polars.Expr.rolling_std_by#

Expr.rolling_std_by(
by: str,
window_size: timedelta | str,
*,
min_periods: int = 1,
closed: ClosedInterval = 'right',
ddof: int = 1,
warn_if_unsorted: bool = True,
) → Self[source]#

Compute a rolling standard deviation based on another column.

Warning

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

Given 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)

Parameters:
by

This column must be of dtype Datetime or Date.

Warning

The column must be sorted in ascending order. Otherwise, results will not be correct.

window_size

The length of the window. Can be 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)

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”.

min_periods

The number of values in the window that should be non-null before computing a result.

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

Define which sides of the temporal interval are closed (inclusive), defaults to 'right'.

ddof

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

warn_if_unsorted

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

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

Create a DataFrame with a datetime column and a row number column

>>> from datetime import timedelta, datetime
>>> start = datetime(2001, 1, 1)
>>> stop = datetime(2001, 1, 2)
>>> df_temporal = pl.DataFrame(
...     {"date": pl.datetime_range(start, stop, "1h", eager=True)}
... ).with_row_index()
>>> df_temporal
shape: (25, 2)
┌───────┬─────────────────────┐
│ index ┆ date                │
│ ---   ┆ ---                 │
│ u32   ┆ datetime[μs]        │
╞═══════╪═════════════════════╡
│ 0     ┆ 2001-01-01 00:00:00 │
│ 1     ┆ 2001-01-01 01:00:00 │
│ 2     ┆ 2001-01-01 02:00:00 │
│ 3     ┆ 2001-01-01 03:00:00 │
│ 4     ┆ 2001-01-01 04:00:00 │
│ …     ┆ …                   │
│ 20    ┆ 2001-01-01 20:00:00 │
│ 21    ┆ 2001-01-01 21:00:00 │
│ 22    ┆ 2001-01-01 22:00:00 │
│ 23    ┆ 2001-01-01 23:00:00 │
│ 24    ┆ 2001-01-02 00:00:00 │
└───────┴─────────────────────┘

Compute the rolling std with the temporal windows closed on the right (default)

>>> df_temporal.with_columns(
...     rolling_row_std=pl.col("index").rolling_std_by("date", window_size="2h")
... )
shape: (25, 3)
┌───────┬─────────────────────┬─────────────────┐
│ index ┆ date                ┆ rolling_row_std │
│ ---   ┆ ---                 ┆ ---             │
│ u32   ┆ datetime[μs]        ┆ f64             │
╞═══════╪═════════════════════╪═════════════════╡
│ 0     ┆ 2001-01-01 00:00:00 ┆ null            │
│ 1     ┆ 2001-01-01 01:00:00 ┆ 0.707107        │
│ 2     ┆ 2001-01-01 02:00:00 ┆ 0.707107        │
│ 3     ┆ 2001-01-01 03:00:00 ┆ 0.707107        │
│ 4     ┆ 2001-01-01 04:00:00 ┆ 0.707107        │
│ …     ┆ …                   ┆ …               │
│ 20    ┆ 2001-01-01 20:00:00 ┆ 0.707107        │
│ 21    ┆ 2001-01-01 21:00:00 ┆ 0.707107        │
│ 22    ┆ 2001-01-01 22:00:00 ┆ 0.707107        │
│ 23    ┆ 2001-01-01 23:00:00 ┆ 0.707107        │
│ 24    ┆ 2001-01-02 00:00:00 ┆ 0.707107        │
└───────┴─────────────────────┴─────────────────┘

Compute the rolling std with the closure of windows on both sides

>>> df_temporal.with_columns(
...     rolling_row_std=pl.col("index").rolling_std_by(
...         "date", window_size="2h", closed="both"
...     )
... )
shape: (25, 3)
┌───────┬─────────────────────┬─────────────────┐
│ index ┆ date                ┆ rolling_row_std │
│ ---   ┆ ---                 ┆ ---             │
│ u32   ┆ datetime[μs]        ┆ f64             │
╞═══════╪═════════════════════╪═════════════════╡
│ 0     ┆ 2001-01-01 00:00:00 ┆ null            │
│ 1     ┆ 2001-01-01 01:00:00 ┆ 0.707107        │
│ 2     ┆ 2001-01-01 02:00:00 ┆ 1.0             │
│ 3     ┆ 2001-01-01 03:00:00 ┆ 1.0             │
│ 4     ┆ 2001-01-01 04:00:00 ┆ 1.0             │
│ …     ┆ …                   ┆ …               │
│ 20    ┆ 2001-01-01 20:00:00 ┆ 1.0             │
│ 21    ┆ 2001-01-01 21:00:00 ┆ 1.0             │
│ 22    ┆ 2001-01-01 22:00:00 ┆ 1.0             │
│ 23    ┆ 2001-01-01 23:00:00 ┆ 1.0             │
│ 24    ┆ 2001-01-02 00:00:00 ┆ 1.0             │
└───────┴─────────────────────┴─────────────────┘