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.
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- Your 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
- How did you get introduced to Python?
- Most people are familiar with Pandas, but can you describe at a high level the new extension interface?
- What is the story behind the implementation of this functionality?
- Prior to this interface what was the option for anyone who wanted to extend Pandas?
- What are some of the new data types that are available as external packages?
- What are some of the unique use cases that they enable?
- How is the new interface implemented within Pandas?
- What were the most challenging or difficult aspects of building this new functionality?
- What are some of the more interesting possibilities that you are aware of for new extension types?
- What are the limitations of the interface for libraries that add new array functionality?
- What is the next major change or improvement that you would like to add in Pandas?
Keep In Touch
- Original IP Address proposal
- Mid-implementation blog post
- Wes McKinney
- Array ufunc