This slot will cover the effort regarding interoperability in the scientific Python ecosystem. Topics:

• Using the Array API for array-producing and array-consuming libraries
• DataFrame interchange and namespace APIs
• Apache Arrow: connecting and accelerating dataframe libraries across the PyData ecosystem
• Entry Points: Enabling backends and plugins for your libraries

Using the Array API for array-producing and array-consuming libraries

Already using the Array API or wondering if you should in a project you maintain? Join this maintainer track session to share your experience and exchange knowledge and tips around building array libraries that implement the standard or libraries that consume arrays.

DataFrame-agnostic code using the DataFrame API standard

The DataFrame Standard provides you with a minimal, strict, and predictable API, to write code that will work regardless of whether the caller uses pandas, polars, or some other library.

DataFrame Interchange protocol and Apache Arrow

The DataFrame interchange protocol and Arrow C Data interface are two ways to interchange data between dataframe libraries. What are the challenges and requirements that maintainers encounter when integrating this into consuming libraries?

Entry Points: Enabling backends and plugins for your libraries

In this talk, we will discuss how NetworkX used entry points to enable more efficient computation backends to plug into NetworkX

Could scikit-learn future be GPU-powered ? This talk will discuss the performance improvements that GPU computing could bring to existing scikit-learn algorithms, and will describe a plugin-based design that is being foresighted to open-up scikit-learn compatibility to faster compute backends, with special concern for user-friendliness, ease of installation, and interoperability.