polars.Series.rolling_map#

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

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.

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:

  • the window size, if window_size is a fixed integer

  • 1, if window_size is a dynamic temporal size

center

Set the labels at the center of the window.

Warning

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

Examples

>>> from numpy import nansum
>>> s = pl.Series([11.0, 2.0, 9.0, float("nan"), 8.0])
>>> s.rolling_map(nansum, window_size=3)
shape: (5,)
Series: '' [f64]
[
        null
        null
        22.0
        11.0
        17.0
]