polars.Series.rolling_var#

Series.rolling_var( window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False, ddof: int = 1, ) → Series[source]#

Compute a rolling variance.

Warning

This functionality is considered unstable. It may be changed at any point without it being considered a breaking change.

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 variance.

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

Parameters:

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:

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

ddof

“Delta Degrees of Freedom”: The divisor for a length N window is N - ddof

Examples

>>> s = pl.Series("a", [1.0, 2.0, 3.0, 4.0, 6.0, 8.0])
>>> s.rolling_var(window_size=3)
shape: (6,)
Series: 'a' [f64]
[
        null
        null
        1.0
        1.0
        2.333333
        4.0
]