ultimately, makes them more valuable. Managing the localization process can be difficult if you don’t have the right tools, so this week Michal čihař tells us about the Weblate project and how it simplifies the process of integrating your translations with your source code.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nYou’ll want to make sure that your users don’t have to put up with bugs, so you should use Rollbar for tracking and aggregating your application errors to find and fix the bugs in your application before your users notice they exist. Use the link rollbar.com/podcastinit to get 90 days and 300,000 errors for free on their bootstrap plan.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour host as usual is Tobias Macey and today I’m interviewing Michal Čihař about Weblate\n\n\nInterview with Michal Čihař\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you explain what Weblate is and the problem that you were trying to solve by creating it?\nWhat are the benefits of using Weblate over other tools for localization and internationalization?\nOne of the advertised features of Weblate is integration with git and mercurial. Can you explain how that works and what a typical translation workflow looks like both for a developer and a translator?\nGiven that part of the focus for the tool is to allow for community translation, how do you simplify the experience for first time contributors?\nI understand that Weblate is written as a django application. Is it possible to use Weblate with other Web frameworks or non-web projects?\n\nCan this be used with projects implemented in other programming laguages? Are there any capabilities that are lot in this scenario?\n\n\n\nWhy should developers and product managers be concerned with localizing an application? How does Weblate help to reduce the level of investment necessary for such an undertaking?\nWhat are some of the biggest difficulties that you have encountered while building and maintaining Weblate?\nWhat are the most common problems that you see people encounter on both the translator and developer side when dealing with internationalization and localization?\n\n\nKeep In Touch\n\n\nWeblate.org\nFacebook\nTwitter\nGitHub\n\n\nPicks\n\n\nTobias\n\nWar Dogs\n\n\n\nMichal\n\n\nJordi’s Chocolate\n\n\n\n\n\nLinks\n\n\nL20N\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Adding translations to our projects makes them usable in more places by more people which, ultimately, makes them more valuable. Managing the localization process can be difficult if you don’t have the right tools, so this week Michal čihař tells us about the Weblate project and how it simplifies the process of integrating your translations with your source code.

\n\n

Brief Introduction

\n\n\n\n

Interview with Michal Čihař

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-12-17T08:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/6ac9265a-ee8f-4391-bcca-85185c44cd35.mp3","mime_type":"audio/mpeg","size_in_bytes":49821765,"duration_in_seconds":1954}]},{"id":"podlove-2016-12-11t02:03:36+00:00-84d056c80e9af3c","title":"SpaCy with Matthew Honnibal","url":"https://www.pythonpodcast.com/episode-87-spacy-with-matthew-honnibal","content_text":"\n\n\n\nSummary\nAs the amount of text available on the internet and in businesses continues to increase, the need for fast and accurate language analysis becomes more prominent. This week Matthew Honnibal, the creator of SpaCy, talks about his experiences researching natural language processing and creating a library to make his findings accessible to industry.\nBrief Introduction\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nYou’ll want to make sure that your users don’t have to put up with bugs, so you should use Rollbar for tracking and aggregating your application errors to find and fix the bugs in your application before your users notice they exist. Use the link rollbar.com/podcastinit to get 90 days and 300,000 errors for free on their bootstrap plan.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour host as usual is Tobias Macey and today I’m interviewing Matthew Honnibal about SpaCy and Explosion.AI\n\nInterview with Matthew Honnibal\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by sharing what SpaCy is and what problem you were trying to solve when you created it?\nAnother project for natural language processing that has been part of the Python ecosystem for a number of years is the Natural Language Tool Kit (NLTK). How does SpaCy differ from the NLTK and are there any cases where that would be the better choice?\nHow much knowledge of NLP and computational linguistics is necessary to be able to use SpaCy?\nWhat does the internal design and architecture of SpaCy look like and what are the biggest challenges associated with its development to date and into the future?\nOne of the projects that you have built around SpaCy which I think is really cool and caught my attention when I first found your project is the displaCy visualization tool. Can you explain what that is and why you think it is important?\nWhat are some kinds of applications where SpaCy would be useful which might not be obvious candidates for it?\nWhy is speed such an important focus for an NLP library?\nOne of the ways that you have been able to gain a speed boost is through releasing the GIL and allowing for true parallelism via Cython. How have you managed to ensure that this doesn’t lead to data races and program failures?\nBuilding on the success of SpaCy you founded a company called Explosion AI. Can you explain what your goals are for this endeavor and the kinds of services that you are offering?\nWhat are some of the most interesting uses of SpaCy that you have seen?\nWhat do you have planned for the future of SpaCy?\n\nKeep In Touch\n\nTwitter\n\nMatthew\nSpaCy\nExplosion AI\n\n\nMailing List\nExplosion AI Contact Form\n\nPicks\n\nTobias\n\nZoom H4N Pro\nShure SM58\n\n\n\nLinks\n\nReddit sense2vec demo\nDisplaCy\nDisplaCy Entity Visualizer\nSpaCy Showcase\nNLTK\nChartbeat\nCytora\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n\n\n\n\n","content_html":"
\n
\n
\n
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Summary

\n

As the amount of text available on the internet and in businesses continues to increase, the need for fast and accurate language analysis becomes more prominent. This week Matthew Honnibal, the creator of SpaCy, talks about his experiences researching natural language processing and creating a library to make his findings accessible to industry.

\n

Brief Introduction

\n
    \n
  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • \n
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.
  • \n
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.
  • \n
  • You’ll want to make sure that your users don’t have to put up with bugs, so you should use Rollbar for tracking and aggregating your application errors to find and fix the bugs in your application before your users notice they exist. Use the link rollbar.com/podcastinit to get 90 days and 300,000 errors for free on their bootstrap plan.
  • \n
  • Visit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.
  • \n
  • To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers
  • \n
  • Join our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.
  • \n
  • Your host as usual is Tobias Macey and today I’m interviewing Matthew Honnibal about SpaCy and Explosion.AI
  • \n
\n

Interview with Matthew Honnibal

\n
    \n
  • Introductions
  • \n
  • How did you get introduced to Python?
  • \n
  • Can you start by sharing what SpaCy is and what problem you were trying to solve when you created it?
  • \n
  • Another project for natural language processing that has been part of the Python ecosystem for a number of years is the Natural Language Tool Kit (NLTK). How does SpaCy differ from the NLTK and are there any cases where that would be the better choice?
  • \n
  • How much knowledge of NLP and computational linguistics is necessary to be able to use SpaCy?
  • \n
  • What does the internal design and architecture of SpaCy look like and what are the biggest challenges associated with its development to date and into the future?
  • \n
  • One of the projects that you have built around SpaCy which I think is really cool and caught my attention when I first found your project is the displaCy visualization tool. Can you explain what that is and why you think it is important?
  • \n
  • What are some kinds of applications where SpaCy would be useful which might not be obvious candidates for it?
  • \n
  • Why is speed such an important focus for an NLP library?
  • \n
  • One of the ways that you have been able to gain a speed boost is through releasing the GIL and allowing for true parallelism via Cython. How have you managed to ensure that this doesn’t lead to data races and program failures?
  • \n
  • Building on the success of SpaCy you founded a company called Explosion AI. Can you explain what your goals are for this endeavor and the kinds of services that you are offering?
  • \n
  • What are some of the most interesting uses of SpaCy that you have seen?
  • \n
  • What do you have planned for the future of SpaCy?
  • \n
\n

Keep In Touch

\n\n

Picks

\n\n

Links

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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\"\"

","summary":"Fast, accurate, and scalable natural language processing in Python","date_published":"2016-12-10T21:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/f43077d3-9518-4591-9529-319d6f1392be.mp3","mime_type":"audio/mpeg","size_in_bytes":55228209,"duration_in_seconds":2207}]},{"id":"podlove-2016-12-03t22:12:39+00:00-270068fd5416874","title":"Kinto with Alexis Metaireau and Mathieu Leplatre","url":"https://www.pythonpodcast.com/episode-86-kinto-with-alexis-metaireau-and-mathieu-leplatre","content_text":"Summary\n\nAre you looking for a backend as a service offering where you have full control of your data? Look no further than Kinto! This week Alexis Metaireau and Mathieu Leplatre share the story of how Kinto was created, how it works under the covers, and some of the ways that it is being used at Mozilla and around the web.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nYou’ll want to make sure that your users don’t have to put up with bugs, so you should use Rollbar for tracking and aggregating your application errors to find and fix the bugs in your application before your users notice they exist. Use the link rollbar.com/podcastinit to get 90 days and 300,000 errors for free on their bootstrap plan.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour host as usual is Tobias Macey and today I’m interviewing Alexis Metaireau and Mathieu Leplatre about Kinto\n\n\nInterview with Alexis and Mathieu\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is Kinto and how did it get started?\nWhat does the internal architecture of Kinto look like?\nGiven that the primary data format being stored is JSON, why did you choose PostGreSQL as your storage backend instead of a NoSQL document database such as CouchDB?\nSynchronization of transactions from multiple users, including offline first support, is a difficult problem. How have you approached that in Kinto and what are some of the alternate solutions that were considered?\nDesigning usable APIs is a complicated subject. What features did you prioritize while creating the interfaces to Kinto?\nWhat are some of the most innovative uses of Kinto that you have seen?\nWhat are some of the biggest challenges that you have faced while building Kinto?\nWhat do you have planned for the future of Kinto?\n\n\nKeep In Touch\n\n\nKinto\n\nGithub\nMailing List\n\n\n\nAlexis\n\n\nEmail\n\n\n\nMathieu\n\n\nTwitter\nEmail\n\n\n\n\n\nPicks\n\n\nTobias\n\nWhat are you working on this week with Python?\n\n\n\nAlexis\n\n\nMiles Davis – Bitches Brew\n\n\n\nMathieu\n\n\nSigal\nSubliminal\n\n\n\n\n\nLinks\n\n\nPocket\nCouchDB\nOpenAPI\nWebCrypto\nFormbuilder\nFirebase\nKinto Comparison Table\nMozilla Persona\nPortier\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Are you looking for a backend as a service offering where you have full control of your data? Look no further than Kinto! This week Alexis Metaireau and Mathieu Leplatre share the story of how Kinto was created, how it works under the covers, and some of the ways that it is being used at Mozilla and around the web.

\n\n

Brief Introduction

\n\n\n\n

Interview with Alexis and Mathieu

\n\n\n\n

Keep In Touch

\n\n

\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-12-03T19:30:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/3adc8ca0-ed0d-4d3e-86d9-ffb78aba385e.mp3","mime_type":"audio/mpeg","size_in_bytes":72941960,"duration_in_seconds":3361}]},{"id":"podlove-2016-11-26t21:29:34+00:00-28772d70bbef41e","title":"Plone with Eric Steele","url":"https://www.pythonpodcast.com/episode-85-plone-with-eric-steele","content_text":"Summary\n\nPlone is one of the first CMS projects to be built using Python and it is still being actively developed. This week Eric Steele, the release manager for Plone, tells us about how it got started, how it is architected, and how the community is one of its greatest strengths\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nYou’ll want to make sure that your users don’t have to put up with bugs, so you should use Rollbar for tracking and aggregating your application errors to find and fix the bugs in your application before your users notice they exist. Use the link rollbar.com/podcastinit to get 90 days and 300,000 errors for free on their bootstrap plan.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour host as usual is Tobias Macey and today I’m interviewing Eric Steele about the Plone CMS.\n\n\nInterview with Eric Steele\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by explaining a bit about what Plone is and how you got involved with it?\nHow did the Plone project get started and how has it evolved over the years?\nWhat makes Plone unique among the myriad CMS tools that are available and which of them do you consider to be direct competitors?\nPlone has managed to keep an impressive track record of security. What are some of the key features that enable that?\nI know that for much of its history, the default data storage for plone was the ZODB (Zope Object DataBase). How would you describe its benefits and drawbacks for someone who is familiar with a relational database?\nPlone is one of the most long-lived Python projects that I am aware of. What are some of the most difficult maintenance challenges that you have encountered over the years of its existence?\nWhat does the internal architecture of Plone look like?\nOne of the major tenets of the project is the ability to install extensions. What are some of the most interesting plugins that you are aware of?\nWhat kinds of projects are Plone best suited for?\nWhat does the workflow look like for a user of Plone?\nWhat are some of the most interesting uses of Plone that you have seen?\nWhat are the biggest challenges facing the Plone project and community as development and deployment paradigms continue to change?\n\n\nKeep In Touch\n\n\nPlone\n\nWebsite\nForum\nIRC: #plone on freenode.net\n\n\n\nEric\n\n\nTwitter\nE-mail\n\n\n\n\n\nPicks\n\n\nTobias\n\nThe Inquiry (podcast)\nPyCon US\n\n\n\nEric\n\n\nReally Bad Chess\nHome Assistant\n\n\n\n\n\nLinks\n\n\nZope\nZEO\nPloneFormGen\nRapido\nCastleCMS\nPlumi\nBika LIMS\nQuaive (Plone Intranet)\nOpen Advice\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n.","content_html":"

Summary

\n\n

Plone is one of the first CMS projects to be built using Python and it is still being actively developed. This week Eric Steele, the release manager for Plone, tells us about how it got started, how it is architected, and how the community is one of its greatest strengths

\n\n

Brief Introduction

\n\n\n\n

Interview with Eric Steele

\n\n\n\n

Keep In Touch

\n\n

\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

\n\n

.\"\"

","summary":"","date_published":"2016-11-26T17:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/41b4d4d7-2d89-48a2-92e7-0250482708c8.mp3","mime_type":"audio/mpeg","size_in_bytes":64511058,"duration_in_seconds":3026}]},{"id":"podlove-2016-11-19t23:20:25+00:00-4754e527248f03d","title":"Retrospective","url":"https://www.pythonpodcast.com/episode-84-retrospective","content_text":"Summary\n\nIn this episode Chris and I look back at the past 83 episodes of the show and talk about what we learned, what we’ve enjoyed, and some of the highlights.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nYou’ll want to make sure that your users don’t have to put up with bugs, so you should use Rollbar for tracking and aggregating your application errors to find and fix the bugs in your application before your users notice they exist. Use the link rollbar.com/podcastinit to get 90 days and 300,000 errors for free on their bootstrap plan.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour hosts as usual are Tobias Macey and Chris Patti\nToday we’re interviewing each other about the past year and a half of the show.\n\n\nInterview with Tobias and Chris\n\n\nIntroductions\nWhat have been some of the most unexpected or surprising aspects of the show for you during the past year and a half? – Tobias\nWhat are your top three favorite shows so far and why? – Chris\nIf you could have a longer conversation with any of the past guests, who would you pick? – Tobias\nWhat has doing the show meant to you? – Chris\nWhat have you learned while doing the show that you wish you had known at the start? – Tobias\nHow has the production process evolved since the beginning of the show? – Chris\n\n\nChris Leaving the Show – Chris\n\n\nTobias and I started new jobs (At MIT Office of Digital Learning and Amazon Web Services, respectively)\nWe’re much, much busier these days, making coordination difficult\nTobias is ready to take the show solo and I (Chris) support him in this\nChris still plans to support the show as an avid fan \n\n\nKeep In Touch\n\n\nChris’s Contact Info\n\n\nPicks\n\n\nTobias\n\nLocust\n\n\n\nChris\n\n\nStaSh – Shell for Pythonista\nProducing a Podcast\nThe Python Community\n\n\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra  / CC BY-SA","content_html":"

Summary

\n\n

In this episode Chris and I look back at the past 83 episodes of the show and talk about what we learned, what we’ve enjoyed, and some of the highlights.

\n\n

Brief Introduction

\n\n\n\n

Interview with Tobias and Chris

\n\n\n\n

Chris Leaving the Show – Chris

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra  / CC BY-SA\"\"

","summary":"","date_published":"2016-11-19T18:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/b157583d-953b-4f10-88e1-37fb0128c7fe.mp3","mime_type":"audio/mpeg","size_in_bytes":55419408,"duration_in_seconds":2249}]},{"id":"podlove-2016-11-12t00:46:21+00:00-7a54a98bfa60ba8","title":"HouseCanary with Travis Jungroth","url":"https://www.pythonpodcast.com/episode-83-house-canary-with-travis-jungroth","content_text":"Summary\n\nHousing is something that we all have experience with, but many don’t understand the complexities of the market. This week Travis Jungroth talks about how HouseCanary uses data to make the business of real estate more transparent.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nYou’ll want to make sure that your users don’t have to put up with bugs, so you should use Rollbar for tracking and aggregating your application errors to find and fix the bugs in your application before your users notice they exist. Use the link rollbar.com/podcastinit to get 90 days and 300,000 errors for free on their bootstrap plan.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour host as usual is Tobias Macey and today I’m interviewing Travis Jungrot about HouseCanary, a company that is using Python and machine learning to help you make real estate decisions.\n\n\nInterview with Travis Jungroth\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is HouseCanary and what problem is it trying to solve?\nWho are your customers?\nIs it possible to get data and predictions at the neighborhood level for individual homebuyers to use in their purchasing decisions?\nWhat do you use for your data sources and how do you validate their accuracy?\n\nWhat are some of the sources of bias that are present in your data and what strategies are you using to account for them?\n\n\n\nCan you describe where Python is leveraged in your environment?\nWhat are some of the biggest software design and architecture challenges that you are facing while you continue to grow?\nWhat are the areas where Python isn’t the right choice and which languages are used in its place?\nWhat are the biggest predictors of future value for residential real estate?\nCan your system be used to identify risks associated with the housing market, similar to those seen in the bubble that triggered the 2008 economic failure?\nWhat are some of the most interesting details that you have discovered about real estate and housing markets while working with HouseCanary?\n\n\nKeep In Touch\n\n\nHouseCanary\n\nWebsite\nTwitter\n\n\n\nTravis\n\n\nTwitter\nGithub\n\n\n\n\n\nPicks\n\n\nTobias\n\nRailsea by China Miéville\nKraken by China Miéville\n\n\n\nTravis\n\n\nDDT\nOn Writing Well by William Zinser\n\n\n\n\n\nLinks\n\n\nHacking Secret Ciphers with Python\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Housing is something that we all have experience with, but many don’t understand the complexities of the market. This week Travis Jungroth talks about how HouseCanary uses data to make the business of real estate more transparent.

\n\n

Brief Introduction

\n\n\n\n

Interview with Travis Jungroth

\n\n

\n\n

Keep In Touch

\n\n

\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-11-12T09:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/144212c1-2336-4bf3-a46d-51bd931ebe8c.mp3","mime_type":"audio/mpeg","size_in_bytes":69284944,"duration_in_seconds":2385}]},{"id":"podlove-2016-10-26t01:30:02+00:00-f737835f9e38586","title":"Mycroft with Steve Penrod","url":"https://www.pythonpodcast.com/episode-82-mycroft-with-steve-penrod","content_text":"Summary\n\nSpeech is the most natural interface for communication, and yet we force ourselves to conform to the limitations of our tools in our daily tasks. As computation becomes cheaper and more ubiquitous and artificial intelligence becomes more capable, voice becomes a more practical means of controlling our environments. This week Steve Penrod shares the work that is being done on the Mycroft project and the company of the same name. He explains how he met the other members of the team, how the project got started, what it can do right now, and where they are headed in the future.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nYou’ll want to make sure that your users don’t have to put up with bugs, so you should use Rollbar for tracking and aggregating your application errors to find and fix the bugs in your application before your users notice they exist. Use the link rollbar.com/podcastinit to get 90 days and 300,000 errors for free on their bootstrap plan.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com to talk to previous guests and other listeners of the show.\nYour host as usual is Tobias Macey and today I’m interviewing Steve Penrod about the company and project Mycroft, a voice controlled, AI powered personal assistant written in Python.\n\n\nInterview with Steve Penrod\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what Mycroft is and how the project and business got started?\nHow is Mycroft architected and what are the biggest challenges that you have encountered while building this project?\nWhat are some of the possible applications of Mycroft?\nWhy would someone choose to use Mycroft in place of other platforms such as Amazon’s Alexa or Google’s personal assistant?\nWhat kinds of machine learning approaches are being used in Mycroft and do they require a remote system for execution or can they be run locally?\nWhat kind of hardware is needed for someone who wants to build their own Mycroft and what does the install process look like?\nIt can be difficult to run a business based on open source. What benefits and challenges are introduced by making the software that powers Mycroft freely available?\nWhat are the mechanisms for extending Mycroft to add new capabilities?\nWhat are some of the most surprising and innovative uses of Mycroft that you have seen?\nWhat are the long term goals for the Mycroft project and the business that you have formed around it?\n\n\nKeep In Touch\n\n\nWebsite\n\n\nPicks\n\n\nTobias\n\nyip\nMyths and Legends Podcast\n\n\n\nSteve\n\n\nEthiopian Cuisine\n\nBlue Nile in KC\n\n\n\nKansas City Barbecue\n\n\nJoe’s KC\n\n\n\n\n\n\n\nLinks\n\n\nGoogle Home\nTom Waits – Heart Attack & Vine\nmycroft.ai\nFLITE\nVocalid\nVocalid TED Talk\nPocketSphinx\nGE FirstBuild\nSonar GNU Linux\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Speech is the most natural interface for communication, and yet we force ourselves to conform to the limitations of our tools in our daily tasks. As computation becomes cheaper and more ubiquitous and artificial intelligence becomes more capable, voice becomes a more practical means of controlling our environments. This week Steve Penrod shares the work that is being done on the Mycroft project and the company of the same name. He explains how he met the other members of the team, how the project got started, what it can do right now, and where they are headed in the future.

\n\n

Brief Introduction

\n\n\n\n

Interview with Steve Penrod

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-11-05T12:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/15db2725-ab99-4477-a81b-c14998d615d5.mp3","mime_type":"audio/mpeg","size_in_bytes":74895245,"duration_in_seconds":3912}]},{"id":"podlove-2016-10-29t16:37:39+00:00-2e14c1beaefb3c5","title":"Annapoornima Koppad","url":"https://www.pythonpodcast.com/episode-81-annapoornima-koppad","content_text":"Summary\n\nAnnapoornima Koppad is a director of the PSF, founder of the Bangalore chapter of PyLadies, and is a Python instructor at the Indian Institute of Science. In this week’s episode she talks about how she got started with Python, her experience running the PyLadies meetup, and working with the PSF.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nYou’ll want to make sure that your users don’t have to put up with bugs, so you should use Rollbar for tracking and aggregating your application errors to find and fix the bugs in your application before your users notice they exist. Use the link rollbar.com/podcastinit to get 90 days and 300,000 errors for free on their bootstrap plan.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour hosts as usual are Tobias Macey and Chris Patti\nToday we’re interviewing Annapoornima Koppad about her career with Python and her experiences running the PyLadies chapter in Bangalore, India and being a director for the Python Software Foundation.\n\n\nInterview with Annapoornima Koppad\n\n\nIntroductions\nHow did you get introduced to Python? – Tobias\nI noticed that you have been freelancing for several years now. How much of that has been in Python and how has that fed back into your other activities? – Tobias\nWhile preparing for this interview I came across the book that you self-published on Amazon. What was your motivation for writing it and who is the target audience? – Tobias\nCan you tell us about your experience with starting the PyLadies group in Bangalore? What were some of the biggest challenges that you encountered and how have you approached the task of growing awareness and membership of the group? – Tobias\nYou recently started teaching Python at the Indian Institute of Science. What kinds of subject matter do you cover in your lessons? – Tobias\nWhat is it about Python and its community that has inspired you to dedicate so much of your time to contributing back to it? – Tobias\nIn what ways would you like to see the Python ecosystem improve? – Tobias\nYou were voted in as a director of the Python Software Foundation in the most recent election. Can you share what responsibilities that entails? – Tobias\nWhat would you like to achieve with your time in the PSF? – Tobias\n\n\nKeep In Touch\n\n\nPyLadies Bangalore Meetup\nBlog\nEmail\nTwitter\n\n\nPicks\n\n\nTobias\n\nFluentd\n\n\n\nAnnapoornina\n\n\nThe Lord of The Rings by J.R.R. Tolkien\nStorks\nFood Street\n\n\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Annapoornima Koppad is a director of the PSF, founder of the Bangalore chapter of PyLadies, and is a Python instructor at the Indian Institute of Science. In this week’s episode she talks about how she got started with Python, her experience running the PyLadies meetup, and working with the PSF.

\n\n

Brief Introduction

\n\n\n\n

Interview with Annapoornima Koppad

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-10-29T13:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/6350540b-7026-4b8e-8b54-e3b65ee261e2.mp3","mime_type":"audio/mpeg","size_in_bytes":28044342,"duration_in_seconds":1163}]},{"id":"http://podcastinit.podbean.com/e/episode-80-gis-with-sean-gillies/","title":"Python for GIS with Sean Gillies","url":"https://www.pythonpodcast.com/episode-80-python-for-gis-with-sean-gillies","content_text":"Summary\n\nLocation is an increasingly relevant aspect of software systems as we have more internet connected devices with GPS capabilities. GIS (Geographic Information Systems) are used for processing and analyzing this data, and fortunately Python has a suite of libraries to facilitate these endeavors. This week Sean Gillies, an author and contributor of many of these tools, shares the story of his career and contributions, and the work that he is doing at MapBox.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.\nWhen you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.\nYou’ll want to make sure that your users don’t have to put up with bugs, so you should use Rollbar for tracking and aggregating your application errors to find and fix the bugs in your application before your users notice they exist. Use the link rollbar.com/podcastinit to get 90 days and 300,000 errors for free on their bootstrap plan.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour host as usual is Tobias Macey\nToday I’m interviewing Sean Gillies about writing Geographic Information Systems in Python.\n\n\nInterview with Sean Gillies\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you start by describing what Geographic Information Systems are and what kinds of projects might take advantage of them?\nHow did you first get involved in the area of GIS and location-based computation?\nWhat was the state of the Python ecosystem like for writing these kinds of applications?\nYou have created and contributed to a number of the canonical tools for building GIS systems in Python. Can you list at least some of them and describe how they fit together for different applications?\nWhat are some of the unique challenges associated with trying to model geographical features in a manner that allows for effective computation?\n\nHow does the complexity of modeling and computation scale with increasing land area?\n\n\n\nMapping and cartography have an incredibly long history with an ever-evolving set of tools. What does our digital age bring to this time-honored discipline that was previously impossible or impractical?\nTo build accurate and effective representations of our physical world there are a number of domains involved, such as geometry and geography. What advice do you have for someone who is interested in getting started in this particular niche?\nWhat level of expertise would you advise for someone who simply wants to add some location-aware features to their application?\nI know that you joined Mapbox a little while ago. Which parts of their stack are written in Python?\nWhat are the areas where Python still falls short and which languages or tools do you turn to in those cases?\n\n\nKeep In Touch\n\n\nEmail\nTwitter\n\n\nPicks\n\n\nTobias\n\nRoku Streaming Stick\n\n\n\nSean\n\n\nThe Tacopedia\nStromae\n\n\n\n\n\nLinks\n\n\nGDAL\nSWIG\nQGIS\nShapefiles\nShapely\nFiona\nRaster File\nGEOS\nRasterio\nPostGIS\nRTree\nGeoPandas\nGeoJSON\nOrthorectification\nMapbox\nSCONS\nMapnik\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Location is an increasingly relevant aspect of software systems as we have more internet connected devices with GPS capabilities. GIS (Geographic Information Systems) are used for processing and analyzing this data, and fortunately Python has a suite of libraries to facilitate these endeavors. This week Sean Gillies, an author and contributor of many of these tools, shares the story of his career and contributions, and the work that he is doing at MapBox.

\n\n

Brief Introduction

\n\n\n\n

Interview with Sean Gillies

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-10-22T18:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/4e305314-5354-488f-8dc5-09ff356e2e2e.mp3","mime_type":"audio/mpeg","size_in_bytes":56210386,"duration_in_seconds":2269}]},{"id":"http://podcastinit.podbean.com/e/episode-79-k-lars-lohn/","title":"K Lars Lohn","url":"https://www.pythonpodcast.com/episode-79-k-lars-lohn","content_text":"Summary\n\nK Lars Lohn has had a long and varied career, spending his most recent years at Mozilla. This week he shares some of his stories about getting involved with Python, his work with Mozilla, and his inspiration for the closing keynote at PyCon US 2016. He also elaborates on the intricate mazes that he draws and his life as an organic farmer in Oregon.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nWe also have a new sponsor this week. Rollbar is a service for tracking and aggregating your application errors so that you can find and fix the bugs in your application before your users notice they exist. Use the link rollbar.com/podcastinit to get 90 days and 300,000 errors for free on their bootstrap plan.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour host as usual is Tobias Macey\nToday we’re interviewing K Lars Lohn about his career, his art, and his work with Mozilla\n\n\nInterview with K Lars Lohn\n\n\nIntroductions\nHow did you get introduced to Python?\nYou have an interesting pair of articles on your website that attempt to detail how you perceive code and why you think that formatting should be configured in a manner analogous to CSS. Can you explain a bit about how your particular perception affects the way that you program?\nOn your website you have some images of incredibly detailed artwork that are actually mazes. Can you describe some of your creation process for those?\nWhat is it about mazes that keeps you interested in them and how did you first start using them as a form of visual art?\nAt Mozilla you have helped to create a project called Socorro which utilizes complexity analysis for correlating stacktraces. How did you conceive of that approach to error monitoring?\nCan you describe how Socorro is architected and how it works under the covers?\nAt this year’s PyCon US you presented the closing keynote and it was one of the most engaging talks that I’ve seen. Where did you get the inspiration for the content and the mixed media approach?\nFor anyone who hasn’t seen it, you managed to weave together a very personal story with a musical performance, and some applications of complexity analysis into a seamless experience. How much did you have to practice before you felt comfortable delivering that in front of an audience?\nIn addition to your technical career you are also very focused on living in a manner that is sustainable and in tune with your environment. What kinds of synergies and conflicts exist between your professional and personal philosophies?\n\n\nKeep In Touch\n\n\nWebsite\nTwitter\n\n\nPicks\n\n\nTobias\n\nTerry Pratchett\n\n\n\nLars\n\n\nBach’s Tocatta & Fugue in D Minor\n\n\n\n\n\nLinks\n\n\nFunctional Geekery Episode 65 – Morten Kromberg talks about APL\nK Lars Lohn’s Portfolio\nThe Well Tempered API\nTemple Grandin\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

K Lars Lohn has had a long and varied career, spending his most recent years at Mozilla. This week he shares some of his stories about getting involved with Python, his work with Mozilla, and his inspiration for the closing keynote at PyCon US 2016. He also elaborates on the intricate mazes that he draws and his life as an organic farmer in Oregon.

\n\n

Brief Introduction

\n\n\n\n

Interview with K Lars Lohn

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-10-15T19:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/be9a3c07-9ac5-485b-9908-0007d7c112e2.mp3","mime_type":"audio/mpeg","size_in_bytes":63516450,"duration_in_seconds":2541}]},{"id":"http://podcastinit.podbean.com/e/episode-78-lorena-mesa/","title":"Lorena Mesa","url":"https://www.pythonpodcast.com/episode-78-lorena-mesa","content_text":"Summary\n\nOne of the great strengths of the Python community is the diversity of backgrounds that our practitioners come from. This week Lorena Mesa talks about how her focus on political science and civic engagement led her to a career in software engineering and data analysis. In addition to her professional career she founded the Chicago chapter of PyLadies, helps teach women and kids how to program, and was voted onto the board of the PSF.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.\nCheck out our sponsor Linode for running your awesome new Python apps. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nYou want to make sure your apps are error-free so give our other sponsor, Rollbar, a look. Rollbar is a service for tracking and aggregating your application errors so that you can find and fix the bugs in your application before your users notice they exist. Use the link rollbar.com/podcastinit to get 90 days and 300,000 errors for free on their bootstrap plan.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nBy leaving a review on iTunes, or Google Play Music it becomes easier for other people to find us.\nJoin our community! Visit discourse.pythonpodcast.com to help us grow and connect our wonderful audience.\nYour host as usual is Tobias Macey\nToday we’re interviewing Lorena Mesa about what inspires her in her work as a software engineer and data analyst.\n\n\nInterview with Lorena Mesa\n\n\nIntroductions\nHow did you get introduced to Python?\nHow did your original interests in political science and community outreach lead to your current role as a software engineer?\nYou dedicate a lot of your time to organizations that help teach programming to women and kids. What are some of the most meaningful experiences that you have been able to facilitate?\nCan you talk a bit about your work getting the PyLadies chapter in Chicago off the ground and what the reaction has been like?\nNow that you are a member of the board for the PSF, what are your goals in that position?\nWhat is it about software development that made you want to change your career path?\nWhat are some of the most interesting projects that you have worked on, whether for your employer or for fun?\nDo you think that the bootcamp you attended did a good job of preparing you for a position in industry?\nWhat is your view on the concept that software development is the modern form of literacy? Do you think that everyone should learn how to program?\n\n\nKeep In Touch\n\nTwitter\n\nPicks\n\n\nTobias\n\nZencastr\n\n\n\nLorena\n\n\nWeapons of Math Destruction\nWhat I Talk About When I talk About Running\n\n\n\n\n\nLinks\n\n\nidealist.org\nSchemas For The Real World\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

One of the great strengths of the Python community is the diversity of backgrounds that our practitioners come from. This week Lorena Mesa talks about how her focus on political science and civic engagement led her to a career in software engineering and data analysis. In addition to her professional career she founded the Chicago chapter of PyLadies, helps teach women and kids how to program, and was voted onto the board of the PSF.

\n\n

Brief Introduction

\n\n\n\n

Interview with Lorena Mesa

\n\n\n\n

Keep In Touch

\n\n

Twitter

\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-10-08T12:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/ed5692ef-49f8-4e02-bdce-f18a5d0959fc.mp3","mime_type":"audio/mpeg","size_in_bytes":55678492,"duration_in_seconds":2542}]},{"id":"http://podcastinit.podbean.com/e/episode-77-podbuzzz-with-kyle-martin/","title":"Podbuzzz with Kyle Martin","url":"https://www.pythonpodcast.com/episode-77-podbuzzz-with-kyle-martin","content_text":"Summary\n\nPodcasts are becoming more popular now than they ever have been. Podbuzzz is a service for helping podcasters to track their reviews and imporove SEO to reach a wider audience. In this episode we spoke with Kyle Martin about his experience using Python to build Podbuzzz and manage it in production.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.\nYou need a place to run your awesome new Python apps, so check out our sponsor Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project.\nYou want to make sure your apps are error-free so give our next sponsor, Rollbar, a look. Rollbar is a service for tracking and aggregating your application errors so that you can find and fix the bugs in your application before your users notice they exist. Use the link rollbar.com/podcastinit to get 90 days and 300,000 errors for free on their bootstrap plan.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nBy leaving a review on iTunes, or Google Play Music it becomes easier for other people to find us.\nJoin our community! Visit discourse.pythonpodcast.com to help us grow and connect our wonderful audience.\nYour hosts as usual are Tobias Macey and Chris Patti\nToday we’re interviewing Kyle Martin about Podbuzzz\n\n\nInterview with Kyle Martin\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nCan you start by explaining what Podbuzz is? – Tobias\nWhy did you end up choosing Python as the language for building thx#is service? – Tobias\nWhat have been the biggest engineering challenges in building Podbuzzz? – Tobias\nHow did you conceive of the idea to build Podbuzzz and what inspired you to provide it as a service? – Tobias\nPart of the service that you are building is a widget that encourages listeners to rate a podcast on iTunes. Why is that important and what are some of the techniques that you have leveraged to determine the most effective messaging? – Tobias\nWhat are some of the features that you plan on adding to your service? – Tobias\nDo you intend to run Podbuzzz as a side project or do you envision it becoming a company with its own staff? – Tobias\nIn addition to your work with Podbuzzz as a way for podcasters to gain visibility for their shows, you’re also working on an analytics platform for the same target audience. Can you explain a bit about that and the problems that you’ve had to overcome? – Tobias\nWhat is it about podcasting that makes it hard to gain useful metrics and what is your strategy for overcoming some of those obstacles? – Tobias\n\n\nKeep In Touch\n\n\nTwitter\nEmail\n\n\nPicks\n\n\nTobias\n\nThank You Scientist\n\n\n\nChris\n\n\nHell or High Water\n\n\n\nKyle\n\n\nUdacity Self-Driving Car Engineering Nanodegree\nStartups For The Rest of Us\nZero To Scale\nSpeechmatics\n\n\n\n\n\nLinks\n\n\nCanva\nInternet Business Mastery Podcast\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Podcasts are becoming more popular now than they ever have been. Podbuzzz is a service for helping podcasters to track their reviews and imporove SEO to reach a wider audience. In this episode we spoke with Kyle Martin about his experience using Python to build Podbuzzz and manage it in production.

\n\n

Brief Introduction

\n\n\n\n

Interview with Kyle Martin

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-10-01T16:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/646ad90c-d696-4e90-a69f-cfd6e4751a52.mp3","mime_type":"audio/mpeg","size_in_bytes":56628893,"duration_in_seconds":2316}]},{"id":"http://podcastinit.podbean.com/e/episode-76-psychopy-with-jonathan-peirce/","title":"PsychoPy with Jonathan Peirce","url":"https://www.pythonpodcast.com/episode-76-psychopy-with-jonathan-peirce","content_text":"Summary\n\nWe’re delving into the complex workings of your mind this week on Podcast.init with Jonathan Peirce. He tells us about how he started the PsychoPy project and how it has grown in utility and popularity over the years. We discussed the ways that it has been put to use in myriad psychological experiments, the inner workings of how to design and execute those experiments, and what is in store for its future.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.\nHired is sponsoring us this week. If you’re looking for a job as a developer or designer then Hired will bring the opportunities to you. Sign up at hired.com/podcastinit to double your signing bonus.\nOnce you land a job you can check out our other sponsor Linode for running your awesome new Python apps. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nYou want to make sure your apps are error-free so give our last sponsor, Rollbar, a look. Rollbar is a service for tracking and aggregating your application errors so that you can find and fix the bugs in your application before your users notice they exist. Use the link rollbar.com/podcastinit to get 90 days and 300,000 errors for free on their bootstrap plan.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nBy leaving a review on iTunes, or Google Play Music it becomes easier for other people to find us.\nJoin our community! Visit discourse.pythonpodcast.com to help us grow and connect our wonderful audience.\nYour hosts as usual are Tobias Macey and Chris Patti\nToday we’re interviewing Jonathan Peirce about PsychoPy, an open source application for the presentation and collection of stimuli for psychological experimentation\n\n\n\n\n\n\n\n\nInterview with Jonathan Peirce\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nCan you start by telling us what PsychoPy is and how the project got started? – Tobias\nHow does PsychoPy compare feature wise against some of the proprietary alternatives? – Chris\nIn the documentation you mention that this project is useful for the fields of psychophysics, cognitive neuroscience and experimental psychology. Can you provide some insight into how those disciplines differ and what constitutes an experiment? – Tobias\nDo you find that your users who have no previous formal programming training come up to speed with PsychoPy quickly? What are some of the challenges there? -Chris\nCan you describe the internal architecture of PsychoPy and how you approached the design? – Tobias\nHow easy is it to extend PsychoPy with new types of stimulus? – Chris\nWhat are some interesting challenges you faced when implementing PsychoPy? – Chris\nI noticed that you support a number of output data formats, including pickle. What are some of the most popular analysis tools for users of PsychoPy? – Tobias\n\nHave you investigated the use of the new Feather library? – Tobias\n\n\n\nHow is data input typically managed? Does PsychoPy support automated readings from test equipment or is that the responsibility of those conducting the experiment? – Tobias\nWhat are some of the most interesting experiments that you are aware of having been conducted using PsychoPy? – Chris\nWhile reading the docs I found the page describing the integration with the OSF (Open Science Framework) for sharing and validating an experiment and the collected data with other members of the field. Can you explain why that is beneficial to the researchers and compare it with other options such as GitHub for use within the sciences? – Tobias\nDo you have a roadmap of features that you would like to add to PsychoPy or is it largely driven by contributions from practitioners who are extending it to suit their needs? – Tobias\n\n\nKeep In Touch\n\n\nPsychoPy Discourse Forum\n\n\nPicks\n\n\nTobias\n\nHackers: Heroes of the Computer Revolution by Steven Levy\n\n\n\nChris\n\n\nCastro 2\n\n\n\nJon\n\n\nDiscourse\n\n\n\n\n\nLinks\n\n\nFeather\nPyglet\nHDF5\nOpen Science Framework\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

We’re delving into the complex workings of your mind this week on Podcast.init with Jonathan Peirce. He tells us about how he started the PsychoPy project and how it has grown in utility and popularity over the years. We discussed the ways that it has been put to use in myriad psychological experiments, the inner workings of how to design and execute those experiments, and what is in store for its future.

\n\n

Brief Introduction

\n\n\n\n
\n\n
\n\n
\n\n

Interview with Jonathan Peirce

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-09-24T20:15:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/52eaa5d1-8ba4-4913-95f4-0216b05e900a.mp3","mime_type":"audio/mpeg","size_in_bytes":109718707,"duration_in_seconds":4330}]},{"id":"http://podcastinit.podbean.com/e/episode-75-sandstormio-with-asheesh-laroia/","title":"Sandstorm.io with Asheesh Laroia","url":"https://www.pythonpodcast.com/episode-75-sandstorm-io-with-asheesh-laroia","content_text":"Summary\n\nSandstorm.io is an innovative platform that aims to make self-hosting applications easier and more maintainable for the average individual. This week we spoke with Asheesh Laroia about why running your own services is desirable, how they have made security a first priority, how Sandstorm is architected, and what the installation process looks like.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nWe are also sponsored by Rollbar. Rollbar is a service for tracking and aggregating your application errors so that you can find and fix the bugs in your application before your users notice they exist. Use the link rollbar.com/podcastinit to get 90 days and 300,000 errors for free on their bootstrap plan.\nHired has also returned as a sponsor this week. If you’re looking for a job as a developer or designer then Hired will bring the opportunities to you. Sign up at hired.com/podcastinit to double your signing bonus.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nI would also like to mention that the organizers of PyCon Zimbabwe are looking to the global Python community for help in supporting their event. If you would like to donate the link will be in the show notes.\nYour hosts as usual are Tobias Macey and Chris Patti\nToday we’re interviewing Asheesh Laroia about Sandstorm.io, a project that is trying to make self-hosted applications easy and secure for everyone.\n\n\n\n\n\n\n\n\nInterview with Asheesh Laroia\n\n\nIntroductions\nHow did you get introduced to Python? – Tobias\nCan you start by telling everyone about the Sandstorm project and how you got involved with it? – Tobias\nWhat are some of the reasons that an individual would want to self-host their own applications rather than using comparable services available through third parties? – Tobias\nHow does Sandstorm try to make the experience of hosting these various applications simple and enjoyable for the broadest variety of people? – Tobias\nWhat does the system architecture for Sandstorm look like? – Tobias\nI notice that Sandstorm requires a very recent Linux kernel version. What motivated that choice and how does it affect adoption? – Chris\nOne of the notable aspects of Sandstorm is the security model that it uses. Can you explain the capability-based authorization model and how it enables Sandstorm to ensure privacy for your users? – Tobias\nWhat are some of the most difficult challenges facing you in terms of software architecture and design? – Tobias\nWhat is involved in setting up your own server to run Sandstorm and what kinds of resources are required for different use cases? – Tobias\nYou have a number of different applications available for users to install. What is involved in making a project compatible with the Sandstorm runtime environment? Are there any limitations in terms of languages or application architecture for people who are targeting your platform? – Tobias\nHow much of Sandstorm is written in Python and what other languages does it use? – Tobias\n\n\nKeep In Touch\n\n\nTwitter\nBlog\nEmail\n\n\nPicks\n\n\nTobias\n\nOpsGenie\n\n\n\nChris\n\n\nViking Godfather Safety Razor\nWho Killed Sherlock Holmes? by Paul Cornell\nPetrus Aged Red\n\n\n\nAsheesh\n\n\nAmtrak\nThe Master Switch by Tim Wu\nRocket Chat\n\n\n\n\n\nLinks\n\n\nNorth Star Post\nContact Otter\nHacker Slides\nPermanote\nRadicale\nMedia Goblin\nIPython Notebook\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Sandstorm.io is an innovative platform that aims to make self-hosting applications easier and more maintainable for the average individual. This week we spoke with Asheesh Laroia about why running your own services is desirable, how they have made security a first priority, how Sandstorm is architected, and what the installation process looks like.

