Data Exploration and Visualization Made Effortless with Lux
May 3rd, 2021
51 mins 5 secs
About this Episode
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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- Your 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
- How did you get introduced to Python?
- Can you start by describing what Lux is and how the project got started?
- What is the role of visualization in a data science workflow?
- What are the challenges that data scientists face in the exploratory phase of their analysis?
- There 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?
- How does Lux compare to tools such as scikit-yb?
- What is the workflow for someone using Lux in their analysis and what problems does it solve for them?
- Can you talk through how Lux is architected?
- How have the goals and design of Lux changed or evolved since you first began working on it?
- Data 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?
- What are some of the capabilities of Lux that are often overlooked or underutilized?
- How has Lux impacted your own work in data analysis/data science?
- What are some of the other gaps that you see in the available tooling for data science?
- What are some of the most interesting, innovative, or unexpected ways that you have seen Lux used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on and with Lux?
- When is Lux the wrong choice?
- What do you have planned for the future of the project?
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