polars.reduce#

polars.reduce( function: Callable[[Series, Series], Series], exprs: Sequence[Expr | str] | Expr, *, returns_scalar: bool = False, return_dtype: DataTypeExpr | PolarsDataType | None = None, ) → Expr[source]#

Accumulate over multiple columns horizontally/ row wise with a left fold.

Parameters:

function: Function to apply over the accumulator and the value. Fn(acc, value) -> new_value
exprs: Expressions to aggregate over. May also be a wildcard expression.
returns_scalar: Whether or not function applied returns a scalar. This must be set correctly by the user.
return_dtype: Output datatype. If not set, the dtype will be inferred based on the dtype of the input expressions.

Notes

See fold for the version with an explicit accumulator.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, 3],
...         "b": [0, 1, 2],
...     }
... )
>>> df
shape: (3, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1   ┆ 0   │
│ 2   ┆ 1   │
│ 3   ┆ 2   │
└─────┴─────┘

Horizontally sum over all columns.

>>> df.select(
...     pl.reduce(function=lambda acc, x: acc + x, exprs=pl.col("*")).alias("sum")
... )
shape: (3, 1)
┌─────┐
│ sum │
│ --- │
│ i64 │
╞═════╡
│ 1   │
│ 3   │
│ 5   │
└─────┘