\n\n

Brief Introduction

\n\n\n\n
\n\n
\n\n
\n\n

Interview with Asheesh Laroia

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-09-17T16:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/1b898350-f5dc-406e-8476-d0d63adb37b5.mp3","mime_type":"audio/mpeg","size_in_bytes":39238004,"duration_in_seconds":3575}]},{"id":"http://podcastinit.podbean.com/e/episode-74-python-at-zalando/","title":"Python at Zalando","url":"https://www.pythonpodcast.com/episode-74-python-at-zalando","content_text":"Summary\n\nOpen source has proven its value in many ways over the years. In many companies that value is purely in terms of consuming available projects and platforms. In this episode Zalando describes their recent move to creating and releasing a number of their internal projects as open source and how that has benefited their business. We also discussed how they are leveraging Python and a couple of the libraries that they have published.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nRollbar is also sponsoring us this week. Rollbar is a service for tracking and aggregating your application errors so that you can find and fix the bugs in your application before your users notice they exist. Use the link rollbar.com/podcastinit to get 90 days and 300,000 errors for free on their bootstrap plan.\nHired has also returned as a sponsor this week. If you’re looking for a job as a developer or designer then Hired will bring the opportunities to you. Sign up at hired.com/podcastinit to double your signing bonus.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour hosts as usual are Tobias Macey and Chris Patti\nToday we’re interviewing Jie Bao and João Santos about their use of Python at Zalando\n\n\n\n\n\n\nInterview with Zalando\n\n\nIntroductions\nHow did you get introduced to Python? – Tobias\nCan you start by telling us a bit about what Zalando does and some of the technologies that you use? – Tobias\nWhat role does Python play in your environment? – Tobias\nIs the use of Python for a particular project governed by any particular operational guidelines or is it largely a matter of developer choice? – Tobias\nGiven that you have such a variety of platforms to support, how do you architect your systems to keep them easy to maintain and reason about? – Tobias\nOne of the projects that you have open sourced is Connexion. Can you explain a bit about what that is and what it is used for at Zalando? – Tobias\nWhat made you choose to standardize on Swagger/OpenAPI vs RAML or some of the other API standards? – Tobias\nDid Connexion start its life as open source or was it extracted from another project? – Tobias\nExpAn is another one of your projects that is written in Python. What do you use that for? – Tobias\nCan you describe the internal implementation of ExpAn and what it takes to get it set up? – Tobias\nGiven the potential complexity of and the need for statistical significance in the data for proper A/B testing, how did you design ExpAn to satisfy those requirements? – Tobias\nGiven the laws in Germany around digital privacy, were there any special considerations that needed to be made in the collection strategy for the data that gets used in ExpAn? – Tobias\n\n\nKeep In Touch\n\n\nJoão\n\nTwitter\n\n\n\nJie\n\n\nTwitter\n\n\n\nLaurie\n\n\nTwitter\n\n\n\n\n\nPicks\n\n\nTobias\n\nHacker’s Keyboard\n\n\n\nJie\n\n\nShah of Shahs by Ryszard Kapuściński\n\n\n\nJoão\n\n\nSerendipity\n\n\n\nLaurie\n\n\nFlow)\n\n\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Open source has proven its value in many ways over the years. In many companies that value is purely in terms of consuming available projects and platforms. In this episode Zalando describes their recent move to creating and releasing a number of their internal projects as open source and how that has benefited their business. We also discussed how they are leveraging Python and a couple of the libraries that they have published.

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Brief Introduction

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Interview with Zalando

\n\n\n\n

Keep In Touch

\n\n

\n\n

Picks

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-09-10T19:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/01b0e710-de0a-47eb-a925-b187d13041b4.mp3","mime_type":"audio/mpeg","size_in_bytes":33092212,"duration_in_seconds":2426}]},{"id":"http://podcastinit.podbean.com/e/episode-73-alex-martelli/","title":"Alex Martelli","url":"https://www.pythonpodcast.com/episode-73-alex-martelli","content_text":"Summary\n\nAlex Martelli has dedicated a large part of his career to teaching others how to work with software. He has the highest number of Python questions answered on Stack Overflow, he has written and co-written a number of books on Python, and presented innumerable times at conferences in multiple countries. We spoke to him about how he got started in software, his work with Google, and the trends in development and design patterns that are shaping modern software engineering.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nWe also have a returning sponsor this week. Rollbar is a service for tracking and aggregating your application errors so that you can find and fix the bugs in your application before your users notice they exist. Use the link rollbar.com/podcastinit to get 90 days and 300,000 errors for free on their bootstrap plan.\nHired is sponsoring us this week. If you’re looking for a job as a developer or designer then Hired will bring the opportunities to you. Sign up at hired.com/podcastinit to double your signing bonus.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers.\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour hosts as usual are Tobias Macey and Chris Patti\nToday we’re interviewing Alex Martelli\n\n\nInterview with Alex Martelli\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nYou have achieved a number of honors and recognitions throughout your career for significant technical achievements. What kind of learning strategies do you use to enable you to achieve mastery of technical topics? – Tobias\nHow do you keep the Python In A Nutshell book current as aspects of the core language and its libraries change? – Chris\nYou are known for your prolific contributions to Stack Overflow, particularly on topics pertaining to Python. Was that a specific goal that you had set for yourself or did it happen organically? – Tobias\nWhen answering Stack Overflow questions, do you usually already know the answers or do you treat it as a learning opportunity? – Tobias\nWhat are some of the most difficult Python questions that you have been faced with? – Tobias\nYou have presented quite a number of times at various Python conferences. What are some of your favorite talks? – Tobias\nDesign patterns and idiomatic code are common themes in a number of your presentations. Why is it important for developers to understand these concepts and what are some of your favorite resources on the topic? – Tobias\nWhat do you see as the most influential trends in software development and design, both currently and heading into the future? – Tobias\nAs a long-time computer engineer, are there any features or ideas from other languages that you would like to see incorporated into Python?\n\n\nPicks\n\n\nTobias\n\nThe Great Gatsby Movie\n\n\n\nChris\n\n\nStone Ruination Double IPA\nGhost Soldiers\n\n\n\nAlex\n\n\nAlexander Hamilton by Ron Chernow\nHamilton Musical\n\n\n\n\n\nLinks\n\n\nPermission or Forgiveness\nGood enough is good enough\nModern Python Patterns and Idioms\nHandling Errors and Exceptions in Modern Python\nMicroservices\nGoogle SRE Book\nPython In A Nutshell use code AUTHD for a discount\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Alex Martelli has dedicated a large part of his career to teaching others how to work with software. He has the highest number of Python questions answered on Stack Overflow, he has written and co-written a number of books on Python, and presented innumerable times at conferences in multiple countries. We spoke to him about how he got started in software, his work with Google, and the trends in development and design patterns that are shaping modern software engineering.

\n\n

Brief Introduction

\n\n\n\n

Interview with Alex Martelli

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-09-03T13:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/c23a2bac-2c0f-4582-a0e3-7bc96739d519.mp3","mime_type":"audio/mpeg","size_in_bytes":83704330,"duration_in_seconds":3889}]},{"id":"http://podcastinit.podbean.com/e/episode-72-dave-beazley/","title":"Dave Beazley","url":"https://www.pythonpodcast.com/episode-72-dave-beazley","content_text":"Summary\n\nDave Beazley has been using and teaching Python since the early days of the language. He has also been instrumental in spreading the gospel of asynchronous programming and the many ways that it can improve the performance of your programs. This week I had the pleasure of speaking with him about his history with the language and some of his favorite presentations and projects.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nWe are also sponsored by Sentry this week. Stop hoping your users will report bugs. Sentry’s real-time tracking gives you insight into production deployments and information to reproduce and fix crashes. Check them out at getsentry.com and use the code podcastinit at signup to get a $50 credit!\nHired has also returned as a sponsor this week. If you’re looking for a job as a developer or designer then Hired will bring the opportunities to you. Sign up at hired.com/podcastinit to double your signing bonus.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour hosts as usual are Tobias Macey and Chris Patti\nToday we’re interviewing Dave Beazley about his career with Python\n\n\n\n\n\n\nInterview with Dave Beazley\n\n\nIntroductions\nHow did you get introduced to Python? – Tobias\nHow has Python and its community helped to shape your career? – Tobias\nWhat are some of the major themes that you have focused on in your work? – Tobias\nOne of the things that you are known for is doing live-coding presentations, many of which are fairly advanced. What is it about that format that appeals to you? – Tobias\n\nWhat are some of your favorite stories about a presentation that didn’t quite go as planned? – Tobias\n\n\n\nYou have given a large number of talks at various conferences. What are some of your favorites? – Tobias\nWhat impact do you think that asynchronous programming will have on the future of the Python language and ecosystem? – Tobias\nAre there any features that you see in other languages that you would like to have incorporated in Python? – Tobias\nOn the about page for your website you talk about some of the low-level code and hardware knowledge that you picked up by working with computers as a kid. Do you think that people who are getting started with programming now are missing out by not getting exposed to the kinds of hardware and software that was present before computing became mainstream?\nYou have had the opportunity to work on a large variety of projects, both on a hobby and professional level. What are some of your favorites? – Tobias\nWhat is it about Python that has managed to hold your interest for so many years? – Tobias\n\n\nKeep In Touch\n\n\nTwitter\n\n\nPicks\n\n\nTobias\n\nCriminal\n\n\n\nDave\n\n\nSamuel Beckett Plays\n\n\n\n\n\nLinks\n\n\nPython Concurrency From The Ground Up\nXKCD compiling\nClifford Stoll\nSuperboard talk\nCurio\nPyOhio async talk\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Dave Beazley has been using and teaching Python since the early days of the language. He has also been instrumental in spreading the gospel of asynchronous programming and the many ways that it can improve the performance of your programs. This week I had the pleasure of speaking with him about his history with the language and some of his favorite presentations and projects.

\n\n

Brief Introduction

\n\n\n\n
\n\n
\n\n

Interview with Dave Beazley

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-08-27T16:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/4a31a779-3dce-4aa7-87f9-5cebdb521804.mp3","mime_type":"audio/mpeg","size_in_bytes":68017682,"duration_in_seconds":2742}]},{"id":"http://podcastinit.podbean.com/e/episode-71-gensim-with-radim-rehurek/","title":"GenSim with Radim Řehůřek","url":"https://www.pythonpodcast.com/episode-71-gensim-with-radim-rehurek","content_text":"Summary\n\nBeing able to understand the context of a piece of text is generally thought to be the domain of human intelligence. However, topic modeling and semantic analysis can be used to allow a computer to determine whether different messages and articles are about the same thing. This week we spoke with Radim Řehůřek about his work on GenSim, which is a Python library for performing unsupervised analysis of unstructured text and applying machine learning models to the problem of natural language understanding.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nWe are also sponsored by Sentry this week. Stop hoping your users will report bugs. Sentry’s real-time tracking gives you insight into production deployments and information to reproduce and fix crashes. Check them out at getsentry.com and use the code podcastinit at signup to get a $50 credit on your account.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour hosts as usual are Tobias Macey and Chris Patti\nToday we’re interviewing Radim Řehůřek about Gensim, a library for topic modeling and semantic analysis of natural language.\n\n\nInterview with Radim Řehůřek\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nCan you start by giving us an explanation of topic modeling and semantic analysis? – Tobias\nWhat is Gensim and what inspired you to create it? – Tobias\nWhat facilities does Gensim provide to simplify the work of this kind of language analysis? – Tobias\nCan you describe the features that set it apart from other projects such as the NLTK or Spacy? – Tobias\nWhat are some of the practical applications that Gensim can be used for? – Tobias\nOne of the features that stuck out to me is the fact that Gensim can process corpora on disk that would be too large to fit into memory. Can you explain some of the algorithmic work that was necessary to allow for this streaming process to be possible? – Tobias\n\nGiven that it can handle streams of data, could it also be used in the context of something like Spark? – Tobias\n\n\n\nGensim also supports unsupervised model building. What kinds of limitations does this have and when would you need a human in the loop? – Tobias\n\n\nOnce a model has been trained, how does it get saved and reloaded for subsequent use? – Tobias\n\n\n\nWhat are some of the more unorthodox or interesting uses people have put Gensim to that you’ve heard about? – Chris\nIn addition to your work on Gensim, and partly due to its popularity, you have started a consultancy for customers who are interested in improving their data analysis capabilities. How does that feed back into Gensim? – Tobias\nAre there any improvements in Gensim or other libraries that you have made available as a result of issues that have come up during client engagements? – Tobias\nIs it difficult to find contributors to Gensim because of its advanced nature? – Tobias\nAre there any resources you’d like to recommend our listeners explore to get a more in depth understanding of topic modeling and related techniques? – Chris\n\n\nKeep In Touch\n\n\nRaRe Technologies\nTwitter\nEmail\nGithub\nMailing List\n\n\nPicks\n\n\nTobias\n\nDark Matter and the Dinosaurs by Lisa Randall\n\n\n\nChris\n\n\nm-cli\n\n\n\nRadim\n\n\n1177 BC: The Year Civilization Collapsed\n\n\n\n\n\nLinks\n\n\nNadia Eghbal\nGensim\nSQL Addict\nNLTK\nSpacy\nLatent Dirichlet Allocation (LDA)\nLSI\nKeynote in Italy on distributed processing\nGoogle Scholar references for Gensim\nStylometric analysis\nOn Writing Well\nStudent Incubator\nWikipedia on topic modeling\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Being able to understand the context of a piece of text is generally thought to be the domain of human intelligence. However, topic modeling and semantic analysis can be used to allow a computer to determine whether different messages and articles are about the same thing. This week we spoke with Radim Řehůřek about his work on GenSim, which is a Python library for performing unsupervised analysis of unstructured text and applying machine learning models to the problem of natural language understanding.

\n\n

Brief Introduction

\n\n\n\n

Interview with Radim Řehůřek

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-08-20T15:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/f4908543-3aba-4d91-8df4-87f526810015.mp3","mime_type":"audio/mpeg","size_in_bytes":82776130,"duration_in_seconds":3208}]},{"id":"http://podcastinit.podbean.com/e/episode-70-python-on-windows-with-steve-dower/","title":"Python on Windows with Steve Dower","url":"https://www.pythonpodcast.com/episode-70-python-on-windows-with-steve-dower","content_text":"Summary\n\nIn order for Python to continue to attract new users, we need to have an easy way for people to get started with it, and Windows is still the most widely used operating system among computers. Steve Dower is the build maintainer for the Windows installers of Python and this week we spoke with him about his work in that role. He told us about the changes that he has made to the installer to make it easier for new users to get started and how modern updates to the packaging ecosystem for libraries has simplified dependency management. He also told us about how the Visual Studio team is building a set of tools to make development of Python code more enjoyable and how Microsoft’s adoption of open source is making Windows a more attractive platform for developers.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nWe are also sponsored by Sentry this week. Stop hoping your users will report bugs. Sentry’s real-time tracking gives you insight into production deployments and information to reproduce and fix crashes. Check them out at getsentry.com and use the code podcastinit at signup to get a $50 credit on your account!\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour hosts as usual are Tobias Macey and Chris Patti\nToday we’re interviewing Steve Dower about Python on Windows\n\n\n\n\n\n\nInterview with Steve Dower\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nYou are currently the release manager for Python on Windows. How did you end up with that responsibility? – Tobias\nWhile Python has supported Windows for a long time, the overall experience has historically been rather poor. Can you give a bit of the background of why that was and tell us about some of the work that you and others have been doing to make it better? – Tobias\nGiven that a large percentage of users are still on Windows, having a good story for getting started with Python on that platform is important for adoption of the language. What are some of the areas where the current situation needs to be improved? – Tobias\nWhat is the most difficult part of building a distribution of Python for a Windows environment? Has it gotten easier in recent years? – Tobias\nWhen we were speaking at PyCon you mentioned that the most frequently downloaded version of Python from the python.org site is the 32 bit version for Windows. Do you think that is an accurate and useful metric? What other statistics do you wish you could capture or improve? – Tobias\nHow does Python Tools for Visual Studio compare with other Python IDEs like Pycharm? – Chris\nWhat are some unique features that Python Tools for Visual Studio offers that other tools don’t? – Chris\nAre there any compelling aspects of developing Python on Windows that could convince users on other platforms to make the switch? – Tobias\nCould you give our listeners a whirlwind tour of the underlying implementation of PTVS? How does Visual Studio provide such in depth introspection for your Python code? – Chris\n\n\nKeep In Touch\n\n\nTwitter\nGithub\n\nMicrosoft\nAzure\n\n\n\nsteve.dower\n\n\nPicks\n\n\nTobias\n\nKdiff3\nSpyderCo Triangle Sharpmaker\n\n\n\nChris\n\n\nAudible\n\n\n\nSteve\n\n\nSandisk Extreme Portable SSD\nSMBC\nRandom Encounters\n\n\n\n\n\nLinks\n\n\nWindows compilers\n\nVisual C++ Build Tools (for Python 3.5 and later)\nVisual C++ Compiler for Python 2.7\n\n\n\nPEP 514\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

In order for Python to continue to attract new users, we need to have an easy way for people to get started with it, and Windows is still the most widely used operating system among computers. Steve Dower is the build maintainer for the Windows installers of Python and this week we spoke with him about his work in that role. He told us about the changes that he has made to the installer to make it easier for new users to get started and how modern updates to the packaging ecosystem for libraries has simplified dependency management. He also told us about how the Visual Studio team is building a set of tools to make development of Python code more enjoyable and how Microsoft’s adoption of open source is making Windows a more attractive platform for developers.

\n\n

Brief Introduction

\n\n\n\n
\n\n
\n\n

Interview with Steve Dower

\n\n\n\n

Keep In Touch

\n\n

\n\n

Picks

\n\n

\n\n

Links

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-08-13T09:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/b4b35108-fe23-4b01-9bf6-19f0a7ed4a1c.mp3","mime_type":"audio/mpeg","size_in_bytes":81227126,"duration_in_seconds":3263}]},{"id":"http://podcastinit.podbean.com/e/episode-69-pycon-canada-with-francis-deslauriers-and-peter-mccormick/","title":"PyCon Canada with Francis Deslauriers and Peter McCormick","url":"https://www.pythonpodcast.com/episode-69-pycon-canada-with-francis-deslauriers-and-peter-mccormick","content_text":"Summary\n\nAside from the national Python conferences such as PyCon US and EuroPyCon there are a number of regional conferences that operate at a smaller scale to service their local communities. This week we interviewed Peter McCormick and Francis Deslauriers about their work organizing PyCon Canada to provide a venue for Canadians to talk about how they are using the language. If you happen to be near Toronto in November then you should get a ticket and help contribute to their success!\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nWe are also sponsored by Sentry this week. Stop hoping your users will report bugs. Sentry’s real-time tracking gives you insight into production deployments and information to reproduce and fix crashes. Check them out at getsentry.com and use the code podcastinit at signup to get a $50 credit!\nHired has also returned as a sponsor this week. If you’re looking for a job as a developer or designer then Hired will bring the opportunities to you. Sign up at hired.com/podcastinit to double your signing bonus.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour hosts as usual are Tobias Macey and Chris Patti\nToday we’re interviewing Peter McCormick and Francis Deslauriers about their experiences organizing PyCon Canada\n\n\n\n\n\n\nInterview with Peter McCormick and Francis Deslauriers\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nHow did you get involved as an organizer of PyCon Canada? – Tobias\nHow does PyCon Canada, and other regional conferences, differ from PyCon US, both in terms of scale and overall experience? – Tobias\nHow do the audience and presenters differ from the US conferences? Is there perhaps a differen mix of industry versus academia, or maybe different disciplines? Chris\nAre you thinking of trying to hold the conference in different cities across Canada, similarly to how PyCon US moves venues every two years? – Tobias\nIn addition to the national and regional conferences, there are a number of special interest Python conferences that take place (e.g. SciPy, PyData, etc.). What kind of relationship do you have with organizers of those events and how do they impact the kinds of talk submissions that you are likely to receive? – Tobias\nThere has been a lot of focus in recent years on trying to increase the diversity of conference speakers. What are some of the methods that you have used to encourage speakers of various backgrounds to submit talks? – Tobias\nOrganizing a conference involves a lot of moving parts. How do you structure the process to ensure a safe and enjoyable experience for the attendees? – Tobias\nWhat are some of the biggest logistical challenges you face as conference organizers? – Chris\nGiven that PyCon Canada is a regional conference, how has that affected your focus in terms of marketing and the general theme? – Tobias\nTell our listeners about your favorite PyCon Canada moments. – Chris\nWhat has been the most surprising part of organizing the conference? – Tobias\n\n\nKeep In Touch\n\n\nPyCon Canada\n\nTwitter\nWebsite\nEmail for sponsorship enquiries\n\n\n\nPeter\n\n\nEmail\nTwitter\nWebsite\n\n\n\nFrancis\n\n\nEmail\nTwitter\n\n\n\n\n\nPicks\n\n\nTobias\n\nJuice SSH\n\n\n\nChris\n\n\nChinese Man\nStiletto\nAmazon Echo\n\n\n\nPeter\n\n\nDjangoCon US documentation\n\n\n\nFrancis\n\n\nSpam Nation\n\n\n\n\n\nLinks\n\n\nPSF Calendar of Events\nSymposion\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Aside from the national Python conferences such as PyCon US and EuroPyCon there are a number of regional conferences that operate at a smaller scale to service their local communities. This week we interviewed Peter McCormick and Francis Deslauriers about their work organizing PyCon Canada to provide a venue for Canadians to talk about how they are using the language. If you happen to be near Toronto in November then you should get a ticket and help contribute to their success!

\n\n

Brief Introduction

\n\n\n\n
\n\n
\n\n

Interview with Peter McCormick and Francis Deslauriers

\n\n\n\n

Keep In Touch

\n\n

\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-08-06T08:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/a36d5324-1212-4ba5-a10d-1ec66eeef24d.mp3","mime_type":"audio/mpeg","size_in_bytes":69346889,"duration_in_seconds":2760}]},{"id":"http://podcastinit.podbean.com/e/episode-68-test-engineering-with-cris-medina/","title":"Test Engineering with Cris Medina","url":"https://www.pythonpodcast.com/episode-68-test-engineering-with-cris-medina","content_text":"Summary\n\nWe all know that testing is an important part of software and systems development. The problem is that as our systems and applications grow, the amount of testing necessary increases at an exponential rate. Cris Medina joins us this week to talk about some of the problems and approaches associated with testing these complex systems and some of the ways that Python can help.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nWe are also sponsored by Sentry this week. Stop hoping your users will report bugs. Sentry’s real-time tracking gives you insight into production deployments and information to reproduce and fix crashes. Check them out at getsentry.com\nHired has also returned as a sponsor this week. If you’re looking for a job as a developer or designer then Hired will bring the opportunities to you. Sign up at hired.com/podcastinit to double your signing bonus.\nThe O’Reilly Velocity conference is coming to New York this September and we have a free ticket to give away. If you would like the chance to win it then just sign up for our newsletter at pythonpodcast.com\nTo help other people find the show you can leave a review on iTunes, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour hosts as usual are Tobias Macey and Chris Patti\nToday we’re interviewing Cris Medina about test engineering for large and complex systems.\n\n\nInterview with Cris Medina\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nTo get us started can you share your definition of test engineering and how it differs from the types of testing that your average developer is used to? – Tobias\nWhat are some common industries or situations where this kind of test engineering becomes necessary? – Tobias\nHow and where does Python fit into the kind of testing that becomes necessary when dealing with these complex systems? – Tobias\nHow do you determine which areas of a system to test and how can Python help in that discovery process? – Tobias\nWhat are some of your favorite tools and libraries for this kind of work? – Tobias\nWhat are some of the areas where the existing Python tooling falls short? – Tobias\nGiven the breadth of concerns that are encompassed with testing the various components of these large systems, what are some ways that a test engineer can get a high-level view of the overall state? – Tobias\n\nHow can that information be distilled for presentation to other areas of the business? – Tobias\nCould that information be used to provide a compelling business case for the resources required to test properly? – Chris\n\n\n\nGiven the low-level nature of this kind of work I imagine that proper visibility of the work being done can be difficult. How do you make sure that management can properly see and appreciate your efforts? – Tobias\n\n\nKeep In Touch\n\n\nTwitter\n\n\nPicks\n\n\nTobias\n\nSamsung Galaxy Tab S2\nAnker SoundCore Bluetooth Speaker\n\n\n\nChris\n\n\nOn Writing Well\nThis Episode Was Written by an AI\nThe Three Rs\n\n\n\nCris\n\n\nCherryPy\nEtcd\nThinking Fast And Slow by Daniel Kahneman\nSpain\n\n\n\n\n\nLinks\n\n\nBehave\nPytest BDD\nHypothesis\nEpisode XX – Hypothesis\nFlask\nCherryPy\nDjango\nPandas\nNumPy\nCelery\nBokeh\nVincent\nToga\nD3 Sunburst\nD3 Chord Diagrams\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

We all know that testing is an important part of software and systems development. The problem is that as our systems and applications grow, the amount of testing necessary increases at an exponential rate. Cris Medina joins us this week to talk about some of the problems and approaches associated with testing these complex systems and some of the ways that Python can help.

\n\n

Brief Introduction

\n\n\n\n

Interview with Cris Medina

\n\n

\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-07-30T15:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/20ddf609-3c78-4d00-9ef1-91f0f6146ed2.mp3","mime_type":"audio/mpeg","size_in_bytes":85619496,"duration_in_seconds":3489}]},{"id":"http://podcastinit.podbean.com/e/episode-67-crossing-the-streams-talk-python-with-michael-kennedy/","title":"Crossing The Streams - Talk Python with Michael Kennedy","url":"https://www.pythonpodcast.com/episode-67-crossing-the-streams-talk-python-with-michael-kennedy","content_text":"Summary\n\nThe same week that we released our first episode of Podcast.__init__, Michael Kennedy was publishing the very first episode of Talk Python To Me. The years long drought of podcasts about Python has been quenched with a veritable flood of quality content as we have both continued to deliver the stories of the wonderful people who make our community such a wonderful place. This week we interviewed Michael about what inspired him to get started, his process and experience as Talk Python continues to evolve, and how that has led him to create online training courses alongside the podcast. He also interviewed us, so check out this weeks episode of Talk Python To Me for a mirror image of this show!\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nWe are also sponsored by Sentry this week. Stop hoping your users will report bugs. Sentry’s real-time tracking gives you insight into production deployments and information to reproduce and fix crashes. Check them out at getsentry.com\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour hosts as usual are Tobias Macey and Chris Patti\nToday we’re interviewing Michael Kennedy about his work with Talk Python to Me, another podcast about Python and its community, and on-demand Python trainings. Michael has also offered to give away one of each of his Python courses to our listeners. If you would like the chance to win, then sign up for our newsletter at pythonpodcast.com, or our forum at discourse.pythonpodcast.com. If you want to double your chances, then sign up for both!\n\n\nInterview with Michael Kennedy\n\n\nIntroductions\nHow did you get into programming?\nHow did you get introduced to Python? (Chris)\nWhat is the craziest piece of software you’ve ever written? – Tobias\nYou’ve taken some pretty drastic steps around Python and your career lately. What inspired you to do that and how’s it going?(yes, quit my job, focus only on podcast and online courses). \nYou are basically self-taught as a developer, how did you get into this teaching / mentor role?\nWhy did you first get started with Talk Python to Me? – Tobias\nDid you know when you started that it would turn into a full-time endeavor? – Tobias\nFor a while there weren’t any podcasts available that focused on Python and now we’re each producing one. What’s it like to run a successful podcast? – Tobias\nWhat have been your most popular episodes? Tell us a bit about each – Tobias\nIn your excellent episode with Kate Heddleston you talked about how we tend to bash other programming languages. We’ve done a fair bit of Java bashing here. How can we help get ourselves and others in our community out of this bad habit? – Chris\nHow do you select the guests and topics for your show? – Tobias\nWhat topics do you have planned for the next few episodes?\nHow do you prepare the questions for each episode? – Tobias\nWhat is the most significant thing you’ve learned from the podcasting experience?\nWhat do you wish you did differently and how are you looking to improve? – Tobias\nI had a great time hanging out with you at PyCon this year. What was your impression of the conference? \nWhat were your favorite sessions and do you have any shows scheduled to follow up on them? – Tobias\nYour sites are 100% “hand-crafted” as they say. Can you give us a look inside? What are the moving parts in there?\nSo you stirred things up with Stitcher this week. What’s up with that?\nCan you recommend some podcasts? What’s in your playlist?\nFinal call to action?\n\n\nKeep In Touch\n\n\nTwitter\nPodcast\nWeb\nGithub\n\n\nPicks\n\n\nTobias\n\nBatman v Superman: Dawn of Justice\nLego Brickumentary\nHashicorp Consul\n\n\n\nChris\n\n\nYarn\nApple Magic Mouse 2\nRemembering Stonewall\n\n\n\nMichael\n\n\nPyPI\npasslib\nPython 2016 Youtube Channel\nK Lars Lohn – Closing Keynote\n\n\n\n\n\nLinks\n\n\nThinking Fast and Slow by Daniel Kahneman\nTrello\nRecommended podcasts:\n\nTest and Code Podcast\nPartially Derivative\nExponent Podcast\nMixergy\nStartup Podcast (season 1 & 2)\nAway from the keyboard\nDeveloper On Fire\n\n\n\nMichael’s courses:\n\n\nPython Jumpstart by Building 10 Apps\nWrite Pythonic Code Like a Seasoned Developer\n\n\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

The same week that we released our first episode of Podcast.__init__, Michael Kennedy was publishing the very first episode of Talk Python To Me. The years long drought of podcasts about Python has been quenched with a veritable flood of quality content as we have both continued to deliver the stories of the wonderful people who make our community such a wonderful place. This week we interviewed Michael about what inspired him to get started, his process and experience as Talk Python continues to evolve, and how that has led him to create online training courses alongside the podcast. He also interviewed us, so check out this weeks episode of Talk Python To Me for a mirror image of this show!

\n\n

Brief Introduction

\n\n\n\n

Interview with Michael Kennedy

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-07-23T17:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/275d31df-d3fc-44ad-ad2e-8b8b54b37533.mp3","mime_type":"audio/mpeg","size_in_bytes":126488993,"duration_in_seconds":4667}]},{"id":"http://podcastinit.podbean.com/e/episode-66-zorg-with-gunther-cox-and-kevin-brown/","title":"Zorg with Gunther Cox and Kevin Brown","url":"https://www.pythonpodcast.com/episode-66-zorg-with-gunther-cox-and-kevin-brown","content_text":"Summary\n\nEveryone loves to imagine what they would do if they had their own robot. This week we spoke with Gunther Cox and Kevin Brown about their work on Zorg, which is a Python library for building a robot of your own! We discussed how the project got started, what platforms it supports, and some of the projects that have been built with it. Give it a listen and then get building!\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nWe are also sponsored by Sentry this week. Stop hoping your users will report bugs. Sentry’s real-time tracking gives you insight into production deployments and information to reproduce and fix crashes. Check them out at getsentry.com\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour host as usual is Tobias Macey\nToday we’re interviewing Gunther Cox and Kevin Brown about Zorg, a Python framework for robotics and physical computing\n\n\nInterview with Gunther Cox and Kevin Brown\n\n\nIntroductions\nHow did you get introduced to Python? – Tobias\nWhat is Zorg and what is its origin story? – Tobias\nHow would you define and differentiate the concepts of robotics, physical computing, and the internet of things? – Tobias\nI noticed in the documentation that Zorg is based on the Cylon.js project. How closely does the implementation of Zorg stick to that of Cylon and how much needs to be changed due to differences in the language? – Tobias \nIs Zorg useful for production applications or is it primarily intended for educational purposes and hobby projects? – Tobias\nZorg currently only supports the Intel Edison, with plans for Raspberry Pi and Arduino Firmata support in the works. What is involved in adding compatibility with other platforms? – Tobias\nWhat are some of the most interesting projects that you have seen created using Zorg? – Tobias\nHow does Zorg compare to other Python robotics projects such as ROSPy? – Tobias\nRobotics is a large and complex problem space. What are some of the other features and projects in Python that are often used when building robots? – Tobias\n\n\nKeep In Touch\n\n\nGitHub\nNewsletter\n\n\nPicks\n\n\nTobias\n\nPadlock Password Manager\nVault\n\n\n\nGunther\n\n\nRobot Builder’s Bonanza\n\n\n\nKevin\n\n\nFacial Recognition with OpenCV in Python\n\n\n\n\n\nLinks\n\n\nRS232\nThe Hybrid Group\nGobot\nArtoo\nCylon.js\nSalvius\nROSPy\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Everyone loves to imagine what they would do if they had their own robot. This week we spoke with Gunther Cox and Kevin Brown about their work on Zorg, which is a Python library for building a robot of your own! We discussed how the project got started, what platforms it supports, and some of the projects that have been built with it. Give it a listen and then get building!

\n\n

Brief Introduction

\n\n\n\n

Interview with Gunther Cox and Kevin Brown

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-07-16T20:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/c0a07cdd-5ad5-4c60-8944-6c829deb9a92.mp3","mime_type":"audio/mpeg","size_in_bytes":24002845,"duration_in_seconds":1518}]},{"id":"http://podcastinit.podbean.com/e/episode-65-mypy-with-david-fisher-and-greg-price/","title":"Mypy with David Fisher and Greg Price","url":"https://www.pythonpodcast.com/episode-65-mypy-with-david-fisher-and-greg-price","content_text":"Summary\n\nAs Python developers we are fond of the dynamic nature of the language. Sometimes, though, it can get a bit too dynamic and that’s where having some type information would come in handy. Mypy is a project that aims to add that missing level of detail to function and variable definitions so that you don’t have to go hunting 5 levels deep in the stack to understand what shape that data structure is supposed to be. This week we spoke with David Fisher and Greg Price about their work on Mypy and its use within Dropbox and the broader community. They explained how it got started, how it works under the covers, and why you should consider adding it to your projects.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nWe are also sponsored by Sentry this week. Stop hoping your users will report bugs. Sentry’s real-time tracking gives you insight into production deployments and information to reproduce and fix crashes. Check them out at getsentry.com\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour hosts as usual are Tobias Macey and Chris Patti\nToday we’re interviewing David Fisher and Greg Price about Mypy, a library for adding optional static types to your Python code.\n\n\nes\n\nInterview with David Fisher and Greg Price\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nCan you explain a bit about what Mypy is and its origin story? – Tobias\nWhat are the benefits of using Mypy for both new and existing projects? – Tobias\nHow does the Mypy compilation step work? – Tobias\nWhat are the biggest technical challenges in implementing Mypy? – Chris\nAre there any limitations imposed by the syntax of Python that prevented you from implementing any features or syntax that you would have liked to include in Mypy? – Tobias\nIn Guido’s keynote from this year’s PyCon he mentioned some tentative plans for adding variable type declarations to the Python syntax in one of the next major releases. How much of that idea was inspired by Mypy? – Tobias\nType theory is a large and complex problem domain. Can you explain where Mypy falls in this space? – Tobias\nWhich language(s) had the biggest influence on the particular syntax and semantics used in Mypy? – Tobias\nWhat kinds of type definitions and guarantees can be encoded using Mypy? – Tobias\nCan you talk a bit about user defined types as implemented in Mypy? – Chris\nHow has the inclusion of the typing module in the Python standard libary influenced the evolution of Mypy? – Tobias\nDid the inclusion of multiple inheritance add any implementation complexity to Mypy? – Chris\nDo you know of any formal studies that have been performed to research the ergonomics or efficiency gains of static or gradual type systems? – Tobias\nWhat does the future roadmap for Mypy look like? – Tobias\n\n\nKeep In Touch\n\n\nDavid\n\nGitHub\n\n\n\nGreg\n\n\nweb page\nGitHub\n\n\n\n\n\n$ pip3 install mypy-lang\n\nBug reports, feature requests, questions welcome on issue tracker: github.com/python/mypy\n\nPicks\n\n\nTobias\n\nFunctional Geekery – Andreas Stefik episode about studies performed on the human factors of development\nSoft Skills Engineering Podcast\n\n\n\nChris\n\n\nGrimm Artisenal Ales Lucky Cloud\njq – json swiss army knife\n\n\n\nDavid\n\n\nfzf – a fuzzy finder\nThinking, Fast And Slow by Daniel Kahneman\nRingworld\n\n\n\nGreg\n\n\nOn Proof and Progress in Mathematics, essay by Bill Thurston\nAxiomatic by Greg Egan\n\n\n\n\n\nLinks\n\n\nGitHub repo, and CONTRIBUTING file\nPEP 484\nPyCon 2016 workshop slides\nTypeshed shared repo for stubs\nOther tools (PyCharm, pylint, pytype, …) using PEP 484 types\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

As Python developers we are fond of the dynamic nature of the language. Sometimes, though, it can get a bit too dynamic and that’s where having some type information would come in handy. Mypy is a project that aims to add that missing level of detail to function and variable definitions so that you don’t have to go hunting 5 levels deep in the stack to understand what shape that data structure is supposed to be. This week we spoke with David Fisher and Greg Price about their work on Mypy and its use within Dropbox and the broader community. They explained how it got started, how it works under the covers, and why you should consider adding it to your projects.

\n\n

Brief Introduction

\n\n\n\n

es

\n\n

Interview with David Fisher and Greg Price

\n\n\n\n

Keep In Touch

\n\n

\n\n

$ pip3 install mypy-lang

\n\n

Bug reports, feature requests, questions welcome on issue tracker: github.com/python/mypy

\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-07-09T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/6595da81-f24b-4f7c-bbcd-0133914e8afa.mp3","mime_type":"audio/mpeg","size_in_bytes":74158794,"duration_in_seconds":3620}]},{"id":"http://podcastinit.podbean.com/e/episode-64-beeware-with-russell-keith-magee/","title":"BeeWare with Russell Keith-Magee","url":"https://www.pythonpodcast.com/episode-64-beeware-with-russell-keith-magee","content_text":"Summary\n\nWhen you have good tools it makes the work you do even more enjoyable. Russel Keith-Magee has been building up a set of tools that are aiming to let you write graphical interfaces in Python and run them across all of your target platforms. Most recently he has been working on a capstone project called Toga that targets the Android and iOS platforms with the same set of code. In this episode we explored his journey through programming and how he has built and designed the Beeware suite. Give it a listen and then try out some or all of his excellent projects!\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nWe are also sponsored by Sentry this week. Stop hoping your users will report bugs. Sentry’s real-time tracking gives you insight into production deployments and information to reproduce and fix crashes. Check them out at getsentry.com and use the code podcastinit to get a $50 credit!\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour hosts as usual are Tobias Macey and Chris Patti\nToday we’re interviewing Russel Keith-Magee about the Beeware project, which is a collection of tools and libraries that are meant to be composed together for building up your Python development environment.\n\n\nInterview with Firstname Lastname\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nWhat is the BeeWare project and what goals do you have for it? – Tobias\nWhat kinds of projects are contained under the BeeWare umbrella and what inspired you to start creating these kinds of tools? – Tobias\nDid each project arise from a particular need that you had at the time or has there been a logical progression from one tool to the next? – Tobias\nAt PyCon US of this year (2016) you made a presentation about the work that you have been doing to bring Python to the iOS and Android platforms. Can you provide a high-level overview for anyone who hasn’t seen that talk yet? – Tobias\nLet’s talk about Toga – how does Toga differ from some of the other cross platform UI framework efforts for various languages like Kivy or Shoes? – Chris\nWhat are some of the biggest challenges that you had to overcome in order to get Python to run on both iOS and Android? – Tobias\nHow does runtime performance for applications written in Python compare with the same program running in the languages that are natively supported on those platforms? – Tobias\nCan you walk us through the low level flow of a single toga API request? – Chris\nDo you view your work on Toga and the associated libraries as a hobby project or do you think that it will turn into a production ready tool set that people will use for shipping applications? – Tobias\nIDEs like Android Studio and XCode have a lot of features that simplify the development and UI creation process. Do you have to forego those niceties when developing a mobile app in Python? – Tobias\nShipping Python applications is a problem that tends to pose a host of issues for people, which you are addressing with the Briefcase project. What are some of the biggest hurdles and design choices that you have encountered while working on that? – Tobias\nDo you think that there will ever be a release of iOS or Android, or even a brand new mobile platform, that will ship with native Python support? – Tobias\n\n\nKeep In Touch\n\n\nTwitter\nWebsite\nGitHub\n\n\nPicks\n\n\nTobias\n\nJapanese cast iron tea set\n\n\n\nChris\n\n\nBantam Cider\nPythonista 3\n\n\n\nRussell\n\n\nMHPrompt\nOpen Sourcing Mental Illness\nBlue Hackers\nBeyond Blue\nBlack Dog institute\nMental Health.gov\n\n\n\n\n\nLinks\n\n\nA Tale of Two Cellphones\nPython interpreter in 500 lines of code\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

When you have good tools it makes the work you do even more enjoyable. Russel Keith-Magee has been building up a set of tools that are aiming to let you write graphical interfaces in Python and run them across all of your target platforms. Most recently he has been working on a capstone project called Toga that targets the Android and iOS platforms with the same set of code. In this episode we explored his journey through programming and how he has built and designed the Beeware suite. Give it a listen and then try out some or all of his excellent projects!

\n\n

Brief Introduction

\n\n\n\n

Interview with Firstname Lastname

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-07-02T14:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/3bf27f2d-8c74-44fa-8ecf-fc892eba418d.mp3","mime_type":"audio/mpeg","size_in_bytes":83201994,"duration_in_seconds":4235}]},{"id":"http://podcastinit.podbean.com/e/episode-63-armin-ronacher/","title":"Armin Ronacher","url":"https://www.pythonpodcast.com/episode-63-armin-ronacher","content_text":"Summary\n\nArmin Ronacher is a prolific contributor to the Python software ecosystem, creating such widely used projects as Flask and Jinja2. This week we got the opportunity to talk to him about how he got his start with Python and what has inspired him to create the various tools that have made our lives easier. We also discussed his experiences working in Rust and how it can interface with Python.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nWe are also sponsored by Sentry this week. Stop hoping your users will report bugs. Sentry’s real-time tracking gives you insight into production deployments and information to reproduce and fix crashes. Check them out at getsentry.com\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour hosts as usual are Tobias Macey and Chris Patti\nToday we’re interviewing Armin Ronacher about his contributions to the Python community.\n\n\nInterview with Armin Ronacher\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nWhat was the first open source project that you created in Python? – Tobias\nWhat is your view of the responsibility for open source project maintainers and how do you manage a smooth handoff for projects that you no longer wish to be involved in? – Tobias\nYou have created a large number of successful open source libraries and tools during your career. What are some of the projects that may be less well known that you think people might find interesting? – Tobias (e.g. logbook)\nI notice that you recently worked on the pipsi project. Please tell us about it! – Chris\nFollowing on from the last question, where would you like to see the Python packaging infrastructure go in the future? – Chris\nYou have had some strong opinions of Python 2 vs Python 3. How has your position on that subject changed over time? – Tobias\nLet’s talk about Lektor – what differentiates it from the pack, and what keeps you coming back to CMS projects? – Chris\nHow has your blogging contributed to the work that you do and the success you have achieved? – Tobias\nLately you have been doing a fair amount of work with Rust. What was your reasoning for learning that language and how has it influenced your work with Python? – Tobias\nIn addition to the code you have written, you also helped to form the Pocoo organization. Can you explain what Pocoo is and what it does? What has inspired the rebranding to the Pallets project? – Tobias\n\n\nKeep In Touch\n\n\nTwitter\n\n\nPicks\n\n\nTobias\n\nRadical Candor\n\n\n\nChris\n\n\nLoverbeer BeerBrugna\nThe Human Resource Machine\n\n\n\nArmin\n\n\nBiermanufaktur Loncium\nMatakustix – Hai Hai Haibodn\n\n\n\n\n\nLinks\n\n\nPHPbb\nPocoo\nPallets Project\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Armin Ronacher is a prolific contributor to the Python software ecosystem, creating such widely used projects as Flask and Jinja2. This week we got the opportunity to talk to him about how he got his start with Python and what has inspired him to create the various tools that have made our lives easier. We also discussed his experiences working in Rust and how it can interface with Python.

