Setting aside complaints about the Pandas API, it's frustrating that we might see the community of a popular "standard" tool fragment into two or even three ecosystems (for libraries with slightly incompatible APIs) -- seemingly all with the value proposition of "making it faster". Based on the machine learning experience over the last decade, this kind of churn in tooling is somewhat exhausting.
I wonder how much of this is fundamental to the common approach of writing libraries in Python with the processing-heavy parts delegated to C/C++ -- that the expressive parts cannot be fast and the fast parts cannot be expressive. Also, whether Rust (for polars, and other newer generation of libraries) changes this tradeoff substantially enough.
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