polars.Series.rolling_apply#

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

Apply a custom rolling window function.

Prefer the specific rolling window functions over this one, as they are faster:

rolling_min

rolling_max

rolling_mean

rolling_sum

The window at a given row will include the row itself and the window_size - 1 elements before it.

Parameters:

function: Aggregation function
window_size: The length of the window.
weights: An optional slice 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

>>> import numpy as np
>>> s = pl.Series("A", [11.0, 2.0, 9.0, float("nan"), 8.0])
>>> print(s.rolling_apply(function=np.nanstd, window_size=3))
shape: (5,)
Series: 'A' [f64]
[
    null
    null
    3.858612
    3.5
    0.5
]