\n\n

Brief Introduction

\n\n\n\n

Interview with Armin Ronacher

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-06-25T20:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/b3afa9dd-68f4-4156-9215-343a098c6673.mp3","mime_type":"audio/mpeg","size_in_bytes":90088891,"duration_in_seconds":3620}]},{"id":"http://podcastinit.podbean.com/e/episode-62-bandit-with-tim-kelsey-travis-mcpeak-and-eric-brown/","title":"Bandit with Tim Kelsey, Travis McPeak, and Eric Brown","url":"https://www.pythonpodcast.com/episode-62-bandit-with-tim-kelsey-travis-mcpeak-and-eric-brown","content_text":"Summary\n\nMaking sure that your code is secure is a difficult task. In this episode we spoke to Eric Brown, Travis McPeak, and Tim Kelsey about their work on the Bandit library, which is a static analysis engine to help you find potential vulnerabilities before your application reaches production. We discussed how it works, how to make it fit your use case, and why it was created. Give the show a listen and then go start scanning your projects!\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project. And they just doubled the RAM for their introductory level servers, so that $20 will get you even more performance.\nWe are also sponsored by Sentry this week. Stop hoping your users will report bugs. Sentry’s real-time tracking gives you insight into production deployments and information to reproduce and fix crashes. Check them out at getsentry.com and use the code podcastinit at signup to get a $50 credit!\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour hosts as usual are Tobias Macey and Chris Patti\nToday we’re interviewing Tim Kelsey and Eric Brown about Bandit which is a static analysis engine for finding security vulnerabilities in your Python code.\n\n\nInterview with Eric Brown, Travis McPeak and Tim Kelsey\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nWhat is Bandit and what was the inspiration for creating it? – Tobias\nHow did you each get involved with the Bandit project? – Tobias\nAt what stage of the development process would you want to use Bandit? – Tobias\nWhat kinds of analysis does Bandit do on the source code that it is run against? – Tobias\nHow does it determine whether a particular segment of code is introducing a vulnerability and what means does it use to determine the severity? – Tobias\nWhat does the generated report include and what can be done with that information? – Tobias\nWhat are some of the biggest design and implementation difficulties that have been encountered in the process of creating Bandit? – Tobias\nHow does bandit compare to similar tools in other languages such as Ruby’s BrakeMan? – Tobias\nWhat are some of the most interesting extensions that you have seen for Bandit? – Tobias\nWhat is on the roadmap for the future of Bandit? – Tobias\n\n\nKeep In Touch\n\n\nOpenStack Security IRC\nOpenStack Security Weekly Meeting\nTim\n\nTwitter\n\n\n\nTravis\n\n\nTwitter\n\n\n\n\n\nPicks\n\n\nTobias\n\nToggl\n\nListener Review of Toggl\n\n\n\nAny.do\n\n\nTim\n\n\nIFTTT (If This Then That)\n\n\n\nEric\n\n\nSlack\n\n\n\nTravis\n\n\nBrilliance Trilogy\nUncharted 4\nRisky Business Podcast\n\n\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Summary

\n\n

Making sure that your code is secure is a difficult task. In this episode we spoke to Eric Brown, Travis McPeak, and Tim Kelsey about their work on the Bandit library, which is a static analysis engine to help you find potential vulnerabilities before your application reaches production. We discussed how it works, how to make it fit your use case, and why it was created. Give the show a listen and then go start scanning your projects!

\n\n

Brief Introduction

\n\n\n\n

Interview with Eric Brown, Travis McPeak and Tim Kelsey

\n\n\n\n

Keep In Touch

\n\n

\n\n

Picks

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-06-18T12:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/1f784155-db92-46c2-86e7-b6f337b99c34.mp3","mime_type":"audio/mpeg","size_in_bytes":39069612,"duration_in_seconds":1728}]},{"id":"http://podcastinit.podbean.com/e/episode-61-sentry-with-david-cramer/","title":"Sentry with David Cramer","url":"https://www.pythonpodcast.com/episode-61-sentry-with-david-cramer","content_text":"Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.\n\nSummary\n\nAs developers we all have to deal with bugs sometimes, but we don’t have to make our users deal with them too. Sentry is a project that automatically detects errors in your applications and surfaces the necessary information to help you fix them quickly. In this episode we interviewed David Cramer about the history of Sentry and how he has built a team around it to provide a hosted offering of the open source project. We covered how the Sentry project got started, how it scales, and how to run a company based on open source.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show, subscribe, join our newsletter, check out the show notes, and get in touch you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nWe are also sponsored by Sentry this week. Stop hoping your users will report bugs. Sentry’s real-time tracking gives you insight into production deployments and information to reproduce and fix crashes. Check them out at getsentry.com and use the code podcastinit at signup to get a $50 credit!- Join our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour hosts as usual are Tobias Macey and Chris Patti\nToday we’re interviewing David Cramer about Sentry which is an open source and hosted service for capturing and tracking exceptions in your applications.\n\n\nInterview with Firstname Lastname\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nWhat is Sentry and how did it get started? – Tobias\nWhat led you to choose Python for writing Sentry and would you make the same choice again? – Tobias\nError reporting needs to be super light weight in order to be useful. What were some implementation challenges you faced around this issue? – Chris\nWhy would a developer want to use a project like Sentry and what makes it stand out from other offerings? – Tobias\nWhen would someone want to use a different error tracking service? – Tobias\nCan you describe the architecture of the Sentry project both in terms of the software design and the infrastructure necessary to run it? – Tobias\nWhat made you choose Django versus another Python web framework, and would you choose it today? – Chris\nWhat languages and platforms does Sentry support and how does a developer integrate it into their application? – Tobias\nOne of the big discussions in open source these days is around maintainability and a common approach is to have a hosted offering to pay the bills for keeping the project moving forward. How has your experience been with managing the open source community around the project in conjunction with providing a stable and reliable hosted service for it? – Tobias\nAre there any benefits to using the hosted offering beyond the fact of not having to manage the service on your own? – Tobias\nHave you faced any performance challenges implementing Sentry’s server side? – Chris\nWhat advice can you give to people who are trying to get the most utility out of their usage of Sentry? – Tobias\nWhat kinds of challenges have you encountered in the process of adding support for such a wide variety of languages and runtimes? – Tobias\nCapturing the context of an error can be immensely useful in finding and solving it effectively. Can you describe the facilities in Sentry and Raven that assist developers in providing that information? – Tobias\nIt’s challenging to create an effective method for aggregating incoming issues so that they are sufficiently visible and useful while not hiding or discarding important information. Can you explain how you do that and what the evolution of that system has been like? – Tobias\nI notice a lot of from future import in Sentry. Does it support Python 3 and/or what’s the plan for getting there? – Chris\nLooking back to the beginning of the project, what are some of the most interesting and surprising changes that have happened during its lifetime? How does it differ from its original vision? – Tobias\n\n\nKeep In Touch\n\n\nTwitter\n\n\nPicks\n\n\nTobias\n\nBPython\n\n\n\nChris\n\n\nDeveloper on Fire\nSong Exploder\n\n\n\nDavid\n\n\nReact\nWebpack\nAlpine Climbing\nPercy.io\nRed Rising Trilogy\n\n\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.

\n\n

Summary

\n\n

As developers we all have to deal with bugs sometimes, but we don’t have to make our users deal with them too. Sentry is a project that automatically detects errors in your applications and surfaces the necessary information to help you fix them quickly. In this episode we interviewed David Cramer about the history of Sentry and how he has built a team around it to provide a hosted offering of the open source project. We covered how the Sentry project got started, how it scales, and how to run a company based on open source.

\n\n

Brief Introduction

\n\n\n\n

Interview with Firstname Lastname

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-06-11T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/1e1d5dbf-be29-4b76-93f4-1bf0f4201218.mp3","mime_type":"audio/mpeg","size_in_bytes":92797262,"duration_in_seconds":4167}]},{"id":"http://podcastinit.podbean.com/e/episode-60-mercurial-with-augie-fackler/","title":"Mercurial with Augie Fackler","url":"https://www.pythonpodcast.com/episode-60-mercurial-with-augie-fackler","content_text":"Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.\n\nSummary\n\nAs developers, one of the most important tools that we use daily is our version control system. Mercurial is one such tool that is written in Python, making it eminently flexible, customizable, and incredibly powerful. This week we spoke with Augie Fackler to learn about the history, features, and future of Mercurial.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nWe are also sponsored by Sentry this week. Stop hoping your users will report bugs. Sentry’s real-time tracking gives you insight into production deployments and information to reproduce and fix crashes. Check them out at getsentry.com and use the code podcastinit at signup to get a $50 credit!\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour hosts as usual are Tobias Macey and Chris Patti\nToday we’re interviewing Augie Fackler about the Mercurial version control system\n\n\nInterview with Augie Fackler\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nCan you describe what Mercurial is and how the project got started? – Tobias\nHow did you get involved with working on Mercurial? – Tobias\nWhat are some of the features that can be found in Mercurial which are lacking in similar tools such as Git or Bazaar? – Tobias\nOne of the common complaints with Git is that its human interface could use some work. How is Mercurial’s UX an improvement over Git? – Chris\nFor someone who is using Mercurial to work with a Git or other VCS repository, what are some of the edge cases that they should watch out for? Are there certain operations that could be performed in Mercurial which would break that compatibility layer? – Tobias\nHow is Mercurial architected and what are some of the design choices that allow for it to be so flexible and extensible? – Tobias\nOne of the core goals of Mercurial is for it to be safe. Can you explain what safety means in this context and how it is architected to achieve that goal? – Tobias\nOne of the noteworthy aspects of Mercurial is the strong focus on making extensions a first-class concern in the project, so much so that a number of the core functions are written as extensions. Can you describe why that is and how the extensions plug into the core execution engine? – Tobias\nWhat are some of the most notable extensions that are available for use with Mercurial? – Tobias\nFor someone who is familiar with Git, what are some of the concepts that they would need to learn about in order to use Mercurial in an idiomatic way? – Tobias\nA large part of the reason that Git has seen such large adoption is due to the prevalence of GitHub. There is the option of using BitBucket when using Mercurial. Are there any other noteworthy Mercurial hosting options? Do you think that the dearth of open source mercurial servers is partially due to the fact that Mercurial ships with a functional server built in? – Tobias\nCan you share some of the most recent features that have been added to Mercurial? – Tobias\nWhat do you have planned for the future of Mercurial? – Tobias\nHow do you think current day DVCS systems like Mercurial, Git and Darcs might evolve in the future? – Chris\n\n\nKeep In Touch\n\n\nTwitter\n\n\nPicks\n\n\nTobias\n\nSapiens: A Brief History of Humankind by Yuval Noah Harrari\nCultures of Continuous Learning Keynote by Vanessa Hurst\n\n\n\nChris\n\n\nIntro to Django Video Series\nTransistor Podcast\nEmbedded Podcast\n\n\n\nAugie\n\n\nLeviathan Wakes\nThree Body Problem\nPrometheus\n\n\n\n\n\nLinks\n\n\nMercurial: The Definitive Guide\n\nOnline\nPrint\n\n\n\nRevsets\nGit Pickaxe\nFacebook Mercurial Post\nRemote File Log\nGerrit\nKallithea\nReviewboard\nMozilla Review Board\nA Case of Computational Thinking: The Subtle Effect ofHidden Dependencies on the User Experience of VersionControl\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.

\n\n

Summary

\n\n

As developers, one of the most important tools that we use daily is our version control system. Mercurial is one such tool that is written in Python, making it eminently flexible, customizable, and incredibly powerful. This week we spoke with Augie Fackler to learn about the history, features, and future of Mercurial.

\n\n

Brief Introduction

\n\n\n\n

Interview with Augie Fackler

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-06-04T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/6884a606-8280-48f3-b3a9-faf678910936.mp3","mime_type":"audio/mpeg","size_in_bytes":86433720,"duration_in_seconds":3311}]},{"id":"http://podcastinit.podbean.com/e/episode-59-pillow-with-alex-clark/","title":"Pillow with Alex Clark","url":"https://www.pythonpodcast.com/episode-59-pillow-with-alex-clark","content_text":"Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.\n\nSummary\n\nIf you need to work with images the Pillow is the library to use. The Python Image Libary (PIL) has long been the gold standard for resizing, analyzing, and processing pictures in Python. Pillow is the modern fork that is bringing the PIL into the future so that we can all continue to use it moving forward. This week I spoke with Alex Clark about what first led him to fork the project and his experience maintaining it, including the migration to Python 3.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nWe also have a new sponsor this week. Rollbar is a service for tracking and aggregating your application errors so that you can find and fix the bugs in your application before your users notice they exist. Use the link rollbar.com/podcastinit to get 90 days and 300,000 errors for free on their bootstrap plan.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour host as usual is Tobias Macey\nToday we’re interviewing Alex Clark about the Pillow project\n\n\nInterview with Alex Clark\n\n\nIntroductions\nHow did you get introduced to Python? – Tobias\nWhat were you working on that led you to forking the Python Image Library (PIL)? – Tobias\nWhat does Fredrik Lundh (author of PIL) think of Pillow?\nWhen you first forked the PIL project did you think that you would still be maintaining and updating that fork by now? – Tobias\nWho else works on the project with you and how did they get involved? – Tobias\nWhat kinds of special knowledge or experience have you found to be necessary for understanding and extending the routines in the library and for adding new capabilities? – Tobias\nCan you describe what PIL and now Pillow are and what kinds of use cases they support? – Tobias\nHow does Pillow compare to libraries with a similar purpose such as ImageMagick? – Tobias\nI have seen Pillow used in computer vision contexts. What are some of the capabilities of the library that lend themselves to this purpose? – Tobias\nWhat architectural patterns does Pillow use to make image operations fast and flexible? Have you found the need to do any significant refactorings of the original code to make it compatible with modern uses and execution environments? – Tobias\nHave you kept up to date with newer image formats, such as webp? Are there any image formats that Pillow does not support that you would like to see added to the project? – Tobias\nWhat are some of the most interesting or innovative uses of Pillow that you have seen? – Tobias\nWhat do you have planned for the future of Pillow? – Tobias\n\n\nKeep In Touch\n\n\nWebsite\n\n\nPicks\n\n\nTobias\n\nMinimalist Baker\nBisect module\n\n\n\nAlex\n\n\nMuse – Uprising\nFanstatic\n\n\n\n\n\nLinks\n\n\nImage-SIG\nRandom (Psychedelic) Art\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.

\n\n

Summary

\n\n

If you need to work with images the Pillow is the library to use. The Python Image Libary (PIL) has long been the gold standard for resizing, analyzing, and processing pictures in Python. Pillow is the modern fork that is bringing the PIL into the future so that we can all continue to use it moving forward. This week I spoke with Alex Clark about what first led him to fork the project and his experience maintaining it, including the migration to Python 3.

\n\n

Brief Introduction

\n\n\n\n

Interview with Alex Clark

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-05-28T16:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/f336949a-7723-4218-a28f-999133f26b8c.mp3","mime_type":"audio/mpeg","size_in_bytes":29722259,"duration_in_seconds":1201}]},{"id":"http://podcastinit.podbean.com/e/episode-58-wagtail-with-tom-dyson/","title":"Wagtail with Tom Dyson","url":"https://www.pythonpodcast.com/episode-58-wagtail-with-tom-dyson","content_text":"Visit our site to sign up for the newsletter, explore past episodes, subscribe to the show, and help support our work.\n\nSummary\n\nIf you are operating a website that needs to publish and manage content on a regular basis, a CMS (Content Management System) becomes the obvious choice for reducing your workload. There are a plethora of options available, but if you are looking for a solution that leverages the power of Python and exposes its flexibility then you should take a serious look at Wagtail. In this episode Tom Dyson explains how Wagtail came to be created, what sets it apart from other options, and when you should implement it for your projects.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nWe also have a new sponsor this week. Rollbar is a service for tracking and aggregating your application errors so that you can find and fix the bugs in your application before your users notice they exist. Use the link rollbar.com/podcastinit to get 90 days and 300,000 errors for free on their bootstrap plan.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Tom Dyson about Wagtail, a modern and sophisticated CMS for Django.\n\n\nInterview with Tom Dyson\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nCan you start by explaining what a content management system is and why they are useful? – Tobias\nHow did the Wagtail project get started and what makes it stand out from other comparable offerings? – Tobias\nWhat made you choose Django as the basis for the project as opposed to another framework or language such as Pyramid, Flask, or Rails? – Tobias\nWhat is your target user and are there any situations in which you would encourage someone to use a different CMS? – Tobias\nCan you explain the software design approach that was taken with Wagtail and describe the challenges that have been overcome along the way? – Tobias\nHow did you approach the project in a way to make the CMS feel well integrated into the other apps in a given Django project so that it doesn’t feel like an afterthought? – Tobias\nFor someone who wants to get started with using Wagtail, what does that experience look like? – Tobias\nWhat are some of the features that are unique to Wagtail? – Tobias\nGiven that Wagtail is such a flexible tool, what are some of the gotchas that people should watch out for as they are working on a new site? – Tobias\nDoes Wagtail have any built-in support for multi-tenancy? – Tobias\nDoes Wagtail have a plugin system to allow developers to create extensions to the base CMS? – Tobias\nHaving built such a sizable plugin with deep integrations to Django, what are some of the shortcomings in the framework that you would like to see improved? – Tobias\n\n\nKeep In Touch\n\n\nTwitter\nSite\nGitHub\n\n\nPicks\n\n\nTobias\n\nPumpkin Pie\n\n\n\nTom\n\n\nHasbean Ethiopian Coffee\nHario V60\n\n\n\n\n\nLinks\n\n\nRoyal College of Arts\nSimon Willison’s Blog\nVagrant\nWillow project\nDjango Model Cluster\nDivio\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to sign up for the newsletter, explore past episodes, subscribe to the show, and help support our work.

\n\n

Summary

\n\n

If you are operating a website that needs to publish and manage content on a regular basis, a CMS (Content Management System) becomes the obvious choice for reducing your workload. There are a plethora of options available, but if you are looking for a solution that leverages the power of Python and exposes its flexibility then you should take a serious look at Wagtail. In this episode Tom Dyson explains how Wagtail came to be created, what sets it apart from other options, and when you should implement it for your projects.

\n\n

Brief Introduction

\n\n\n\n

Interview with Tom Dyson

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-05-21T12:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/9dfc4f39-356a-48e0-9fc1-c8cd5597e6cd.mp3","mime_type":"audio/mpeg","size_in_bytes":69939939,"duration_in_seconds":3152}]},{"id":"http://podcastinit.podbean.com/e/episode-57-buildbot-with-pierre-tardy/","title":"Buildbot with Pierre Tardy","url":"https://www.pythonpodcast.com/episode-57-buildbot-with-pierre-tardy","content_text":"Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.\n\nSummary\n\nAs technology professionals, we need to make sure that the software we write is reliably bug free and the best way to do that is with a continuous integration and continuous deployment pipeline. This week we spoke with Pierre Tardy about Buildbot, which is a Python framework for building and maintaining CI/CD workflows to keep our software projects on track.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show, subscribe, join our newsletter, check out the show notes, and get in touch you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nWe are also sponsored by Rollbar this week. Rollbar is a service for tracking and aggregating your application errors so that you can find and fix the bugs in your application before your users notice they exist. Use the link rollbar.com/podcastinit to get 90 days and 300,000 errors for free on their bootstrap plan.\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nYour hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Pierre Tardy about the Buildbot continuous integration system.\n\n\nInterview with Pierre Tardy\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nFor anyone who isn’t familiar with it can you explain what Buildbot is? – Tobias\nWhat was the original inspiration for creating the project? – Tobias\nHow did you get involved in the project? – Tobias\nCan you describe the internal architecture of Buildbot and outline how a typical workflow would look? – Tobias\nThere are a number of packages out on PyPI for doing subprocess invocation and control, in addition to the functions in the standard library. Which does buildbot use and why? – Chris\nWhat makes Buildbot stand out from other CI/CD options that are available today? – Tobias\nScaling a large CI/CD system can become a challenge. What are some of the limiting factors in the Buildbot architecture and in what ways have you seen people work to overcome them? – Tobias\nAre there any design or architecture choices that you would change in the project if you were to start it over? – Tobias\nIf you were starting from scratch on implementing buildbot today, would you still use Python? Why? – Chris\nWhat are some of the most difficult challenges that have been faced in the creation and evolution of the project? – Tobias\nWhat are some of the most notable uses of Buildbot and how do they uniquely leverage the capabilities of the framework? – Tobias\nWhat are some of the biggest challenges that people face when beginning to implement Buildbot in their architecture? – Tobias\nDoes buildbot support the use of docker or public clouds as a part of the build process? – Chris\nI know that the execution engine for Buildbot is written in Twisted. What benefits does that provide and how has that influenced any efforts for providing Python 3 support? – Tobias\nDoes buildbot support build parallelization at all? For instance splitting one very long test run up into 3 instances each running a section of tests to cut build time? – Chris\nWhat are some of the most requested features for the project and are there any that would be unreasonably difficult to implement due to the current design of the project? – Tobias\nDoes buildbot offer a plugin system like Jenkins does, or is there some other approach it uses for custom extensions to the base buildbot functionality? – Chris\nManaging a reliable build pipeline can be operationally challenging. What are some of the thorniest problems for Buildbot in this regard and what are some of the mechanisms that are built in to simplify the operational characteristics? – Tobias\nWhat were some of the challenges around supporting slaves running on platforms with very different environmental characteristics like Microsoft Windows? – Chris\nWhat is on the roadmap for Buildbot? – Tobias\n\n\nKeep In Touch\n\n\nBuildbot Website\nGitHub\n\n\nPicks\n\n\nTobias\n\nViking Safety Razor\n\n\n\nChris\n\n\nLifeline\nSuzaku Sake\n\n\n\n\n\nLinks\n\n\nCrossbar.io\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.

\n\n

Summary

\n\n

As technology professionals, we need to make sure that the software we write is reliably bug free and the best way to do that is with a continuous integration and continuous deployment pipeline. This week we spoke with Pierre Tardy about Buildbot, which is a Python framework for building and maintaining CI/CD workflows to keep our software projects on track.

\n\n

Brief Introduction

\n\n\n\n

Interview with Pierre Tardy

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-05-14T14:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/8924c725-6596-4efe-8776-dc606c6c48f8.mp3","mime_type":"audio/mpeg","size_in_bytes":93408365,"duration_in_seconds":5107}]},{"id":"http://podcastinit.podbean.com/e/episode-56-onion-iot-with-lazar-and-zheng/","title":"Onion IoT with Lazar and Zheng","url":"https://www.pythonpodcast.com/episode-56-onion-iot-with-lazar-and-zheng","content_text":"Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.\n\nSummary\n\nOne of the biggest new trends in technology is the Internet of Things and one of the driving forces is the wealth of new sensors and platforms that are being continually introduced. In this episode we spoke with the founder and head engineer of one such platform named Onion. The Omega board is a new hardware platform that runs OpenWRT and lets you configure it using a number of languages, not least of which is Python.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nWe are also sponsored by Rollbar this week. Rollbar is a service for tracking and aggregating your application errors so that you can find and fix the bugs in your application before your users notice they exist. Use the link rollbar.com/podcastinit to get 90 days and 300,000 errors for free on their bootstrap plan.\nVisit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.\nTo help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nThe Open Data Science Conference in Boston is happening on May 21st and 22nd. If you use the code EP during registration you will save 20% off of the ticket price. If you decide to attend then let us know, we’ll see you there!\nYour hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Lazar and Zheng about the Onion IoT platform\n\n\nInterview with Lazar and Zheng\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nWhat is the Onion platform and how does it leverage Python? – Tobias\nCan you compare and contrast the Python support you provide for Onion as compared with Raspberry Pi? – Chris\nI noticed that you are using the OpenWRT distribution of Linux in order to provide support for multiple languages. What was the driving intent behind choosing it and why is multiple language support so important for an IoT product? – Tobias\nDo you provide any libraries for using with the Omega to abstract away some of the hardware level tasks? What are some of the design considerations that were involved when developing that? – Tobias\nWhat are some of the most interesting projects you have seen people build with Python on your platform? – Tobias\n\n\nKeep In Touch\n\n\nForum\nTwitter\n\n\nPicks\n\n\nTobias\n\nNow You See Me\n\n\n\nChris\n\n\nPortrait / Landscape Phone / Tablet Stand\nTom Bihn Bags\n\n\n\nLazar\n\n\nEx Machina\n\n\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.

\n\n

Summary

\n\n

One of the biggest new trends in technology is the Internet of Things and one of the driving forces is the wealth of new sensors and platforms that are being continually introduced. In this episode we spoke with the founder and head engineer of one such platform named Onion. The Omega board is a new hardware platform that runs OpenWRT and lets you configure it using a number of languages, not least of which is Python.

\n\n

Brief Introduction

\n\n\n\n

Interview with Lazar and Zheng

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-05-07T17:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/77567525-56bb-4b5b-b34d-6840344b9a94.mp3","mime_type":"audio/mpeg","size_in_bytes":42512049,"duration_in_seconds":2151}]},{"id":"http://podcastinit.podbean.com/e/episode-55-libcloud-with-anthony-shaw/","title":"LibCloud with Anthony Shaw","url":"https://www.pythonpodcast.com/episode-55-libcloud-with-anthony-shaw","content_text":"Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.\n\nSummary\n\nMore and more of our applications are running in the cloud and there are increasingly more providers to choose from. The LibCloud project is a Python library to help us manage the complexity of our environments from a uniform and pleasant API. In this episode Anthony Shaw joins us to explain how LibCloud works, the community that builds and supports it, and the myriad ways in which it can be used. We also got a peek at some of the plans for the future of the project.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nThe Open Data Science Conference in Boston is happening on May 21st and 22nd. If you use the code EP during registration you will save 20% off of the ticket price. If you decide to attend then let us know, we’ll see you there!\nYour hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Anthony Shaw about the Apache LibCloud project\n\n\nInterview with Anthony Shaw\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nWhat is LibCloud and how did it get started? – Tobias\nHow much overhead does using libcloud impose versus native SDKs for performance sensitive APIs like block storage? – Chris\nWhat are some of the design patterns and abstractions in the library that allow for supporting such a large number of cloud providers with a mostly uniform API? – Tobias\nGiven that there are such differing services provided by the different cloud platforms, do you face any difficulties in exposing those capabilities? – Tobias\nHow does LibCloud compare to similar projects such as the Fog gem in Ruby? – Tobias\nWhat inspired the choice of Python as the language for creating the LibCloud project? Would you make the same choice again? – Tobias\nWhich versions of Python are supported and what challenges has that created? – Tobias\nWhat is your opinion on the state of PyPI as a package maintainer? What statistics are most useful to you and what else do you wish you could track? – Tobias\nCould you walk our listeners through the under the cover process details of instantiating a computer instance in say, Azure using libcloud? – Chris\nDoes LibCloud have any native support for parallelization, such as for the purpose of launching a large number of compute instances simultaneously? – Tobias\nWhat does it mean to be an Apache project and what benefits does it provide? – Tobias\nWhat are some of the most notable projects that leverage LibCloud for interacting with platform and infrastructure service providers? – Tobias\nCould you describe how libcloud could be extended to abstract away a new type of service that’s not yet supported – e.g. a database? – Chris\nWould you suggest that libcloud users extend libcloud to cover ‘native’ services they might use like AWS Lambda, or should they mix libcloud and ‘native’ SDKs in cases like this? – Chris\nCould you talk a little bit about the cloud oriented network services that libcloud supports? Is it possible to create AWS VPCs, subnets, etc using libcloud? – Chris\nDo you know if people use LibCloud for abstracting the APIs of a single cloud provider, even if they don’t have any intention of using a different platform? – Tobias\nDo you think that people are more likely to use LibCloud for bridging across muliple public cloud platforms, or is it more commonly used in a hybrid cloud type of environment? – Tobias\nWhat is on the roadmap for LibCloud that people should keep an eye out for? – Tobias\n\n\nKeep In Touch\n\n\nTwitter\nGitHub\nGitHub\n\n\nPicks\n\n\nTobias\n\nBlue Yeti Microphone\nDiablo Swing Orchestra\n\n\n\nChris\n\n\nRosewill RK Keycaps\nEnki\nCatch 22\n\n\n\nAnthony\n\n\nHidden Brain Podcast\nPyKwalify\nDoing Nothing\n\n\n\n\n\nLinks\n\n\nDimension Data\nAustin Bingham and Robert Smallshire Pluralsight Python Training\nCloudKick\nPyPI Ranking website\nApache JClouds\nSaltStack\nScalr\nApache Software Foundation\nMist.io\nStackStorm\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.

\n\n

Summary

\n\n

More and more of our applications are running in the cloud and there are increasingly more providers to choose from. The LibCloud project is a Python library to help us manage the complexity of our environments from a uniform and pleasant API. In this episode Anthony Shaw joins us to explain how LibCloud works, the community that builds and supports it, and the myriad ways in which it can be used. We also got a peek at some of the plans for the future of the project.

\n\n

Brief Introduction

\n\n\n\n

Interview with Anthony Shaw

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-04-30T20:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/2b661d75-defc-4a21-af9d-46121970ce97.mp3","mime_type":"audio/mpeg","size_in_bytes":98965226,"duration_in_seconds":5074}]},{"id":"http://podcastinit.podbean.com/e/episode-54-pip-and-the-python-package-authority-with-donald-stufft/","title":"Pip and the Python Package Authority with Donald Stufft","url":"https://www.pythonpodcast.com/episode-54-pip-and-the-python-package-authority-with-donald-stufft","content_text":"Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.\n\nSummary\n\nAs Python developers we have all used pip to install the different libraries and projects that we need for our work, but have you ever wondered about who works on pip and how the package archive we all know and love is maintained? In this episode we interviewed Donald Stufft who is the primary maintainer of pip and the Python Package Index about how he got involved with the projects, what kind of work is involved, and what is on the roadmap. Give it a listen and then give him a big thank you for all of his hard work!\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nGoogle Play Music just launched support for podcasts, so now you can check us out there and subscribe to the show.\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nWe also have a new sponsor this week. Rollbar is a service for tracking and aggregating your application errors so that you can fix the bugs in your application before your users notice they exist. Use the link rollbar.com/podcatinit to get 90 days and 300,000 errors for free on their bootstrap plan.\nThe Open Data Science Conference in Boston is happening on May 21st and 22nd. If you use the code EP during registration you will save 20% off of the ticket price. If you decide to attend then let us know, we’ll see you there!\nYour hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Donald Stufft about Pip and the Python Packaging Authority\n\n\nInterview with Donald Stufft\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nHow did you get involved with the Pip project? – Tobias\nWhat is the Python Packaging Authority and what does it do? – Tobias\nHow is PyPi / the Python Packaging Authority funded? – Chris\nWhat is your opinion on the current state of Python packaging? Are there lessons from other languages and package managers that you think should be adopted by Python? – Tobias\nWhat was involved in getting pip into the standard Python distribution? Was there any controversy around this? – Chris\nCan you describe some of the mechanics of Pip and how it differs from the other packaging systems that Python has used in the past? – Tobias\nDoes pip interact at all with virtualenv, pyenv and the like? – Chris\nThe newest package format for Python is the wheel system. Can you describe what that is and what its benefits are? – Tobias\nWhat are the biggest challenges that you have encountered while working on Pip? – Tobias\nWhat does the infrastructure for the Python Package Index look like? – Tobias\nWhat have been some of the challenges around scaling Pypi’s infrastructure to meet demand? – Chris\nYou’re currently working on a replacement for the PyPI site with the Warehouse project. Can you explain your motivation for that and how it improves on the current system? – Tobias\nWhere do you see the future of dependency management in Python headed? – Chris\nA few days ago there was a big story about how an NPM library was removed from the index, breaking a large number of dependent projects and applications. Do you think that anything like that could happen in the Python ecosystem? – Tobias\nWhat’s on the roadmap for Pip? – Tobias\n\n\nKeep In Touch\n\n\nGitHub\nDistUtils Special Interest Group\nEmail\n@dstufft on Twitter\n\n\nPicks\n\n\nTobias\n\nXiki\n\n\n\nChris\n\n\nAgar.io\nCulprate\nTCP/IP Illustrated Volume I: The Protocols\n\n\n\nDonald\n\n\nLinux on Windows 10\n\n\n\n\n\nLinks\n\n\nBandersnatch\nWheel\nWarehouse pypa/warehouse\nPyPI Sponsors56\nDevPI\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.

\n\n

Summary

\n\n

As Python developers we have all used pip to install the different libraries and projects that we need for our work, but have you ever wondered about who works on pip and how the package archive we all know and love is maintained? In this episode we interviewed Donald Stufft who is the primary maintainer of pip and the Python Package Index about how he got involved with the projects, what kind of work is involved, and what is on the roadmap. Give it a listen and then give him a big thank you for all of his hard work!

\n\n

Brief Introduction

\n\n\n\n

Interview with Donald Stufft

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-04-23T14:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/2a8488d2-5cf5-49bd-8481-f72992909606.mp3","mime_type":"audio/mpeg","size_in_bytes":60390636,"duration_in_seconds":3179}]},{"id":"http://podcastinit.podbean.com/e/episode-53-stackstorm-with-tomaz-muraus-and-patrick-hoolboom/","title":"StackStorm with Tomaž Muraus and Patrick Hoolboom","url":"https://www.pythonpodcast.com/episode-53-stackstorm-with-tomaz-muraus-and-patrick-hoolboom","content_text":"Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.\n\nSummary\n\nIf you are responsible for managing any amount of servers, then you know that automation is critical for maintaining your sanity. This week we spoke with Tomaž Muraus and Patrick Hoolboom about their work on StackStorm, which is a platform for tracking and reacting to events in your infrastructure. By allowing you to register actions with event triggers it frees you from having to worry about a whole class of concerns so that you can focus on building new capabilities rather than babysitting what you already have.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nI would also like to thank Hired, a job marketplace for developers and designers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.\nODSC East in Boston is happening on May 21st – 22nd. Use the discount code EP for 20% off when you register\nYour hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Tomaž Muraus and Patrick Hoolboom about the StackStorm project, which is an event-driven system automation framework.\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nWhat is StackStorm and what problems does it solve? – Tobias\nWhat was your inspiration for creating StackStorm and what were some of the biggest architectural and design challenges? – Tobias\nWhat made you choose Python for StackStorm’s implementation rather than another language like Go? – Chris\nCan you describe the architecture of StackStorm and what the setup looks like? – Tobias\nOther than chat driven events, what types of event sources does StackStorm support, and what use cases do those alternate event streams enable? – Chris\nThe home page describes StackStorm as being an event-driven framework for automating the users infrastructure. What kinds of capabilities are made possible by this and do you think that it simplifies or complicates the work of operations engineers? – Tobias\nIs there a minimum or maximum size of infrastructure for which it would make sense to use StackStorm? – Tobias\nIt looks like StackStorm is made up of a number of discrete components. What do the components use to communicate, and how did those choices influence the design of StackStorm’s overall architecture? – Chris\nI use SaltStack in my work which is a tool that also focuses on event-driven architecture. Can you compare and contrast the capabilities and focus of StackStorm with the features of SaltStack? Would it make sense to use both frameworks in the same infrastructure? – Tobias\nOne of the advertised features of StackStorm is a strong focus on ChatOps. Can you explain that concept for people who might not be familiar with it and describe why it is such a useful paradigm? – Tobias\nExtensibility is a critical capability for an operations platform due to the wide variety of environments that people are inclined to build. In StackStorm the unit of extensibility is a pack. Can you describe what a pack is and how you arrived at that abstraction? – Tobias\n\nHave you encountered any situations in which the concept of a pack has been the wrong abstraction and made something more difficult than it may have been otherwise? – Tobias\n\n\n\nIn very large scale environments like Netflix, how would one build a StackStorm cluster to handle the immense load. More specifically, how does one determine what kinds of machine resources each component needs? – Chris\nManagement of credentials is always a difficult problem in operations. Does StackStorm attempt to tackle that issue or does it defer that responsibility to other systems, such as the user’s configuration management platform? – Tobias\nDoes StackStorm interface with Kibana, Splunk or other log / metric aggregation packages? – Chris\nWhat are some of the most surprising uses that you have heard of from people using the platform? – Tobias\n\n\nKeep In Touch\n\n\nTomaž\n\nTwitter\nwebsite/blog\n\n\n\nPatrick\n\n\nTwitter\n\n\n\n\n\nPicks\n\n\nTobias\n\nSAWS\nBill Peet\n\n\n\nChris\n\n\nGrimm Brewing Subliminal Message Sour Red Ale\nLobste.rs\nMedium\n\n\n\nTomaž\n\n\nUnderstanding Air France 447\nAviation Herald\n\n\n\nPatrick\n\n\nTrue Nutrition\nJP Cycles\n\n\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.

\n\n

Summary

\n\n

If you are responsible for managing any amount of servers, then you know that automation is critical for maintaining your sanity. This week we spoke with Tomaž Muraus and Patrick Hoolboom about their work on StackStorm, which is a platform for tracking and reacting to events in your infrastructure. By allowing you to register actions with event triggers it frees you from having to worry about a whole class of concerns so that you can focus on building new capabilities rather than babysitting what you already have.

\n\n

Brief Introduction

\n\n\n\n

Interview

\n\n

\n\n

Keep In Touch

\n\n

\n\n

Picks

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-04-16T11:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/51cf0b1d-2082-4a20-be63-50b2f6819514.mp3","mime_type":"audio/mpeg","size_in_bytes":70172317,"duration_in_seconds":3562}]},{"id":"http://podcastinit.podbean.com/e/episode-52-hypothesis-with-david-maciver/","title":"Hypothesis with David MacIver","url":"https://www.pythonpodcast.com/episode-52-hypothesis-with-david-maciver","content_text":"Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.\n\nSummary\n\nWriting tests is important for the stability of our projects and our confidence when making changes. One issue that we must all contend with when crafting these tests is whether or not we are properly exercising all of the edge cases. Property based testing is a method that attempts to find all of those edge cases by generating randomized inputs to your functions until a failing combination is found. This approach has been popularized by libraries such as Quickcheck in Haskell, but now Python has an offering in this space in the form of Hypothesis. This week, the creator and maintainer of Hypothesis, David MacIver, joins us to tell us about his work on it and how it works to improve our confidence in the stability of our code.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nOpen Data Science Conference on May 21-22nd in Boston. 20%\nYour hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing David MacIver about the Hypothesis project which is an advanced Quickcheck implementation for Python.\n\n\nInterview with David MacIver\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nCan you provide some background on what Quickcheck is and what inspired you to write an implementation in Python? – Tobias\nAre there any ways in which Hypothesis improves on the original design of Quickcheck? – Tobias\nCan you walk us through the execution of a simple Hypothesis test to give our listeners a better sense for what Hypothesis does? – Chris\nHave you had trouble getting people to use Hypothesis? How has adoption been? – David\nWhat does this sort of testing get you that conventional testing doesn’t? – David\nWhy do you think this sort of testing hasn’t caught on in the Python world before? – David\nAre there any facilities of the Python language that make your job easier? Are there aspects of the language that make this style of testing more difficult? – Tobias\nWhat are some of the design challenges that you have been presented with while working on Hypothesis and how did you overcome them? – Tobias\nGiven that testing is an important part of the development process for ensuring the reliability and correctness of the system under test, how do you make sure that Hypothesis doesn’t introduce uncertainty into this step? – Tobias\nGiven the sophisticated nature of the internals of Hypothesis, do you find it difficult to attract contributors to the project? – Tobias\nA few months ago you went through some public burnout with regards to open source and Hypothesis in particular, but circumstances have brought you back to it with a more focused plan for making it sustainable. Can you provide some background and detail about your experiences and reasoning? – Tobias\nWhat’s next for Hypothesis? – Chris\n\n\nKeep In Touch\n\n\nTwitter\nBlog\nNewsLetter\n\n\nPicks\n\n\nTobias\n\nTypeForm\nListener Survey\nCI Survey\n\n\n\nChris\n\n\nSeashine\nCheckIO\nMike Coutermarsh’s Jr. Developer series\n\n\n\nDavid\n\n\nMake It Stick by Peter Brown \nBeeminder\nVorkosigan Saga by Lois McMaster Bujold \n\n\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.

\n\n

Summary

\n\n

Writing tests is important for the stability of our projects and our confidence when making changes. One issue that we must all contend with when crafting these tests is whether or not we are properly exercising all of the edge cases. Property based testing is a method that attempts to find all of those edge cases by generating randomized inputs to your functions until a failing combination is found. This approach has been popularized by libraries such as Quickcheck in Haskell, but now Python has an offering in this space in the form of Hypothesis. This week, the creator and maintainer of Hypothesis, David MacIver, joins us to tell us about his work on it and how it works to improve our confidence in the stability of our code.

\n\n

Brief Introduction

\n\n\n\n

Interview with David MacIver

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-04-09T05:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/06d20def-d617-43f5-a42b-5b24a5ef2b45.mp3","mime_type":"audio/mpeg","size_in_bytes":53865533,"duration_in_seconds":2821}]},{"id":"http://podcastinit.podbean.com/e/episode-51-pyjion-with-dino-viehland-and-brett-cannon/","title":"Pyjion with Dino Viehland and Brett Cannon","url":"https://www.pythonpodcast.com/episode-51-pyjion-with-dino-viehland-and-brett-cannon","content_text":"Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.\n\nSummary\n\nIn an attempt to improve the performance characteristics of the CPython implementation, Dino Viehland began work on a patch to allow for a pluggable interface to a JIT (Just In Time) compiler. His employer, Microsoft, decided to sponsor his efforts and the result is the Pyjion project. In this episode we spoke with Dino Viehland and Brett Cannon about the goals of the project, the progress they have made so far, and the issues they have encountered along the way. We also made an interesting detour to discuss the general state of performance in the Python ecosystem and why the GIL isn’t the bogeyman it’s made out to be.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nI would also like to thank Hired, a job marketplace for developers and designers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.\nYour hosts as usual are Tobias Macey and Chris Patti\nOpen Data Science Conference, Boston MA May 21st – 22nd, use the discount code EP at registration for 20% off\nToday we are interviewing Brett Cannon and Dino Viehland about their work on Pyjion, a CPython extension that provides an API to allow for plugging a JIT compilation engine into the CPython runtime.\n\n\nInterview with Brett Cannon and Dino Viehland\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nWhat was the inspiration for the Pyjion project and what are its goals? – Tobias\nThe FAQ mentions that Pyjion could easily be made cross platform, but this being a Microsoft project it was bootstrapped on Windows. Have any of the discrete tasks required to get Pyjion running under OSX or Linux been laid out even in outline form? – Chris\nGiven that this is a Microsoft backed project it makes sense that the first JIT engine to be implemented is for the CoreCLR. What would an alternative implementation provide and in what ways can a JIT framework be tuned for particular workloads? – Tobias\nWhat kinds of use cases and problem domains that were previously impractical will be enabled by this? – Tobias\nDoes Microsoft’s recent acquisition of Xamarin and the Mono project change things for the Pyjion project at all? – Chris\nWhat are the challenges associated with your work on Pyjion? Are there certain aspects of the Python language and the CPython implementation that make the work more difficult than it might be otherwise? – Tobias\nWhen I think of Microsoft and programming languages I generally think of C++ and C#. Did your team have to go through an approval process in order to utilize Python, and further to open source your work on Pyjion? – Chris\nHow does Pyjion hook into the CPython runtime and what kinds of primitives does it expose to JIT engines for them to be able to work with? – Tobias\nWould an entire project be run through the JIT engine during runtime or is it possible to target a subset of the code being executed? – Tobias\nIn what ways can a JIT compiler implementation be purpose-built for a given workload and how would someone go about creating one? – Tobias\nCould a JIT plugin be designed with different trade-offs, like no C API compatibility, but that worked around the GIL to provide real concurrency in Python? – Chris\nOne of the most notable benefits of having a JIT implementation for the CPython runtime is the fact that modules with C extensions can be used, such as NumPy. Does that pose any difficulties in the compilation methods used for optimizing the Python portion of the code? – Tobias\nWhat kinds of performance improvements have you seen in your experimentation? – Tobias\nWhich release of Python do you hope to have Pyjion incorporated into? – Tobias\nHas any thought been given to making Python a first class citizen in Visual Studio Code? – Chris\nWhat areas of the project could use some help from our listeners? – Chris\n\n\nKeep In Touch\n\n\nDino\n\nGitHub\n\n\n\nBrett\n\n\nTwitter\nBlog\nPython Engineering @ Microsoft Blog\n\n\n\n\n\nPicks\n\n\nTobias\n\nLogitech Wave MK550\nSaltStack\nTestInfra\nSaltStack Formula Cookiecutter\n\n\n\nChris\n\n\nAnchor – Public Radio for the People\nThe Magicians\nPortal is a Feminist Masterpiece – PBS Gameshow\n\n\n\nBrett\n\n\nBreville Tea Maker\nBodom Mugs\nAlto’s Adventure\n\n\n\nDino\n\n\nCome Dine With Me\n\n\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.

