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,
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 ]