So in pandas you might get that warning when you’re trying to do an in place operation like so: df.loc['2023-01-01': '2023-12-31'] *= 2 This doubles every

So in pandas you might get that warning when you’re trying to do an in place operation like so: df.loc['2023-01-01': '2023-12-31'] *= 2 This doubles every row in 2023 in your frame. In polars there’s no mutating operations, everything is pure, so you won’t see any warnings of that nature, but what you will see instead is having to do something like this to solve the same problem: df.with_column([ pl.when(pl.col('date').is_between( datetime(2023, 1, 1), datetime(2023, 12, 31), )).then(pl.col(x) * 2) .otherwise(pl.col(x)) .alias(x) for x in df.columns if x != 'date' ]) So there’s definitely some trade offs, but mutating operations are sometimes worth learning how to use them properly to avoid that pandas warning (or know when to ignore it, because unfortunately there can be false positives).