\n\n

Summary

\n\n

In an attempt to improve the performance characteristics of the CPython implementation, Dino Viehland began work on a patch to allow for a pluggable interface to a JIT (Just In Time) compiler. His employer, Microsoft, decided to sponsor his efforts and the result is the Pyjion project. In this episode we spoke with Dino Viehland and Brett Cannon about the goals of the project, the progress they have made so far, and the issues they have encountered along the way. We also made an interesting detour to discuss the general state of performance in the Python ecosystem and why the GIL isn’t the bogeyman it’s made out to be.

\n\n

Brief Introduction

\n\n\n\n

Interview with Brett Cannon and Dino Viehland

\n\n\n\n

Keep In Touch

\n\n

\n\n

Picks

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-03-31T21:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/0ea4ea81-5ba3-4768-882f-198153548849.mp3","mime_type":"audio/mpeg","size_in_bytes":85224365,"duration_in_seconds":4226}]},{"id":"http://podcastinit.podbean.com/e/episode-50-transcrypt-with-jacques-de-hooge/","title":"Transcrypt with Jacques de Hooge","url":"https://www.pythonpodcast.com/episode-50-transcrypt-with-jacques-de-hooge","content_text":"Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.\n\nSummary\n\nAny programmer who has dealt with a website for any length of time knows that writing JavaScript isn’t always the most enjoyable. Wouldn’t you rather write that code in Python and just have it work on your website? In this episode we learn about Transcrypt with its creator Jacques de Hooge. Transcrypt is a Python to JavaScript transpiler that embraces the JavaScript ecosystem while letting you use the familiar syntax of Python for writing your logic, rather than trying to shoehorn a Python runtime into your browser.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nI would also like to thank Hired, a job marketplace for developers and designers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.\nODSC East in Boston is happening on May 21st – 22nd. Use the discount code EP for 20% off when you register\nYour host today is Tobias Macey\nToday I am interviewing Jacques de Hooge about his work on the Transcrypt Project\n\n\nInterview with Jacques de Hooge\n\n\nIntroductions\nHow did you get introduced to Python? – Tobias\nWhat is Transcrypt and what inspired you to create it? – Tobias\nAs you mention in the documentation, there are a number of projects that attempt to shoehorn Python into the browser. What makes Transcrypt different? – Tobias\nI like that you decided to embrace the web environment by calling into JavaScript libraries. What are some of the challenges that you encountered while creating that functionality? – Tobias\nHow is the transpilation performed and what are some of the methods that you used to get the build size as small as it is? – Tobias\nGiven the nature of JavaScripts prototypical inheritance and differences in class semantics, I imagine that adding support for multiple inheritance and reflecting the structure of Python classes must have been challenging. Can you describe that process and how you arrived at your current solution? – Tobias\nWhich aspects of the language were most difficult to translate to JavaScript? – Tobias\nIs Transcrypt complete and stable enough to be used in production? – Tobias\n\n\nKeep in Touch\n\n\nTranscrypt.org\nForum\nEmail\n\n\nPicks\n\n\nTobias\n\nCookiecutter\n\n\n\nJacques\n\n\nProgramming\n\n\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.

\n\n

Summary

\n\n

Any programmer who has dealt with a website for any length of time knows that writing JavaScript isn’t always the most enjoyable. Wouldn’t you rather write that code in Python and just have it work on your website? In this episode we learn about Transcrypt with its creator Jacques de Hooge. Transcrypt is a Python to JavaScript transpiler that embraces the JavaScript ecosystem while letting you use the familiar syntax of Python for writing your logic, rather than trying to shoehorn a Python runtime into your browser.

\n\n

Brief Introduction

\n\n\n\n

Interview with Jacques de Hooge

\n\n\n\n

Keep in Touch

\n\n\n\n

Picks

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-03-26T06:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/69747555-5a8a-4991-9b1e-5a8e84a7c8d6.mp3","mime_type":"audio/mpeg","size_in_bytes":26488788,"duration_in_seconds":2530}]},{"id":"http://podcastinit.podbean.com/e/episode-49-vpython-with-ruth-chabay-and-bruce-sherwood/","title":"VPython with Ruth Chabay and Bruce Sherwood","url":"https://www.pythonpodcast.com/episode-49-vpython-with-ruth-chabay-and-bruce-sherwood","content_text":"Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.\n\nSummary\n\nWouldn’t it be nice to be able to generate interactive 3D visualizations of physical systems in a declarative manner with Python? In this episode we spoke with Ruth Chabay and Bruce Sherwood about the VPython project which does just that. They tell us about how the use VPython in their classrooms, how the project got started, and the work they have done to bring it into the browser.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nI would also like to thank Hired, a job marketplace for developers and designers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.\nYour hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Ruth Chabay and Bruce Sherwood about their work on VPython\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nWhat is VPython and how did it get started? – Tobias\nWhat problems inspired you to create VPython? – Chris\nHow do you design an API that allows for such powerful 3D visualization while still making it accessible to students who are focusing on learning new concepts in mathematics and physics so that they don’t get overwhelmed by the tool? – Tobias\nI know many schools have embraced the open curriculum idea, have any of your physics courses using VPython been made available to the non matriculating public? – Chris\nHow does VPython perform its rendering? If you were to reimplement it would you do anything differently? – Tobias\nOne of the remarkable points about VPython is its ability to execute the simulations in a browser environment. Can you explain the technologies involved to make that work? – Tobias\nGiven the real-time rendering capabilities in VPython I’m sure that performance is a core concern for the project. What are some of the methods that are used to ensure an appropriate level of speed and does the cross-platform nature of the package pose any additional challenges? – Tobias\nHow does collision detection work in VPython, and does it handle more complex assemblies of component objects? – Chris\nCan you talk a little bit about VPython’s design, and perhaps walk us through how a simple scene is rendered, say the results of the sphere() call? – Chris\n\n\nKeep In Touch\n\n\nVPython Forum\nGlowscript Forum\nGithub\n\n\nPicks\n\n\nTobias\n\nLand of Lisp by Conrad Barsky M.D.\n\n\n\nChris\n\n\nThe Magicians\nSwift\nAtari Logo\n\n\n\nBruce\n\n\nVPython.org\nGlowscript.org\n\n\n\nRuth\n\n\nmatterandinteractions.org/student\nNetLogo\n\n\n\n\n\nLinks\n\n\nCoursera GATech Intro to Physics\nAlice Project\nglowscript.org\nJupyter VPython\nRapydScript\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.

\n\n

Summary

\n\n

Wouldn’t it be nice to be able to generate interactive 3D visualizations of physical systems in a declarative manner with Python? In this episode we spoke with Ruth Chabay and Bruce Sherwood about the VPython project which does just that. They tell us about how the use VPython in their classrooms, how the project got started, and the work they have done to bring it into the browser.

\n\n

Brief Introduction

\n\n\n\n

Interview

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-03-18T06:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/80220ee1-74b9-4aff-bce1-be8980f45e9a.mp3","mime_type":"audio/mpeg","size_in_bytes":41893810,"duration_in_seconds":3782}]},{"id":"http://podcastinit.podbean.com/e/episode-48-pydata-london-with-ian-ozsvald-and-emlyn-clay/","title":"PyData London with Ian Ozsvald and Emlyn Clay","url":"https://www.pythonpodcast.com/episode-48-pydata-london-with-ian-ozsvald-and-emlyn-clay","content_text":"Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.\n\nSummary\n\nIan Ozsvald and Emlyn Clay are co-chairs of the London chapter of the PyData organization. In this episode we talked to them about their experience managing the PyData conference and meetup, what the PyData organization does, and their thoughts on using Python for data analytics in their work.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nI would also like to thank Hired, a job marketplace for developers and designers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.\nYour hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Ian Ozsvald and Emlyn Clay about their work with PyData London, a group within the PyData organization. PyData London represents the largest Python group in London at ~2850 members, they hold regular monthly meetups for ~200 members at AHL near Bank and a yearly conference for around ~300 members. Last year, they and their sponsors raised over £26,000 to sponsor the development of core numerical libraries in Python.\n\n\n\nUse the promo code podcastinit20 to get a $20 credit when you sign up!\n\n\n\n\nOn Hired software engineers & designers can get 5+ interview requests in a week and each offer has salary and equity upfront. With full time and contract opportunities available, users can view the offers and accept or reject them before talking to any company. Work with over 2,500 companies from startups to large public companies hailing from 12 major tech hubs in North America and Europe. Hired is totally free for users and If you get a job you’ll get a $2,000 “thank you” bonus. If you use our special link to signup, then that bonus will double to $4,000 when you accept a job. If you’re not looking for a job but know someone who is, you can refer them to Hired and get a $1,337 bonus when they accept a job.\n\n\nInterview\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nWhat is the PyData organization, how does PyData London fit into it and what is your relationship with it? – Tobias\nIn what ways does a PyData conference differ from a PyCon? – Tobias\nDoes PyData do anything in particular to encourage users from disciplines that might not be aware of how much our community has to offer to choose the Python suite of data analysis tools? – Chris\nYou have both spent a good portion of your careers using Python for working with and analyzing data from various domains. How has that experience evolved over the past several years as newer tools have become available? – Tobias\nFor someone who is just getting started in the data analytics space, what advice can you give? – Tobias\nHow can conferences like PyData help strengthen the bonds and synergies between the Python software community and the sciences? – Chris\nThere are a number of different subtopics within the blanket categorization of data science. Is it difficult to balance the subject matter in PyData conferences and meetups to keep members of the audience from being alienated? – Tobias\nData science is a young field and we’ve yet to see lots of examples of the successful use of data. How are London-based companies using data with Python? – Ian\nIs there a Python data science library you think needs a little love? – Emlyn\n\n\nKeep In Touch\n\n\nIan\n\nBlog\nTwitter\n\n\n\nEmlyn\n\n\nTwitter\n\n\n\n\n\nPicks\n\n\nTobias\n\nxcape\nKeybase Filesystem\n\n\n\nChris\n\n\nThe Player of Games\nUndertale\nThe Big Short\n\n\n\nIan\n\n\nSeaborn: Python visualisation tool\nMastering Predictive Analytics with R: Rui Miguel Forte\nAllergect Rhinitis research using ML\nLondon Unreal City Audio Tour\n\n\n\nEmlyn\n\n\nipython nbconvert –template flag\nDamian Avila’s Blog post on making slides with iPython Notebook\n\n\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.

\n\n

Summary

\n\n

Ian Ozsvald and Emlyn Clay are co-chairs of the London chapter of the PyData organization. In this episode we talked to them about their experience managing the PyData conference and meetup, what the PyData organization does, and their thoughts on using Python for data analytics in their work.

\n\n

Brief Introduction

\n\n\n\n
\"Linode
\nUse the promo code podcastinit20 to get a $20 credit when you sign up!

\n
\n\n
\n

\"Hired

\n

On Hired software engineers & designers can get 5+ interview requests in a week and each offer has salary and equity upfront. With full time and contract opportunities available, users can view the offers and accept or reject them before talking to any company. Work with over 2,500 companies from startups to large public companies hailing from 12 major tech hubs in North America and Europe. Hired is totally free for users and If you get a job you’ll get a $2,000 “thank you” bonus. If you use our special link to signup, then that bonus will double to $4,000 when you accept a job. If you’re not looking for a job but know someone who is, you can refer them to Hired and get a $1,337 bonus when they accept a job.

\n
\n\n

Interview

\n\n\n\n

Keep In Touch

\n\n

\n\n

Picks

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-03-12T11:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/70a5be6c-957b-4723-a496-ddd1c6cdba7b.mp3","mime_type":"audio/mpeg","size_in_bytes":55532594,"duration_in_seconds":3791}]},{"id":"http://podcastinit.podbean.com/e/episode-47-efene-with-mariano-guerra/","title":"Efene with Mariano Guerra","url":"https://www.pythonpodcast.com/episode-47-efene-with-mariano-guerra","content_text":"Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.\n\nSummary\n\nEfene is a language that runs on the Erlang Virtual Machine (BEAM) and is inspired by the Zen of Python. It is intended as a bridge language that serves to ease the transition into the Erlang ecosystem for people who are coming from languages like Python. In this episode I spoke with Mariano Guerra, the creator of Efene, about how Python influenced his design choices, why you might want to use it, and when Python is the better tool.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nI would also like to thank Hired, a job marketplace for developers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.\nYour host today is Tobias Macey\nToday we are interviewing Mariano Guerra about his work on the Efene language.\n\n\nInterview with Mariano Guerra\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nSo Efene is a language that runs on the BEAM VM which you say was at least partially inspired by the Zen of Python. Can you explain in greater detail in what form that inspiration manifested and some of the process involved in the creation of Efene? – Tobias\nWhat inspired you to create Efene and what problems does it solve? – Tobias\nHow does Efene compare to other BEAM based languages such as Elixir? – Tobias\nWhen would a Python developer want to consider using Efene? – Tobias\nWhat benefits does the BEAM provide that can’t be easily replicated in the Python ecosystem? – Tobias\nDoes the Efene language ease the transition to a more functional mindset for developers who are already familiar with Python paradigms? – Tobias\nI understand that you are experimenting with another language implementation that runs on the BEAM. Can you describe that project and compare it to Efene? What were your inspirations? – Tobias\n\n\nKeep In Touch\n\n\nTwitter\nGitHub\nBlog\nEfene\nEmesene\nPython Argentina\n\n\nPicks\n\n\nTobias\n\nDotphiles\nThe Unreasonable Effectiveness of Dynamic Typing for Practical Programs\n\n\n\nMariano\n\n\nOm Next\nDavid Nolan on Om Next\nClojurescript\nThings Network\n\n\n\n\n\nLinks\n\n\nErlang\nElixir\nLisp Flavored Erlang\nJoxa\nRebar3\nErlang MK\nHex\nInterfix\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.

\n\n

Summary

\n\n

Efene is a language that runs on the Erlang Virtual Machine (BEAM) and is inspired by the Zen of Python. It is intended as a bridge language that serves to ease the transition into the Erlang ecosystem for people who are coming from languages like Python. In this episode I spoke with Mariano Guerra, the creator of Efene, about how Python influenced his design choices, why you might want to use it, and when Python is the better tool.

\n\n

Brief Introduction

\n\n\n\n

Interview with Mariano Guerra

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-03-03T20:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/8479e7c2-c1e3-4af6-88e4-f29f214102fc.mp3","mime_type":"audio/mpeg","size_in_bytes":38843228,"duration_in_seconds":3575}]},{"id":"http://podcastinit.podbean.com/e/episode-46-functional-python-with-matthew-rocklin-and-alexander-schepanovsky/","title":"Functional Python with Matthew Rocklin and Alexander Schepanovsky","url":"https://www.pythonpodcast.com/episode-46-functional-python-with-matthew-rocklin-and-alexander-schepanovsky","content_text":"Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.\n\nSummary\n\nWhat is functional programming, why would you want to use it, and how can you get started with it in Python? Our guests this week, Matthew Rocklin and Alexander Schepanovsky, help us understand all of that and more. Matthew and Alexander have each created their own Python libraries to make it easier to employ functional paradigms in your Python code. In this episode they help us understand the benefits that functional styles can have and the benefits that can be realized by trying them out for yourself.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nI would also like to thank Hired, a job marketplace for developers and designers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.\nYour host today is Tobias Macey\nToday we are interviewing Matthew Rocklin and Alexander Schepanovski about their work on functional libraries for Python.\n\n\nInterview with Alexander and Matthew\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nCan you first explain what functional programming is and how it differs from the procedural or object oriented programming that most Pythonistas are familiar with? – Tobias\nHow did you get started with functional programming? – Tobias\nWhat are the benefits of functional programming and when might someone want to use functional paradigms in their projects? – Tobias\nWhat is it about functional programming that people find so intimidating and what do you think has led to its recent rise in popularity? – Tobias\nWhat aspects of the Python language lend themselves to being used in a functional manner and where does it fall down? – Tobias\nCan you each describe what your respective libraries provide in terms of functional capabilities and what their particular focus is? Are they distinct enough from each other that it would make sense to use them both in a single project? – Tobias\nWhat inspired each of you to create your respective libraries? – Tobias\nThere is a functools module in the Python standard library that provides some methods that enable functional paradigms. Where does that module fall short and how do your respective libraries augment or replace the functionality in that module? – Tobias\nThere is also a library named fn.py which provides functional paradigms for use in Python. Can you each compare and contrast it with your own work? – Tobias\nThere are a number of concepts involved in functional programming such as currying, function composition, immutable data, and pure functions. Can you describe some of those concepts and then explain which of them you tried to incorporate into your libraries? – Tobias\nWhat are some of the resources that you have found to be most helpful when trying to learn and apply functional principles to your programs? – Tobias\n\n\nKeep In Touch\n\n\nAlexander\n\nTwitter\nBlog\n\n\n\nMatthew\n\n\nWebsite\nToolz\nTwitter\nGitHub\n\n\n\n\n\nPicks\n\n\nTobias\n\nDataDog\n\n\n\nAlexander\n\n\nThe Expanse\nRevolut\n\n\n\nMatthew\n\n\nRiemann\nFive Dances\nDistributed\n\n\n\n\n\nLinks\n\n\nRosetta Code\nPyToolz\nFuncy\nFn.py\nMacroPy\nCode Transformer\nSimple Made Easy by Rich Hickey\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.

\n\n

Summary

\n\n

What is functional programming, why would you want to use it, and how can you get started with it in Python? Our guests this week, Matthew Rocklin and Alexander Schepanovsky, help us understand all of that and more. Matthew and Alexander have each created their own Python libraries to make it easier to employ functional paradigms in your Python code. In this episode they help us understand the benefits that functional styles can have and the benefits that can be realized by trying them out for yourself.

\n\n

Brief Introduction

\n\n\n\n

Interview with Alexander and Matthew

\n\n\n\n

Keep In Touch

\n\n

\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-02-28T22:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/db2651f8-7773-4190-9c42-d2520cfb3263.mp3","mime_type":"audio/mpeg","size_in_bytes":94239852,"duration_in_seconds":4802}]},{"id":"http://podcastinit.podbean.com/e/episode-45-cython-with-craig-citro-and-robert-bradshaw/","title":"Cython with Craig Citro and Robert Bradshaw","url":"https://www.pythonpodcast.com/episode-45-cython-with-craig-citro-and-robert-bradshaw","content_text":"Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.\n\nSummary\n\nDo you find yourself reaching for a different language when you need some extra speed? With Cython you can get the best of both worlds by writing your code in Python and executing it as compiled code. In this episode we were joined by Craig Citro and Robert Bradshaw from the Cython project to discuss how and when you might want to incorporate it into your applications.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nI would also like to thank Hired, a job marketplace for developers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.\nYour hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Craig Citro and Robert Bradshaw\n\n\nInterview with Craig Citro and Robert Bradshaw\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nWhat is Cython and how did the project get started? – Tobias\nMy understanding is that Cython can improve the performance of a Python program without even having to provide any type annotations. How does it manage to do that? – Tobias\nCan a Cython module be used as a way to sidestep the GIL? What are some of the pitfalls that can be caused by doing so? – Tobias\nCan you give some examples of how Cython can be used to improve the perfomance of Python programs? – Tobias\nHow does Cython work under the covers? – Tobias\nWhat were some of the challenges during the creation of Cython and what design decisions were made to overcome them? – Tobias\nDoes Python’s cross platform nature create any unique challenges when compiling down to the C level? – Chris\nWhat processor and system architectures does Cython support and are there plans to expand that support? – Tobias\nHow do generators and list comprehensions map to C, and did those higher level language constructs pose any special challenges in Cython’s design? – Chris\nWould Rust ever be a potential compile target for performance and safety optimized modules? – Tobias\n\n\nKeep In Touch\n\n\nCraig\n\nTwitter\nGitHub\nWebsite\n\n\n\nRobert\n\n\nEmail\n\n\n\n\n\nPicks\n\n\nTobias\n\nCertificates, Reputation, and the Blockchain\n\n\n\nCraig\n\n\nCurious Kids Science Book by Asia Citro\ndplyr\nmagrittr\nEverything Is Obvious: How Common Sense Fails Us by Duncan Watts\n\n\n\nRobert\n\n\nMo Willems\nPhilips Hue Lights\nSage Math Cloud\n\n\n\n\n\nLinks\n\n\nSage (Math)\nPyrex)\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.

\n\n

Summary

\n\n

Do you find yourself reaching for a different language when you need some extra speed? With Cython you can get the best of both worlds by writing your code in Python and executing it as compiled code. In this episode we were joined by Craig Citro and Robert Bradshaw from the Cython project to discuss how and when you might want to incorporate it into your applications.

\n\n

Brief Introduction

\n\n\n\n

Interview with Craig Citro and Robert Bradshaw

\n\n\n\n

Keep In Touch

\n\n

\n\n

Picks

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-02-18T21:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/015e644c-cbe0-4dc3-8859-f7e917ba237a.mp3","mime_type":"audio/mpeg","size_in_bytes":58083626,"duration_in_seconds":3122}]},{"id":"http://podcastinit.podbean.com/e/episode-44-airflow-with-maxime-beauchemin/","title":"Airflow with Maxime Beauchemin","url":"https://www.pythonpodcast.com/episode-44-airflow-with-maxime-beauchemin","content_text":"Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.\n\nSummary\n\nAre you struggling with trying to manage a series of related, interdependent batch jobs? Then you should check out Airflow. In this episode we spoke with the project’s creator Maxime Beauchemin about what inspired him to create it, how it works, and why you might want to use it. Airflow is a data pipeline management tool that will simplify how you build, deploy, and monitor your complex data processing tasks so that you can focus on getting the insights you need from your data.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nI would also like to thank Hired, a job marketplace for developers and designers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.\nYour hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Maxime Beauchemin about his work on the Airflow project.\n\n\nInterview with Maxime Beauchemin\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nWhat is Airflow and what are some of the kinds of problems it can be used to solve? – Chris\nWhat are some of the biggest challenges that you have seen when implementing a data pipeline with a workflow engine? – Tobias\nWhat are some of the signs that a workflow engine is needed? – Tobias\nCan you share some of the design and architecture of Airflow and how you arrived at those decisions? – Tobias\nHow does Airflow compare to other workflow management solutions, and why did you choose to write your own? – Chris\nOne of the features of Airflow that is emphasized in the documentation is the ability to dynamically generate pipelines. Can you describe how that works and why it is useful? – Tobias\nFor anyone who wants to get started with using Airflow, what are the infrastructure requirements? – Tobias\nAirflow, like a number of the other tools in the space, support interoperability with Hadoop and its ecosystem. Can you elaborate on why JVM technologies have become so prevalent in the big data space and how Python fits into that overall problem domain? – Tobias\nAirflow comes with a web UI for visualizing workflows, as do a few of the other Python workflow engines. Why is that an important feature for this kind of tool and what are some of the tasks and use cases that are supported in the Airflow web portal? – Tobias\nOne problem with data management is tracking the provenance of data as it is manipulated and shuttled between different systems. Does Airflow have any support for maintaining that kind of information and if not do you have recommendations for how practitioners can approach the issue? – Tobias\nWhat other kinds of metadata can Airflow track as it executes tasks and what are some of the interesting uses you have seen or created for that information? – Tobias\nWith all the other languages competing for mindshare, what made you choose Python when you built Airflow? – Chris\nI notice that Airflow supports Kerberos. It’s an incredibly capable security model but that comes at a high price in terms of complexity. What were the challenges and was it worth the additional implementation effort? – Chris\nWhen does the data pipeline/workflow management paradigm break down and what other approaches or tools can be used in those cases? – Tobias\nSo, you wrote another tool recently called Panoramix. Can you describe what it is and maybe explain how it fits in the data management domain in relation to Airflow? – Tobias\n\n\nKeep In Touch\n\n\nGoogle Group\nGitter\nGitHub\n\n\nPicks\n\n\nTobias\n\nEmpire of the East by Fred Saberhagen\nThe Book of Swords by Fred Saberhagen\n\n\n\nChris\n\n\nBuraka Son Sistema\nStar Wars – Despecialized Edition\nThe Iron Druid Chronicles\n\n\n\nMaxime\n\n\nFlask App Builder\n\n\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.

\n\n

Summary

\n\n

Are you struggling with trying to manage a series of related, interdependent batch jobs? Then you should check out Airflow. In this episode we spoke with the project’s creator Maxime Beauchemin about what inspired him to create it, how it works, and why you might want to use it. Airflow is a data pipeline management tool that will simplify how you build, deploy, and monitor your complex data processing tasks so that you can focus on getting the insights you need from your data.

\n\n

Brief Introduction

\n\n\n\n

Interview with Maxime Beauchemin

\n\n\n\n

Keep In Touch

\n\n\n\n

Picks

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-02-13T06:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/d5447065-7299-480a-bc20-375cc9dea2ae.mp3","mime_type":"audio/mpeg","size_in_bytes":74751846,"duration_in_seconds":3797}]},{"id":"http://podcastinit.podbean.com/e/episode-43-wsgi-2/","title":"WSGI 2","url":"https://www.pythonpodcast.com/episode-43-wsgi-2","content_text":"Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.\n\nSummary\n\nThe Web Server Gateway Interface, or WSGI for short, is a long-standing pillar of the Python ecosystem. It has enabled a vast number of web frameworks to proliferate by not having to worry about how exactly to interact with the HTTP protocol and focus instead on building a library that is robust, extensible, and easy to use. With recent evolutions to how we interact with the web, it appears that WSGI may be in need of an update and that is what our guests on this episode came to discuss. Cory Benfield is leading an effort to determine what if any modifications should be made to the WSGI standard or if it is time to retire it in favor of something new. Andrew Godwin has been hard at work building the Channels framework for Django to allow for interoperability with websockets. They bring their unique perspectives to bear on how and why we may want to consider bringing WSGI into the current state of the web.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nI would also like to thank Hired, a job marketplace for developers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.\nYour hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Cory Benfield and Andrew Godwin about a proposed update to the WSGI specification.\n\n\nInterview with Cory Benfield and Andrew Godwin\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nFirst off, what is WSGI? – Tobias\nWhat are some of the ways the current WSGI spec has fallen out of step with the needs of the modern developer? – Chris\nHow did you come to be involved with the new WSGI specification? What brought you into this process? – Chris\nDo you think the WSGI name itself brings a lot of expectation, or is it good to keep it as a well-recognised Python landmark? – Tobias\nWould it be better to make a clean break and implement an entirely new set of APIs and style of interaction? – Tobias\nWhat kind of compatibility guarantees should be made between the current spec and the proposed upgrade? What would the impact be if the new specification was incompatible? – Tobias\nHow has the response been to your call for comments? What are some of the most frequently raised concerns or suggestions? – Tobias\nWhat are some of the proposed changes to the specification? – Tobias\nAre there any future directions you think WSGI should take that perhaps haven’t been considered yet? – Chris\nHas your opinion or vision of the proposed update changed as you reviewed responses to the conversation on the mailing list? – Tobias\nDo you have any ideas of how to design the new specification in order to avoid a similar situation of needing to deprecate the current standards in order to accomodate new web protocols? – Tobias\nWhat are some of the points of contention or rigorous debate that have kept previous WSGI 2 attempts from succeeding? – Chris\n\n\nKeep In Touch\n\n\nAndrew\n\nTwitter\nGitHub\n\n\n\nCory\n\n\nTwitter\nGitHub\n\n\n\n\n\nPicks\n\n\nTobias\n\nDiscourse\n\n\n\nChris\n\n\nThe Expanse\nPuerto Rico for IOS\nDominion for IOS\nSplendor for IOS\n\n\n\nCory\n\n\nWusthof Knives\nAustralian Football\nXCOM 2\n\n\n\nAndrew\n\n\nArchery\nTromsø Norway\n\n\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.

\n\n

Summary

\n\n

The Web Server Gateway Interface, or WSGI for short, is a long-standing pillar of the Python ecosystem. It has enabled a vast number of web frameworks to proliferate by not having to worry about how exactly to interact with the HTTP protocol and focus instead on building a library that is robust, extensible, and easy to use. With recent evolutions to how we interact with the web, it appears that WSGI may be in need of an update and that is what our guests on this episode came to discuss. Cory Benfield is leading an effort to determine what if any modifications should be made to the WSGI standard or if it is time to retire it in favor of something new. Andrew Godwin has been hard at work building the Channels framework for Django to allow for interoperability with websockets. They bring their unique perspectives to bear on how and why we may want to consider bringing WSGI into the current state of the web.

\n\n

Brief Introduction

\n\n\n\n

Interview with Cory Benfield and Andrew Godwin

\n\n\n\n

Keep In Touch

\n\n

\n\n

Picks

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-02-06T21:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/81419273-d5a8-408b-b7a8-97fd00f6fb7c.mp3","mime_type":"audio/mpeg","size_in_bytes":72868831,"duration_in_seconds":3886}]},{"id":"http://podcastinit.podbean.com/e/episode-42-sympy-with-aaron-meurer/","title":"SymPy With Aaron Meurer","url":"https://www.pythonpodcast.com/episode-42-sympy-with-aaron-meurer","content_text":"Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.\n\nSummary\n\nLooking for an open source alternative to Mathematica or MatLab for solving algebraic equations? Look no further than the excellent SymPy project. It is a well built and easy to use Computer Algebra System (CAS) and in this episode we spoke with the current project maintainer Aaron Meurer about its capabilities and when you might want to use it.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nJoin our community at discourse.pythonpodcast.com to follow up with the guests and help us make the show better!\nnn\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nI would also like to thank Hired, a job marketplace for developers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit and double your signing bonus to $4,000.\nWe are recording today on January 18th, 2016 and your hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Aaron Meurer about SymPy\n\n\nInterview with Aaron Meurer\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nWhat is Sympy and what kinds of problems does it aim to solve? – Chris\nHow did the SymPy project get started? – Tobias\nHow did you get started with the SymPy project? – Chris\nAre there any limits to the complexity of the equations SymPy can model and solve? – Chris\nHow does SymPy compare to similar projects in other languages? – Tobias\nHow does Sympy render results using such beautiful mathematical symbols when the inputs are simple ASCII? – Chris\nWhat are some of the challenges in creating documentation for a project like SymPy that is accessible to non-experts while still having the necessary information for professionals in the fields of mathematics? – Tobias\nWhich fields of academia and business seem to be most heavily represented in the users of SymPy? – Tobias\nWhat are some of the uses of Sympy in education outside of the obvious like students checking their homework? – Chris\nHow does SymPy integrate with the Jupyter Notebook? – Chris\nIs SymPy generally used more as an interactive mathematics environment or as a library integrated within a larger application? – Tobias\nWhat were the challenges moving SymPy from Python 2 to Python 3? – Chris\nAre there features of Python 3 that simplify your work on SymPy or that make it possible to add new features that would have been too difficult previously? – Tobias\nWere there any performance bottlenecks you needed to overcome in creating Sympy? – Chris\nWhat are some of the interesting design or implementation challenges you’ve found when creating and maintaining SymPy? – Chris\nAre there any new features or major updates to SymPy that are planned? – Tobias\nHow is the evolution of SymPy managed from a feature perspective? Have there been any occasions in recent memory where a pull request had to be rejected because it didn’t fit with the vision for the project? – Tobias\nWhich of the features of SymPy do you find yourself using most often? – Tobias\n\n\nPicks\n\n\nTobias\n\nFunctional Geekery\nNekrogoblikon\n\nHeavy Meta\n\n\n\nMarble Fun Run\n\n\nChris\n\n\nSurprisingly Awesome\nAll Watched Over by Machines of Loving Grace\nPizzicato 5\nMayflower Hoppy Brown Ale\n\n\n\nAaron\n\n\nFermat’s Library\ncatimg\niTerm2\n\n\n\n\n\nKeep In Touch\n\n\nTwitter\nMailing List\nGitter Channel\n\n\nLinks\n\n\nProject Euler\nRichardson’s Theorem\nDoing Math With Python by Amit Saha (and Aaron’s book review)\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.

\n\n

Summary

\n\n

Looking for an open source alternative to Mathematica or MatLab for solving algebraic equations? Look no further than the excellent SymPy project. It is a well built and easy to use Computer Algebra System (CAS) and in this episode we spoke with the current project maintainer Aaron Meurer about its capabilities and when you might want to use it.

\n\n

Brief Introduction

\n\n\n\n

Interview with Aaron Meurer

\n\n\n\n

Picks

\n\n

\n\n

Keep In Touch

\n\n\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-01-31T09:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/0b1cb211-0e3f-4ca8-9b99-8b1e11b27051.mp3","mime_type":"audio/mpeg","size_in_bytes":72610847,"duration_in_seconds":3786}]},{"id":"http://podcastinit.podbean.com/e/episode-41-rpython-with-maciej-fijalkowski/","title":"RPython with Maciej Fijalkowski","url":"https://www.pythonpodcast.com/episode-41-rpython-with-maciej-fijalkowski","content_text":"Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.\n\nSummary\n\nRPython is a subset of Python that is used for writing high performance interpreters for dynamic languages. The most well-known product of this tooling is the PyPy interpreter. In this episode we had the pleasure of speaking with Maciej Fijalkowski about what RPython is, what it isn’t, what kinds of projects it has been used for, and what makes it so interesting.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nJoin our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nI would also like to thank Hired, a job marketplace for developers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.\nWe are recording today on December 17th, 2015 and your hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Maciej Fijalkowski on RPython\n\n\nInterview with Maciej Fijalkowski\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nWhat is RPython and how does it differ from CPython? – Tobias\nCan you share some of the history of RPython in terms of the major improvements and design choices? – Tobias\nIn the documentation it says that RPython is able to generate a Just In Time compiler for dynamic languages. Can you explain why that is significant and some of the ways that it does that? – Tobias\nThe most well-known use of RPython is the PyPy interpreter for Python. Can you share some of the other languages that have been ported to the RPython runtime and how their performance has been improved or altered in the process? – Tobias\nAre there any languages that have been designed entirely for use with RPython, rather than translating an existing language to run on it? – Tobias\nDo you know of any cases where an application has been written to run directly on RPython? – Tobias\nWhat are the computer architecture and operating system platforms that RPython supports and do you have any plans to expand that support? – Tobias\nAre there any minimum hardware specifications that are necessary to be able to effectively run a language written against the RPython platform? – Tobias\nIs RPython similar in concept to other efforts like Parrot in the Perl world? – Chris\nAre there any particular areas of the project that you need help with and how can people get involved with the project? – Tobias\n\n\nPicks\n\n\nTobias\n\nPyCoders 2015 Recap\nShape Up\nXbox One\nXbox One Kinect\nSelfless\n\n\n\nChris\n\n\nSkunk Bear\nCategory 6\nEnvironments)\n\n\n\nMaciej\n\n\nPyCon South Africa\n\n\n\n\n\nKeep In Touch\n\n\nIRC\nMailing List\nPyPy consultancy\n\n\nLinks\n\n\nPsyco (Python JIT)\nTruffle\nHippyVM\nTopaz\nPycket\nPyxie-lang\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, join our community, and sign up for our mailing list.

\n\n

Summary

\n\n

RPython is a subset of Python that is used for writing high performance interpreters for dynamic languages. The most well-known product of this tooling is the PyPy interpreter. In this episode we had the pleasure of speaking with Maciej Fijalkowski about what RPython is, what it isn’t, what kinds of projects it has been used for, and what makes it so interesting.

\n\n

Brief Introduction

\n\n\n\n

Interview with Maciej Fijalkowski

\n\n\n\n

Picks

\n\n

\n\n

Keep In Touch

\n\n\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-01-22T13:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/33491ab2-d420-4b52-8b65-e83d7eb2d1b8.mp3","mime_type":"audio/mpeg","size_in_bytes":40069253,"duration_in_seconds":2134}]},{"id":"http://podcastinit.podbean.com/e/episode-40-ben-darnell-on-tornado/","title":"Ben Darnell on Tornado","url":"https://www.pythonpodcast.com/episode-40-ben-darnell-on-tornado","content_text":"Visit our site to listen to past episodes, support the show, join our Discourse community, and sign up for our mailing list.\n\nSummary\n\nIf you are trying to build a web application in Python that can scale to a high number of concurrent users, or you want to leverage the power of websockets, then Tornado just may be the library you need. In this episode we interview Ben Darnell about his work as the maintainer of the Tornado project and how it can be used in a number of ways to power your next high traffic site.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nWe are also running a listener survey to get feedback about the show. You can find it at bit.do/podcastinit-survey.\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project\nI would also like to thank Hired, a job marketplace for developers and designers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus to $4,000.\nYour hosts as usual are Tobias Macey and Chris Patti\nWe recently launched a new Discourse forum for the show which you can find at discourse.pythonpodcast.com. Join us to discuss the show, the episodes, and ideas for future interviews.\nToday we are interviewing Ben Darnell about his work on Tornado\n\n\nInterview with Ben Darnell\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nWhat is Tornado and what sets it apart from other HTTP servers? – Chris\nHow did you get involved with Tornado? – Ben\nWhat was the inspiration for the name? – Tobias\nTornado was created before the recent focus on asynchronous applications. What prompted that design choice and when might someone care about using async in their development? – Tobias\nWhat is involved in creating an event loop and what are some of the specific design decisions that you made when implementing one for Tornado? – Tobias\nHow does Tornado’s event loop compare to other packages such as Twisted or the asyncio module in the standard library? – Tobias\nThe web module appears to provide a minimal framework for developing web apps. How scalable are those capabilities and is there a recommended architecture for people using Tornado to develop web applications? – Tobias\nWhat are some use cases in which a developer might choose Tornado over other similar options? – Chris\nCould you please give our listeners an overview of Tornado’s concurrency options including coroutines? – Chris\nI see that Tornado supports interoperability with the WSGI protocol and one of the use cases mentioned is for running a Django application alongside a Tornado app. Is that a common way for providing websocket capabilities alongside an existing web app? – Tobias\nI noticed that Tornado provides non-blocking versions of bare sockets and TCP connections. Are there any add-on packages available to simplify the use of various network protocols along the lines of what Twisted includes? – Tobias\nPlease tell us about the transition of Tornado to Python 3. What obstacles did you face and how did you overcome them? – Chris\nBased on your issue tracker it looks like http2 support is definitely on the roadmap. Could you please detail your future plans in this area? – Chris\nWhat are some of the common “gotcha’s” for people who are just starting to use Tornado? – Tobias\n\n\nPicks\n\n\nTobias\n\nAdventures of Riley\nDayworld Trilogy by Philip José Farmer\n\n\n\nChris\n\n\nSense8\nHabits of a Happy Brain\nEthereum\n\n\n\nBen\n\n\nThe Memory Palace\nNewsblur\n\n\n\n\n\nKeep In Touch\n\n\nMailing List\n\n\nLinks\n\n\nMotor\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, join our Discourse community, and sign up for our mailing list.

\n\n

Summary

\n\n

If you are trying to build a web application in Python that can scale to a high number of concurrent users, or you want to leverage the power of websockets, then Tornado just may be the library you need. In this episode we interview Ben Darnell about his work as the maintainer of the Tornado project and how it can be used in a number of ways to power your next high traffic site.

\n\n

Brief Introduction

\n\n\n\n

Interview with Ben Darnell

\n\n\n\n

Picks

\n\n

\n\n

Keep In Touch

\n\n\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-01-16T15:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/8b1a6166-b293-4047-810a-7e542226e177.mp3","mime_type":"audio/mpeg","size_in_bytes":56736830,"duration_in_seconds":3987}]},{"id":"http://podcastinit.podbean.com/e/episode-39-yves-hilpisch-on-quantitative-finance/","title":"Yves Hilpisch on Quantitative Finance","url":"https://www.pythonpodcast.com/episode-39-yves-hilpisch-on-quantitative-finance","content_text":"Visit our site to listen to past episodes, join our community Discourse, support the show, and sign up for our mailing list.\n\nSummary\n\nYves Hilpisch is a founder of The Python Quants, a consultancy that offers services in the space of quantitative financial analysis. In addition, they have created open source libraries to help with that analysis. In this episode we spoke with him about what quantitative finance is, how Python is used in that domain, and what kinds of knowledge are necessary to do these kinds of analysis.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nI would also like to thank Hired, a job marketplace for developers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus to $4,000.\nWe are recording today on December 30th, 2015 and your hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Yves Hilpisch about Quantitative Finance\n\n\nOn Hired software engineers & designers can get 5+ interview requests in a week and each offer has salary and equity upfront. With full time and contract opportunities available, users can view the offers and accept or reject them before talking to any company. Work with over 2,500 companies from startups to large public companies hailing from 12 major tech hubs in North America and Europe. Hired is totally free for users and If you get a job you’ll get a $2,000 “thank you” bonus. If you use our special link to signup, then that bonus will double to $4,000 when you accept a job. If you’re not looking for a job but know someone who is, you can refer them to Hired and get a $1,337 bonus when they accept a job.\n\n\nInterview with Yves Hilpisch\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nCan you explain what Quantitative Finance is? – Tobias\nHow common is it for Python to be used in an investment bank or hedge fund? – Tobias\nWhat factors contribute to the choice of whether or not to use Python in a Quantitative Finance role? – Tobias\nAre there any performance bottle necks or other considerations inherent in using Python for quantitative finance? – Chris\nWhat kind of background is necessary for getting started in Quantitative Finance? – Tobias\nWhat kinds of libraries or algorithms in Python are useful for the day-to-day work of a quant? – Tobias\nIs Python actually used to enact the trades? What protocols, APis, and libraries are used in this process? – Chris\nCould you please walk us through how a simple analysis using DXAnalytics might work? – Chris\nYou work for a company called ‘The Python Quants‘. What kinds of services do you provide and what kinds of organizations typically hire you? – Tobias\n\n\nPicks\n\n\nTobias\n\nKraken by China Miéville\nHeroes in Training series\nOlympians Graphic Novels\nData Elixir Newsletter\n\n\n\nChris\n\n\nHill Farmstead – Edward\nLong Trail – Brush & Barrel Series – Culmination Chocolate Porter\nLong Trail – Spaaaaaace Juice Double IPA\nFlask-RESTLess\n\n\n\nYves\n\n\nThe Willpower Instinct\nThe Way of the Seal\nSapiens: A Brief History of Humankind\nPython High Performance Computing\n\n\n\n\n\nKeep In Touch\n\n\nTwitter\nWebsite\n\n\nLinks\n\n\nQuandl\nYahoo Finance Market Data\nRavenpack\nDX Analytics\nDataPark.io\nPython for Finance\nDerivatives Analytics With Python\nPython Quants Conference\nOpen Source for Quant Finance\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, join our community Discourse, support the show, and sign up for our mailing list.

