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

Computing custom functions is extremely slow. Use specialized rolling functions such as Series.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
>>> 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
]