How can I check if all values of a polars DataFrame, containing only boolean columns, are True? Example df: df = pl.DataFrame({"a": [True, True, None], "b": [True, True, True], })  The reason for my question is that sometimes I want to check if all values of a df fulfill a condition, like in the following: df = pl.DataFrame({"a": [1, 2, None], "b": [4, 5, 6], }).select(pl.all() >= 1)  By the way, I didn't expect that .select(pl.all() >= 1) keeps the null (None) in last row of column "a", maybe that's worth noting.
As of the date of this answer, I found the following snippet most appropriate for polars: df.fill_null(False).min_horizontal().min()  If no null values exist in df, one could omit .fill_null(False). Credit goes to roman, the logic of min_horizotnal().min() was first described by him in this answer on a similar issue on any. Example with the df from above: >>> df.fill_null(False).min_horizontal().min() False >>> df = pl.DataFrame({"a": [True, True, True], ... "b": [True, True, True], ... }) ... ... df.fill_null(False).min_horizontal().min() True 