\n\n

Summary

\n\n

Yves Hilpisch is a founder of The Python Quants, a consultancy that offers services in the space of quantitative financial analysis. In addition, they have created open source libraries to help with that analysis. In this episode we spoke with him about what quantitative finance is, how Python is used in that domain, and what kinds of knowledge are necessary to do these kinds of analysis.

\n\n

Brief Introduction

\n\n\n\n
\"HiredOn Hired software engineers & designers can get 5+ interview requests in a week and each offer has salary and equity upfront. With full time and contract opportunities available, users can view the offers and accept or reject them before talking to any company. Work with over 2,500 companies from startups to large public companies hailing from 12 major tech hubs in North America and Europe. Hired is totally free for users and If you get a job you’ll get a $2,000 “thank you” bonus. If you use our special link to signup, then that bonus will double to $4,000 when you accept a job. If you’re not looking for a job but know someone who is, you can refer them to Hired and get a $1,337 bonus when they accept a job.

\n
\n\n

Interview with Yves Hilpisch

\n\n\n\n

Picks

\n\n

\n\n

Keep In Touch

\n\n\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-01-08T11:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/3e001784-e28f-44b4-bb39-3c40a2f950d8.mp3","mime_type":"audio/mpeg","size_in_bytes":55876045,"duration_in_seconds":4230}]},{"id":"http://podcastinit.podbean.com/e/episode-38-scott-sanderson-on-algorithmic-trading/","title":"Scott Sanderson on Algorithmic Trading","url":"https://www.pythonpodcast.com/episode-38-scott-sanderson-on-algorithmic-trading","content_text":"Visit our site to listen to past episodes, support the show, and sign up for our mailing list.\n\nSummary\n\nBecause of its easy learning curve and broad extensibility Python has found its way into the realm of algorithmic trading at Quantopian. In this episode we spoke with Scott Sanderson about what algorithmic trading is, how it differs from high frequency trading, and how they leverage Python for empowering everyone to try their hand at it.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nWe are recording today on December 16th, 2015 and your hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Scott Sanderson on Algorithmic Trading\n\n\nInterview with Scott Sanderson\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nCan you explain what algorithmic trading is and how it differs from high frequency trading? – Tobias\nWhat kinds of algorithms and libraries are commonly leveraged for algorithmic trading? – Tobias\nQuantopian aims to make algorithmic trading accessible to everyone. What do people need to know in order to get started? Is it necessary to have a background in mathematics or data analysis? – Tobias\nDoes the Quantopian platform build in any safe guards to prevent user’s algorithms from spiraling out of control and creating or contributing to a market crash? – Chris\nHow is Python used within Quantopian and when do you leverage other languages? – Tobias\nWhat Pypi packages does Quantopian leverage in its platform? – Chris\nHow do the financial returns compare between algorithmic vs human trading on the stock market? – Tobias\nCan you speak about any trends you see in the trading algorithms people are creating for the Quantopian platform? – Chris\n\n\nPicks\n\n\nTobias\n\nKinetic Sand\nTrivium\nThrift Books\n\n\n\nChris\n\n\nThrees\nJessica Jones)\nSerial\n\n\n\nScott\n\n\nDota 2\nPhilosophical Investigations\nLogicomix\nInfinite Jest\n\n\n\n\n\nKeep In Touch\n\n\nTwitter\nEmail\nGitHub\n\n\nLinks\n\n\nQGrid\nSlickGrid\nJupyter Hub\nLight Table\nCodeMirror\nCython\nPyData NYC Talk by Scott\nBlaze\nDask\nTheano\nTensorFlow\nZipline\nPyfolio\nPGContents\nSQLAlchemy\nGevent\nquantopian.com/lectures\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, and sign up for our mailing list.

\n\n

Summary

\n\n

Because of its easy learning curve and broad extensibility Python has found its way into the realm of algorithmic trading at Quantopian. In this episode we spoke with Scott Sanderson about what algorithmic trading is, how it differs from high frequency trading, and how they leverage Python for empowering everyone to try their hand at it.

\n\n

Brief Introduction

\n\n\n\n

Interview with Scott Sanderson

\n\n\n\n

Picks

\n\n

\n\n

Keep In Touch

\n\n\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2016-01-03T11:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/87be47b0-e63a-47de-a0ef-e68d412f646a.mp3","mime_type":"audio/mpeg","size_in_bytes":119987908,"duration_in_seconds":5273}]},{"id":"http://podcastinit.podbean.com/e/episode-37-the-pep-talk/","title":"The PEP Talk","url":"https://www.pythonpodcast.com/episode-37-the-pep-talk","content_text":"Visit our site to listen to past episodes, support the show, and sign up for our mailing list.\n\nSummary\n\nThe Python language is built by and for its community. In order to add a new feature, change the specification, or create a new policy the first step is to submit a proposal for consideration. Those proposals are called PEPs, or Python Enhancement Proposals. In this episode we had the great pleasure of speaking with three of the people who act as stewards for this process to learn more about how it got started, how it works, and what impacts it has had.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nThis episode is sponsored by Zato – Microservices, ESB, SOA, REST, API, and Cloud Integrations in Python. Visitzato.io to learn more about how to integrate smarter in the modern world.\nI would also like to thank Hired, a job marketplace for developers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.\nSearching for Pythonistas with Disabilities\nWe are recording today on December 7th, 2015 and your hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing some of the PEP editors\n\n\nInterview with PEP editors\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nFor anyone who isn’t familiar with them, can you explain what a PEP is and how they influence the Python language? – Tobias\nWhat are the requirements for a PEP to be considered for approval and what does the overall process look like to get it finalized? – Tobias\nHow has the PEP process evolved to meet challenges posed by changes in the Python community? – Chris\nHow many reviewers are there and how did each of you end up in that role? Is there a set number of editors that must be maintained and if so how did you arrive at that number? – Tobias\nWhat mistakes have other communities made when creating similar processes, and how has PEP learned from those mistakes? – Chris\nThere are different categories for PEPs. Can you describe what those are and how you arrived at that ontology? – Tobias\nIs there any significance to the numbering system used for identifying different PEPs? – Tobias\nHow does the PEP process maintain its sense of humor (e.g. PEP 20) while being sure to be taken seriously where it really counts? – Chris\nAlong the lines of humorous PEPs, can you share the story of PEP 401? – Tobias\nHow does the PEP process strive to prevent an undesirable level of control by any one company or other special interest group? – Chris\nHow much control does Guido have over the PEP process? Has a PEP ever directly countered Guido’s wishes? How did it turn out? – Chris\nWhat is your favorite PEP and why? – Tobias\n\nBarry: PEP 20\nChris: PEP 479\nDavid: PEP 20\n\n\n\nWhat, in your opinion, has been the most important or far-reaching PEP, whether it was approved or not? – Tobias\n\n\nDavid: PEP 20\nChris: PEP 466\nBarry: PEP 8\n\n\n\nWhat was the strangest / most extreme PEP proposal you’ve ever seen? – Chris\n\n\nChris: PEP 501\nBarry: PEP 507\nDavid: PEP 666\n\n\n\n\n\nPicks\n\n\nTobias\n\nWagtail CMS\nInside Out\nSpark Podcast\nHymn for Atheists\n\n\n\nChris\n\n\nTrumbo\nKivy Crash Course\nJihadology Podcast\n\n\n\nBarry\n\n\nTox\nNose2\nJessica Jones\nThe Joy of Science\n\n\n\nChris\n\n\nThe Git Manpage Generator\nDaily MTG\n\n\n\nDavid\n\n\nTim’s Vermeer\nReady Player One\nThe Aristocrats\nScientific Songs of Praise\nHollywood Babble On\n\n\n\n\n\nKeep In Touch\n\n\nBarry\n\nBlog\n\n\n\nChris\n\n\nBlog\nGitHub\n\n\n\nDavid\n\n\nWebsite\nBlog\n\n\n\n\n\nLinks\n\n\nMonty Python – All the Words\nMonty Python – On YouTube\nPEP 404\nPEP 666\nRaymond Hettinger PyCon 2015 PEP8 talk\nPython Dev Mailing List\nPython Ideas Mailing List\nPython Bug Mailing List\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, and sign up for our mailing list.

\n\n

Summary

\n\n

The Python language is built by and for its community. In order to add a new feature, change the specification, or create a new policy the first step is to submit a proposal for consideration. Those proposals are called PEPs, or Python Enhancement Proposals. In this episode we had the great pleasure of speaking with three of the people who act as stewards for this process to learn more about how it got started, how it works, and what impacts it has had.

\n\n

Brief Introduction

\n\n\n\n

Interview with PEP editors

\n\n

\n\n

Picks

\n\n

\n\n

Keep In Touch

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2015-12-26T21:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/50abf9dd-6f5d-4dc8-9c83-adf2fa22ccc9.mp3","mime_type":"audio/mpeg","size_in_bytes":117868118,"duration_in_seconds":6341}]},{"id":"http://podcastinit.podbean.com/e/episode-36-eric-holscher-on-documentation-and-read-the-docs/","title":"Eric Holscher on Documentation and Read The Docs","url":"https://www.pythonpodcast.com/episode-36-eric-holscher-on-documentation-and-read-the-docs","content_text":"Visit our site to listen to past episodes, support the show, and sign up for our mailing list.\n\nSummary\n\nThe first place we all go for learning about new libraries is the documentation. Lack of effective documentation can limit the adoption of an otherwise excellent project. In this episode we spoke with Eric Holscher, co-creator of Read The Docs, about why documentation is important and how we can all work to make it better.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nI would also like to thank Hired, a job marketplace for developers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $10 credit to try out their fast and reliable Linux virtual servers for your next project\nWe are recording today on November 30th, 2015 and your hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Eric Holscher about Documentation\n\n\nUse the promo code podcastinit10 to get a $10 credit when you sign up!\n\n\nOn Hired software engineers & designers can get 5+ interview requests in a week and each offer has salary and equity upfront. With full time and contract opportunities available, users can view the offers and accept or reject them before talking to any company. Work with over 2,500 companies from startups to large public companies hailing from 12 major tech hubs in North America and Europe. Hired is totally free for users and If you get a job you’ll get a $2,000 “thank you” bonus. If you use our special link to signup, then that bonus will double to $4,000 when you accept a job. If you’re not looking for a job but know someone who is, you can refer them to Hired and get a $1,337 bonus when they accept a job.\n\n\nInterview with Eric Holscher\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nYou are one of the people behind the Read The Docs project. What was your inspiration for creating that platform and why is documentation so important in software? – Tobias\nWhat makes Read The Docs different from other static sources for documentation? – Chris\nThe Python community seems to have a stronger focus on well-documented projects than some other languages. Do you have any theories as to why that is the case? – Tobias\nCan you outline the landscape of projects that leverage the documentation capabilities that are built in to the Python language? – Tobias\nCan you estimate the overall user base for Read The Docs? – Chris\nDo you have any advice around methods or approaches that can help developers create and maintain effective documentation? – Tobias\nCan you list some projects that you have found to provide the best documentation and what was remarkable about them? – Tobias\nNewcomers to open source are often encouraged to submit improvements to a projects documentation as a way to get started and become involved with the community. Do you have any general advice on how to find and understand undocumented features? – Tobias\nDo you have any statistics on the languages represented among the projects that host their documentation with you? – Tobias\nWhat are some of the challenges you’ve faced and overcome in maintaining such a large repository of documentation from so many projects? – Chris\nHow can our listeners contribute to the project? – Chris\n\n\nPicks\n\n\nTobias\n\nThe Man from Uncle\nMinute Physics\n\n\n\nChris\n\n\nSigAvdi\nBlack Flags: The Rise of ISIS\nVeritassium\n\n\n\nEric\n\n\nKhao Soi\nClimate Change\nGardening & healthy eating – Classic\n\n\n\n\n\nKeep In Touch\n\n\nTwitter\n\n@ericholscher\n@readthedocs\n@writethedocs\n\n\n\n\n\nLinks\n\n\nStripe docs\nDjango Girls Tutorial\nWrite The Docs\nWrite The Docs Meetup Talk\nWrite The Docs Slack Channel\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, and sign up for our mailing list.

\n\n

Summary

\n\n

The first place we all go for learning about new libraries is the documentation. Lack of effective documentation can limit the adoption of an otherwise excellent project. In this episode we spoke with Eric Holscher, co-creator of Read The Docs, about why documentation is important and how we can all work to make it better.

\n\n

Brief Introduction

\n\n\n\n
\"LinodeUse the promo code podcastinit10 to get a $10 credit when you sign up!

\n
\n\n
\"HiredOn Hired software engineers & designers can get 5+ interview requests in a week and each offer has salary and equity upfront. With full time and contract opportunities available, users can view the offers and accept or reject them before talking to any company. Work with over 2,500 companies from startups to large public companies hailing from 12 major tech hubs in North America and Europe. Hired is totally free for users and If you get a job you’ll get a $2,000 “thank you” bonus. If you use our special link to signup, then that bonus will double to $4,000 when you accept a job. If you’re not looking for a job but know someone who is, you can refer them to Hired and get a $1,337 bonus when they accept a job.

\n
\n\n

Interview with Eric Holscher

\n\n\n\n

Picks

\n\n

\n\n

Keep In Touch

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2015-12-20T11:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/4256f205-5814-4647-b38b-b05850d5517f.mp3","mime_type":"audio/mpeg","size_in_bytes":76716015,"duration_in_seconds":3933}]},{"id":"http://podcastinit.podbean.com/e/episode-35-sylvain-thenault-on-astroid/","title":"Sylvain Thénault on ASTroid","url":"https://www.pythonpodcast.com/episode-35-sylvain-thenault-on-astroid","content_text":"Visit our site to listen to past episodes, support the show, and sign up for our mailing list.\n\nSummary\n\nThe Python AST (Abstract Syntax Tree) is a powerful abstraction that allows for a number of innovative projects. ASTroid is a library that provides additional convenience methods to simplify working with the AST. In this episode we spoke with Sylvain Thénault from Logilab about his work on ASTroid and how it is used to power the popular PyLint static analysis tool.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nI would also like to thank Hired, a job marketplace for developers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $10 credit to try out their fast and reliable Linux virtual servers for your next project\nWe are recording today on November 23rd, 2015 and your hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Sylvain Thénault about ASTroid\n\n\nOn Hired software engineers & designers can get 5+ interview requests in a week and each offer has salary and equity upfront. With full time and contract opportunities available, users can view the offers and accept or reject them before talking to any company. Work with over 2,500 companies from startups to large public companies hailing from 12 major tech hubs in North America and Europe. Hired is totally free for users and If you get a job you’ll get a $2,000 “thank you” bonus. If you use our special link to signup, then that bonus will double to $4,000 when you accept a job. If you’re not looking for a job but know someone who is, you can refer them to Hired and get a $1,337 bonus when they accept a job.\n\n\nUse the promo code podcastinit10 to get a $10 credit when you sign up!\n\n\nInterview with Sylvain Thénault\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nCan you explain what an Abstract Syntax Tree is and why it is a useful language feature? – Tobias\nWhat was your inspiration for creating ASTroid? – Chris\nWhat features does ASTroid offer over Python’s standard AST package, and what makes those features important? – Chris\nI know that the ASTroid package is used in Pylint which is also maintained by Logilab. How does the AST facilitate static analysis of Python projects and are there cases where you have to fall back to text parsing? – Tobias\nBeyond static analysis, what are some of the other possible uses for the Python AST? – Tobias\nThe documentation for the AST package in Python mentions that the specific syntax objects in the tree are subject to change between releases. Does the ASTroid package provide any abstractions to maintain a consistent API between versions or does it just provide a pass-through? – Tobias\nHave you encountered any challenges in testing ASTroid given that it operates at such a low level in the language? – Chris\nDo you have trouble attracting contributors given the great understanding of Python’s inner working required? – Chris\nDoes the implementation or representation of the AST differ between different distributions of Python such as CPython, PyPy and Jython? – Tobias\nWhat are some of the most interesting applications ASTroid has been used in? – Chris\n\n\nPicks\n\n\nTobias\n\nPre-Commit\nExistential Comics\nhtmlPy\n\n\n\nChris\n\n\nPretty Things – Fluffy White Rabbits\nFallout 4\n\n\n\nSylvain\n\n\nPyReverse\nCubicWeb\n\n\n\n\n\nKeep In Touch\n\n\nCode Quality Mailing List\nPyLint Dev Mailing List\nTwitter\n\n@sythenault\n@logilab\n\n\n\nLogilab\n\n\nLinks\n\n\nVisitor pattern\nPylint\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, and sign up for our mailing list.

\n\n

Summary

\n\n

The Python AST (Abstract Syntax Tree) is a powerful abstraction that allows for a number of innovative projects. ASTroid is a library that provides additional convenience methods to simplify working with the AST. In this episode we spoke with Sylvain Thénault from Logilab about his work on ASTroid and how it is used to power the popular PyLint static analysis tool.

\n\n

Brief Introduction

\n\n\n\n
\"HiredOn Hired software engineers & designers can get 5+ interview requests in a week and each offer has salary and equity upfront. With full time and contract opportunities available, users can view the offers and accept or reject them before talking to any company. Work with over 2,500 companies from startups to large public companies hailing from 12 major tech hubs in North America and Europe. Hired is totally free for users and If you get a job you’ll get a $2,000 “thank you” bonus. If you use our special link to signup, then that bonus will double to $4,000 when you accept a job. If you’re not looking for a job but know someone who is, you can refer them to Hired and get a $1,337 bonus when they accept a job.

\n
\n\n
\"LinodeUse the promo code podcastinit10 to get a $10 credit when you sign up!

\n
\n\n

Interview with Sylvain Thénault

\n\n\n\n

Picks

\n\n

\n\n

Keep In Touch

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2015-12-11T21:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/0fe7868a-d3e8-4a9d-8c4f-911cbd3295c4.mp3","mime_type":"audio/mpeg","size_in_bytes":53206415,"duration_in_seconds":2848}]},{"id":"http://podcastinit.podbean.com/e/episode-34-stuart-mumford-on-sunpy/","title":"Stuart Mumford on SunPy","url":"https://www.pythonpodcast.com/episode-34-stuart-mumford-on-sunpy","content_text":"Visit our site to listen to past episodes, support the show, and sign up for our mailing list.\n\nSummary\n\nWhat is Solar Physics? How does it differ from AstroPhysics? What does this all have to do with Python? In this episode we answer all of those questions when we interview Stuart Mumford about his work on SunPy. So put on your sunglasses and learn about how to use Python to decipher the secrets of our closest star.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nI would also like to thank Hired, a job marketplace for developers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $10 credit to try out their fast and reliable Linux virtual servers for your next project\nWe are recording today on November 17th, 2015 and your hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Stuart Mumford about SunPy\n\n\nUse the promo code podcastinit10 to get a $10 credit when you sign up!\n\n\nOn Hired software engineers & designers can get 5+ interview requests in a week and each offer has salary and equity upfront. With full time and contract opportunities available, users can view the offers and accept or reject them before talking to any company. Work with over 2,500 companies from startups to large public companies hailing from 12 major tech hubs in North America and Europe. Hired is totally free for users and If you get a job you’ll get a $2,000 “thank you” bonus. If you use our special link to signup, then that bonus will double to $4,000 when you accept a job. If you’re not looking for a job but know someone who is, you can refer them to Hired and get a $1,337 bonus when they accept a job.\n\n\nInterview with Stuart Mumford\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nCan you explain what the research and applications of solar physics are and how SunPy facilitates those activities? – Tobias\nWhat was your inspiration for the SunPy project and what are you using it for in your research? – Tobias\nCan you tell us what SunPy’s map and light curve classes are and how they might be used? – Chris\nAre there any considerations that you need to be aware of when writing software libraries for practitioners of the hard sciences that would be different if the target audience were software engineers? – Tobias\nCan SunPy consume data directly from telescopes and other observational apparatus? – Chris\nI noticed on the project site that SunPy leverages AstroPy internally. Can you describe the relationship between the two projects and why someone might want to use SunPy in place of or in addition to AstroPy? – Tobias\nLooking at the documentation I got the impression that there is a fair amount of visual representation of data for analysis. Can you describe some of the challenges that has posed? Is there integrated support for project Jupyter and are there other graphical environments that SunPy supports? – Tobias\nWhat are some of the most interesting applications that SunPy has been used for? – Chris\n\n\nPicks\n\n\nTobias\n\nElm\nAvro\nCommon Sense Media\n\n\n\nChris\n\n\nMassdrop\n21st Amendment Fireside Chat\nExtra Creditz\n\n\n\nStuart\n\n\nLive ISS Stream with space-to-ground radio\nLive ISS HD video stream 24/7\nyt\nCalf Studio – Live Audio Processing\n\n\n\n\n\nKeep In Touch\n\n\nTwitter(@sunpyproject)\nSunPy.org\nGitHub\nIRC\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, and sign up for our mailing list.

\n\n

Summary

\n\n

What is Solar Physics? How does it differ from AstroPhysics? What does this all have to do with Python? In this episode we answer all of those questions when we interview Stuart Mumford about his work on SunPy. So put on your sunglasses and learn about how to use Python to decipher the secrets of our closest star.

\n\n

Brief Introduction

\n\n\n\n
\"LinodeUse the promo code podcastinit10 to get a $10 credit when you sign up!

\n
\n\n
\"HiredOn Hired software engineers & designers can get 5+ interview requests in a week and each offer has salary and equity upfront. With full time and contract opportunities available, users can view the offers and accept or reject them before talking to any company. Work with over 2,500 companies from startups to large public companies hailing from 12 major tech hubs in North America and Europe. Hired is totally free for users and If you get a job you’ll get a $2,000 “thank you” bonus. If you use our special link to signup, then that bonus will double to $4,000 when you accept a job. If you’re not looking for a job but know someone who is, you can refer them to Hired and get a $1,337 bonus when they accept a job.

\n
\n\n

Interview with Stuart Mumford

\n\n\n\n

Picks

\n\n

\n\n

Keep In Touch

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2015-12-04T12:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/0f1e8103-e9f8-40ec-ba12-c2047de1d8fd.mp3","mime_type":"audio/mpeg","size_in_bytes":45976179,"duration_in_seconds":2438}]},{"id":"http://podcastinit.podbean.com/e/episode-33-maneesha-sane-on-software-and-data-carpentry/","title":"Maneesha Sane on Software and Data Carpentry","url":"https://www.pythonpodcast.com/episode-33-maneesha-sane-on-software-and-data-carpentry","content_text":"Visit our site to listen to past episodes, support the show, and sign up for our mailing list.\n\nSummary\n\nThe Software and Data Carpentry organizations have a mission of making it easier for scientists and data analysts in academia to replicate and review each others work. In order to achieve this goal they conduct training and workshops that teach modern best practices in software and data engineering, including version control and proper data management. In this episode we had the opportunity to speak with Maneesha Sane, the program coordinator for both organizations, so that we could learn more about how these projects are related and how they approach their mission.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nThis episode is sponsored by Zato – Microservices, ESB, SOA, REST, API, and Cloud Integrations in Python. Visit zato.io to learn more about how to integrate smarter in the modern world.\nI would also like to thank Hired, a job marketplace for developers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $10 credit to try out their fast and reliable Linux virtual servers for your next project\nWe are recording today on November 10th, 2015 and your hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Maneesha Sane about Software Carpentry and Data Carpentry\n\n\n\n\n\n\nInterview with Maneesha Sane\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you explain what the Software and Data Carpentry organizations are and what their respective goals are?\nWhat is the history of these organizations and how are they related?\nWhat does a typical Software Carpentry or Data Carpentry workshop look like?\nWhat is the background of your instructors?\nCan you explain why Python was chosen as the language for your workshops and why it is such a good language to use for teaching proper software engineering practices to scientists?\nIn what ways do the lessons taught by both groups differ and what parts are common between the two organizations?\nWhat are some of the most important tools and lessons that you teach to scientists in academia?\nDo you tend to focus mostly on procedural development or do you also teach object oriented programming in Software Carpentry?\nWhat is the target audience for Data Carpentry and what are some of the most important lessons and tools taught to them?\nDo you teach any particular method of pre-coding design like flowcharting, pseudocode, or top down decomposition in software carpentry?\nWhat scientific domains are most commonly represented among your workshop participants for Software Carpentry?\nWhat are some specific things the Python community and the Python core team could do to make it easier to adopt for your students?\nWhat are the most common concepts students have trouble with in software & data carpentry?\nHow can our audience help support the goals of these organizations?\n\n\nPicks\n\n\nTobias\n\nVivaldi Browser\nvyte.in\nPocket Casts\n\n\n\nChris\n\n\nChiptunes = Win\nESM – Electronic Study Music\nSupergalactic Expansive\n\n\n\nManeesha\n\n\nQPython\nNew Boston\nLunar Baboon\n\n\n\n\n\nKeep In Touch\n\n\nTwitter\n\n@swcarpentry\n@datacarpentry\n@maneeshasane\n\n\n\nBlog\nSoftware Carpentry\nData Carpentry\n\n\nLinks\n\n\nNumFocus\nSoftware Carpentry GitHub – Training Courses\nInstructor Training\nDiscussion Mailing List\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, and sign up for our mailing list.

\n\n

Summary

\n\n

The Software and Data Carpentry organizations have a mission of making it easier for scientists and data analysts in academia to replicate and review each others work. In order to achieve this goal they conduct training and workshops that teach modern best practices in software and data engineering, including version control and proper data management. In this episode we had the opportunity to speak with Maneesha Sane, the program coordinator for both organizations, so that we could learn more about how these projects are related and how they approach their mission.

\n\n

Brief Introduction

\n\n\n\n
\n\n
\n\n

Interview with Maneesha Sane

\n\n\n\n

Picks

\n\n

\n\n

Keep In Touch

\n\n

\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2015-11-25T10:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/2935348b-2e4c-4309-ab6a-09bac6589326.mp3","mime_type":"audio/mpeg","size_in_bytes":28836478,"duration_in_seconds":2668}]},{"id":"http://podcastinit.podbean.com/e/episode-32-erik-tollerud-on-astropy/","title":"Erik Tollerud on AstroPy","url":"https://www.pythonpodcast.com/episode-32-erik-tollerud-on-astropy","content_text":"Visit our site to listen to past episodes, support the show, and subscribe to our mailing list.\n\nSummary\n\nErik Tollerud is an astronomer with a background in software engineering. He leverages these backgrounds to help build and maintain the AstroPy framework and its associated modules. AstroPy is a set of Python libraries that provide useful mechanisms for astronomers and astrophysicists to perform analyses on the data that they receive from observational equipment such as the mountain observatory that Erik was preparing to visit when we talked to him about his work. If you like Python and space then you should definitely give this episode a listen!\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nI would also like to thank Hired, a job marketplace for developers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.\nLinode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $10 credit to try out their fast and reliable Linux virtual servers for your next project\nWe are recording today on November 2nd, 2015 and your hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Erik Tollerud about AstroPy\n\n\nOn Hired software engineers & designers can get 5+ interview requests in a week and each offer has salary and equity upfront. With full time and contract opportunities available, users can view the offers and accept or reject them before talking to any company. Work with over 2,500 companies from startups to large public companies hailing from 12 major tech hubs in North America and Europe. Hired is totally free for users and If you get a job you’ll get a $2,000 “thank you” bonus. If you use our special link to signup, then that bonus will double to $4,000 when you accept a job. If you’re not looking for a job but know someone who is, you can refer them to Hired and get a $1,337 bonus when they accept a job.\n\n\nUse the promo code podcastinit10 to get a $10 credit when you sign up!\n\n\nInterview with Erik Tollerud\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat was the inspiration to create AstroPy and what kinds of astronomical research can it be used for?\nCan you tell us what AstroPy’s modeling functions are and give us examples of where they might be used?\nAre there any considerations that you need to be aware of when writing software libraries for practitioners of the hard sciences that would be different if the target audience were software engineers?\nWhat are some of the most interesting applications that AstroPy has been used for?\nAre there open data sets that are available for people outside of academia to do analysis of astronomical data using AstroPy?\n\nHave there been any useful discoveries made in this way?\n\n\n\nCould you please tell us about AstroPy’s Virtual Observatory capabilities?\nWhat are some interesting use cases for AstroPy’s Cosmological calculations?\nAre there other libraries available that provide similar capabilities, perhaps in other languages? What makes AstroPy unique among them?\nCan AstroPy consume data directly from telescopes and other observational apparatus?\nThe amount of data generated from observing astronomical phenomena must be immense. What are some of the tools used to manage that data and how does AstroPy interface with them?\nHow might AstroPy be used to prove or disprove the cold dark matter hypothesis?\nWhat are some of the architectural choices that have been made to allow for the AstroPy library to serve as the core for a number of other add-ons?\n\n\nDoes AstroPy provide a common data format to allow for easy interoperability between the various addons?\n\n\n\nI noticed that AstroPy adheres to the PSF code of conduct, as well as having adopted an enhancement proposal process modelled after PEPs. Can you explain why that is important and what kind of an impact it has had on the community around AstroPy?\n\n\nPicks\n\n\nTobias\n\nCitizen Ex\npiprot\nOpen Culture\n\n\n\nChris\n\n\nThe Allusionist\nCriminal\nHardcore History\n\n\n\nErik\n\n\nHubbleSite\nGreat Courses – History of the Ancient World\n\n\n\n\n\nKeep In Touch\n\n\nastropy.org\nAstroPy User Mailing List\nAstroPy Dev Mailing List\n\n\nLinks\n\n\ntutorials.astropy.org\nAstroQuery\nCython\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, and subscribe to our mailing list.

\n\n

Summary

\n\n

Erik Tollerud is an astronomer with a background in software engineering. He leverages these backgrounds to help build and maintain the AstroPy framework and its associated modules. AstroPy is a set of Python libraries that provide useful mechanisms for astronomers and astrophysicists to perform analyses on the data that they receive from observational equipment such as the mountain observatory that Erik was preparing to visit when we talked to him about his work. If you like Python and space then you should definitely give this episode a listen!

\n\n

Brief Introduction

\n\n\n\n
\"hired-logo-dark-padding.png\"On Hired software engineers & designers can get 5+ interview requests in a week and each offer has salary and equity upfront. With full time and contract opportunities available, users can view the offers and accept or reject them before talking to any company. Work with over 2,500 companies from startups to large public companies hailing from 12 major tech hubs in North America and Europe. Hired is totally free for users and If you get a job you’ll get a $2,000 “thank you” bonus. If you use our special link to signup, then that bonus will double to $4,000 when you accept a job. If you’re not looking for a job but know someone who is, you can refer them to Hired and get a $1,337 bonus when they accept a job.

\n
\n\n
\"linode-banner-sponsor-large.png\"Use the promo code podcastinit10 to get a $10 credit when you sign up!

\n
\n\n

Interview with Erik Tollerud

\n\n

\n\n

Picks

\n\n

\n\n

Keep In Touch

\n\n\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2015-11-19T21:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/2016b204-3a52-4f9b-92c5-b3d1b8f2aa01.mp3","mime_type":"audio/mpeg","size_in_bytes":55803415,"duration_in_seconds":2958}]},{"id":"http://podcastinit.podbean.com/e/episode-31-dariusz-suchojad-on-zato/","title":"Dariusz Suchojad on Zato","url":"https://www.pythonpodcast.com/episode-31-dariusz-suchojad-on-zato","content_text":"Visit our site to listen to past episodes, support the show, and sign up for our mailing list.\n\nSummary\n\nService integration platforms have traditionally been the realm of Java projects. Zato is a project that shows Python is a great choice for systems integration due to its flexibility and wealth of useful libraries. In this episode we had the opportunity to speak with Dariusz Suchojad, the creator of Zato about why he decided to make it and what makes it interesting. Listen to the episode and then take it for a spin.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email, leave us a message on Google+, or leave a comment on our show notes\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at\nI would also like to thank Hired, a job marketplace for developers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.\nLinode is also sponsoring us this week. Check them out at linode.com/podcastinit and get a $10 credit to try out their fast and reliable Linux virtual servers for your next project.\nWe are recording today on October 27th, 2015 and your hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Dariusz Suchojad about Zato\n\n\n\n\nInterview with Dariusz Suchojad\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you explain what Zato is and what motivated you to create it?\nWhat makes Zato stand out from other service bus implementations?\nWhat are some signs that someone should consider incorporating Zato into their software architecture?\nDoes zato perform well in restricted resource environments like ec2? What performance bottlenecks are common when using zato?\nIt seems that most other ESB projects are written in Java. What advantages does Python have over Java for this kind of project and in what ways is it inferior?\nThe architectural nature of ESBs are such that they form the central backbone of a software system. How have you been able to ensure an appropriate level of reliability and stability in Zato while still delivering new features and improvements?\nWhat are the scalability and high availability characteristics of Zato?\nDoes zato run well using pypy?\nFor anyone wanting to use Zato, what are the infrastructure requirements for deployment?\nWhat are some of the security ramifications you took into account in zato’s design?\nWhat are some of the most novel uses for Zato that you have seen or heard about?\n\n\nPicks\n\n\nTobias\n\nSPY\nEric Royer’s One Man Band\npip-tools\n\n\n\nChris\n\n\nRational Security\nNew Rustacean Podcast\nJohan Goes to Mexico\n\n\n\nDariusz\n\n\nSublime Text Editor\n\n\n\n\n\nKeep In Touch\n\n\nzato.io\nTwiter\nGithub\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, and sign up for our mailing list.

\n\n

Summary

\n\n

Service integration platforms have traditionally been the realm of Java projects. Zato is a project that shows Python is a great choice for systems integration due to its flexibility and wealth of useful libraries. In this episode we had the opportunity to speak with Dariusz Suchojad, the creator of Zato about why he decided to make it and what makes it interesting. Listen to the episode and then take it for a spin.

\n\n

Brief Introduction

\n\n\n\n
\n\n

Interview with Dariusz Suchojad

\n\n\n\n

Picks

\n\n

\n\n

Keep In Touch

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2015-11-12T21:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/b08d104c-ded6-48d9-a1a8-5b2bc8b5e1ae.mp3","mime_type":"audio/mpeg","size_in_bytes":47741162,"duration_in_seconds":2546}]},{"id":"http://podcastinit.podbean.com/e/episode-30-tom-rothamel-on-renpy/","title":"Tom Rothamel on Ren’Py","url":"https://www.pythonpodcast.com/episode-30-tom-rothamel-on-renpy","content_text":"Visit our site to listen to past episodes, support the show, and sign up for our mailing list.\n\nSummary\n\nTom Rothamel is an embedded systems engineer who spends his free time working on Ren’Py, a visual novel engine written in Python. Ren’Py allows you to write interactive fiction experiences and deploy them across desktop and mobile platforms. By creating a purpose-built DSL for describing the interactions, users of Ren’Py can focus on crafting polished experiences without fighting through the vagaries of programming languages, while still providing access to the internals when necessary. Listen to our interview with Tom to learn more about this long-running project and what makes it so interesting.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at\nI would also like to thank Hired, a job marketplace for developers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.\nLinode is also sponsoring us this week. Check them out at linode.com/podcastinit and get a $10 credit to try out their fast and reliable Linux virtual servers for your next project.\nWe are recording today on October 19th, 2015 and your hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Tom Rothamel about RenPy\n\n\nOn Hired software engineers & designers can get 5+ interview requests in a week and each offer has salary and equity upfront. With full time and contract opportunities available, users can view the offers and accept or reject them before talking to any company. Work with over 2,500 companies from startups to large public companies hailing from 12 major tech hubs in North America and Europe. Hired is totally free for users and If you get a job you’ll get a $2,000 “thank you” bonus. If you use our special link to signup, then that bonus will double to $4,000 when you accept a job. If you’re not looking for a job but know someone who is, you can refer them to Hired and get a $1,337 bonus when they accept a job.\n\n\nInterview with Tom Rothamel\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat is Ren’Py and what was your inspiration for starting it?\nI noticed that Ren’Py supports a number of different styles of gameplay. Can you explain the differences between interactive fiction, kinetic fiction and RPGs?\nI notice that RenPy has clearly been around a while (Some of the games for OSX are PowerPC binaries!) – what problems have you encountered maintaining such a long lived project and keeping it current?\nWhat libraries does Ren’Py leverage and how did you go about selecting them to allow for cross-platform development and deployment?\nWhat underlying Python graphics toolkit does RenPy use for display, and how did that choice affect RenPy’s design?\nWhile reading through the quickstart in the documentation I noticed that there is a special syntax that you have created for defining the dialog and narratives. Can you explain how you created the DSL for building the storylines?\nIt feels to me like RenPy was heavily inspired by the JRPG genre and as such there are games where sex plays a prominent role(I noticed a mention of Hentai in the docs), which is less readily accepted in the west. Have you ever encountered any pushback on this issue?\nI noticed that some of the games that were created with Ren’Py are available on the Steam platform. What elements of the Ren’Py project lend themselves to producing games with enough polish to be published on such a mainstream platform?\nIf you were just starting out today implementing RenPy, would you still use Python? Why?\n\n\nPicks\n\n\nTobias\n\nDJ Logic\ngit-extras\nRadon\n\n\n\nChris\n\n\nNarcos\nThe Rust Programming Language\nKent Falls Brewing Shower Beer\n\n\n\nTom\n\n\nCython\nNPR One\nThe Seinfeld Method\n\n\n\n\n\nKeep In Touch\n\n\nrenpy.org\nTwitter\n\n\nLinks\n\n\nLong Live The Queen\nMoonlight Walks\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, and sign up for our mailing list.

\n\n

Summary

\n\n

Tom Rothamel is an embedded systems engineer who spends his free time working on Ren’Py, a visual novel engine written in Python. Ren’Py allows you to write interactive fiction experiences and deploy them across desktop and mobile platforms. By creating a purpose-built DSL for describing the interactions, users of Ren’Py can focus on crafting polished experiences without fighting through the vagaries of programming languages, while still providing access to the internals when necessary. Listen to our interview with Tom to learn more about this long-running project and what makes it so interesting.

\n\n

Brief Introduction

\n\n\n\n
\"HiredOn Hired software engineers & designers can get 5+ interview requests in a week and each offer has salary and equity upfront. With full time and contract opportunities available, users can view the offers and accept or reject them before talking to any company. Work with over 2,500 companies from startups to large public companies hailing from 12 major tech hubs in North America and Europe. Hired is totally free for users and If you get a job you’ll get a $2,000 “thank you” bonus. If you use our special link to signup, then that bonus will double to $4,000 when you accept a job. If you’re not looking for a job but know someone who is, you can refer them to Hired and get a $1,337 bonus when they accept a job.

\n
\n\n

Interview with Tom Rothamel

\n\n\n\n

Picks

\n\n

\n\n

Keep In Touch

\n\n\n\n

Links

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2015-11-06T15:00:00.000-05:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/86f31161-2b43-459e-99b1-3da878f76f1d.mp3","mime_type":"audio/mpeg","size_in_bytes":67918051,"duration_in_seconds":3532}]},{"id":"http://podcastinit.podbean.com/e/episode-29-anthony-scopatz-on-xonsh/","title":"Anthony Scopatz on Xonsh","url":"https://www.pythonpodcast.com/episode-29-anthony-scopatz-on-xonsh","content_text":"Visit our site to listen to past episodes, support the show, and sign up for our mailing list.\n\nSummary\n\nAnthony Scopatz is the creator of the Python shell Xonsh in addition to his work as a professor of nuclear physics. In this episode we talked to him about why he created Xonsh, how it works, and what his goals are for the project. It is definitely worth trying out Xonsh as it greatly simplifies the day-to-day use of your terminal environment by adding easily accessible python interoperability.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nI would also like to thank Hired, a job marketplace for developers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.\nLinode is also sponsoring us this week. Check them out at linode.com/podcastinit and get a $10 credit to try out their fast and reliable Linux virtual servers for your next project\nWe are recording today on October 12th, 2015 and your hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Anthony Scopatz about Xonsh\n\n\nOn Hired software engineers & designers can get 5+ interview requests in a week and each offer has salary and equity upfront. With full time and contract opportunities available, users can view the offers and accept or reject them before talking to any company. Work with over 2,500 companies from startups to large public companies hailing from 12 major tech hubs in North America and Europe. Hired is totally free for users and If you get a job you’ll get a $2,000 “thank you” bonus. If you use our special link to signup, then that bonus will double to $4,000 when you accept a job. If you’re not looking for a job but know someone who is, you can refer them to Hired and get a $1,337 bonus when they accept a job.\n\n\nUse the promo code podcastinit10 to get a $10 credit when you sign up!\n\n\nInterview with Anthony Scopatz\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you explain what Xonsh is and your motivation for creating it?\nFor people transitioning to Xonsh from a shell like Bash or Zsh, what are some of the biggest differences that they will see?\nWhat are some really powerful one-liners that showcase Xonsh’s capabilities?\nWhat is it about Python that lends itself to this kind of a project and what are your thoughts on building something like Xonsh in another language such as Ruby or Node.js?\nIf you had to single out one killer feature that Xonsh brings to the table, what would that be?\nIs it possible to specify which shell, such as bash or zsh, gets used in subprocess mode?\nI started using the Xonsh shell as my daily terminal recently and have been enjoying it so far. One of the things that I have been wondering is how to hook into the completion system to provide eldoc style completion from parsing the output of help flags. Do you have any advice on where to start? Perhaps using the docopt library to handle parsing of help output and generate completions from that?\nWhat are your thoughts on adding a section to the project documentation for people to list various extension modules that people can take advantage of? Or perhaps creating something along the lines of Oh my Xonsh?\nHow do bash function definitions interoperate with the Xonsh environment and functions defined in Python?\nIt seems as though there could be some potential path or compatibility issues when moving between virtual environments and having access to extension modules loaded into Xonsh. Can you shed some light on that?\nDo you have any suggestions for people who may not have the privileges to set their own login shell but who want to try Xonsh?\nWhat are some of the most interesting uses of Xonsh that you have seen?\nWhat does the future hold for the Xonsh project and how can our audience help?\n\n\nPicks\n\n\nTobias\n\nMortdecai\nAlembic\nSQLAlchemy\npopulation.io\n\n\n\nChris\n\n\nConsider Phlebas\nThe Martian – Movie\nFantastic Planet\n\n\n\nAnthony\n\n\nThe Worst Journey In The World\n\n\n\n\n\nKeep In Touch\n\n\nMailing List\nxonsh.org\n#xonsh on OFTC\nGitHub\nTwitter: @scopatz\n\n\nLinks\n\n\nEffective Computation in Physics\nPython Prompt Toolkit\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, and sign up for our mailing list.

\n\n

Summary

\n\n

Anthony Scopatz is the creator of the Python shell Xonsh in addition to his work as a professor of nuclear physics. In this episode we talked to him about why he created Xonsh, how it works, and what his goals are for the project. It is definitely worth trying out Xonsh as it greatly simplifies the day-to-day use of your terminal environment by adding easily accessible python interoperability.

\n\n

Brief Introduction

\n\n\n\n
\"HiredOn Hired software engineers & designers can get 5+ interview requests in a week and each offer has salary and equity upfront. With full time and contract opportunities available, users can view the offers and accept or reject them before talking to any company. Work with over 2,500 companies from startups to large public companies hailing from 12 major tech hubs in North America and Europe. Hired is totally free for users and If you get a job you’ll get a $2,000 “thank you” bonus. If you use our special link to signup, then that bonus will double to $4,000 when you accept a job. If you’re not looking for a job but know someone who is, you can refer them to Hired and get a $1,337 bonus when they accept a job.

\n
\n\n
\"LinodeUse the promo code podcastinit10 to get a $10 credit when you sign up!

