polars.Expr.rolling_quantile#

Expr.rolling_quantile(
quantile: float,
interpolation: RollingInterpolationMethod = 'nearest',
window_size: int | timedelta | str = 2,
weights: list[float] | None = None,
min_periods: int | None = None,
*,
center: bool = False,
by: str | None = None,
closed: ClosedInterval = 'right',
warn_if_unsorted: bool = True,
) Self[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.

If by has not been specified (the default), the window at a given row will include the row itself, and the window_size - 1 elements before it.

If you pass a by column <t_0, t_1, ..., t_n>, then closed="left" means the windows will be:

  • [t_0 - window_size, t_0)

  • [t_1 - window_size, t_1)

  • [t_n - window_size, t_n)

With closed="right", the left endpoint is not included and the right endpoint is included.

Parameters:
quantile

Quantile between 0.0 and 1.0.

interpolation{‘nearest’, ‘higher’, ‘lower’, ‘midpoint’, ‘linear’}

Interpolation method.

window_size

The length of the window. Can be a fixed integer size, or a dynamic temporal size indicated by a timedelta or the following string language:

  • 1ns (1 nanosecond)

  • 1us (1 microsecond)

  • 1ms (1 millisecond)

  • 1s (1 second)

  • 1m (1 minute)

  • 1h (1 hour)

  • 1d (1 calendar day)

  • 1w (1 calendar week)

  • 1mo (1 calendar month)

  • 1q (1 calendar quarter)

  • 1y (1 calendar year)

  • 1i (1 index count)

By “calendar day”, we mean the corresponding time on the next day (which may not be 24 hours, due to daylight savings). Similarly for “calendar week”, “calendar month”, “calendar quarter”, and “calendar year”.

If a timedelta or the dynamic string language is used, the by and closed arguments must also be set.

weights

An optional slice with the same length as the window that determines the relative contribution of each value in a window to the output.

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

by

If the window_size is temporal for instance "5h" or "3s", you must set the column that will be used to determine the windows. This column must be of dtype Datetime or Date.

Warning

If passed, the column must be sorted in ascending order. Otherwise, results will not be correct.

closed{‘left’, ‘right’, ‘both’, ‘none’}

Define which sides of the temporal interval are closed (inclusive); only applicable if by has been set.

warn_if_unsorted

Warn if data is not known to be sorted by by column (if passed).

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             │
└─────┴──────────────────┘