Native support for polars dataframes in imbalanced-learn

[kumar-abhishek]

Polars is a high-performance DataFrame library for Python, celebrated for its fast data processing capabilities and efficient, concise syntax. Its multi-threaded query engine and strong integration with the Python ecosystem make it an outstanding choice for managing large datasets. Polars has been gaining popularity as a fast and memory-efficient alternative to pandas, especially for big data applications.

While several libraries like scikit-learn and seaborn have added support for Polars DataFrames, I am not sure if imbalanced-learn currently requires users to directly use polars dataFrames or they need to convert Polars DataFrames to pandas (e.g., polars_df.to_pandas()) before applying the sampling methods.

I do see that imblearn depends upon polars and some APIs like set_output accept polars as parameter, it's unclear if imblearn APIs can directly work with polars dataframes

[jamblejoe]

Native support for polars would be really great!

[jamblejoe]

Maybe someone could point out the necessary steps to get at least rudimentary functionality with polars dataframes. I would be willing to investigate implementing those. Thanks!

[chkoar]

It is on my todo list for 2025