\n
\n\n

Interview with Anthony Scopatz

\n\n\n\n

Picks

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Keep In Touch

\n\n\n\n

Links

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2015-10-30T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/33c1c08a-88a0-479d-a8ec-6925c7b3c193.mp3","mime_type":"audio/mpeg","size_in_bytes":65954172,"duration_in_seconds":3473}]},{"id":"http://podcastinit.podbean.com/e/episode-28-kay-hayen-on-nuitka/","title":"Kay Hayen on Nuitka","url":"https://www.pythonpodcast.com/episode-28-kay-hayen-on-nuitka","content_text":"Visit our site to listen to past episodes, support the show, and sign up for our mailing list.\n\nSummary\n\nKay Hayen is a systems engineer from Germany who has dedicated his spare time to the creation of Nuitka, a library that will compile your Python project to C++. In this episode we talked to Kay about what inspired him to create the project, how it operates, and some of the challenges he has faced. It is a very interesting project and it has the potential to let you run your Python code in a whole new way!\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email, leave us a message on Google+, or leave a comment on our show notes\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com\nI would also like to thank Hired, a job marketplace for developers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus. Linode has also sponsored this episode and you can get a $10 credit using the link linode.com/podcastinit to try out their fast and reliable linux virtual servers.\nWe are recording today on October 6th, 2015 and your hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Kay Hayen about the Nuitka project\n\n\n\n\n\nOn Hired software engineers & designers can get 5+ interview requests in a week and each offer has salary and equity upfront. With full time and contract opportunities available, users can view the offers and accept or reject them before talking to any company. Work with over 2,500 companies from startups to large public companies hailing from 12 major tech hubs in North America and Europe. Hired is totally free for users and If you get a job you’ll get a $2,000 “thank you” bonus. If you use our special link to signup, then that bonus will double to $4,000 when you accept a job. If you’re not looking for a job but know someone who is, you can refer them to Hired and get a $1,337 bonus when they accept a job.\n\n\nUse the promo code podcastinit10 to get a $10 credit when you sign up!\n\n\n\n\nInterview with Kay Hayen\n\n\nIntroductions\n\nGerman, family with 2 kids, one cat\nWorking in ATM (Air Traffic Management), tracker product\nSystems Engineer\nNuitka as a hobbyist\n\n\n\nHow did you get introduced to Python?\n\n\nOnce was Perl “Guru”.\nPython was getting a lot of positive press\nTeam decision to want to use readable stuff\nCPAN was still more complete, but Python was making inroads\n\n\n\nCan you describe how to pronounce the name of your project?\n\n\nWife Anna, Russian, Annuitka -> Nuitka\n\n\n\nCan you briefly describe what Nuitka is and what your motivation was for creating it?\n\n\nI was thinking a fully integrated and compatible compiler should be possible.\nWhy is nobody doing it?\nI can do it.\nI am doing it.\nTake Python beyond current use cases.\n\nEverbody currently using Python needs no compiler, or wouldn’t use it\nLess need for time consuming C++/Python hybrid coding\nSimple code should compile to fast code by default\nComplex code should still work\n\n\n\n\n\nOn the project web site it says that Nuitka does a lot of clever things after being fed a Python project. Can you provide some details as to what some of that cleverness is?\n\n\nRe-formulations of Python into simpler Python\n\nNo “class”\nNo “assert”\nNo complex assignments\n\n\n\nSSA tracing\n\n\nAttaching uses to assignments properly\n\nDespite try/finally\nLoops\n\n\n\nAvoids checks for known defined/undefined values\n\n\nFunction inlining (coming)\nConstant propagation\nClosure variable removal\n\n\nWhat is libpython and how is it used in both Nuitka and CPython?\n\n\nCore of the Python interpreter\nWith Python VM and C interface\nNuitka can fall back to it\nAvoiding it as often as we can, key to performance\n\n\n\nIs there any way to provide hints to Nuitka to generate more optimized output?\n\n\nNuitka is yet to make a difference based on type information\nNot yet there, but coming soonish. SSA was pre-requisite\nPEP 484 will be unreliable type information, mostly useless\nI want type hints that are checked at Python run time\n\n\n\nWhat are some of the biggest challenges in generating statically compiled code from a language as dynamic as Python?\n\n\nPython is compiled to .pyc files\nCompatible Frame stack, cached\nException handling of Python is terrible\nCPython type system designed to be extensible\n\nExtension types for functions, bound/unbound methods, generators, etc.\n\n\n\nMany details to get right\n\n\nAre there any particular Python constructs that Nuitka is unable to translate and as a corollary to that is the compilation step lossy at all or do you have some way of ensuring that the functionality of the program remains unaltered?\n\n\nBig point, no price attached\nExcept for not having bytecode, there is nothing missing\nNo pdb support\nEdit / run cycle is not accelerated\nThat said: PyQt (integrated), PySide (available, unmerged), wxPython (available, maybe merged) needed patches to take compiled function/method objects for function objects too\n\n\n\nAre there any particular types of programs that benefit the most from Nuitka’s compilation?\n\n\nBindings with ctypes of cffi compile into zero overhead C calls (planned)\nScientific programs are the most obvious goal (float type inference)\nCPU bound or low latency programs\n\n\n\nIs it possible to feed an entire project with multiple modules into Nuitka all at once or is the standard use to perform compilation one source file or submodule at a time?\n\n\nYou give it the main program and it recurses imports according to “PYTHONPATH”\nnuitka –recurse-all “/usr/bin/hg” supposed to work\nMight have to give directories with program plug-ins\n\n\n\nI’m curious about what led you to choose compilation to C++ for Nuitka rather than making Nuitka an LLVM back end like Numba?\n\n\nWhen I started Nuitka, I was using C++0x and variadic templates\nWanted to make a proof of concept that compatibility and integration is feasible\nFrom there, code generation got less high level to goto ridden C\n\n\n\nHow does Nuitka compare to projects like Numba or Cython?\n\n\nGraceful degradation goal\nComplete compatibility with Python whole stack\n\n\n\nHow does Nuitka compare to PyPy? – Kay\n\n\nPyPy is the coolest project ever\nPure Python goals shared\n\n\n\nHow can users evaluate the performance of Nuitka – Kay\n\n\nThey currently cannot\nDeveloping a tool to compare CPython and Nuitka runs\n\nBased on vmprof from PyPy people\nIdentify parts of program where Nuitka is slower\nLinks to source code\n\n\n\nTo be done, help needed.\nNuitka is only starting to get to serious performance\n\n\nCompatibility is such a high bar to take\nC++ to C took a year (avoiding C++ exceptions)\nSSA literally took forever\n\n\n\n\n\n\n\nPicks\n\n\nTobias\n\nForbidden Island\nForbidden Desert\nOtto Project\n\n\n\nChris\n\n\nGrimm Super Symmetry\nAre You Listening To?: Boston\nRipple\n\n\n\nKay\n\n\nLearn being skeptic, Atheist Experience\nMicroPython\n\n\n\n\n\nKeep In Touch\n\n\nNuitka Homepage\nGoogle+\nEmail\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, support the show, and sign up for our mailing list.

\n\n

Summary

\n\n

Kay Hayen is a systems engineer from Germany who has dedicated his spare time to the creation of Nuitka, a library that will compile your Python project to C++. In this episode we talked to Kay about what inspired him to create the project, how it operates, and some of the challenges he has faced. It is a very interesting project and it has the potential to let you run your Python code in a whole new way!

\n\n

Brief Introduction

\n\n\n\n
\"HiredOn Hired software engineers & designers can get 5+ interview requests in a week and each offer has salary and equity upfront. With full time and contract opportunities available, users can view the offers and accept or reject them before talking to any company. Work with over 2,500 companies from startups to large public companies hailing from 12 major tech hubs in North America and Europe. Hired is totally free for users and If you get a job you’ll get a $2,000 “thank you” bonus. If you use our special link to signup, then that bonus will double to $4,000 when you accept a job. If you’re not looking for a job but know someone who is, you can refer them to Hired and get a $1,337 bonus when they accept a job.

\n
\n\n
\"LinodeUse the promo code podcastinit10 to get a $10 credit when you sign up!

\n
\n\n


\n\n

Interview with Kay Hayen

\n\n

\n\n

Picks

\n\n

\n\n

Keep In Touch

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2015-10-23T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/3fa9687d-9dee-48c8-8d09-d08dcde1c48d.mp3","mime_type":"audio/mpeg","size_in_bytes":109386625,"duration_in_seconds":5675}]},{"id":"http://podcastinit.podbean.com/e/episode-27-trent-nelson-on-pyparallel/","title":"Trent Nelson on PyParallel","url":"https://www.pythonpodcast.com/episode-27-trent-nelson-on-pyparallel","content_text":"Visit our site to listen to past episodes, support the show, and sign up for our mailing list.\n\nSummary\n\nTrent Nelson is a software engineer working with Continuum Analytics and a core contributor to CPython. He started experimenting with a way to sidestep the restrictions of the Global Interpreter Lock without discarding its benefits and that has become the PyParallel project. We had the privilege of discussing the details around this innovative experiment with Trent and learning more about the challenges he has experienced, what motivated him to start the project, and what it can offer to the community.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at\nI would also like to thank Hired, a job marketplace for developers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.\nWe are recording today on September 7th, 2015 and your hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Trent Nelson about PyParallel\n\n\nOn Hired software engineers & designers can get 5+ interview requests in a week and each offer has salary and equity upfront. With full time and contract opportunities available, users can view the offers and accept or reject them before talking to any company. Work with over 2,500 companies from startups to large public companies hailing from 12 major tech hubs in North America and Europe. Hired is totally free for users and If you get a job you’ll get a $2,000 “thank you” bonus. If you use our special link to signup, then that bonus will double to $4,000 when you accept a job. If you’re not looking for a job but know someone who is, you can refer them to Hired and get a $1,337 bonus when they accept a job.\n\n\nInterview with Trent Nelson\n\n\nIntroductions\nHow did you get introduced to Python?\nFor our listeners who may not be aware, can you give us an overview of what Pyparallel is and what makes it different from other Python implementations?\nHow did PyParallel come about?\nWhat are some of the biggest technical hurdles that you have been faced with during your work on PyParallel?\nI understand that PyParallel currently only works on Windows. What was the motivation for that and what would be required for enabling PyParallel to run on a Linux or BSD style operating system?\nHow does Pyparallel get around the limitations of the global interpreter lock without removing it?\nIs there any special syntax required to take advantage of the parallelism offered by PyParallel? How does it interact with the threading module in the standard library?\nIn the abstract for the Pyparallel paper, you cite a simple rule – “Don’t persist parallel objects” – how easy is this to do with currently available concurrency paradigms and APIs, and would it make sense to add such support?\n\nFor instance, how would one be sure to follow this rule when using Twisted or asyncio?\n\n\n\nAre there any operations that are not supported in parallel threads?\nWhat drove the decision to fork Python 3.3 as opposed to the 2.X series?\nIn the documentation you mention that the long term goal for PyParallel is to merge it back into Python mainline, possibly within 5 years. Has anything changed with that goal or timeline? What milestones do you need to hit before that becomes a realistic possibility?\nCan you compare PyParallel to PyPy-STM and Go with Goroutines in terms of performance and user implementation?\nWhat are some particular problem areas that you are looking for help with?\nAssuming that it does get merged in as Python 4, how do you think that would affect the features and experiments that went into Python 5?\nTo be continued…\n\n\nPicks\n\n\nTobias\n\nTestinfra\nSoftware Engineering Daily\n\n\n\nChris\n\n\nHello Webapp – Intermediate Concepts\nGrimm Rainbow Dome\nPBS Idea Channel\n\n\n\nTrent\n\n\nShow Stopper by G. Pascal Zachary\n\n\n\n\n\nKeep In Touch\n\n\nGitHub\nTwitter\n\n@PyParallel\n@TrentNelson\n\n\n\n\n\n","content_html":"

Visit our site to listen to past episodes, support the show, and sign up for our mailing list.

\n\n

Summary

\n\n

Trent Nelson is a software engineer working with Continuum Analytics and a core contributor to CPython. He started experimenting with a way to sidestep the restrictions of the Global Interpreter Lock without discarding its benefits and that has become the PyParallel project. We had the privilege of discussing the details around this innovative experiment with Trent and learning more about the challenges he has experienced, what motivated him to start the project, and what it can offer to the community.

\n\n

Brief Introduction

\n\n\n\n
\"HiredOn Hired software engineers & designers can get 5+ interview requests in a week and each offer has salary and equity upfront. With full time and contract opportunities available, users can view the offers and accept or reject them before talking to any company. Work with over 2,500 companies from startups to large public companies hailing from 12 major tech hubs in North America and Europe. Hired is totally free for users and If you get a job you’ll get a $2,000 “thank you” bonus. If you use our special link to signup, then that bonus will double to $4,000 when you accept a job. If you’re not looking for a job but know someone who is, you can refer them to Hired and get a $1,337 bonus when they accept a job.

\n
\n\n

Interview with Trent Nelson

\n\n

\n\n

Picks

\n\n

\n\n

Keep In Touch

\n\n

\n\n

\"\"

","summary":"","date_published":"2015-10-14T09:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/a8db05d1-4da8-480a-b248-50e73145c8f9.mp3","mime_type":"audio/mpeg","size_in_bytes":48263824,"duration_in_seconds":4363}]},{"id":"http://podcastinit.podbean.com/e/episode-26-dag-brattli-on-rxpy/","title":"Dag Brattli on RxPy","url":"https://www.pythonpodcast.com/episode-26-dag-brattli-on-rxpy","content_text":"Visit our site to listen to past episodes, support the show, and sign up for our newsletter!\n\nSummary\n\nDag Brattli is an engineer with Microsoft and in his spare time he created the ported the Reactive Xtensions framework to Python in the form of the RxPy library. In this episode we had the opportunity to speak with Dag and learn more about what ReactiveX is, why it is useful and how you can use it in your Python programs. It is definitely a very powerful programming patern when manipulating data streams which is becoming increasingly common in modern software architectures.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at\nI would also like to thank Hired, a job marketplace for developers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.\nWe are recording today on October 2nd, 2015 and your hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Dag Brattli about the RxPy project\n\n\nOn Hired software engineers & designers can get 5+ interview requests in a week and each offer has salary and equity upfront. With full time and contract opportunities available, users can view the offers and accept or reject them before talking to any company. Work with over 2,500 companies from startups to large public companies hailing from 12 major tech hubs in North America and Europe. Hired is totally free for users and If you get a job you’ll get a $2,000 “thank you” bonus. If you use our special link to signup, then that bonus will double to $4,000 when you accept a job. If you’re not looking for a job but know someone who is, you can refer them to Hired and get a $1,337 bonus when they accept a job.\n\n\n\nInterview with Dag Brattli\n\nIntroductions\nHow did you get introduced to Python?\nFor our listeners who haven’t heard of it before, can you describe what RxPy is and why someone might want to use it?\nWhat problem domains are best suited for using the Reactive X approach?\nWhat is involved in integrating RxPy into an existing code base?\nWhen should we use RxPy over asyncio or asynchronous workers like Celery?\nWhat resources or tutorials do you recommend people use when trying to understand how and when to use the Reactive X tools?\nWhat in particular about Python lends itself to the ReactiveX pattern, and what features of the language does RxPy leverage in particular in its implementation?\nIn what ways does the Python implementation of the Reactive X framework differ from those of other languages?\nThe project description references the use of LINQ for querying the various data streams that RxPy enables consumption of. I had always heard of LINQ in the context of traditional database queries. What makes LINQ a good choice for stream processing?\nI mostly hear about ReactiveX in terms of UI design, but the project description seemed to indicate it was much more generally useful. What are some of the less common and more interesting problems that RxPy lends itself to solving?\n\nPicks\n\nTobias\n\nicdiff\nTimeline card game\nGriatch’s Digital Art\n\n\nChris\n\nelpy\nsshuttle\nChimay Grand Reserve\n\n\nDag\n\nASTor\nHow To Bake Pi – A book about the mathematics of mathematics\n\n\n\nKeep In Touch\n\nGitHub\nLinks\n\nMain ReactiveX Site\nrxjava site for documentation\nrxmarbles\nMSDN Channel 9\nFunction Overloading in Python 3\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\n\n\n\n","content_html":"

Visit our site to listen to past episodes, support the show, and sign up for our newsletter!

\n\n

Summary

\n\n

Dag Brattli is an engineer with Microsoft and in his spare time he created the ported the Reactive Xtensions framework to Python in the form of the RxPy library. In this episode we had the opportunity to speak with Dag and learn more about what ReactiveX is, why it is useful and how you can use it in your Python programs. It is definitely a very powerful programming patern when manipulating data streams which is becoming increasingly common in modern software architectures.

\n\n

Brief Introduction

\n\n\n\n
\"hired-logo-dark-padding.png\"On Hired software engineers & designers can get 5+ interview requests in a week and each offer has salary and equity upfront. With full time and contract opportunities available, users can view the offers and accept or reject them before talking to any company. Work with over 2,500 companies from startups to large public companies hailing from 12 major tech hubs in North America and Europe. Hired is totally free for users and If you get a job you’ll get a $2,000 “thank you” bonus. If you use our special link to signup, then that bonus will double to $4,000 when you accept a job. If you’re not looking for a job but know someone who is, you can refer them to Hired and get a $1,337 bonus when they accept a job.

\n
\n\n
\n

Interview with Dag Brattli

\n\n

Picks

\n\n

Keep In Touch

\n\n
\n\n

\"\"

","summary":"","date_published":"2015-10-09T08:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/9349ea8b-160b-420f-a255-f2c7aa632671.mp3","mime_type":"audio/mpeg","size_in_bytes":40303989,"duration_in_seconds":1981}]},{"id":"http://podcastinit.podbean.com/e/episode-25-uwsgi-core-developers/","title":"uWSGI Core Developers","url":"https://www.pythonpodcast.com/episode-25-uwsgi-core-developers","content_text":"Visit our site to listen to past episodes, join the mailing list and support the show.\n\nSummary\n\nuWSGI is one of the most versatile application servers available. It was originally written for running Python applications and has since gained functionality to support Perl, Ruby, PHP, and more in addition to the incredible feature set. In this episode Tobias got to interview three of the core developers of this project and find out more about how the different pieces of it fit together and what its future holds.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at\nI would also like to thank Hired, a job marketplace for developers, for sponsoring this episode of Podcast.init. Sign up at hired.com/podcastinit to double your signing bonus.\nWe are recording today on September 22nd, 2015 and your hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing the core developers of uWSGI (Adriano Di Luzio, Riccardo Magliocchetti, and Roberto De Ioris)\n\n\nInterview with uWSGI core developers\n\n\nIntroductions\nHow did you get introduced to Python?\nFor anyone who hasn’t come across the project before, can you explain what uWSGI is and what makes it unique?\nHow did you architect uWSGI in order to allow for supporting so many different languages?\nThe feature set of uWSGI is truly incredible. Does this make the code complicated to understand and modify?\nCan you describe some of your favorite features in uWSGI?\nWhat have you found to be the most overlooked or underutilized features of uWSGI?\nCan you briefly describe how Emperor mode works and how that can be used to handle routing between microservices?\nCould you discuss some of the particular features UWSGI provides around load balancing?\n\nIs connection draining supported?\nCan nodes be dynamically added and removed from the pool or does the config need to be rewritten and UWSGI restarted?\n\n\n\nThe configuration syntax looks like it provides a very rich set of capabilities. Is it based on a general purpose programming language or is it a DSL?\nWhat might be some common use cases for using UWSGI in tandem with another web server like NGINX?\nI have read that WSGI does not get along with http/2. Are there any plans to look towards supporting that protocol in some way?\nWhat new capabilities can we look forward to in the future of uWSGI?\n\n\nPicks\n\n\nTobias\n\nManjaro Linux\nKontact\nBlackhat\n\n\n\nRiccardo\n\n\nBuilding Microservices book\nDjango-Denis\n\n\n\nAdriano\n\n\nPaxos Algorithm\n\n\n\nRoberto\n\n\nThe Brink\n\n\n\n\n\nKeep In Touch\n\n\nMailing List\n#uWSGI on IRC\nGitHub\nlatest docs\nRoberto\n\nTwitter\nGitHub\n\n\n\nAdriano\n\n\nGitHub\nTwitter\n\n\n\nRiccardo\n\n\nGitHub\nTwitter\n\n\n\n\n\n","content_html":"

Visit our site to listen to past episodes, join the mailing list and support the show.

\n\n

Summary

\n\n

uWSGI is one of the most versatile application servers available. It was originally written for running Python applications and has since gained functionality to support Perl, Ruby, PHP, and more in addition to the incredible feature set. In this episode Tobias got to interview three of the core developers of this project and find out more about how the different pieces of it fit together and what its future holds.

\n\n

Brief Introduction

\n\n\n\n

Interview with uWSGI core developers

\n\n

\n\n

Picks

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\n\n

Keep In Touch

\n\n

\n\n

\"\"

","summary":"","date_published":"2015-10-02T23:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/ee5aed4f-7155-46ac-83a0-21695528d8b2.mp3","mime_type":"audio/mpeg","size_in_bytes":25483151,"duration_in_seconds":2099}]},{"id":"http://podcastinit.podbean.com/e/episode-24-griatch-on-evennia/","title":"Griatch on Evennia (Making MUDs with Python)","url":"https://www.pythonpodcast.com/episode-24-griatch-on-evennia","content_text":"Visit our site to listen to past episodes, sign up for our mailing list and support the show.\n\nSummary\n\nGriatch is an incredibly talented digital artist, professional astronomer and the maintainer of the Evennia project for creating MUDs in Python. We got the opportunity to speak with him about what MUDs are, why they’re interesting and how Evennia simplifies the process of creating and extending them. If you’re interested in building your own virtual worlds, this episode is a great place to start.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at\nWe are recording today on September 15th, 2015 and your hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Griatch about the Evennia project\n\n\nInterview with Griatch\n\n\nIntroductions\nHow did you get introduced to Python?\nCan you explain what MUDs are and what that has to do with Evennia?\nWhat is it about MUDs that keeps them interesting long after the technical restrictions that led to their creation are no longer present, especially in light of 3D multiplayer games like WoW and EVE Online?\nCan you give us a rundown of the various parts of Evennia (MUD engine, web interface, etc.) and how they fit together?\nHow does Evennia handle the fact that a MUD world is comprised of many hundreds of objects containing various properties, maintaining consistent, persistent state as players interact with them?\nWhat concurrency tools or paradigms does Evennia use?\nDuring the height of MUDs popularity, one highly sought after feature was the idea of being able to have players travel from one MUD instance to another, would it be possible to implement this in Evennia?\nHas the Evennia core team given any thought to adding features to support a richer client interface? Graphical maps or the like?\nHow difficult would it be to use Evennia to interface with something like Slack or Hipchat for a company-wide MUD? Have you ever heard of someone doing something like that?\nAre there any fully fledged running MUDs built with Evennia out in the wild?\n\n\nPicks\n\n\nTobias\n\nlibraries.io\njsonapi.org\nMarshmallow Marshalling Library\n\n\n\nChris\n\n\nThe End of All Things\nDavid’s Tea Steeper\nHello Webapp – Intermediate Concepts\n\n\n\nGriatch\n\n\nF2Py\nDesigning Virtual Worlds\nImaginary Realities\nOptional Realities\n\n\n\n\n\nKeep In Touch\n\n\nEvennia Website\nEvennia Github\nFreenode IRC Channel #Evennia\n\n\nLinks\n\n\nroll20\n\n\n","content_html":"

Visit our site to listen to past episodes, sign up for our mailing list and support the show.

\n\n

Summary

\n\n

Griatch is an incredibly talented digital artist, professional astronomer and the maintainer of the Evennia project for creating MUDs in Python. We got the opportunity to speak with him about what MUDs are, why they’re interesting and how Evennia simplifies the process of creating and extending them. If you’re interested in building your own virtual worlds, this episode is a great place to start.

\n\n

Brief Introduction

\n\n\n\n

Interview with Griatch

\n\n\n\n

Picks

\n\n

\n\n

Keep In Touch

\n\n\n\n

Links

\n\n\n\n

\"\"

","summary":"Making MUDs with Python","date_published":"2015-09-28T23:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/82b5fbb0-c359-4b09-b091-73870f2faf0b.mp3","mime_type":"audio/mpeg","size_in_bytes":49853493,"duration_in_seconds":4443}]},{"id":"http://podcastinit.podbean.com/e/episode-23-hylang-core-developers/","title":"Hylang Core Developers","url":"https://www.pythonpodcast.com/episode-23-hylang-core-developers","content_text":"Visit our site to listen to past episodes, support the show, and sign up for our mailing list\n\nSummary\n\nWe got the chance to talk to some of the core developers of Hylang, which is a Lisp dialect that runs on the Python VM! We talked about how it got started, how it works and why you should try it. Of particular interest is our discussion about using Hylang to backport language features, or create entirely new ones due to the power of Lisp and the Python AST (Abstract Syntax Tree). If you need to level up your Lisp knowledge, they gave us a great list of references to help out.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at\nWe are recording today on August 27, 2015 and your hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Paul Tagliamonte, Tuukka Turto, and Morten Linderud\n\n\nInterview with Hylang Developers\n\n\nIntroductions\nHow did you get introduced to Python?\nBefore we get too far along can you explain what Hy is?\nWhat inspired you to create Hy?\nWhat do you recommend as reference material for Python developers to gain familiarity with idiomatic Lisp?\nWhat are some of the problem domains where implementation becomes easier or more elegant as a result of Hy’s LISP syntax?\nGiven the ability to create powerful macros in Lisp, could Hy be used as a way of prototyping or backporting new language features in Python?\nWhat are some of the most challenging and interesting problems you encountered bringing an alternate syntax to the Python runtime?\nWhile playing around with the Hy REPL I noticed that it does visual matching of parentheses when closing an expression. What other niceties have been included in the REPL?\nWhat are your thoughts on adding autocompletion to the REPL as a way of encouraging discovery and exploration of the Hy language?\nWhich LISP variant is Hy most similar to, and why?\nHow does garbage collection work in Hy, and why?\nHow hard would it be to port existing LISP packages to Hy like MACSYMA or CLOS?\nWhat kind of overhead in terms of runtime performance and memory usage does Hy impose? Has this been a challenge in Hy’s development?\nWhat are some of the most innovative uses for Hy that you have seen or created?\nWhat does the future hold for Hy?\nI noticed that there are a large number of core contributors to Hylang and I’m curious how you determine what features to work on?\n\n\nPicks\n\n\nTobias\n\nDisplacy\nThe Golem and the Jinni by Helene Wecker – Read it on Scribd\nSafari Online\n\n\n\nChris\n\n\nDash and Zeal\nReasonably sound (podcast)\nPBS Idea Channel (Youtube)\n\n\n\nPaul\n\n\nReproducible Build Project\nModel View Culture\n\n\n\nTuukka\n\n\nSICP Lecture\nF#\nReactiveX\n1 Game Per Month (#!GAM)\n\n\n\nMorten\n\n\nHackers\nMr. Robot\n\n\n\n\n\nKeep In Touch\n\n\nPaul\n\nTwitter\npaultag on IRC\nWebsite\n\n\n\nTuukka\n\n\nTwitter\n\n\n\nMorten\n\n\nTwitter\nLinks\n\n\n\n\nCore features of Hylang\nAdderall – minicanron in hylang\nBooks\n\n\nJoy of Clojure\nLet over Lambda\nLand of Lisp\nClojure programming\n\n\n\nHerculeum – Tukka’s DSL for roguelikes\nPixie – Lisp in RPython\nDogelang\nBPython\nGithub trending repos with Hylang\nPineal\nhydiomatic – Algernon\n\n\n","content_html":"

Visit our site to listen to past episodes, support the show, and sign up for our mailing list

\n\n

Summary

\n\n

We got the chance to talk to some of the core developers of Hylang, which is a Lisp dialect that runs on the Python VM! We talked about how it got started, how it works and why you should try it. Of particular interest is our discussion about using Hylang to backport language features, or create entirely new ones due to the power of Lisp and the Python AST (Abstract Syntax Tree). If you need to level up your Lisp knowledge, they gave us a great list of references to help out.

\n\n

Brief Introduction

\n\n\n\n

Interview with Hylang Developers

\n\n\n\n

Picks

\n\n

\n\n

Keep In Touch

\n\n

\n\n

\"\"

","summary":"","date_published":"2015-09-18T23:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/19900407-0341-4cbf-b58d-2beddb085d15.mp3","mime_type":"audio/mpeg","size_in_bytes":66055750,"duration_in_seconds":3348}]},{"id":"http://podcastinit.podbean.com/e/episode-22-bryan-van-de-ven-on-bokeh/","title":"Bryan Van de Ven on Bokeh","url":"https://www.pythonpodcast.com/episode-22-bryan-van-de-ven-on-bokeh","content_text":"Visit our site to listen to past episodes, subscribe to our mailing list, and donate to the show.\n\nSummary\n\nBryan Van de Ven is the project maintainer for Bokeh, a plotting and visualization toolkit that allows Python developers to easily create attractive interactive visualizations for the web. We talked about the project’s history, some interesting use cases for it, and what its near future looks like. Bryan also told us about how Bokeh compares to some of the other visualization libraries in both Python and Javascript, as well as how to use Bokeh from other languages such as Scala and Lua.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at\nThere is a new Python podcast that just started up recently! It’s called the Python Test Podcast and covers the world of testing in Python, so go ahead and give it a listen. You can find it at\nWe are recording today on Aug 18th, 2015 and your hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Bryan Van de Ven about the Bokeh project\n\n\nInterview with Bryan Van de Ven\n\n\nIntroductions\nHow did you get introduced to Python?\nFor our listeners who aren’t familiar with what Bokeh is, can you describe it?\nWhat inspired you to create Bokeh?\nBokeh has integrations with some of the other Python graphing libraries such as matplotlib and seaborn. I can see how this would be useful to easily update existing code to publish visualizations on the web. Are there other use cases for these integrations?\nI noticed that Bokeh has bindings for some languages other than Python. R and Julia are obvious candidates due to their strong focus on analytics work, I’m curious what made you choose Scala and Lua as languages worth targeting?\nDo you lose any capabilities using the javascript library by itself?\nOther than the sample data sets that come with Bokeh, can you suggest a good publicly available data set with accompanying tutorial for people who want to get started with data visualization using Bokeh?\nCan you provide some comparisons between D3.js and the Bokeh javascript library in terms of capabilities and performance?\nThe Bokeh project has a server component that allows for streaming data to clients. Can you describe the architecture of that and some example uses for it?\nWhy was the server written as a Flask blueprint as opposed to making it a component of another framework such as Django or Pyramid and how difficult would it be to port the functionality to another system?\nWhat’s the most interesting use of Bokeh you’ve seen?\nAre you aware of any projects in other languages that are comparable to Bokeh?\n\n\nPicks\n\n\nTobias\n\nwappalyzer\nThe Graveyard Book by Neil Gaiman\n\n\n\nChris\n\n\nEdward Snowden Meets the IETF\nBetween the World and Me\nUntapp’d\n\n\n\nBryan\n\n\nAudiobooks\n\nScribd – Subscription service for ebooks and audio books with a great selection\nTry Audible and Get Two Free Audiobooks\n\n\n\nCartographies of Time\nThe Post-Modern Jukebox\n\n\n\n\nKeep In Touch\n\n\nTwitter\nMailing List\nBokeh Web Site\n\n\nLinks\n\n\nvispy\nVincent\nvega\nD3.js\nnbviewer.org bokeh page\nmillion song dataset\ndata.gov\nggplot / ggvis\nmathematica\n\n\n","content_html":"

Visit our site to listen to past episodes, subscribe to our mailing list, and donate to the show.

\n\n

Summary

\n\n

Bryan Van de Ven is the project maintainer for Bokeh, a plotting and visualization toolkit that allows Python developers to easily create attractive interactive visualizations for the web. We talked about the project’s history, some interesting use cases for it, and what its near future looks like. Bryan also told us about how Bokeh compares to some of the other visualization libraries in both Python and Javascript, as well as how to use Bokeh from other languages such as Scala and Lua.

\n\n

Brief Introduction

\n\n\n\n

Interview with Bryan Van de Ven

\n\n\n\n

Picks

\n\n

\n\n

Keep In Touch

\n\n\n\n

Links

\n\n\n\n

\"\"

","summary":"","date_published":"2015-09-08T08:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/a920282e-6224-4c9e-bf72-71d7fde94609.mp3","mime_type":"audio/mpeg","size_in_bytes":73186664,"duration_in_seconds":3438}]},{"id":"http://podcastinit.podbean.com/e/episode-21-jessica-mckellar/","title":"Jessica McKellar","url":"https://www.pythonpodcast.com/episode-21-jessica-mckellar","content_text":"Visit our site to listen to past episodes, support the show and sign up for our mailing list.\n\nSummary\n\nWe got the chance to talk to Jessica McKellar about her work in the Python community. She told us about her experience as a director for the PSF, working as the diversity outreach manager for PyCon, and being a champion for improving the on-boarding experience for new users of Python. We also discussed perceptions around the performance of Python and some of the work being done to improve concurrency, as well as her work with OpenHatch.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at\nWe are recording today on Aug, 12 2015 and your hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Jessica McKellar\n\n\nInterview with Jessica McKellar\n\n\nIntroductions\nHow did you get introduced to Python?\n\nAttended MIT, originally for Chemistry\nHad friends pursuing CS degrees\nToolset and skills seemed worth investingating\nLed to BA and MS\nMIT was in transition from LISP to Python\n\n\n\nCan you describe what your responsibilities are as a director of the PSF?\n\n\nA lot of outreach and investment in the community\n\n\n\nDo you think the PSF does a good job of making people aware of what it is, what it does for the community, and how they can help?\n\n\nStruggled with this historically but has gotten better in recent years\nWebsite re-design has helped\n\n\n\nA large focus of your work in the community has been around improving the experience of users who are new to Python and programming in general and I noticed that you just received the Frank Willison Memorial Award for your contributions to outreach and education in the Python community. What is your motivation behind this particular focus?\n\n\nGreat deal of empathy for newcomers due to personal history\nKnowing how to program changes how you think about the world\n\n\n\nHas the situation for newcomers running Windows who wish to try Python gotten any better since your keynote at Kiwi PyCon?\n\n\nSome vaguaries of setup have gotten better with recent versions (e.g. setting path variables)\nRuby has in-browser tutorial to get people hooked\n\n\n\nDo “Batteries Included’ distributions like Anaconda help or is it the same problem of visibility you discussed in your talk?\n\n\nInformatino flow / what are you default options question\nWe could be much more opinionated about this\n\n\n\nYou have presented a number of times about the future of Python and how we can all help to make sure that story is a happy one. How has the material for that talk changed over the past few years?\n\n\nAs a largely volunteer community, how to maximize the impact of the bandwidth that we have\nFocus on the ‘top of the funnel’ to win over new users\nPython has the steepest positive curve of any language\nCommunity should invest in AP high school Python curriculum\n\n\n\nWhat do you anticipate will be the talking points for this topic over the next few years?\n\n\nWe need to be smart about which areas we invest in to ensure success e.g. mobile, web, desktop.\n\n\n\nIf you could grade the Python community on how well they have listened to and acted on the calls to action in your talks over the past few years, what would you give them?\n\n\nRallying large groups of volunteers is a hard problem\nWe need to think about commercial partnerships in key areas\n\n\n\nIn your Kiwi PyCon talk you mentioned Kivy as an example of a great way to do mobile software development in Python. It feels to me like the Kivy team are still not getting the community involvement and buy in they should. How can we help make Kivy the mobile app development platform of choice for beginners?\n\n\nThis will be a tough battle because Python is not the default platform for mobile compared to Java for Android, Objective C, Swift\nUsers vote with their feet depending on what provides the most value to them\nOpportunity for a virtuous cycle here\n\n\n\nGame development as an entree to programming has been a recurring theme on our podcast. Has the Python game dev scene improved at all since 2013? And do you still see the same pitfalls holding people back (like app packaging), or have we moved on to different problems?\n\n\nThe problems are largely the same\nStatus quo still feels pretty broken\nCreative experiments around this definitely make sense for the community\nKivEnt could be a win here because Kivy apps are free standing binaries and require no dependencies.\n\n\n\nWhat do you view as the biggest threats to the popularity of Python currently and what can we do to address them?\n\n\nOther languages gaining popularity where Python has historically been strong (e.g. server-side development)\nA lot of this may be a perception issue\nMay be largely a marketing problem\n\n\n\nI understand that you were involved in the formation of the Open Hatch organization. Can you describe what Open Hatch does and how our listeners can get involved?\n\n\nNon-profit dedicated to lowering barriers to entry for open source contribution\nHost workshops in colleges, underserved communities, etc.\n\n\n\n\n\nPicks\n\n\nTobias\n\nF.lux\nLightyear.fm\nPEP 0401\n\n\n\nChris\n\n\nThe Alex Verus Series by Benedict Jacka\nRick Dillon’s Org-mode structure manipulation tutorial\nDominion\n\n\n\nJessica\n\n\nReply All Podcast\nRFC 959 – original FTP RFC\n\nGo read some RFCs!\n\n\n\nThink Stats\n\n\n\n\nKeep In Touch\n\n\nGoogle for “Jesstess”\n\n\nConference Presentations\n\n\nhttps://www.youtube.com/watch?v=CI_RPSbsRw8&utm_source=rss&utm_medium=rss\nhttps://www.youtube.com/watch?v=2p-FecWny_Q&utm_source=rss&utm_medium=rss\nhttps://www.youtube.com/watch?v=lH9KJBr_R1Q&utm_source=rss&utm_medium=rss\nhttps://www.youtube.com/watch?v=d1a4Jbjc-vU&utm_source=rss&utm_medium=rss\n\n\n","content_html":"

Visit our site to listen to past episodes, support the show and sign up for our mailing list.

\n\n

Summary

\n\n

We got the chance to talk to Jessica McKellar about her work in the Python community. She told us about her experience as a director for the PSF, working as the diversity outreach manager for PyCon, and being a champion for improving the on-boarding experience for new users of Python. We also discussed perceptions around the performance of Python and some of the work being done to improve concurrency, as well as her work with OpenHatch.

\n\n

Brief Introduction

\n\n\n\n

Interview with Jessica McKellar

\n\n

\n\n

Picks

\n\n

\n\n

Keep In Touch

\n\n\n\n

Conference Presentations

\n\n\n\n

\"\"

","summary":"","date_published":"2015-08-31T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/00855b45-f0bf-4454-8a52-fdc05fd088d7.mp3","mime_type":"audio/mpeg","size_in_bytes":40173100,"duration_in_seconds":3083}]},{"id":"http://podcastinit.podbean.com/e/episode-20-static-site-generators-with-justin-mayer-and-roberto-alsina/","title":"Static Site Generators with Justin Mayer and Roberto Alsina","url":"https://www.pythonpodcast.com/episode-20-static-site-generators-with-justin-mayer-and-roberto-alsina","content_text":"Visit our site to listen to past episodes, comment on the show or find out more about us.\n\nSummary\n\nIn this episode we had the opportunity to discuss the world of static site generators with Roberto Alsina of the Nikola project and Justin Mayer of the Pelican project. They explained what static site generators are and why you might want to use one. We asked about why you should choose a Python based static site generator, theming and markup support as well as metadata formats and documentation. We also debated what makes Pelican and Nikola so popular compared to other projects.\n\nBrief Introduction\n\n\nWelcome to Podcast.__init__ the podcast about Python and the people who make it great\nFollow us on iTunes, Stitcher or TuneIn\nGive us feedback on iTunes, Twitter, email or Disqus\nWe donate our time to you because we love Python and its community. If you would like to return the favor you can send us a donation}. Everything that we don’t spend on producing the show will be donated to the PSF to keep the community alive.\nDate of recording – August 08, 2015\nHosts Tobias Macey and Chris Patti\nToday we are interviewing the core developers of Nikola and Pelican about static site generators\n\n\nInterview\n\n\nIntroductions\n\nMonitorial.net <- Justin\nUpriise <- Justin\nWorks for Canonical <- Roberto\n\n\n\nHow did you get introduced to Python?\n\n\nJustin:\n\nNeeded a way to get order data to payment processor for commerce company\n\n\n\nRoberto:\n\n\n1996 got involved with Linux\nFound XForms\nWrote Python bindings\n\n\n\n\n\nFor our listeners who might not know, what are static site generators and what are some of the advantages they bring to the table over other similar systems that perform the same function?\n\n\nRoberto\n\nRemove all the effort from the computer that serves the website\nServer runs no code\nSmaller ssurface area for security purposes\n\n\n\nJustin\n\n\nBetter performance – important for responsiveness and uptime\nEasier deployment and maintenance\nEasier versioning and migration\nCan version both input and output\n\n\n\n\n\nThere are a number of static site generators available in virtually every language. Why would a user want to leverage a Python solution vs Ruby, javascript, Go, etc.?\n\n\nReStructured TeXT is best supported in Python\nGood language for supporting various markup syntaxes\n\n\n\nMost static site generators seem to have a primary focus on blogging. What is it about these tools that lend themselves so well to that use case?\n\n\nThe author of the tools shape the purpose of the tool\nMost popular among programmers which is a demographic that is likely to have a blog\n\nWorkflow is similar to what programmers are used to\n\n\n\nStill useful for non-chronological pages due to templating system\n\n\nSomething that struck me comparing the two systems is that they have largely the same kinds of data going into the metadata block for each post, but it’s expressed in a different / incompatible way in each. Have you ever considered agreeing on a standard and even advertising it as such so all static site generators could make use of it?\nChallenging because of the idiosyncratic way problems are solved in each system\nWouldn’t end up with the same site even if metadata were identical\nRoberto & Justin are talking, this may happen!\nThe themes in Pelican and Nikola have very different feels and one of the things that initially drew me to Pelican is the larger catalog of themes available. What are some of the challenges involved in creating a theme for a static site generator?\nMany programmers who write SSGs aren’t amazing at HTML\nPelican and Nikola seem to be the most widely used projects for creating static sites using Python. What do you think is the key to that popularity?\n\n\nFrequent updates, good documentation and large community\nEasy to get up and running\n\nNeed to be productive inside of 2 minutes\n\n\n\nGood first impressions are key\nImportance of extensibility\nCore modularity and availability of plugins\n\n\nA lot of people have written about the importance (and difficulty) of writing and maintaining good documentation in open source projects. Nikola’s documentation is excellent. How did Nikola manage this in its development process and what can other open source projects learn from this?\n\n\nNo secrets – just do it and keep it updated.\nNeed to look at the tool as if using it for the first time\n\n\n\nWhat are some specific examples of unique and interesting uses your site generators have been put to?\n\n\nJustin:\n\nkernel.org, Debian, Chicago Linux Users, TransFX (translation house) all use Pelican\nEmbedding Jupyter notebooks and MathML rendering in posts\nSite search plugin\n\n\n\nNikola:\n\n\nBig adoption in the sciences (Jupyter notebook embedding supported in core)\nOutput is forever\nPlugin to trigger internet archive to reindex site\n\n\n\n\n\nNikola’s flexible deployment architecture (e.g. the use of doit tasks) seems to lend itself to some interesting use cases. What was the inspiration for this?\n\n\nBuild was taking 1 1/2 hours, doit allowed for incremental generation\nDoit is a generic task system. Nikola has no “main” it’s a collection of doit tasks.\n\n\n\nIs there any specific help that you would like to ask of the audience?\n\n\nContribute themes\nHelp with reviewing issues and pull requests\n\n\n\n\n\nPicks\n\n\nTobias\n\nTermux\nMagic Wormhole\nArrow\n\n\n\nChris\n\n\nEmacs Lisp Introduction\n3D Cellular Automata in Minecraft\nPrompt 2\n\n\n\nJustin\n\n\nMonitorial.net\nUpriise\nErgodox\nJarvis Bamboo Sit/Stand Desk\nTalky.io\nFish shell\n\nTacklebox\n\n\n\niTerm v3.0 beta\nBrother Thelonious Belgian Ale\nFrog’s Leap Winery\nPyCon Italia and Italy in general\n\n\nRoberto\n\n\nNeal Stephenson\nDocopt\nFried Pickles\nPyAr Python Argentina User Group\nPyCon Argentina in Mendosa\nPyCamp\n\n\n\n\n\nKeep In Touch\n\n\nJustin\n\nPersonal\nPelican\n\n\n\nRoberto\n\n\nNikola\n\nForums and mailing list\n\n\n\n\n\n\n\n","content_html":"

Visit our site to listen to past episodes, comment on the show or find out more about us.

