polars.Expr.rolling_quantile#

Expr.rolling_quantile( quantile: float, interpolation: RollingInterpolationMethod = 'nearest', window_size: int = 2, weights: list[float] | None = None, *, min_periods: int | None = None, center: bool = False, ) → Expr[source]#

Compute a rolling quantile.

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 weights vector. The resulting values will be aggregated to their quantile.

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

Parameters:

quantile: Quantile between 0.0 and 1.0.
interpolation{‘nearest’, ‘higher’, ‘lower’, ‘midpoint’, ‘linear’}: Interpolation method.
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 to window_size.
center: Set the labels at the center of the window.

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_quantile=pl.col("A").rolling_quantile(
...         quantile=0.25, window_size=4
...     ),
... )
shape: (6, 2)
┌─────┬──────────────────┐
│ A   ┆ rolling_quantile │
│ --- ┆ ---              │
│ f64 ┆ f64              │
╞═════╪══════════════════╡
│ 1.0 ┆ null             │
│ 2.0 ┆ null             │
│ 3.0 ┆ null             │
│ 4.0 ┆ 2.0              │
│ 5.0 ┆ 3.0              │
│ 6.0 ┆ 4.0              │
└─────┴──────────────────┘

Specify weights for the values in each window:

>>> df.with_columns(
...     rolling_quantile=pl.col("A").rolling_quantile(
...         quantile=0.25, window_size=4, weights=[0.2, 0.4, 0.4, 0.2]
...     ),
... )
shape: (6, 2)
┌─────┬──────────────────┐
│ A   ┆ rolling_quantile │
│ --- ┆ ---              │
│ f64 ┆ f64              │
╞═════╪══════════════════╡
│ 1.0 ┆ null             │
│ 2.0 ┆ null             │
│ 3.0 ┆ null             │
│ 4.0 ┆ 2.0              │
│ 5.0 ┆ 3.0              │
│ 6.0 ┆ 4.0              │
└─────┴──────────────────┘

Specify weights and interpolation method

>>> df.with_columns(
...     rolling_quantile=pl.col("A").rolling_quantile(
...         quantile=0.25,
...         window_size=4,
...         weights=[0.2, 0.4, 0.4, 0.2],
...         interpolation="linear",
...     ),
... )
shape: (6, 2)
┌─────┬──────────────────┐
│ A   ┆ rolling_quantile │
│ --- ┆ ---              │
│ f64 ┆ f64              │
╞═════╪══════════════════╡
│ 1.0 ┆ null             │
│ 2.0 ┆ null             │
│ 3.0 ┆ null             │
│ 4.0 ┆ 1.625            │
│ 5.0 ┆ 2.625            │
│ 6.0 ┆ 3.625            │
└─────┴──────────────────┘

Center the values in the window

>>> df.with_columns(
...     rolling_quantile=pl.col("A").rolling_quantile(
...         quantile=0.2, window_size=5, center=True
...     ),
... )
shape: (6, 2)
┌─────┬──────────────────┐
│ A   ┆ rolling_quantile │
│ --- ┆ ---              │
│ f64 ┆ f64              │
╞═════╪══════════════════╡
│ 1.0 ┆ null             │
│ 2.0 ┆ null             │
│ 3.0 ┆ 2.0              │
│ 4.0 ┆ 3.0              │
│ 5.0 ┆ null             │
│ 6.0 ┆ null             │
└─────┴──────────────────┘