polars.all#

polars.all(*names: str, ignore_nulls: bool = True) → Expr[source]#

Either return an expression representing all columns, or evaluate a bitwise AND operation.

If no arguments are passed, this function is syntactic sugar for col("*"). Otherwise, this function is syntactic sugar for col(names).all().

Parameters:

*names

Name(s) of the columns to use in the aggregation.

ignore_nulls

Ignore null values (default).

If set to False, Kleene logic is used to deal with nulls: if the column contains any null values and no False values, the output is null.

See also

all_horizontal

Examples

Selecting all columns.

>>> df = pl.DataFrame(
...     {
...         "a": [True, False, True],
...         "b": [False, False, False],
...     }
... )
>>> df.select(pl.all().sum())
shape: (1, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ u32 ┆ u32 │
╞═════╪═════╡
│ 2   ┆ 0   │
└─────┴─────┘

Evaluate bitwise AND for a column.

>>> df.select(pl.all("a"))
shape: (1, 1)
┌───────┐
│ a     │
│ ---   │
│ bool  │
╞═══════╡
│ false │
└───────┘