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Polars: Lightning-fast DataFrame library for Rust and Python
https://github.com/austospumanto/minimal-pandas-api-for-pola...
pip install minimal-pandas-api-for-polars
I wrote a library that wraps polars DataFrame and Series objects to allow you to use them with the same syntax as with pandas DataFrame and Series objects. The goal is not to be a replacement for polars' objects and syntax, but rather to (1) Allow you to provide (wrapped) polars objects as arguments to existing functions in your codebase that expect pandas objects and (2) Allow you to continue writing code (especially EDA in notebooks) using the pandas syntax you know and (maybe) love while you're still learning the polars syntax, but with the underlying objects being all-polars. All methods of polars' objects are still available, allowing you to interweave pandas syntax and polars syntax when working with MppFrame and MppSeries objects.
Furthermore, the goal should always be to transition away from this library over time, as the LazyFrame optimizations offered by polars can never be fully taken advantage of when using pandas-based syntax (as far as I can tell). In the meantime, the code in this library has allowed me to transition my company's pandas-centric code to polars-centric code more quickly, which has led to significant speedups and memory savings even without being able to take full advantage of polars' lazy evaluation. To be clear, these gains have been observed both when working in notebooks in development and when deployed in production API backends / data pipelines.
I'm personally just adding methods to the MppFrame and MppSeries objects whenever I try to use pandas syntax and get AttributeErrors.
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