polars.Series.rolling_mean

Series.rolling_mean(
    window_size: int,
    weights: list_[float] | None = None,
    *,
    min_samples: int | None = None,
    center: bool = False,
) → Series

Apply a rolling mean (moving mean) over the values in this array.

A window of length window_size will traverse the array. The values that fill this window will (optionally) be multiplied with the weights given by the weight vector. The resulting values will be aggregated to their mean.

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

Changed in version 1.21.0: The min_periods parameter was renamed min_samples.

Parameters:

window_size

The length of the window in number of elements.

weights

An optional slice with the same length as the window that will be multiplied elementwise with the values in the window.

min_samples

The number of values in the window that should be non-null before computing a result. If set to None (default), it will be set equal to window_size.

center

Set the labels at the center of the window.

Examples

>>> s = pl.Series("a", [100, 200, 300, 400, 500])
>>> s.rolling_mean(window_size=2)
shape: (5,)
Series: 'a' [f64]
[
    null
    150.0
    250.0
    350.0
    450.0
]