\n\n

Summary

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In this episode we had the opportunity to discuss the world of static site generators with Roberto Alsina of the Nikola project and Justin Mayer of the Pelican project. They explained what static site generators are and why you might want to use one. We asked about why you should choose a Python based static site generator, theming and markup support as well as metadata formats and documentation. We also debated what makes Pelican and Nikola so popular compared to other projects.

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Brief Introduction

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Interview

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Picks

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Keep In Touch

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","summary":"","date_published":"2015-08-25T07:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/f3261281-8917-4bb1-bfc2-83ab64f83b7b.mp3","mime_type":"audio/mpeg","size_in_bytes":62453014,"duration_in_seconds":5555}]},{"id":"http://podcastinit.podbean.com/e/episode-19-al-sweigart-on-python-for-non-programmers/","title":"Al Sweigart on Python for Non-Programmers","url":"https://www.pythonpodcast.com/episode-19-al-sweigart-on-python-for-non-programmers","content_text":"Visit our site to listen to past episodes, learn more about us, and support the show.\n\nSummary\n\nWe got the opportunity to speak with Al Sweigart about his work on books like ‘Automate The Boring Stuff With Python’ and ‘Invent With Python’. We discussed how Python can be useful to people who don’t work as software engineers, why coding literacy is important for the general populace and how that will affect the ways in which we interact with software.\n\nBrief Introduction\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nSubscribe on iTunes, Stitcher, TuneIn or RSS\nFollow us on Twitter or Google+\nGive us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+\nI would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at\nWe are recording today on July 27th, 2015 and your hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Al Sweigart about Python for non-programmers\n\n\nInterview with Al Sweigert\n\n\nIntroductions\nHow did you get introduced to Python?\n\nStarted in PHP/Perl, introduced to Python in 2006\nLack of curly braces took some getting used to\nClarity of standard library was refreshing\n\n\n\nWhat inspired you to start writing books for non-programmers?\n\n\nFriend who took care of 10 year old interested in programming\nLack of coherent introductory material\nStarted writing a tutorial which grew to book length\nAll books published under Creative Commons license\n\n\n\nYou have written a few books about teaching Python to people who have never programmed, can you share your thoughts on the best order in which to introduce the various aspects of programming?\n\n\nBlog post driven development – http://blog.estimote.com/post/119525082855/user-stories-on-steroids-how-estimote-uses-blog?utm_source=rss&utm_medium=rss\n\n\n\nWhere does software testing come in when teaching new coders how to program?\n\n\nUse the logger, debugger, and assertions effectively\n\n\n\nIn invent with Python you use games as the vehicle to discuss the principles involved with writing code. What is it about computer games that makes them so popular as a means to introduce programming to newcomers?\n\n\nSomething everyone is familiar with\nEasy to make a simple game to get started\nGood way to get creative with programming\n\n\n\nFor automate the boring stuff with Python you focused on explaining how programming can be useful even if it is not someone’s occupation. How did you determine which kinds of activities to focus on for the book?\n\n\nGot the idea at a meetup talking to someone who works in an office doing repetitive tasks\nA lot of office jobs that involve tedious computer work which could be automated\n\n\n\nWhat are your thoughts on the need for software literacy among the general population?\n\n\nHow much programming knowledge do you think is sufficient for a member of our modern society?\n\n\n\nYou also wrote about using Python to decrypt simple ciphers as a means to learn about code. What was the inspiration for this approach to software education?\n\n\nOne of the projects in invent with Python was a simple cypher, inspired further interest in the subject\n\n\n\nIn episode 7 with Jacob Kaplan-Moss we talked about how we define what a programmer is. Can you share your opinions on what separates someone who can understand code from someone who is a programmer?\n\n\nBarriers to entry have been significantly lowered, making the distinction very fuzzy\nDefinition of programmer is becoming much wider\n\n\n\nBooks available at:\n\n\nAutomate the Boring Stuff\nInvent With Python\n\n\n\n\n\nPicks\n\n\nTobias\n\nLogbook\nEmacs Psychotherapist\nEx Machina\nMining the social web\n\n\n\nChris\n\n\nEmacs Rocks\nWorking Copy\nFeedly\nTom Collins\n\n\n\nAl\n\n\nPyCon\nSelenium Python Module\nSeven Eaves by Neal Stephenson\n\n\n\n\n\nKeep In Touch\n\n\nTwitter\nEmail\n\n\n","content_html":"

Visit our site to listen to past episodes, learn more about us, and support the show.

\n\n

Summary

\n\n

We got the opportunity to speak with Al Sweigart about his work on books like ‘Automate The Boring Stuff With Python’ and ‘Invent With Python’. We discussed how Python can be useful to people who don’t work as software engineers, why coding literacy is important for the general populace and how that will affect the ways in which we interact with software.

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Brief Introduction

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Interview with Al Sweigert

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Picks

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Keep In Touch

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","summary":"","date_published":"2015-08-15T20:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/f53b0c9e-1433-40ed-b896-73dc1dbc51a4.mp3","mime_type":"audio/mpeg","size_in_bytes":42256398,"duration_in_seconds":3171}]},{"id":"http://podcastinit.podbean.com/e/episode-18-liza-avramenko-on-checkio-and-empire-of-code/","title":"Liza Avramenko on CheckIO and Empire of Code","url":"https://www.pythonpodcast.com/episode-18-liza-avramenko-on-checkio-and-empire-of-code","content_text":"Visit our site to listen to past episodes, find additional content, sign up for our newsletter or learn about the hosts.\n\nSummary\n\nIn this episode we talked to Liza Avramenko, the CEO of CheckIO, about Empire of Code and CheckIO. We discussed what differentiates them from each other and from the other coding games that have been spreading on the internet. One of the main differentiators for CheckIO in particular is the strong focus on community. The bottom line is that if you use Python then you should check out CheckIO and Empire of Code as a great way to practice your skills.\n\nBrief Intro\n\n\nHello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.\nFollow us on iTunes, Stitcher or TuneIn\nGive us feedback on iTunes, Twitter, email or Disqus\nWe donate our time to you because we love Python and its community. If you would like to return the favor you can send us a donation. Everything that we don’t spend on producing the show will be donated to the PSF to keep the community alive.\nWe are recording today on July 27th, 2015 and your hosts as usual are Tobias Macey and Chris Patti\nToday we are interviewing Liza Avramenko about CheckIO\n\n\nInterview\n\n\nPlease introduce yourself\nHow did you get introduced to Python?\n\nLearned about it from Co-Founder Alex\n\n\n\nFor anyone not familiar with CheckIO, can you explain what it is?\nWhat was the inspiration for creating the CheckIO platform?\n\n\nAlex was bored working in a bank and wanted to create a place for sharing practice problems\n\n\n\nWhat is your goal with this platform?\n\n\nBecome global community for most popular coding languages\nRemain open and supportive\n\n\n\nHow do you deal with the question of ownership and licensing in CheckIO? Was this a tricky hurdle to get past in the site’s creation?\n\n\nBeing willing to share solutions publicly is a core part of the site.\n\nThis had to be more explicitly stated due to some users confusion early on.\n\n\n\n\n\nGrowing a community is difficult because of the chicken and egg problem. How did you kickstart the growth of the CheckIO community?\n\n\nCommunity always number one priority\nStarted organically\nInitially had 24/7 live chat to help new users\nOpenness was attractive, led to critical mass\nAs community grew, need for live chat decreased\nNature of Python community lends itself well to a collaborative, open community\nGuido provided advice on how to grow and foster community\n\n\n\nGuido himself has participated in a number of conversations on your platform to critique submissions. Have you received any feedback from him directly about his impressions of the system?\nHow does diversity play into CheckIO? Are there aspects of the site’s design that are purposefully meant to attract a diverse audience?\n\n\nCheckIO has always targeted people with basic coding experience\nEarly live chat feedback focused around very new coders wishing there was more material for them\nThese early challenges resulted in the development of Empire of Code\n\n\n\nThere are a number of other online programming-oriented games available. What makes CheckIO and Empire of Code stand out from them?\n\n\nPriority of community\nOthers are more about gaming, showcasing talent\n\n\n\nHow did you design the gamification aspects of CheckIO, and how important do you think they are to the site’s success?\n\n\nCheckIO was never a game, more of a library of challenges that have game elements\nEmpire of Code is all about gamification, code and algo improvement are baked into the gameplay\n\nYou choose Python or Javascript “legions” at character creation time, this is a one time choice.\nBuildings, troop movements, materials, etc. are all based in code\nPlayers can steal code and algorithms from other players\n\nIncredible innovation\n\n\n\nGreat adoption story for new users – can start playing without writing any code\n\n\nBut in order to really excel you will WANT to start writing code\nSo many people have their original motivations for coding come from playing games\n\n\n\nCooperative play in the form of training missions with other players\n\n\nThis is an opportunity to learn how people on the other side are solving the same problem\n\n\n\nNew languages are planned – Ruby, maybe Java?\n\n\n\n\nDo you think that there is something about the Python language or community that inspires adoption of this kind of gamified practice?\nYou recently released the beta of a new experience called Empire of Code which is more akin to the type of video game that many people are familiar with. What inspired that evolution?\n\n\nAs part of the new experience, you also added JavaScript as an available language. Do you intend to add new languages in the future?\nIs there a particular demographic or set of demographics that you are targeting with Empire of Code vs CheckIO?\n\n\n\nWhat’s the monetization strategy for Empire of Code or CheckIO?\n\n\nFor Empire, you can play for free but you might keep losing your resources until you can learn to code more effectively, OR you can buy a shield which will protect your resources for a time.\n\n\n\nIn CheckIO, how do you label the difficulty level of the individual puzzles, is there a set of guidelines for that or is it up to the puzzle writer / submitter?\n\n\nCheckIO trusts its community\n\nThe community rates each challenge\n\n\n\n\n\nPart of the CheckIO platform is the ability for users to submit their own problems. How much vetting is involved before these submissions are available to users of the site?\nWhere do you see CheckIO and Empire of Code going in the future?\n\n\nWant to have Empire of Code known as the best online game that blends in programming by the end of 2016\nIn ~5 years want to see people saying the CheckIO/Empire of Code inspired people to program as a career\nIn ~10 years want to see all major languages represented\nAiming to become a major game publisher\n\n\n\n\n\nPicks\n\n\nTobias\n\nJSON Web Tokens\nSource Code Pro\nDirEnv\nChappie\n\n\n\nChris\n\n\nPrune\nNikola\nWarday’s Cocktail\n\n\n\nLiza\n\n\nKiev, Ukraine\nBulletproof Coffee\n\n\n\n\n\nKeep In Touch\n\n\nTwitter: @avrliza\n\n\n","content_html":"

Visit our site to listen to past episodes, find additional content, sign up for our newsletter or learn about the hosts.

\n\n

Summary

\n\n

In this episode we talked to Liza Avramenko, the CEO of CheckIO, about Empire of Code and CheckIO. We discussed what differentiates them from each other and from the other coding games that have been spreading on the internet. One of the main differentiators for CheckIO in particular is the strong focus on community. The bottom line is that if you use Python then you should check out CheckIO and Empire of Code as a great way to practice your skills.

\n\n

Brief Intro

\n\n\n\n

Interview

\n\n

\n\n

Picks

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\n\n

Keep In Touch

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\"\"

","summary":"","date_published":"2015-08-06T13:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/9fc16a1a-a27b-4e79-a04e-e546de864e98.mp3","mime_type":"audio/mpeg","size_in_bytes":33310582,"duration_in_seconds":2895}]},{"id":"http://podcastinit.podbean.com/e/episode-17-glyph-on-ethics-in-software/","title":"Glyph on Ethics in Software","url":"https://www.pythonpodcast.com/episode-17-glyph-on-ethics-in-software","content_text":"Visit our site for past episodes and extra content.\n\nSummary\n\nIn this episode we had a nice long conversation with Glyph Lefkowitz of Twisted fame about his views on the need for an established code of ethics in the software industry. Some of the main points that were covered include the need for maintaining a proper scope in the ongoing discussion, the responsibilities of individuals and corporations, and how any such code might compare with those employed by other professions. This is something that every engineer should be thinking about and the material that we cover will give you a good starting point when talking to your compatriots.\n\nBrief Introduction\n\n\nWelcome to Podcast.__init__ the podcast about Python and the people who make it great\nDate of recording – July 21, 2015\nHosts Tobias Macey and Chris Patti\nFollow us on iTunes, Stitcher, TuneIn, Google+ and Twitter\nGive us feedback! (iTunes, Twitter, email, Disqus comments)\nWe donate our time to you because we love Python and its community. If you would like to return the favor you can send us a donation. Everything that we don’t spend on producing the show will be donated to the PSF to keep the community alive.\nOverview – Interview with Firstname Lastname about Topic\n\n\nInterview with Glyph\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\n\n2000 – large scale collaborative gaming system in Java\n\nAsynchronous IO\nTwisted\n\n\n\n\n\nLet’s start with the bad news What are some of the potential wide spread implications of less than ethical software that you were referring to in your Pycon talk? – Chris\n\n\nRobot Apocalypse (Not really)\n\nMuch of the discussion around this derails into unrealistic nightmare scenarios\nTHERAC 25 radiation machine\nToyota unintended acceleration scandal\n\n\n\nReal worry – gradual erosion of trust in programmers and computers\nFirst requirement for a code of ethics – a clear understanding of the reality you’re trying to litigate\nThe search for ethics will likely begin in academia where this aspect of software dev is more like psychology.\n\n\nIn your talk you commented on the training courses that Lawyers are required to take as part of their certification. Do you think the fact that there is no standardized certification body for software development contributes to a lack of widely held ethical principles in software engineering? – Tobias\n\n\nDo you think that it is necessary to form such a certification mechanism for developers as part of the effort to establish a recognized ethical code? – Tobias\nIf we were to create a certification to indicate proper training in the software engineers code of ethics, how do you think that would affect the rate at which people enter the industry? – Tobias\n\n\n\nAssuming we can all agree on a set of relatively strict professional ethics that would prevent the above from happening, how would we enforce those ethics? Or do you advocate an honor system? – Chris\n\n\nEthics are by definition an honors system\nEnforcement would be straight forward – professional organizations to maintain a record and deviations from that record\nNeed better laws & better jurisprudence\nWe need an Underwriters Laboratory seal for software development ethics\nCode of software ethics will not and should not tell you how to be a decent human being.\nDevs / companies can create software that could be used for evil – “We are merchants of death and these are lethal weapons” – could conceivably earn the ethical software developer’s seal of approval.\n\n\n\nWhere does accessibility of the software we make fit into a code of ethics? Do you think there should be a minimum level of support for technologies such as screen readers or captioning for audio content in the software that we build? – Tobias\n\n\nMinimum levels of knowledge required\nMinimum levels of content in curriculum\n\n\n\nIn your talk you mentioned how Rackspace’s stance on user support matches the ideals you’d previously laid out, can you flesh that out a bit for us? What does that mean to individual Rackers in their day to day work lives? – Chris\nIn your talk you mentioned that availability of the software source should be mandatory for compliance with a properly defined ethical framework. What mechanisms for providing that access do you think would be acceptable? Should there be a central repository for housing and providing access to that source? – Tobias\n\n\nWould the list of acceptable mechanisms change according to the intended audience of the software? – Tobias\nWhat responsibility do you think producers of software should have to maintain an archive of the source for past versions? – Tobias\nHow should we define what level of access is provided? In the case of commercial software should the source only be available to paying customers, perhaps delivered along with the product? This also poses an interesting quandary for SaaS providers. Should they provide the source to their systems only to paying customers, or to potential customers as well? – Tobias\nThis question of transparency and availability of source is especially interesting in the light of a number of stories that have come out recently about patients who have been provided with prostheses and other medical devices. In a number of cases, shortly after receiving the device, the company who made it, which are increasingly startups, goes out of business, leaving the patient with no way of obtaining support for something that they are dependent on for their health and well-being. Having the source for those devices available would help mitigate the impact of such a situation. – Tobias\n\n\n\nYou brought up an interesting aspect of the trust equation and its relevance to the need for an ethical code. Because what we do as software engineers is effectively viewed as sorcery by a vast majority of the public, they must therefore wholly place their trust in us as part of using the products that we create. As you mentioned with the demise of the scribe with the rise of literacy, increasing the overall awareness of how software works at a basic level partially reduces that depency of trust. At what level of aptitude do you think our relationship with our users becomes more equitable? How does the concept of source availability play into this topic of general education? – Tobias\nWhat can the Python community in particular do to start the ball rolling towards defining a set of professional ethics, and what has it already done in this area? – Chris\n\n\nPSF Code of Conduct is a starting point\n\nPSF is an organization of individuals\nCorporations are cagey about getting involved for fear of it becoming a legally binding contract\n\n\n\nDjango Code of Conduct more specific\n\n\n\n\nPicks\n\n\nTobias\n\nPhillips SHP9500\nkeybase.io – Tweet us with your favorite thing about the show to get an invite\nPaul Blart: Mall Cop 2\n\n\n\nChris\n\n\nDon’t Starve for IOS\nWant to understand Pythonâ€s comprehensions? Think in Excel or SQL.\nBarr Hill Gin\n\n\n\nGlyph\n\n\nPy2App\nBlog post\nPyObjC\nSensair Sou Vide immersion circulator\n\n\n\n\n\nKeep In Touch\n\n\nTwitter\nKeybase.io\nemail\nGlyph everywhere on the internet\n\n\n","content_html":"

Visit our site for past episodes and extra content.

\n\n

Summary

\n\n

In this episode we had a nice long conversation with Glyph Lefkowitz of Twisted fame about his views on the need for an established code of ethics in the software industry. Some of the main points that were covered include the need for maintaining a proper scope in the ongoing discussion, the responsibilities of individuals and corporations, and how any such code might compare with those employed by other professions. This is something that every engineer should be thinking about and the material that we cover will give you a good starting point when talking to your compatriots.

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Brief Introduction

\n\n\n\n

Interview with Glyph

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Picks

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Keep In Touch

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\"\"

","summary":"","date_published":"2015-08-02T20:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/82ba4673-4bbd-4327-bb1c-d48efc094893.mp3","mime_type":"audio/mpeg","size_in_bytes":47187313,"duration_in_seconds":4763}]},{"id":"http://podcastinit.podbean.com/e/episode-16-holger-krekel-on-pytest/","title":"Holger Krekel on Py.Test","url":"https://www.pythonpodcast.com/episode-16-holger-krekel-on-py-test","content_text":"Visit our site to listen to past episodes, learn more about the show and sign up for our mailing list.\n\nSummary\n\nIn this episode we talked to Holger Krekel about the py.test library. We discussed the various styles of testing that it supports, the plugin system and how it compares to the unittest library. We also reviewed some of the challenges around packaging and releasing Python software and our thoughts on some ways that they can be improved.\n\nBrief Introduction\n\n\nWelcome to Podcast.__init__ the podcast about Python and the people who make it great\nDate of recording – July 8th, 2015\nHosts Tobias Macey and Chris Patti\nFollow us on iTunes, Stitcher or TuneIn\nGive us feedback on iTunes, Twitter, email or Disqus)\nWe donate our time to you because we love Python and its community. If you would like to return the favor you can send us a donation}. Everything that we don’t spend on producing the show will be donated to the PSF to keep the community alive.\nOverview – Interview with Holger Krekel about his work on Pytest\n\n\nInterview with Holger Krekel\n\n\nIntroductions\n\nProgramming for 25 years\nRuns a consultancy\nBeen to almost every EuroPyCon and PyCon US\n\n\n\nHow did you get introduced to Python? – Chris\n\n\nWanted to write an HTTP proxy and Java I/O was too confusing. Jython took less than a day to get it working after 2-3 days on it with Java.\n\n\n\nWhat inspired you to create Pytest, and how did the existing unittest framework play into the story? – Chris\n\n\nIntroduced to agile methods through the Zope community\nZope used unittest – didn’t like the boiler plate\nNot in the spirit of Python\nOnly took ~200 lines of code to get a testing tool working\nOriginal name was ‘utest’ – 2003\nPytest name came in 2004 on Pypy project\nHuge number of tests on that project (20,000) – distributed test runner – xdist helped solve this.\n\n\n\nThere are many different styles of testing, such as BDD, unit testing, integration testing, functional testing, what attributes of py.test make it suitable or unsuitable for these different approaches? – Tobias\n\n\nWhat are your views on black box testing and how would someone use py.test to implement this approach? – Tobias\nPytest’s plugin architecture enables you to hook into the various phases of test execution enabling you to extend Pytest in all kinds of ways beyond the original design.\nI have been hearing a lot about property based testing which was popularized by the Quickcheck module in Haskell. Does py.test support anything like that? – Tobias\nhypothesis-pytest\n\n\n\nDo you think the characteristics and nature of the unit testing framework being used have any effect on the number and quality of the tests developers write? – Chris\n\n\nDevelopers find writing tests in Pytest to be fun compared to unittest\nWhich will help people write better tests\nEncourages refactoring\n\n\n\nIs there ever a time when you would advice against writing tests? – Tobias\n\n\nWhen exploring a problem, writing tests first doesn’t make sense\nWhen getting feedback on a potential approach, writing tests first can be a waste of time\n\n\n\nWhat are some signs that you watch out for when writing tests that tell you that a particular feature needs to be refactored? – Tobias\n\n\nWhen the test code is fragile it should be refactored\nRequires experience to really understand when to refactor\nWhen it’s not fun anymore or the tests are repetitive\n\n\n\nFor someone who is converting their existing unit tests from UnitTest/Nose style to use py.test in an idiomatic manner, what are some of the biggest differences to be aware of? – Tobias\n\n\nGenerator/yield based testing should move to property based testing\nIf py.test can’t run a UnitTest/Nose style test it is considered a bug and gets fixed\n\n\n\nHas the strict backwards compatibility policy presented any interesting technical challenges thus far? – Chris\n\n\nYes it definitely makes more work\nHowever breaking the API in a large project like this will cause too many problems for users\n\n\n\npy.test supports execution of tests written with other frameworks, how much ongoing maintenance does this feature require as changes are made to the other implementations? – Tobias\nThe web page says that Pytest is designed to work with domain specific and non Python tests, and in fact a coworker is using it to test a node.js project – how did Pytest’s design enable this? – Chris\n\n\nPytest uses a collection tree model to represent your project\n\nThis is not Python specific\nAll classes and functions are just mapped into this tree, not directly on the Python function\n\n\n\nThere are few Python specific hooks for fixtures etc.\nPeople have written plugins so they can express their tests in YAML, Microsoft Excel\nTests are represented as items\nAll plugins are written in Python\n\n\nWhat are some of the most interesting applications of py.test that you have seen? – Tobias\n\n\nPlugins!\nPytest-BDD\nPytest-C++\nPytest-sugar\nPy.test plugin list\n\n\n\nSpeaking about adoption, do you have any sense of the relative adoption of Pytest versus unitest or other tools? – Tobias\n\n\nVery hard to actually know\nDownload numbers are not a clear indicator due to robots, CI systems, etc.\nQuantifying market share is hard to do\nPopularity is not a useful heuristic in determining a good fot for technology adoption\n\nBut popularity is an indicator for the level of support you might receive\nTech can be popular but very poorly maintained\n\n\n\n\n\nAre there any features of py.test that would make it suitable for use with configuration management tools and infrastructure testing? – Tobias\n\n\nExample driven testing\nRun py.test from a blackbox approach\nLargest benefit would be from having one testing tool used across the organization\n\n\n\nWhere do you see Pytest and more generally test frameworks headed in the future? – Chris\n\n\nNo big changes for Pytest – lots of incremental things\nPlugins will add functionality\nHolger is also the author of Tox\nIntegration testing and testing in more complex environments are a direction that test management tools will likely go\nTools like Jenkins can be a real headache in trying to have a good testing story for your company\nhttps://devpi.net/hpk/dev/devpi-server/2.2.0/+toxresults/devpi-server-2.2.0.tar.gz?utm_source=rss&utm_medium=rss\n\n\n\nAny questions we didn’t ask?\n\n\nPytest is a very healthy project! There are 10 regular contributors – this is exceptional among OSS projects\n\n\n\n\n\nPicks\n\n\nTobias\n\npython-future\nsix\nThe Way Back\nRosewill BK-500A} or BK-500i\npipdeptree\npundler\n\n\n\nChris\n\n\nCrop Bavarian Weizen\nDutch Pancakes\nProphet\n\n\n\nHolger\n\n\nThe Utopia of Rules\nIPFS.io – The interplanetary file system\nA New Way to Look at Networking\n\n\n\n\n\nKeep In Touch\n\n\nTwitter\nBlog\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site to listen to past episodes, learn more about the show and sign up for our mailing list.

\n\n

Summary

\n\n

In this episode we talked to Holger Krekel about the py.test library. We discussed the various styles of testing that it supports, the plugin system and how it compares to the unittest library. We also reviewed some of the challenges around packaging and releasing Python software and our thoughts on some ways that they can be improved.

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Brief Introduction

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Interview with Holger Krekel

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Picks

\n\n

\n\n

Keep In Touch

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2015-07-23T20:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/1cdbfab8-6a86-408c-a17c-9b0ffdbdf8aa.mp3","mime_type":"audio/mpeg","size_in_bytes":70238855,"duration_in_seconds":4260}]},{"id":"http://podcastinit.podbean.com/e/episode-15-damien-george-talks-to-us-about-micropython/","title":"Damien George Talks To Us About MicroPython","url":"https://www.pythonpodcast.com/episode-15-damien-george-talks-to-us-about-micropython","content_text":"Visit our site for more news, information and past episodes of Podcast.__init__!\n\nSummary\n\nWe talked to Damien George about his work on the Micro Python interpreter and the PyBoard SOC (Systom On a Chip). The combination of the interpreter and SOC allows Python developers to get involved in hardware hacking, as well as letting electronics afficionados try their hand at development. Damien explained to us where this fits in with the expanding landscape of low cost embedded devices and why you should get one to start playing with it.\n\nBrief Introduction\n\n\nDate of recording – June 29th, 2015\nHosts – Tobias Macey and Chris Patti\nFollow us on iTunes, Stitcher or TuneIn\nGive us feedback! (iTunes, Twitter, email, Disqus comments)\nYou can donate (if you want)!\nOverview – Interview with Damien George from the Micro Python project\n\n\nInterview with Damien George\n\n\nIntroductions\n\nPostdoc in Theoretical Physics\n\n\n\nHow did you get introduced to Python?\nWhat problem were you trying to solve when you first had the idea to create the Micro Python board and interpreter?\n\n\nNot really \nPython lets you get things done quickly\nAbstracts the hardware really well\n\n\n\nIn the Kickstarter video you mention that Micro Python is a complete re-implementation of Python optimized to run on a micro-controller. How hard was it to create an alternative Python implementation? Did you have hard decisions to make as to what to include given the limitations of the hardware?\n\n\nTo start with, was it even possible?\n\nProof of Concept: Get a REPL running on the board\n\n\n\nLots of tricks to get things to fit into RAM\n\n\nStuffing integers into pointers\nOptimizing RAM at various points\nRuns the parser 4 times, looking for different things each time\nLots of things are stored in ROM in the built-in Flash\n\n\n\nVery fine efficiency trade off between code size, memory usage, speed.\nREPL runs in 1K of RAM!\n\n\nMost of this is the parse tree\n\n\n\n20 line script might take ~5K RAM\n128K RAM on the Micro Python board\nNot 100% Python – but 90% – the most useful parts\n\n\nI know that people who have developed alternative Ruby implementations have run into issues due to the lack of a formal specification. Has the fact that there is a specification for Python made your job easier?\n\n\nDefinitely, Python is very well defined\nWell documented\nAlready multiple implementations\n\n\n\nThe WiPy chip seems like an interesting device. What are some ways in which it could be put to use? A Micro Python cluster for instance?\n\n\nSmall, cheap, low power little wireless chip that also runs Python\nYou can telnet in and have a Python REPL\nPart of the Internet of Things\n\n\n\nWhat changes did you have to make to get the Python interpreter to run without an underlying operating system?\nWhen you were designing the hardware, what were some of the requirements that you were targeting in terms of performance or peripherals?\n\n\nWanted the best chip for the least money\nDidn’t know ahead of time how many resources were required\n\n\n\nWhat level of hardware knowledge is required to start working with the Micro Python board?\n\n\nVirtually none\nJust need to plug into USB and login with a terminal program to get a Python prompt\nCan change frequency of CPU, turn on/off LEDs, etc.\nConnecting peripherals requires some hardware knowledge\nModule namespace to make hardware management easier\n\n\n\nFor anyone who is interested in writing libraries, what kinds of restrictions do they need to be aware of?\n\n\nBe aware of RAM size limitations\nPrety much anything that will fit will work\nLibraries with C extensions won’t work because they rely on the CPython API\n\n\n\nWhat license is used for the Micro Python interpreter and the PyBoard? Are the compatible with commercial uses?\n\n\nMIT License\nHardware schematics are open source as well, open and accessible design\n\n\n\nWhat are some of the most interesting/innovative projects that you have seen people make with the Micro Python board or runtime?\n\n\nDamien attempted to make a quadcopter – not completely finished\nMicro Python controlled guitar – PyBoard connected to actuators to play guitar\n\n\n\nHow does the experience of using Micro Python compare to some of the other hardware projects that are popular right now such as Arduino, Raspberry Pi or Tessel?\n\n\nPyBoard in between Arduino and Raspberry Pi\n\nMore approachable than Arduino\nNot a full OS like Raspberry Pi\n\n\n\nTessel similar to Micro Python but runs Javascript\n\n\nEU Space Agency (Europe’s version of NASA) interested in Micro Python\n\n\nPrepared to fund Micro Python development to explore possibilities of space based applications\n\nCode needs to be well written and with few bugs\nSee if it can be used for real-time systems\n\n\n\n\n\n\n\nPicks\n\n\nTobias\n\nMachine Gun Preacher – Real life story of Sam Childers’ work in Southern Sudan\nPocket Book Android App – E-Book app with good UI/UX and solid feature set\nOnline access to digital media through local library memberships\n\nHoopla Digital\nOverdrive\n\n\n\n\n\nChris\n\n\nReal Ramen\nRedHat Summit\nThe SELinux Coloring Book\n\n\n\nDamien\n\n\nMOSH – Mobile shell, resilient SSH that allows for resuming sessions across networks, computer sleeps, etc.\n\n\n\n\n\nKeep in Touch\n\n\nTwitter\n\n@micropython\n@damienpgeorge\n\n\n\nGitHub – micropython\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Visit our site for more news, information and past episodes of Podcast.__init__!

\n\n

Summary

\n\n

We talked to Damien George about his work on the Micro Python interpreter and the PyBoard SOC (Systom On a Chip). The combination of the interpreter and SOC allows Python developers to get involved in hardware hacking, as well as letting electronics afficionados try their hand at development. Damien explained to us where this fits in with the expanding landscape of low cost embedded devices and why you should get one to start playing with it.

\n\n

Brief Introduction

\n\n\n\n

Interview with Damien George

\n\n

\n\n

Picks

\n\n

\n\n

Keep in Touch

\n\n

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2015-07-16T11:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/b7f2dd10-2a73-464e-bef9-d302b7672d33.mp3","mime_type":"audio/mpeg","size_in_bytes":44082904,"duration_in_seconds":2957}]},{"id":"http://podcastinit.podbean.com/e/episode-14-allen-downey-on-teaching-computer-science-with-python/","title":"Allen Downey on Teaching Computer Science with Python","url":"https://www.pythonpodcast.com/episode-14-allen-downey-on-teaching-computer-science-with-python","content_text":"Find past episodes and more information about the show at iTunes, Stitcher or TuneIn\n\nGive us feedback! (iTunes, Twitter, email, Disqus comments)\nYou can donate (if you want)\nOverview – Interview with Allen Downey, Prolific Author and Professor of Computer Science\n\nInterview with Allen Downey\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\n\nWrote a Java book with an open license to allow anyone to make changes\nJeff Elkner translated it to Python\n\n\n\nWhat attributes of Python make it well suited for use in teaching computer science principles?\n\n\nSyntax is simple, makes a difference for beginners\nGood error messages\nBatteries included\n\n\n\nOne of the things I found very compelling about Think Like a Computer Scientist is its use of interactive turtle graphics early on. What makes the turtle continue to be a compelling educational tool and what made you choose it for this book in particular?\n\n\nEverything you do has a visible effect, makes it easier to see what’s happening and debug\nUsed to introduce functional decomposition because of no return value in turtle graphics\nGreat way to explore complex geometric concepts\n\n\n\nDid the structure of your courses change when you started using Python as the language used in the classroom? Were you able to cover more material as a result?\n\n\nAble to make material more interesting\nLess time spent fighting with syntax\n\n\n\nAs a professor of computer science, do you attempt to incorporate the realities of software development in a business environment, such as unit testing and working with legacy code, into your lesson plans?\n\n\nUnit tests useful as a teaching tool\nVersion control getting introduced earlier\n\n\n\nA number of your books are written around the format of ‘Think X’. Can you describe what a reader can expect from this approach and how you came up with it?\n\n\nLearning how to program can be used as a lever to learn everything else\nYou can understand what a thing is by understanding what it does\n\n\n\nWhat are some of the more common stumbling blocks students and developers encounter when trying to learn about stastics and modeling, and how can they be overcome?\n\n\nTraditional analytic methods for statistical computation – get in the way and impede understanding\n\nP-values are a great example\nWhat test should I do? is the wrong question\n\n\n\n\n\nI’ve heard you refer to yourself as a ‘bayesian’. Can you elaborate on what that means and how bayesian statistics fits into the larger landscape of data science?\n\n\nFrustration with frequentist approach to statistics\n\nWasted time over debate of objectivity vs subjectivity\n\n\n\nBayesian approach takes modeling ideas and makes them explicit\n\n\nCan directly compare and contrast results of competing models\n\n\n\nClassical approaches don’t answer the most interesting questions\n*We’re big fans of iPython notebook which you’ve used in at least one of your books already – can you describe some of the ways you have implemented it in an educational context, as well as some of the benefits and drawbacks?\nStarted using about 2 years ago\nAppreciated usefulness for books and teaching because of synthesis of text, code and results\nWorking on DSP really highlighted the usefulness of IPython notebooks\n\n\n\n\nPicks\n\n\nTobias\n\nIMAPy – IMAP for humans\nScudCloud – Linux desktop Slack client\nThrive – Online purchasing club for healthy and organic foods\nFloobits – remote pair programming\n\n\n\nChris\n\n\nTestament of Youth\nMastering Emacs – The Website / Blog\nStayFocused\nFallout Shelter\n\n\n\n\n\nKeep in Touch\n\n\nTwitter\nBlog\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Find past episodes and more information about the show at iTunes, Stitcher or TuneIn

\n\n

Give us feedback! (iTunes, Twitter, email, Disqus comments)
\nYou can donate (if you want)
\nOverview – Interview with Allen Downey, Prolific Author and Professor of Computer Science

\n\n

Interview with Allen Downey

\n\n

\n\n

Picks

\n\n

\n\n

Keep in Touch

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2015-07-09T05:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/570edb52-440c-491a-a6de-99d58695cec2.mp3","mime_type":"audio/mpeg","size_in_bytes":32436905,"duration_in_seconds":2262}]},{"id":"http://podcastinit.podbean.com/e/episode-13-jacob-kovac-on-kivent/","title":"Jacob Kovac on KivEnt","url":"https://www.pythonpodcast.com/episode-13-jacob-kovac-on-kivent","content_text":"Listen to past episodes and find out more about the show at our website pythonpodcast.com\n\nSynopsis\n\nIn this episode we talked to Jacob Kovac, creator of the KivEnt game engine and one of the Kivy core developers. He told us about what inspired him to create the KivEnt project, some of the ways that he has managed to optimize rendering time and some of the problems that he has encountered as part of his work on the project. We also discussed what the use cases and limitations of the KivEnt engine are and he shared some of the projects that have been made with it.\n\nBrief Introduction\n\n\nDate of recording – June 17th, 2015\nHosts – Tobias Macey and Chris Patti\nFollow us on iTunes, Stitcher or TuneIn\nGive us feedback! (iTunes, Twitter, email, Disqus comments)\nWe don’t have any corporate sponsorship or advertisements in the show because we are making it for the community and we respect our listeners and value your time. If you would like to help support the show and keep it ad-free you can find out how by visiting our website\nOverview – Interview with Jacob Kovac about the KivEnt Game Engine, based off of Kivy\n\n\nInterview with Jacob Kovac\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nCould you please give us a high level overview of KivEnt and how it differs from other game builder frameworks like Unity or Unreal?\n\nManages memory for game objects and stores them contiguously in memory for greater efficiency\nReal-time focused rendering engine for Kivy\nCython interface to provide performant game objects with Python API\nIncreased speed of main render loop by 38X by removing a single Python list lookup\nKivent is mainly 2D focused, vs 3D for Unity/Unreal\nPython all the way down\n\nCython and pointer magic for optimization purposes\n\n\n\nMade to be familiar to Pythonistas\nAiming for “A” level games\nBringing modern advancements in making games to Python – GPU awareness\nBuilt with constraints in mind\nThe Pacman Dossier\n\n\nWhat inspired you to create the KivEnt engine?\n\n\nTried to create an Android infinite runner in Kivy, performance was unacceptable\nLooking for how to build games in Python with large amounts of data\n\n\n\nIs there a particular kind of game KivEnt is particularly suited for versus any of the other popular frameworks?\n\n\nFocuses mainly on 2D, agnostic as to ‘type’ of game\nJacob’s interests largely focused on procedurally generated environments\n\n\n\nCould KivEnt be used to create networked multiplayer games and what challenges might that bring to the table for the aspiring KivEnt game developer?\n\n\nMultiplayer thought to be largely out of scope\n\nThis doesn’t mean KivEnt is bad for multiplayer games, but that KivEnt in and of itself doesn’t wholly solve this problem.\nPlenty of other frameworks to draw on for handling the multi-player server or pulling data from it, KivEnt solves the client side problems germane to making a game in Python\n\n\n\n\n\nDoes the fact that KivEnt games need to run on so many platforms present any unique difficulties in KivEnt’s development?\n\n\nKivy has solved most of the cross-platform problems\nDifference in GPU vendors has proved the most difficult\n\n\n\nI hear game developers talk a lot about assets and asset formats. What kinds of assets can be used with KivEnt?\n\n\n2D assets are simple – especially as compared to 3D\nKivEnt supports any image format that Kivvy does for your platform\nComing next release – you can specify the vertex format for your model\nhttps://youtu.be/qe9fWC-2e3M?utm_source=rss&utm_medium=rss\n\n\n\nI have heard that unit testing games is difficult and rarely done for reasons of time pressure, as well as lack of determinism in the interactions. Does KivEnt provide any utilities to make this easier?\n\n\nNot currently well tested, but targeting that for next release\nTrying to add tooling to make testing games easier, though still somewhat difficult\nPlatform Biased Podcast – by a bunch of Microsoft Studios SDETs\n\n\n\nHow does KivEnt handle input and what kids of input devices are supported?\n\n\nInput handled entirely by Kivy, so any inputs supported by Kivy are accessible in KivEnt\nRumors of using Kinect camera with Kivy/KivEnt applications\n\n\n\nIs there a built in physics engine or is that something that is pluggable?\n\n\nMostly pluggable\nChipmunk 2D integration provided via a module\nParticle Panda – one of the major inspirations for KivEnt\nNew Particle engine coming in the next version of KivEnt\n\n\n\nHow does KivEnt handle collision tracking?\n\n\nMathematically difficult, very hard to get right\nDon’t do it! Use the physics engine – Chipmunk 2D is also a collision detection engine\nKivy enables devs to use C, C++, Java and Objective C code in their games\nGame development has been democratized\nEntity / Component architecture enables great modularity\nGame objects that appear on the screen (Gun, ball, etc.) are not represented as such in the system\n\n\n\nCan you tell us about some of the projects that you have seen built in KivEnt which you are most excited by?\n\n\nhttps://github.com/chozabu/KivEntEd?utm_source=rss&utm_medium=rss\nhttps://play.google.com/store/apps/details?id=org.chozabu.boardzfree&hl=en&utm_source=rss&utm_medium=rss\n\n\n\nWhat are some ways in which our listeners could help contribute to the project?\n\n\nWould like to see more people build games in KivEnt\n\nGive feedback about the experience and what can be improved\n\n\n\nIf you have Apple hardware, try out KivEnt and file issues with any errors that occur\n\n\n\n\nPicks\n\n\nTobias\n\nEIN (Emacs IPython Notebook)\nPip 7.x\nRESTful Web APIs\n\n\n\nChris\n\n\nThe Killing\nData Science on the iPad with RethinkDB\nLeft Hand Nitro Milk Stout\n\n\n\nJacob\n\n\nPelican Static Site Generator\nTerraria 1.3\nAmorone Homemade Red Wine\n\n\n\n\n\nKeep in Touch\n\n\nE-Mail – kovac\nBlog – chaosbuffalogames.com/blog\nIRC – #kivy\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Listen to past episodes and find out more about the show at our website pythonpodcast.com

\n\n

Synopsis

\n\n

In this episode we talked to Jacob Kovac, creator of the KivEnt game engine and one of the Kivy core developers. He told us about what inspired him to create the KivEnt project, some of the ways that he has managed to optimize rendering time and some of the problems that he has encountered as part of his work on the project. We also discussed what the use cases and limitations of the KivEnt engine are and he shared some of the projects that have been made with it.

