polars.Expr.rolling_var#
- Expr.rolling_var(
- window_size: int,
- weights: list[float] | None = None,
- *,
- min_periods: int | None = None,
- center: bool = False,
- ddof: int = 1,
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 theweights
vector. The resulting values will be aggregated to their var.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 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_periods
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 towindow_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
Notes
If you want to compute multiple aggregation statistics over the same dynamic window, consider using
rolling
- this method can cache the window size computation.Examples
>>> df = pl.DataFrame({"A": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]}) >>> df.with_columns( ... rolling_var=pl.col("A").rolling_var(window_size=2), ... ) shape: (6, 2) ┌─────┬─────────────┐ │ A ┆ rolling_var │ │ --- ┆ --- │ │ f64 ┆ f64 │ ╞═════╪═════════════╡ │ 1.0 ┆ null │ │ 2.0 ┆ 0.5 │ │ 3.0 ┆ 0.5 │ │ 4.0 ┆ 0.5 │ │ 5.0 ┆ 0.5 │ │ 6.0 ┆ 0.5 │ └─────┴─────────────┘
Specify weights to multiply the values in the window with:
>>> df.with_columns( ... rolling_var=pl.col("A").rolling_var( ... window_size=2, weights=[0.25, 0.75] ... ), ... ) shape: (6, 2) ┌─────┬─────────────┐ │ A ┆ rolling_var │ │ --- ┆ --- │ │ f64 ┆ f64 │ ╞═════╪═════════════╡ │ 1.0 ┆ null │ │ 2.0 ┆ 0.1875 │ │ 3.0 ┆ 0.1875 │ │ 4.0 ┆ 0.1875 │ │ 5.0 ┆ 0.1875 │ │ 6.0 ┆ 0.1875 │ └─────┴─────────────┘
Center the values in the window
>>> df.with_columns( ... rolling_var=pl.col("A").rolling_var(window_size=3, center=True), ... ) shape: (6, 2) ┌─────┬─────────────┐ │ A ┆ rolling_var │ │ --- ┆ --- │ │ f64 ┆ f64 │ ╞═════╪═════════════╡ │ 1.0 ┆ null │ │ 2.0 ┆ 1.0 │ │ 3.0 ┆ 1.0 │ │ 4.0 ┆ 1.0 │ │ 5.0 ┆ 1.0 │ │ 6.0 ┆ null │ └─────┴─────────────┘