polars.Expr.rolling_map

Expr.rolling_map(
    function: Callable[[Series],
    Any],
    window_size: int,
    weights: list_[float] | None = None,
    *,
    min_samples: int | None = None,
    center: bool = False,
) → Expr

Compute a custom rolling window function.

Warning

This functionality is considered unstable. It may be changed at any point without it being considered a breaking change.

Changed in version 1.21.0: The min_periods parameter was renamed min_samples.

engine:In Memory

Parameters:

function

Custom aggregation function.

window_size

The length of the window in number of elements.

weights

An optional slice with the same length as the window that will be multiplied elementwise with the values in the window.

min_samples

The number of values in the window that should be non-null before computing a result. If set to None (default), it will be set equal to window_size.

center

Set the labels at the center of the window.

Warning

Computing custom functions is extremely slow. Use specialized rolling functions such as Expr.rolling_sum() if at all possible.

Examples

>>> from numpy import nansum
>>> df = pl.DataFrame({"a": [11.0, 2.0, 9.0, float("nan"), 8.0]})
>>> df.select(pl.col("a").rolling_map(nansum, window_size=3))
shape: (5, 1)
┌──────┐
│ a    │
│ ---  │
│ f64  │
╞══════╡
│ null │
│ null │
│ 22.0 │
│ 11.0 │
│ 17.0 │
└──────┘