\n\n

Brief Introduction

\n\n\n\n

Interview with Jacob Kovac

\n\n

\n\n

Picks

\n\n

\n\n

Keep in Touch

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2015-07-02T22:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/f31e69ba-b003-4360-8fed-f65508f272c5.mp3","mime_type":"audio/mpeg","size_in_bytes":76594996,"duration_in_seconds":4121}]},{"id":"http://podcastinit.podbean.com/e/episode-12-eric-schles-on-fighting-human-trafficking-with-python/","title":"Eric Schles on Fighting Human Trafficking with Python","url":"https://www.pythonpodcast.com/episode-12-eric-schles-on-fighting-human-trafficking-with-python","content_text":"Listen to past episodes, read about the hosts or donate to the show at podcastinit.com\n\nBrief Introduction\n\n\nDate of recording – June 10th, 2015\nHosts Tobias Macey and Chris Patti\nFollow us on iTunes, Stitcher or TuneIn\nGive us feedback! (iTunes, Twitter, email, Disqus comments)\nYou can donate (if you want)!\nOverview – Interview with Eric Schles\n\n\nInterview with Eric Schles\n\n\nIntroductions\nHow did you get introduced to Python?\nWhat inspired you to take up the fight against slavery? Is there personal story behind this choice?\nSome of your work touches on the “Deep Web”. Can you provide listeners with some context around what that term means and role it plays in what you do?\n\nTor .onion sites (Hidden Services) are examples\nAnonymous Web Experience\nAnonymity allows for illegal, immoral things like buying selling people\nConceptually very important idea\nBruce Schneier – Web technologies need to be more privacy aware\nLike a really scary version of “The Internet of the Old Days”\nPhotos of young, exploited men and women\nPedophiles are building communities, having parties through these hidden services\nEric feels that Tor is an extreme\nFeels there had to be a way to protect the rights of legitimate while protecting against pedophiles\nMaybe a voting system?\nThe Tor project feels that any compromise lessens the that’s so important for people in embattled or countries (Worded that poorly -Chris)\nNo metrics on the amount of pedophilia that actually happens Tor – probably a lot\nSexually abused victims of trafficking grow up damanged unable to do anything else\nConsumers of this type of porn were often themselves victims sexual abuse\nStructural dissonance which exists to create this problem society needs to be addressed\nGoogle puts the number to the anti-trafficking hotline at top of any trafficking search results\nDarren (Derek?) Hayes – redirect to trafficking resources when viewing advertisements for victims trafficking\n\n\n\nWhy did you choose Python as opposed to any other tool for your search engine?\n\n\nNeeded solutions quickly with the ability to evolve as needed\nAble to rapidly develop and incorporate new features rapidly\nEasy to scale as needed\nFlask is easier to prototype and iterate with\nPython data science tools make the analysis easy\nAble to finish a 2 year C++ project in 3 weeks using Python\nDoing data science in Ruby is challenging\nPandas Dataframe galvanized the creation of a lot of other useful tools\nVincent – write Python which compiles to D3\n\n\n\nCan you provide a high level description of the technical details the search engine that you created, and what it’s like to with Tor through Python?\n\n\nDirected search engine\n“It would be like if you went to Google but everything watched was Porn which you were uncomfortabl seeing and you sad”\nGet most case information through regular old detective work\nPerson arrested / in holding yields phone number, other attributes that can feed the search engine\nGoogle can’t scrape the deep web\nMemex tool indexes the deep web – Eric’s search engine uses that\nEric does design work for the Memex project\nDeveloped by the amazing Chris White\nEric’s search engine uses the Tor driver in Selenium to .onion sites\n\n\n\nWhat are some of the technical and legal challenges that you experienced in the course of your work?\n\n\nMost of the technical challenges are around automated processing\nLegal structure provides some limits on what can be worked on\n\n\n\nDoes your search engine try to infer who might be engaged in work voluntarily as opposed to those being forced into it their will?\n\n\nNo, because they get all their case referrals from detective work\nYou have to have been hospitalized or in some other way come the attention of the authorities for being deprived of rights\nTrafficking looks very different in different cultures\nGlobal similarities\nAfraid to say why if hurt\nForced into having sex against your will\nClear patterns of indication\nUrban versus Suburban versus Rural\nFracking towns\nDemographics are very different – mostly men very women, LOTS of ads for sex workers\nOnly helping people that want to be helped\n\n\n\nWhat was the most surprising fact you uncovered as part of research?\n\n\nImagery of exploited children is so depressing and sad\n\n\n\nWithout revealing anything you shouldn’t, are you aware of being set free as a result of your work?\n\n\n“Not my work, our work”\nNot an individual effort\nlawyers, analysts, larger DAs office\n\n\n\nGiven the complicated socio-economic aspects of human and prosecution of those who are responsible, can you discuss of the moral and ethical considerations that you have confronted with while building these tools?\n\n\nPrivacy is the biggest concern\nOpen source book to teach colleagues at the DA’s office how program to in Python\nSometimes Eric works at Civic Hall\n\n\n\nAre there any projects out there that you consider similar to you are working on?\n\n\nThorn’s Spotlight tool\nMemex Project\nPolaris Project\nDatakind Anti Trafficking\ndosomething.org – more broadly focused – help center for teens\nRescueForensics – stage startup\n\n\n\nWhat would it take for other municipalities and law agencies to get started with using your tools?\n\n\nGo to https://github.com/EricSchles?utm_source=rss&utm_medium=rss\nAlert System and investa_gator\nContact Eric at ericschles@gmail.com to collaborate\n\n\n\nHow can our listeners get involved and help you with this Chris\n\n\nTweet at @EricSchles or E-mail Eric\nVolunteer for any of the non profit anti-trafficking groups\n\n\n\nMessage to the community: There is a world of good waiting to happen\n\n\nPicks\n\n\nTobias\n\n@accidental_aRt\ntldrlegal.com\nRishloo\n\n\n\nChris\n\n\nNeil Gaiman’s Sandman Overture\nAlchemist Brewing’s Heady Topper\nHen of the Wood\n\n\n\nEric\n\n\nJames Powell’s Blog\nJulia Nunes\nXKCD\nExplain XKCD\n\n\n\n\n\nKeep in Touch\n\n\nTwitter: @EricSchles\nEric’s About.me page\n\n\nMore From Eric\n\n\nHe presented at PyGotham 2014\nHe also talked at the Open Data Science Conference 2015 Boston\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Listen to past episodes, read about the hosts or donate to the show at podcastinit.com

\n\n

Brief Introduction

\n\n\n\n

Interview with Eric Schles

\n\n

\n\n

Picks

\n\n

\n\n

Keep in Touch

\n\n\n\n

More From Eric

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2015-06-25T06:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/e858db2c-1e94-4ffc-8901-bbdc695ba629.mp3","mime_type":"audio/mpeg","size_in_bytes":84928552,"duration_in_seconds":4389}]},{"id":"http://podcastinit.podbean.com/e/episode-11-naomi-ceder-lynn-root-and-tracy-osborn-on-diversity-in-the-python-community/","title":"Naomi Ceder, Lynn Root and Tracy Osborn on Diversity in the Python Community","url":"https://www.pythonpodcast.com/episode-11-naomi-ceder-lynn-root-and-tracy-osborn-on-diversity-in-the-python-community","content_text":"Listen to past episodes, read about the show and check out our donations section at podcastinit.com\n\nBrief Introduction\n\n\nDate of recording – Jun-10th, 2015\nHosts Macey and Chris Patti\nFollow us on iTunes, Stitcher or TuneIn\nGive us feedback! (iTunes, Twitter, email, Disqus comments)\nYou can donate (if you want)!\nOverview – Interview with Tracy Osborn, Naomi Ceder, Lynn Root\n\n\nInterview with Prominent PyLadies\n\n\nIntroductions\n\nTracy Osborn\nNaomi Ceder\nLynn Root\n\n\n\nHow did you get introduced to Python?\nIn what ways do you think the Python community has succeeded in making itself more friendly and welcoming to women and other under represented minorities, and where could it do better?\n\n\nPython community leadership takes a positive stance on diversity\nCodes of conduct are taken very seriously\nFinancial diversity needs more focus\n\n\n\nWhat can you tell us about PyLadies and DJango Girls?\n\n\nPyLadies\n\nstarted in a coffee shop in LA\npip install PyLadies\nOver 70 locations on almost every continent – half on meetup.com\n\n\n\n\n\nWhat are some of the challenges you still face in being a part of the Python community, and how can our listeners help?\n\n\nDon’t be disparaging about women-focused events\n\n\n\nI had to read up to page 17 of the top authors list on PyPi to find a woman. Can you provide some insight into what may be contributing to this state of affairs and how we can help to improve it?\n\n\npypi is confusing and intimidating\nProcess and tools are tough to use\nMaybe Pyladies should host a “make your own package” night\nMentorship and easy HOWTOs are needed\n\n\n\nYou have all gained some notoriety in the Python community through work that you have done. Do you feel that you were faced with greater adversity than your peers in the course of your careers?\n\n\nStartup community more hostile than Python community\n\n\n\nWe are talking to each of you because of your involvement in the Python community. Have you worked with and been involved in other language communities? If so, can you provide some comparisons between that and Python in how they manage the subject of diversity, gender and otherwise?\n\n\nDesign community – lots of conferences with “all dude” conference speaker line up\nStartups very focused on males for employees and customers\n\n\n\nWhat effect do you think job descriptions play in excluding women and other minorities from roles in development positions? (In reference to https://blog.safaribooksonline.com/2015/06/08/on-recruiting-inclusiveness-and-crafting-better-job-descriptions/?utm_source=rss&utm_medium=rss)\n\n\nDiscourage more appropriate term than exclude\nWomen less likely to apply for roles that they are not completely qualified for\nSpotify experimenting with blind resume review and cross-checking of job descriptions\n\nResult is more women applying and having better results\n\n\n\n\n\nFor any women and young girls who may be considering a career in technology, do you have any words of advice?\n\n\nGo for it, but be aware that it’s hard\n\n\n\nDo you have any advice for the men in the Python community and technology as a whole?\n\n\nActually listen when somebody tells you that it’s not the same for them (race, economics, gender)\nHave some compassion and empathy\nMen should educate themselves\nOld habits die hard but getting over them is important\n\n\n\nIs there anything we haven’t discussed that any of you would like to bring up?\n\nPicks\n\n\nTobias\n\n\nThe Banned and the Banished series by James Clemens\nCool Hand Luke with Paul Newman\n\n\n\nChris\n\n\nBaxter Stowaway IPA\nMastering Emacs\n99% Invisible – The Nutshell Studies\n\n\n\nNaomi Ceder\n\n\nKorey Schrum – Dying for a Living\nInto the Brambles – by “PyDanny – Danny Greenfeld”\n\n\n\nLynn Root\n\n\nJupyter – tmpnb – Kyle Kelly blog post\nKnit Your Own Zoo\nBechdel Test\nThe Good Wife\n\nPasses the Bechdel Test\nInspiration for women being awesome in a male dominated industry\n\n\n\n\n\nTracy Osborn\n\n\nEasyPost – Simplifies generating shipping labels for USPS\nKeep in Touch\n\n\n\n\nNaomi Ceder\n\n\n@naomiceder\n\n\n\nLynn Root\n\n\n@roguelynn\n\n\n\nTracy Osborn\n\n\n@limedaring\nBlog\nHello Webapp\n\n\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Listen to past episodes, read about the show and check out our donations section at podcastinit.com

\n\n

Brief Introduction

\n\n\n\n

Interview with Prominent PyLadies

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2015-06-18T08:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/b6fbbecc-514d-4d84-962f-8a5772f9df31.mp3","mime_type":"audio/mpeg","size_in_bytes":25403314,"duration_in_seconds":2954}]},{"id":"http://podcastinit.podbean.com/e/episode-10-brian-granger-and-fernando-perez-of-the-ipython-project-1434193715/","title":"Brian Granger and Fernando Perez of the IPython Project","url":"https://www.pythonpodcast.com/episode-10-brian-granger-and-fernando-perez-of-the-ipython-project","content_text":"You can find past episodes and other information about the show at podcastinit.com\n\nBrief Introduction\n\n\nDate of recording – June 3rd, 2015\nHosts – Tobias Macey and Chris Patti\nOverview – Interview with Fernando Perez and Brian Granger, core developers of IPython/Project Jupyter\nFollow us on iTunes, Stitcher or TuneIn\nGive us feedback! (iTunes, Twitter, email, Disqus comments)\nYou can donate (if you want)!\n\n\nInterview with Brian Granger and Fernando Perez\n\n\nIntroductions\nHow did you get introduced to Python? – Chris\nFor anyone who may not have heard of or used IPython, can you describe what it is?\nHow challenging was it to port IPython to Python 3?\n\nThomas Kluyver\n\n\n\nWhat prompted the name change from IPython to Project Jupyter and were there any associated changes in the project itself?\n\n\nName inspired by Julia, Python and R – the three programming languages of data science\n\n\n\nData scientists have adopted the use of IPython notebooks in their work on a large scale, what is it about notebooks that lend themselves to this particular problem domain?\n\n\nBayesian methods for Hackers – Cameron Davidson-Pilon\nSignal processing in Python\nO’Reilly added support for notebooks into Atlas publishing platform\n\n\n\nIPython Notebook seems like an incredible tool for educators is advanced fields. Have you seen wide spread adoption in this area and is it a focus for the project?\n\n\nNBGrader – notebook grader\n\n\n\nGithub recently added the ability to render notebooks in a repo. Did you work with them to build that integration?\nWhat are some of the most interesting uses of IPython notebooks that you have seen?\n\n\nGallery of interesting notebooks on the wiki\n\nReproducible academic publications\nCouple of dozen scientific papers, some very high profile\n\n\n\nEducational notebooks on various subjects\nGreat learning resource, as well as entertaining\nMOOC taught between distributed team on Open EdX using IPython notebooks about numerical computing with Python\nPeter Norvig collection of IPython notebooks\n\n\nIncludes analysis of traveling salesman problem\n\n\n\nnotebooks.codeneuro.org– time series data analysis <- Couldn’t get this to work. -Chris\n\n\nAre there any notable projects that use IPython as one of their components?\n\n\nKBase for computational biology\nSage – Open source mathematics project written in Python\n\nCreated by number theorist William Stein\nCustom parser to allow for non-python syntax\n\n\n\nQuantopian – Collaborative platform for financial modeling. Runs on top of IPython\nWakari from Continuum Analytics – hosted IPython with computing environment\nRackspace hosts TempNB and other IPython services\n\n\nWhere do you see Project Jupyter going in the future? Are there any particular new features you’d like to see added? – Tobias\n\n\nOne of the biggest targeted features is real-time collaboration\n\nPrototyped by engineers from Google\n\n\n\nMore modular UI and architecture\nMulti-user deployments with Jupyter Hub\n\n\nA few weeks ago we interviewed Jonathan Slenders who wrote ptpython, which brings IDE like capabilities to interactive Python. Have you ever considered including this in IPython?\nWhat are some of the features that an average user might not know about?\nIs there anything in particular that you would like to ask our listeners for help with?\n\n\nPitch in with the development effort\nOrganize community events on behalf of IPython/Jupyter\nBe patient while documentation improves \n\n\n\n\n\nPicks\n\n\nTobias\n\nDayworld trilogy by Phillip Jose Farmer\nReadRuler.com\n\n\n\nChris\n\n\nRubyTapas by Avdi Grimm\nCodeNewbies\nTweetbot\n\n\n\nBrian Granger\n\n\nData Science from Scratch – Joel Gruß\nElements of Graphing Data – William Cleveland\n\n\n\nFernando Perez\n\n\nRepublic Lost – Lawrence Lessig\nAlvaro Mutis\n\n\n\n\n\nKeep in Touch\n\n\nTwitter @projectjupyter, @ipythondev, @ellisonbg, @fperez_org\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango\nOrchestra\n/ CC BY-SA","content_html":"

You can find past episodes and other information about the show at podcastinit.com

\n\n

Brief Introduction

\n\n\n\n

Interview with Brian Granger and Fernando Perez

\n\n

\n\n

Picks

\n\n

\n\n

Keep in Touch

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango
\nOrchestra

\n/ CC BY-SA\"\"

","summary":"","date_published":"2015-06-13T07:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/cdb05630-cee8-4dde-8288-0187d7965570.mp3","mime_type":"audio/mpeg","size_in_bytes":64774887,"duration_in_seconds":4908}]},{"id":"http://podcastinit.podbean.com/e/episode-9-david-baumgold-on-flask-dance-webhookdb-and-open-edx/","title":"David Baumgold on Flask-Dance, WebhookDB and Open EdX","url":"https://www.pythonpodcast.com/episode-9-david-baumgold-on-flask-dance-webhookdb-and-open-edx","content_text":"You can find out more about us and view previous episodes at podcastinit.com.\n\nBrief Introduction\n\n\nDate of recording – 2015-06-02\nHosts – Tobias Macey and Chris Patti\nFollow us on – iTunes, Stitcher or TuneIn\nGive us feedback on iTunes, Twitter, email or Disqus\n\n\nInterview with David Baumgold\n\n\nIntroduction\nHow did you get introduced to Python?\nWhat problem does Flask-Dance solve that wasn’t covered by other libraries?\nWhat were some of the technical issues that you encountered while building Flask-Dance?\nWhat are some of the design considerations that you had when building Flask-Dance?\nYou also built webhookdb for replicating GitHub’s information to be queryable. What are some use cases for which you would want to do that?\nWhat is Open EdX and what is its intended audience?\nWhat are some of the challenges implementing a system like Open EdX, and what can Python developers learn from the implementation of the project?\n\n\nPicks\n\n\nTobias\n\nEvil mode\nForgotify\nWolf of Wall Street\npipreqs\n\n\n\nChris\n\n\nDark Horse Brewing – “Smells Like a Safety Meeting”\nMedium\nModern Gnu Emacs\n\n\n\nDavid\n\n\nHomebrewhttps://open.edx.org/ for OSX\nHomebrew Cask\nArrow\nMoment.js\nThe Imitation Game\n\n\n\n\n\nKeep in touch\n\n\nTwitter: @singingwolfboy\nGitHub\nWebsite\nEmail\n\n\n","content_html":"

You can find out more about us and view previous episodes at podcastinit.com.

\n\n

Brief Introduction

\n\n\n\n

Interview with David Baumgold

\n\n\n\n

Picks

\n\n

\n\n

Keep in touch

\n\n\n\n

\"\"

","summary":"","date_published":"2015-06-07T11:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/ea65f062-3abe-4424-abf7-309653751f12.mp3","mime_type":"audio/mpeg","size_in_bytes":24451615,"duration_in_seconds":1941}]},{"id":"http://podcastinit.podbean.com/e/episode-8-mark-baggett-on-python-for-infosec/","title":"Mark Baggett on Python for InfoSec","url":"https://www.pythonpodcast.com/episode-8-mark-baggett-on-python-for-infosec","content_text":"Read all of our show notes and find more information about us at Beautiful Soup\n\nBrief Introduction\n\n\nDate of recording – May 28th, 2015\nHosts – Tobias Macey and Chris Patti\nOverview – Interview with Mark Bagett\nFollow us on iTunes, Stitcher or TuneIn\nGive us feedback! (iTunes, Twitter, email, Disqus comments)\nYou can donate (if you want)!\n\n\nInterview with Mark Bagett\n\n\nIntroductions\nHow were you first introduced to Python? – Chris\n\nStarted using it for automating tasks while working as a sysadmin\nFound code that launched an attack on FTP server – in Python\n\n\n\nWhat are some of the tasks in your job that you use Python for? -Tobias\n\n\nTrusted command & control backdoor for Windows\n\nMostly not used by malware authors – thus far (at least Mark hasn’t seen it used that way)\nFlame virus – 5MB payload – incredibly advanced\n\nLua interpreter bundled along with the scripts\n\n\n\nVale framework – Python framework that takes payloads out of penetration testing executables\n\n\n\n\nWhat is it about Python that makes it useful for penetration testing and other information security tasks?\n\n\nSame thing that makes it useful for anything else\nmpacket from core security\n\n\n\nWhat are some of the more useful Python penetration testing tools?\n\n\nOFFENSE\n\nBeautiful Soup\nscapy\nVolatility\n\n\n\nDEFENSE\n\n\nCounter dictionary from collections\nPandas\niPython\nmatplotlib\n\n\n\n\n\nWe’ve noticed that a lot of the literature around information security and penetration testing focuses on targeting Windows. Can you enlighten us as to why that is?\n\n\nWindows event tracing\n\nlogman\nevent trace providers – implement packet sniffing (Can turn every browser into a key logger)\n\n\n\nPrimary attack surface – Where most attacks are targeted\nFewer purely Linux systems\n\n\nVery few ports open – maybe 80, 22\nVery likely no user just sitting there waiting to run an executable you send\n\n\n\nMore freedom on Linux – less formalized patching process, more variable tools = more exploits\nWill write code to only use built in modules for Python that will run in customer target environments\n\n\nWhat are some of the legal considerations that you have to deal with on a regular basis as a penetration tester?\nThere have recently been a number of attacks based on hijacking the TCP/IP stack. Is Python being used for any of these exploits or tools to defend against them?\n\n\nData analytics\nDetect repeated sequence numbers – Man in the Middle Attack\n\nAs simple as 5 lines of Python code\nimport scapy, start sniffing packets, pull together all packets – make list of associated packets\nCan pull together all packets inside of stream\nTime spefic source communicates with specific destination\nBro – intrusion detection suite\n\nBuilt into Security Onion – Doug Berks\nFLOSS Weekly episode 296 with Bro developers\n\n\n\n\n\n\n\nWhat are some activities that you do on a regular basis for which you would turn to another language or toolchain, rather than using Python?\n\n\nPowershell – The Python of windows\n\nWhitelisted and ubiquitous\n\n\n\nPassword cracking – compiled language like C or assembly\n\n\nFor anyone who is interested in getting involved in the security industry, and penetration testing in particular, what resources or tools would you recommend?\n\n\nDevelopers make the best InfoSec professionals\n\nLots of jobs and opportunities\n\n\n\nDeveloper -> Systems Administration -> Information Security\nSecurity conferences – BSides, Defcon, Black Hat\nOnline capture the flag challenges (google it) – good practice for critical thinking and using code for security exercises\nGet involved in the industry – Meetups, etc.\nSANS institute course, Python for Penetration Testers, SEC573 by Mark Baggett – sans.org\nLots of free online resources\nViolent Python\nPicoCTF\nCounter Hack Challenges\n\n\n\n\nPicks\n\n\nTobias\n\nAuthy\nOpenWRT\nTP-Link Archer C7\nSchemas For The Real World by Carina C. Zona\nThe Soul of Software by Avdi Grimm\nChina Mieville\n\n\n\nChris\n\n\nRapscallion Munich Dark\nWrite\nMarginal Way\nFrankie and Johnny’s\npyenv\n\n\n\nMark Bagett\n\n\nCorelabs impacket\nGoogle Labs – Rekall\nAdams peanut butter cup fudge ripple cheesecake\nBSides security conference\n\n\n\n\n\nKeep in Touch\n\n\nTwitter: @markbaggett\nIn Depth Defense\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

Read all of our show notes and find more information about us at Beautiful Soup

\n\n

Brief Introduction

\n\n\n\n

Interview with Mark Bagett

\n\n

\n\n

Picks

\n\n

\n\n

Keep in Touch

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2015-06-03T11:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/c44afaf0-f595-4ad5-bdc6-fbe3a9f273ac.mp3","mime_type":"audio/mpeg","size_in_bytes":55411200,"duration_in_seconds":4470}]},{"id":"http://podcastinit.podbean.com/e/episode-7-jacob-kaplan-moss-on-addressing-cultural-issues-in-tech/","title":"Jacob Kaplan-Moss on Addressing Cultural Issues in Tech","url":"https://www.pythonpodcast.com/episode-7-jacob-kaplan-moss-on-addressing-cultural-issues-in-tech","content_text":"Read all of our show notes and find more information about us at podcastinit.com\n\nBrief Introduction\n\n\nDate of recording – May 18th, 2015\nHosts – Tobias Macey and Chris Patti\nFollow us on iTunes, Stitcher or TuneIn\nGive us feedback! (iTunes, Twitter, email, Disqus comments)\nOverview – Interview with Jacob Kaplan-Moss\n\n\nInterview with Jacob Kaplan-Moss\n\n\nIntroductions\nHow were you first introduced to Python?\nSo, we wanted to invite you on the show to discuss the keynote that you gave at this years PyCon. Can you tell us what you mean when you say that you’re a mediocre programmer and why that is such an important admission to make?\nWhat are some ways that we can change the tone of the conversation around programming skill?\nWhat do we gain by admitting to ourselves and others that we are not all phenomenal engineers?\nWhere does the myth of exceptional vs terrible programmers come from? Can you provide some examples of times that you came in contact with this narrative?\nHow do you think hiring tactics in technology companies contribute to this misconception and how can they be more accepting of average programmers?\nWhat are some ways that we can work toward eradicating the myth of the 10x programmer?\nThinking about our industry’s problems retaining women and other undervalued groups, do you think the way many managers do performance reviews play a role? If so, how can we do better?\n\nWhat Works For Women At Work\n\n\n\nCan you tell us about some other ongoing narratives in the technology industry that you find equally as damaging as our misconceptions around skills and knowledge? – Tobias\n\n\nindie.vc\n\n\n\n\n\nPicks\n\n\nTobias\n\nTrue Ability\nManjaro Linux\nVultr VPS\nMage Wars\n\n\n\nChris\n\n\nK is for Kriek\nTrello\nDan Carlin’s Hardcore History\n\n\n\nJacob Kaplan-Moss\n\n\nHello Web App\nWhat Works For Women At Work\nWhy Women Leave Tech: What the Research Says\nLibrary Extension for Chrome and Firefox\n\n\n\n\n\nKeep In Touch\n\n\n@jacobian\n\n\n","content_html":"

Read all of our show notes and find more information about us at podcastinit.com

\n\n

Brief Introduction

\n\n\n\n

Interview with Jacob Kaplan-Moss

\n\n

\n\n

Picks

\n\n

\n\n

Keep In Touch

\n\n\n\n

\"\"

","summary":"","date_published":"2015-05-26T09:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/6654eb64-7fb9-4343-9877-4cd58ff0166a.mp3","mime_type":"audio/mpeg","size_in_bytes":46669889,"duration_in_seconds":2975}]},{"id":"http://podcastinit.podbean.com/e/episode-6-jonathan-slenders-talks-about-prompt-toolkit/","title":"Jonathan Slenders Talks About Prompt Toolkit","url":"https://www.pythonpodcast.com/episode-6-jonathan-slenders-talks-about-prompt-toolkit","content_text":"Visit our site at podcastinit.com for more show notes and news.\n\nBrief Introduction\n\n\nDate of recording – May 17th, 2015\nHosts – Tobias Macey and Chris Patti\nFollow us on iTunes, Stitcher or TuneIn\nGive us feedback! (iTunes, Twitter, email, Disqus comments)\nOverview – Interview with Jonathan Slenders\nInterview with Jonathan Slenders\n\nIntroductions\nHow were you first introduced to Python? -Chris\nWhat inspired you to create the python-prompt-toolkit?\nWhat are some design considerations that you made when building prompt-toolkit?\n\nMake minimal use of inheritance\n\nOverly strong coupling\nBetter clarity for the API of your library\nCompletely event driven / asynchronous\nNo global state\n\n\n\nptpython completion benefits from asynchrony – The jedi completion library is too slow – completion happens in its own thread\n\n\nYou have built a number of projects that use the prompt-toolkit as a core component, did you have them in mind from the beginning, or are they experiments to test the capabilities of the toolkit?\n\n\ntmux rewrite in Python, abandoned, original motivation for prompt-toolkit\nptpython\npgcli\nptpdb\npyvim\n\n\n\nDo you intend to bring PyVim to feature parity with Vim, or is it just intended for experimentation?\n\n\nShort answer: Don’t know – but will probably never be in full parity with Vim\n\n\n\nWhat inspired you to create ptpython and why did you choose to make it a stand-along project rather than extending iPython?\nHow difficult was it to integrate with IPython and what were the benefits?\n\n\nIPython has its own event loop – this presented difficulties as prompt-toolkit has its own as well\n\n\n\nWhat are some of the most interesting uses that you have seen of the prompt-toolkit?\n\n\nPyVim – really challenged the design\npgcli\nPicks\n\n\n\n\nTobias\n\n\nvimsert\nJohnny Cash Project\nInterstellar\n\n\n\nChris\n\n\nGrimm Telekinesis\npandoc\nvimpager\nHomebrew Cask\n\n\n\nJonathan Slenders\n\n\nBelgian Beer\n\nRochefort\n\n\n\nWestern European Folk Dancing\n\nKeep in touch\n\n\n\n\nTwitter – @jonathan_s\nGitHub – jonathanslenders\n\n\n","content_html":"

Visit our site at podcastinit.com for more show notes and news.

\n\n

Brief Introduction

\n\n

\n\n

\"\"

","summary":"","date_published":"2015-05-19T09:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/5c4bb488-c428-492c-b9ed-b974c2a84dd2.mp3","mime_type":"audio/mpeg","size_in_bytes":35868189,"duration_in_seconds":2453}]},{"id":"http://podcastinit.podbean.com/e/episode-5-ned-batchelder/","title":"Ned Batchelder","url":"https://www.pythonpodcast.com/episode-5-ned-batchelder","content_text":"Visit podcastinit.com for information about the show and links to our iTunes and Stitcher feeds.\n\nBrief Introduction\n\n\nDate of recording – May 4th, 2015\nHosts – Tobias Macey and Chris Patti\nOverview – Interview with Ned Batchelder\nFollow us on iTunes, Stitcher or TuneIn\nGive us feedback! (iTunes, Twitter, email, Disqus comments)\nYou can donate (if you want)!\n\n\nInterview with Ned Batchelder\n\n\nIntroductions\nHow did you get introduced to Python?\n\nZope\n… Implemented in Python\n\n\n\nHow did you get started as the organizer for Boston Python Meetup?\n\n\nHistory is long and varied (Why is this switching to numbers?\nStarted – 6 people sitting around a coffee table\n5 or 6 years\nCo-organizer Jessica McKeller\n\nBuilt structures to help keep the community goingr\n\n\n\nWeekend Python Workshop\n\n\nPeople ‘adjacent’ to the male members – wives, mothers, etc.\n\n\n\n“What comes next” from weekend workshops – became Project Night\n\n\nHow much of your time ends up being dedicated to the Python community?\n\n\nAlso maitainer of coverage.py\nActive on Freenode IRC #python\n20 hours a week\n\n\n\nWhat are your goals for the Boston Python community?\n\n\nContinue to grow\nMore events, different events?\nchipy – Chicago UG very active – 1 on 1 mentoring program\nSmaller events – 5 person events – study groups\n\nAll levels not just beginners\nComputational Biologists – study genomics\nThree user groups\n\nPyladies Boston\nDJango Boston\nBoston Python Meetup\n\n\n\n\n\n\n\nWhat do you find to be the most important thing(s) for building a healthy community (particularly in reference to programming)?\n\n\nConsistency – good to know what to expect\nPick a cadence – don’t burn out\n\n\n\nSpeakers aren’t superheroes, they’re just people. ‘Everyone has at least one talk in them’.\nValue in having a blog, twitter stream – people talk back to you and by correcting your mistakes everyone benefits.\nHow do you keep people engaged outside of the monthly meetings?\n\n\nMeetup.com – requires moderation\npython.org mailing lists – unmoderated – low traffic\nNeed to do more in that regard\n\n\n\nWhat do you like the most/least about the Python community?\n\n\nCommunities can improve – IRC has gotten better\nTurmoil on PSF mailing list over election for directors\n\n\n\nHow do you strike a balance between sponsors and the rest of the community? Do you have policies around sponsored presentations / talks?\n\n\nTend not to do sponsored talks\nMicrosoft NERD – great benefit to Boston Python\nProvides monthly space for the group\n1 minute slots for sponsors\nNo sales pitches\n\n\n\nWhat are the steps I can take to start my own tech community?\n\n\nHow can you get the word out?\nMeetup.com is useful\nPeople like free food and beer \nBe predictable. Pick something sustainable\n\n\n\nWhat is the State of Python, from your perspective?\n\n\nNo signs of slowing down\nRuby people are moving to other environments\nPython people are still using Python\nPython 2 to 3 conflict is unfortunate – transition could have been handled more smoothly\nPython 3 ecosystem is getting much better\nNext big drama – type hinting proposal\nAppears to be contrary to one of the basics tenets of the language at first blush\n\n\n\nDo you feel that Boston will ever have its own regional Python conference?\n\n\nToyed with bid to bring Pycon to Boston\nWould require someone stepping up to do it\nNot sure how a regional conference ‘feels’ as a local event\nTry to have Boston Python be like a year long conference all year long\nHuge undertaking\n\n\n\n\n\nPicks\n\n\nTobias\n\nScribd\nKonch\nDupeGuru\n\n\n\nChris\n\n\nThe River Cafe\nPythonista\nRototo – IOS Game\nStone Brewing Arrogant Bastard\n\n\n\nNed\n\n\nTox\nPythonz\nSpell Tower\nRichard Feynman’s Cornell Lectures\n\n\n\n\n\nKeep in Touch\n\n\nTwitter: @nedbat and @bostonpython\nIRC: nedbat\nnedbatchelder.com\nbostonpython.com\n\n\n","content_html":"

Visit podcastinit.com for information about the show and links to our iTunes and Stitcher feeds.

\n\n

Brief Introduction

\n\n\n\n

Interview with Ned Batchelder

\n\n

\n\n

Picks

\n\n

\n\n

Keep in Touch

\n\n\n\n

\"\"

","summary":"","date_published":"2015-05-12T08:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/99802cb5-4a22-417e-b2cd-beeea2c95deb.mp3","mime_type":"audio/mpeg","size_in_bytes":71677653,"duration_in_seconds":4555}]},{"id":"http://podcastinit.podbean.com/e/episode-4-travis-oliphant/","title":"Travis Oliphant","url":"https://www.pythonpodcast.com/episode-4-travis-oliphant","content_text":"For show notes and other content, visit our site at http://www.pythonpodcast.com?utm_source=rss&utm_medium=rss\n\nBrief Introduction\n\n\nDate of recording – Apr 28th 2015\nHosts – Tobias Macey and Chris Patti\nOverview – Interview with Travis Oliphant\n\n\nInterview with Travis Oliphant\n\n\nIntroductions\nHow did you get introduced to Python?\nI’m curious what inspired you to create NumPy and SciPy?\nWhy did you choose Python for those libraries?\n\nNumeric, Jim Hugunin\nMorphology library in NumArray\n\n\n\nFor those of us who aren’t in the know, can you provide a brief definition of what data science is and how you got involved in it?\n\n\nTerm coined by DJ Patil\nAnswer: Anybody who takes data and tries to derive insights from it\nNobody really knows what this means \n\n\n\nCan you tell us the story of how Continuum Analytics came to be?\nWhat are some interesting projects that you have worked on with Continuum Analytics?\n\n\nBokeh\nWakari\nAnaconda\nNumba\nBlaze\n\n\n\nCan you explain a bit about what NumFocus is and how it got started?\nHow can our audience get involved with NumFocus?\nFor someone just starting out in the data science and data analytics space, what advice would you give?\n\n\nDownload Anaconda, learn as much Python as you can\nGoogle search “Data Analysis in Python”\niPython Notebooks in data analysis\nR community\nMeetups\nOnline classes\nR Community can be helpful\n\n\n\nOf your myriad achievements, what are you most proud of?\n\n\nPicks\n\n\nTobias\n\nUsed bookstores\n\nThe Book Barn\n\n\n\nCloudy with a Chance of Meatballs\nKickin’ it Old School\n\n\nChris\n\n\nKids In The Hall\nMFA Boston Art in Bloom\nCodeNewbies\nApple 27″ Retina iMac 5K\n\n\n\nTravis Oliphant\n\n\nData Carpentry\n\nTracy Teal (@tracykteal)\nPatterned on Software Carpentry\n\n\n\nBrain Science Podcast – Ginger Campbell, MD\nMoney, Bank Credit and Economic Cycles\n\n\nTravis Contacts\n\n\nTwitter:\n\nTravis – @teoliphant\nNumFocus – @numfocus\nContinuum Analytics – @ContinuumIO\n\n\n\n\n\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

For show notes and other content, visit our site at http://www.pythonpodcast.com?utm_source=rss&utm_medium=rss

\n\n

Brief Introduction

\n\n\n\n

Interview with Travis Oliphant

\n\n

\n\n

Picks

\n\n

\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"","date_published":"2015-05-04T08:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/2f822e15-314f-4b4a-9438-7c34174d4f2a.mp3","mime_type":"audio/mpeg","size_in_bytes":49108751,"duration_in_seconds":3136}]},{"id":"http://podcastinit.podbean.com/e/episode-3-kivy-core-developers/","title":"Kivy Core Developers","url":"https://www.pythonpodcast.com/episode-3-kivy-core-developers","content_text":"You can view all of the show notes for every episode at http://podcastinit.com?utm_source=rss&utm_medium=rss\n\nBrief Introduction\n\n\nDate of recording – Apr 21st 2015\nHosts – Tobias Macey and Chris Patti\nOverview – Interview with members of the Kivy core development team\n\n\nInterview with Kivy Core Developers\n\n\nIntroductions\nHow did you get introduced to Python?\nHow did the Kivy project get started?\nWhat made you choose Python as the basis for Kivy?\nWhat were some influences on and inspirations for Kivy’s design?\n\nRaymond Hettinger – Beyond Pep 8\n\n\n\nOne of the amazing things about Kivy is that it’s comparatively simple to learn and get started with. Did this ease of use occur by design or accident?\nWhat were some of the biggest challenges to designing or implementing Kivy?\nIf you could start the project over, what would you do differently?\nWhat are some of the most interesting things you’ve seen Kivy used for?\n\n\nGabriel Pettier – http://www.tangibledisplay.com/en/?utm_source=rss&utm_medium=rss\nMathieu Virbel – https://www.digital-stories.fr/?utm_source=rss&utm_medium=rss and https://vimeo.com/80051846?utm_source=rss&utm_medium=rss\n\n\n\nWhat are some changes/features that you are particularly excited about for the future of Kivy?\n\n\nWiki for roadmap to 2.0\nPyJnius\nPyObjus\nKivy-iOS\nBuildozer\nKivy Remote Shell\nPlyer\n\n\n\nAre there any platforms/operating systems that you are trying to add support for (e.g. Sailfish OS, Ubuntu Phone, Firefox OS)?\nIs there anything in particular that you would like to ask for our listeners to help with?\n\n\nGoogle Summer of Code – If you didn’t get accepted, DO it anyway! \nStart small – documentation fixes\nFix issues\nHuge backlog – help answering questions\nMaintainers for subprojects – like PyJnius\nSponsors – Kivy core team looking for new hardware\nIncrease unit test coverage\n\nIf you find a bug submit a test case\n\n\n\n\n\n\n\nPicks\n\n\nTobias\n\nZeal\nCommitStrip\n\n\n\nChris\n\n\nJack’s Abbey Smoke & Dagger\nWoman in Gold\n\n\n\nMathieu Virbel\n\n\nYAPF Yet Another Python Formatter\nLearn Chinese With Cats!\nRince Cochon\n\n\n\nAkshay Aurora\n\n\nMangoes!\nTic-Tac-Toe machine controlled by Kivy\n\n\n\nRyan Pessa\n\n\nE-Cigarettes – The MilkMan by Vaping Rabbit\n\n\n\nGabriel Pettier\n\n\nI3WM Tiling window manager\nBoulet Corp\nSMBC\n\n\n\n\n\nContacting the Kivy Core Team\n\n\nKivy.org – About Us page\n\n\nThe intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA","content_html":"

You can view all of the show notes for every episode at http://podcastinit.com?utm_source=rss&utm_medium=rss

\n\n

Brief Introduction

\n\n\n\n

Interview with Kivy Core Developers

\n\n

\n\n

Picks

\n\n

\n\n

Contacting the Kivy Core Team

\n\n\n\n

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA\"\"

","summary":"Cross Platform GUI Development in Python","date_published":"2015-04-27T09:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/4a007da3-5e0b-4c36-9790-1d9ae6450b22.mp3","mime_type":"audio/mpeg","size_in_bytes":79176300,"duration_in_seconds":5433}]},{"id":"http://podcastinit.podbean.com/e/episode-2-reuven-lerner/","title":"Reuven Lerner","url":"https://www.pythonpodcast.com/episode-2-reuven-lerner","content_text":"Full show notes can be found at http://podcastinit.com/episode-2-reuven-lerner.html?utm_source=rss&utm_medium=rss\n\nEpisode 2 Brief intro\n\n\nRecording date/time\nHosts\nOverview\n\n\nReuven Lerner Interview\n\n\nPlease introduce yourself\nHow did you get introduced to Python?\nHow did you break into the field of providing Python trainings?\nWhat are the most common languages that your students are coming from?\nWhat are some of the biggest obstacles that people encounter when learning Python?\nWhere does Python draw the inspiration for its object system from?\nIn what way(s) does learning Python differ from learning other languages?\nWhat sorts of materials/mediums do you use for training people in Python?\n\nPython Tutor\n\n\n\nDo you use your book (Practice make Python) as follow up material for your trainings?\nIn your freelance work, what portion of your projects use Python?\n\n\nRuby is Oscar, Python is Felix\n\n\n\nHave you seen a change in the demand for Python skills in the time between when you first started using it and now?\nWhat types of projects would cause you to choose something other than Python?\n\n\nPicks\n\n\nReuven Lerner\n\nDaily Tech Video\nMindless Eating: Why We Eat More Than We Think by Brian Wansink\nAge of Ambition: Chasing Fortune, Truth, and Faith in the New China by Evan Osnos\n\n\n\nChris Patti\n\n\nSpencer Trappist Ale\nRich Hickey’s The Value of Values\nYouCompleteMe – Vim auto-completion\nSizeUp for OSX\n\n\n\nTobias Macey\n\n\nCheckIO – Gamified practice programming\nSnap Circuits\nNvidia Shield Tablet\nSamson Go Mic Portable USB Condenser Microphone\nZoho Apps\n\n\n\n\n\nClosing remarks\n\n\nReuven Contact:\n\nWebsite\nblog\nTwitter: @reuvenmlerner\n\n\n\n\n\n","content_html":"

Full show notes can be found at http://podcastinit.com/episode-2-reuven-lerner.html?utm_source=rss&utm_medium=rss

\n\n

Episode 2 Brief intro

\n\n\n\n

Reuven Lerner Interview

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\n\n

Picks

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\n\n

Closing remarks

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\n\n

\"\"

","summary":"","date_published":"2015-04-23T09:45:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/9caa007d-f9cf-4610-af61-f631a85577ff.mp3","mime_type":"audio/mpeg","size_in_bytes":62638609,"duration_in_seconds":4051}]},{"id":"http://podcastinit.podbean.com/e/episode-1-thomas-hatch/","title":"Thomas Hatch","url":"https://www.pythonpodcast.com/episode-1-thomas-hatch","content_text":"Full show notes can be found at http://podcastinit.com/episode-1-thomas-hatch.html?utm_source=rss&utm_medium=rss\n\nBrief Intro\n\n\nHosts\nOverview\nPython at Chefconf!\nPlug for Talk Python To Me\n\n\nThomas Hatch Interview\n\nPicks\n\n\nThomas Hatch\n\nFlow Based Programming\n\nIOFlo\n\n\n\nImagine Dragons\n\n\nChris Patti\n\n\nStone Imperial Russian Stout\nPython One Liner Games\nBoston Python User Group\n\n\n\nTobias Macey\n\n\nNoisli\nCopyQ\nPelican\nMoving From Heroku to AWS With Salt Part 1\nMoving From Heroku to AWS With Salt Part 2\n\n\n\n\n\nClosing Remarks\n\n","content_html":"

Full show notes can be found at http://podcastinit.com/episode-1-thomas-hatch.html?utm_source=rss&utm_medium=rss

\n\n

Brief Intro

\n\n\n\n

Thomas Hatch Interview

\n\n

Picks

\n\n

\n\n

Closing Remarks

\n\n

\"\"

","summary":"","date_published":"2015-04-11T16:00:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/2105296e-b382-4fc0-b0a9-edf8d730ce98.mp3","mime_type":"audio/mpeg","size_in_bytes":55674787,"duration_in_seconds":4010}]},{"id":"http://podcastinit.podbean.com/e/podbean_best_podcast_hosting_audio_video_blog_hosting/","title":"Podcast.__init__ - Introduction","url":"https://www.pythonpodcast.com/podcast-__init__-introduction","content_text":"Welcome to the first episode of a new podcast focused on bringing you the stories of the people who make the Python language and ecosystem great.\n\n\n\n\nOutline\n\n\n\nIntroduction\nBrief Host Biographies\nWhy We’re Doing This\nWhy We Love Python & Favorite Tools\nThank You\nPicks!\n\nPicks\n\n\nTobias\n\nSummoner Wars\nDbeaver\nKDE Connect\nPlayerctl\n\n\nChris\n\nptpython\nDuchesse de Bourgogne\n\n\n\n\n\nThe intro and outro music is from \nRequiem for a Fish (The Freak Fandango Orchestra) / CC BY-SA 3.0\n\n\n\n\n","content_html":"

Welcome to the first episode of a new podcast focused on bringing you the stories of the people who make the Python language and ecosystem great.\n

\n\n
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Outline
\n\n
\n\n
Picks
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\n\n
The intro and outro music is from

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Requiem for a Fish (The Freak Fandango Orchestra) / CC BY-SA 3.0
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","summary":"","date_published":"2015-03-21T10:21:00.000-04:00","attachments":[{"url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/aphid.fireside.fm/d/1437767933/a8ff81d7-d84b-4c87-872a-dcde96c0b97b/4a1695fc-26b5-4bfc-8519-f041eec2910f.mp3","mime_type":"audio/mpeg","size_in_bytes":26299794,"duration_in_seconds":1643}]}]}