We could expand this topic for a broader perspective. Pandas offers "custom accessors," empowering users to extend DataFrame functionality, while Polars introduces "Expression plugins" for customization, enhancing DataFrame operations. These features are pretty awesome. The obvious advantage, the users are writing and maintaining additional methods.
https://pandas.pydata.org/docs/reference/api/pandas.api.extensions.register_dataframe_accessor.html https://docs.pola.rs/user-guide/expressions/plugins/ For NumPy arrays, integrating similar functionalities, such as a pipe function for method chaining and "custom accessors" for increased flexibility, would improve the user experience. These features would not only encourage cleaner, reusable, and more expressive code but also align NumPy with other data processing libraries. Furthermore, enabling method chained pipelines to leverage acceleration techniques like JIT compilation at a later stage would further optimize performance. Implementing a pipe method could serve as an excellent starting point for these enhancements since it is the least effort. "Custom accessors" and leveraging acceleration techniques might be more ambitious. _______________________________________________ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-le...@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: arch...@mail-archive.com
