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Comment by gigatexal | Hacker News Reader

I see your pandas (even the arrow based pandas 2.0) and raise you polars. ;)

I see your:

    (
        polars_df1
        .join(polars_df2, on=['state', 'county', 'timestamp'], suffix='_r')
        .with_column(
           ( pl.col('val') + pl.col('val_r')).alias('val')
        )
        .select(['state', 'county', 'timestamp', 'val'])
    )

and raise you:

    pandas_df1 + pandas_df2

Hmmm touché

Fwiw I actually do prefer polars for standard long format relational operations. But sometimes it’s just more convenient to work with data in other ways. Another example:

Polars:

    polars_df.with_column(
        pl.when(pl.col('timestamp').is_between(
            datetime('2023-03-01'),
            datetime('2023-03-31'),
            include_bounds=True
        )).then(pl.col('val') * 1.1)
        .otherwise(pl.col('val'))
        .alias('val')
    )

Pandas:

     pandas_df.loc['2023-03'] *= 1.1

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