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If we implement @with_columns for Polars, we can leverage Narwhals to support cuDF, Modin, pandas, Polars, Pyarrow, Dask, Ibis, Duckdb, Vaex, and more. We have to distinguish eager and lazy execution though.
Narwhals is an ok dependency to add because @with_columns is already an extension feature and Narwhals itself only depends on the standard library.
Is your feature request related to a problem? Please describe.
We have @with_columns for pyspark, but not for other dataframes.
Describe the solution you'd like
@with_columns
works for any dataframe type.Describe alternatives you've considered
N/A
Additional context
This would help with reusing transforms across dataframes without getting into naming issues.
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