Data Analysis

Salabim: Logistics Simulation with Ruud van der Ham - Episode 151

Summary

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.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
  • 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.
  • 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.
  • 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 [email protected])
  • 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.
  • Your 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

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by explaining what Discrete Event Simulation is and how Salabim helps with that?
    • Can you explain how you chose the name?
  • What was your motivation for creating Salabim and how does it compare to other tools for discrete event simulation?
  • How does discrete event simulation compare with state machines?
  • How is Salabim implemented and how has the design evolved over the time that you have been working on it?
  • I 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?
  • What are some examples of the types of models that you can model with Salabim?
    • What would an implementation of one of these models look like for someone using Salabim?
  • What options does a user have to verify the accuracy of a simulation created with Salabim?
  • One 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?
  • At 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?
  • When is Salabim not the right tool to use and what would you suggest for people who find themselves in that situation?
  • What have been some of the most complicated or difficult aspects of building and maintaining Salabim?
  • What are some of the new features or improvements that you have planned for the future of Salabim?

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

PyRay: Pure Python 3D Rendering with Rohit Pandey - Episode 147

Summary

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.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
  • 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.
  • 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.
  • 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 [email protected])
  • 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.
  • A few announcements before we start the show:
    • There’s still time to get your tickets for PyCon Colombia, happening February 9th and 10th. Go to pycon.co to learn more and register.
    • There 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%
    • If 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.
  • Your host as usual is Tobias Macey and today I’m interviewing Rohit Pandey about PyRay, a 3d rendering library written completely in python

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by explaining what PyRay is and what motivated you to create it?
    [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.
  • What does the internal architecture of PyRay look like and how has that design evolved since you first started working on it?
  • What 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?
    [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.
  • What have been some of the most challenging aspects of building and maintaining PyRay?
    [rohit] 3d objects – getting mesh plots. I have to develop routines from scratch for almost everything – shading objects, etc. Animated routines for characters.

  • What are some of the most interesting or unexpected uses of PyRay that you are aware of?
    [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.

  • For someone who wanted to contribute to PyRay are there any particular skills or experience that would be most helpful?
    Basic linear algebra and python
  • What are some of the features or improvements that you have planned for the future of PyRay?

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pyray repo – https://github.com/ryu577/pyray
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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Learn Leap Fly: Using Python To Promote Global Literacy with Kjell Wooding - Episode 145

Summary

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.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
  • 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.
  • 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.
  • 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 [email protected])
  • 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.
  • Your 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

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by describing what Learn Leap Fly does and how the company got started?
  • What 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?
  • What 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.
  • How do you measure the effectiveness of the work that you are doing?
  • How has the framework of the XPRIZE influenced the way in which you have approached the design and development of your work?
  • What have been some of the biggest challenges that you faced in the process of developing and deploying your submission for the XPRIZE?
  • What are some of the features that you have planned for future releases of your platform?

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

Orange: Visual Data Mining Toolkit with Janez Demšar and Blaž Zupan - Episode 142

Summary

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.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
  • 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.
  • 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.
  • 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 [email protected])
  • 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.
  • Your 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

Interview

  • Introductions
  • How did you get introduced to Python?
  • What is Orange and what was your motivation for building it?
  • Who is the target audience for this project?
  • How is the graphical interface implemented and what kinds of workflows can be implemented with the visual components?
  • What are some of the most notable or interesting widgets that are available in the catalog?
  • What are the limitations of the graphical interface and what options do user have when they reach those limits?
  • What have been some of the most challenging aspects of building and maintaining Orange?
  • What 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?
  • What are some of the most interesting or innovative uses of Orange that you are aware of?
  • What are some of the projects or technologies that you consider to be your competition?
  • Under what circumstances would you advise against using Orange?
  • What are some widgets that you would like to see in future versions?
  • What do you have planned for future releases of Orange?

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

Data Retriever with Henry Senyondo - Episode 122

Summary

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.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.
  • Visit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.
  • 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.
  • Your host as usual is Tobias Macey and today I’m interviewing Henry Senyondo about Data Retriever, the package manager for public data sets.

