Description
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