A few months ago I tried migrating a large pandas codebase to polars. I'm not much of a fan of doing analytics/data pipelining in Python - a complex transformation takes me 2-5x as long in pandas compared to Julia or R (using dataframes.jl & dplyr).
Unfortunately polars was not it. Too many bugs on standard operations, unreliable interoperability with pandas (which is an issue since so many libraries require pandas dataframes as inputs), the API is also very verbose for a modern dataframe library, though it's still better than pandas.
Hopefully these will get resolved out over time but for now I had the best luck using duckdb on top of pandas, it is as fast as polars but more stable/better interoperability.
Eventually I hope the Python dataframe ecosystem gets to the same point as R's, where you have a analytics-oriented dataframe library with an intuitive API (dplyr) that can be easily used alongside a high-performance dataframe library (data.table).
Unfortunately polars was not it. Too many bugs on standard operations, unreliable interoperability with pandas (which is an issue since so many libraries require pandas dataframes as inputs), the API is also very verbose for a modern dataframe library, though it's still better than pandas.
Hopefully these will get resolved out over time but for now I had the best luck using duckdb on top of pandas, it is as fast as polars but more stable/better interoperability.
Eventually I hope the Python dataframe ecosystem gets to the same point as R's, where you have a analytics-oriented dataframe library with an intuitive API (dplyr) that can be easily used alongside a high-performance dataframe library (data.table).