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you explain what data retriever is and the problem that it was built to solve?
  • Are there limitations as to the types of data that can be managed by data retriever?
  • What kinds of data sets are currently available and who are the target users?
  • What is involved in preparing a new dataset to be available for installation?
  • How 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?
  • How is the project designed and what are some of the most difficult technical aspects of building it?
  • What is in store for the future of data retriever?

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

Nuclear Engineering with Dr. Katy Huff - Episode 115

Summary

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.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.
  • Visit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.
  • 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.
  • Your host as usual is Tobias Macey and today I’m interviewing Dr. Katy Huff about using Python for nuclear engineering

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by explaining what nuclear engineering is and give some examples of current research in the field?
  • The 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?
  • One 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?
  • In 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?
  • What are some of the most pressing issues in nuclear engineering and how are you leveraging Python to help with addressing them?
  • How does open source software relate to open science, and how do they impact the impact the ways that research is performed?
  • What are some of the current or future developments in nuclear engineering that you are most excited about?

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

MetPy: Taming The Weather With Python - Episode 100

Summary

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.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.
  • 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.
  • Visit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.
  • 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
  • Your 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.

Interview

  • Introductions
  • How did you get introduced to Python?
  • What is MetPy and what is the problem that prompted you to create it?
  • Can you explain the problem domain for Meteorology and how it compares to other domains such as the physical sciences?
  • How do you deal with the inherent uncertainty of atmospheric and weather data?
  • What are some of the data sources and data formats that a meteorologist works with?
  • To what degree is machine learning or artificial intelligence employed when modelling climate and local weather patterns?
  • The 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?
  • I 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.
  • What are some of the interesting innovations in weather science that you are looking forward to?

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

Pandas with Jeff Reback - Episode 98

Summary

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.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.
  • 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.
  • When 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> and use the promo code podcastinit during checkout.
  • Visit the site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.
  • 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
  • Your host as usual is Tobias Macey and today I’m interviewing Jeff Reback about Pandas, the swiss army knife of data analysis in Python.

Interview

  • Introductions
  • How did you get introduced to Python?
  • To start off, what is Pandas and what is its origin story?
    • How did you get involved in the project’s development?
  • For 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?
  • Pandas 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?
  • There 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?
  • What are some of the biggest challenges that you have encountered while working on Pandas?
  • Do 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?
  • What’s coming for pandas? Pandas 2.0!

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

PyTables with Francesc Alted - Episode 97

Summary

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.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.
  • 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. 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.
  • Visit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.
  • 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
  • Your host as usual is Tobias Macey and today I’m interviewing Francesc Alted about PyTables

Interview

  • Introductions
  • How did you get introduced to Python?
  • To start with, what is HDF5 and what was the problem that motivated you to wrap Python around it to create PyTables?
  • Which are the most relevant contributors for PyTables? How you interacted?
  • How is the project architected and what are some of the design decisions that you are most proud of?
  • What are some of the typical use cases for PyTables and how does it tie into the broader Python data ecosystem?
  • How common is it to use an HDF5 file as a data interchange format to be shared between researchers or between languages?
  • Given the ability to create custom node types, does that inhibit the ability to interact with the stored data using other libraries?
  • What are some of the capabilities of HDF5 and PyTables that can’t be reasonably replicated in other data storage systems?
  • One 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?
  • What are some of the most interesting or unexpected uses of PyTables that you are aware of?

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

Python for GIS with Sean Gillies - Episode 80

Summary

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.

Brief Introduction

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.
  • 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.
  • 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.
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  • Join our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.
  • Your host as usual is Tobias Macey
  • Today I’m interviewing Sean Gillies about writing Geographic Information Systems in Python.

Interview with Sean Gillies

  • Introductions
  • How did you get introduced to Python?
  • Can you start by describing what Geographic Information Systems are and what kinds of projects might take advantage of them?
  • How did you first get involved in the area of GIS and location-based computation?
  • What was the state of the Python ecosystem like for writing these kinds of applications?
  • You 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?
  • What are some of the unique challenges associated with trying to model geographical features in a manner that allows for effective computation?
    • How does the complexity of modeling and computation scale with increasing land area?
  • Mapping 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?
  • To 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?
  • What level of expertise would you advise for someone who simply wants to add some location-aware features to their application?
  • I know that you joined Mapbox a little while ago. Which parts of their stack are written in Python?
  • What are the areas where Python still falls short and which languages or tools do you turn to in those cases?

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