polars.Series.rolling_var_by#

Series.rolling_var_by(
by: IntoExpr,
window_size: timedelta | str,
*,
min_samples: int = 1,
closed: ClosedInterval = 'right',
ddof: int = 1,
) → Self[source]#

Compute a rolling variance based on another series.

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="right" (the default) 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

Should be DateTime, Date, UInt64, UInt32, Int64, or Int32 data type (note that the integral ones require using 'i' in window size).

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)

  • 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”.

min_samples

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

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 series with a row index value

>>> from datetime import timedelta, datetime
>>> start = datetime(2001, 1, 1)
>>> stop = datetime(2001, 1, 2)
>>> s = pl.Series("index", range(25))
>>> s
shape: (25,)
Series: 'index' [i64]
[
    0
    1
    2
    3
    4
    …
    20
    21
    22
    23
    24
]

Create another series to apply the window mask:

>>> d = pl.Series("date", pl.datetime_range(start, stop, "1h", eager=True))
>>> d
shape: (25,)
Series: 'date' [datetime[μs]]
[
    2001-01-01 00:00:00
    2001-01-01 01:00:00
    2001-01-01 02:00:00
    2001-01-01 03:00:00
    2001-01-01 04:00:00
    …
    2001-01-01 20:00:00
    2001-01-01 21:00:00
    2001-01-01 22:00:00
    2001-01-01 23:00:00
    2001-01-02 00:00:00
]

Compute the rolling std with the temporal windows from the second series closed on the right:

>>> s.rolling_std_by(d, "3h")
shape: (25,)
Series: 'index' [f64]
[
    null
    0.707107
    1.0
    1.0
    1.0
    …
    1.0
    1.0
    1.0
    1.0
    1.0
]