polars.Expr.rolling_map#

Expr.rolling_map( function: Callable[[Series], Any], window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False, ) → Self[source]#

Compute a custom rolling window function.

Warning

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

Parameters:

function: Custom aggregation function.
window_size: Size of the window. The window at a given row will include the row itself and the window_size - 1 elements before it.
weights: A list of weights with the same length as the window that will be multiplied elementwise with the values in the window.
min_periods: The number of values in the window that should be non-null before computing a result. If None, it will be set equal to window size.
center: Set the labels at the center of the window.

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 │
└──────┘