polars.apply#

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

Apply a custom/user-defined function (UDF) in a GroupBy context.

Depending on the context it has the following behavior:

Select
Don’t use apply, use map
GroupBy
expected type f: Callable[[Series], Series] Applies a python function over each group.

Parameters:

exprs: Input Series to f
function: Function to apply over the input
return_dtype: dtype of the output Series
returns_scalar: If the function returns a single scalar as output.

Returns:

Expr

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [7, 2, 3, 4],
...         "b": [2, 5, 6, 7],
...     }
... )
>>> df
shape: (4, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 7   ┆ 2   │
│ 2   ┆ 5   │
│ 3   ┆ 6   │
│ 4   ┆ 7   │
└─────┴─────┘

Calculate product of a.

>>> df.with_columns(pl.col("a").apply(lambda x: x * x).alias("product_a"))
shape: (4, 3)
┌─────┬─────┬───────────┐
│ a   ┆ b   ┆ product_a │
│ --- ┆ --- ┆ ---       │
│ i64 ┆ i64 ┆ i64       │
╞═════╪═════╪═══════════╡
│ 7   ┆ 2   ┆ 49        │
│ 2   ┆ 5   ┆ 4         │
│ 3   ┆ 6   ┆ 9         │
│ 4   ┆ 7   ┆ 16        │
└─────┴─────┴───────────┘