polars.Expr.rolling#

Expr.rolling(
index_column: str,
*,
period: str | timedelta,
offset: str | timedelta | None = None,
closed: ClosedInterval = 'right',
check_sorted: bool = True,
) → Self[source]#

Create rolling groups based on a temporal or integer column.

If you have a time series <t_0, t_1, ..., t_n>, then by default the windows created will be

  • (t_0 - period, t_0]

  • (t_1 - period, t_1]

  • …

  • (t_n - period, t_n]

whereas if you pass a non-default offset, then the windows will be

  • (t_0 + offset, t_0 + offset + period]

  • (t_1 + offset, t_1 + offset + period]

  • …

  • (t_n + offset, t_n + offset + period]

The period and offset arguments are created either from a timedelta, or by using 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)

Or combine them: “3d12h4m25s” # 3 days, 12 hours, 4 minutes, and 25 seconds

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”.

Parameters:
index_column

Column used to group based on the time window. Often of type Date/Datetime. This column must be sorted in ascending order. In case of a rolling group by on indices, dtype needs to be one of {UInt32, UInt64, Int32, Int64}. Note that the first three get temporarily cast to Int64, so if performance matters use an Int64 column.

period

Length of the window - must be non-negative.

offset

Offset of the window. Default is -period.

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

Define which sides of the temporal interval are closed (inclusive).

check_sorted

Whether to check that index_column is sorted. If you are sure the data is sorted, you can set this to False. Doing so incorrectly will lead to incorrect output.

Examples

>>> dates = [
...     "2020-01-01 13:45:48",
...     "2020-01-01 16:42:13",
...     "2020-01-01 16:45:09",
...     "2020-01-02 18:12:48",
...     "2020-01-03 19:45:32",
...     "2020-01-08 23:16:43",
... ]
>>> df = pl.DataFrame({"dt": dates, "a": [3, 7, 5, 9, 2, 1]}).with_columns(
...     pl.col("dt").str.strptime(pl.Datetime).set_sorted()
... )
>>> df.with_columns(
...     sum_a=pl.sum("a").rolling(index_column="dt", period="2d"),
...     min_a=pl.min("a").rolling(index_column="dt", period="2d"),
...     max_a=pl.max("a").rolling(index_column="dt", period="2d"),
... )
shape: (6, 5)
┌─────────────────────┬─────┬───────┬───────┬───────┐
│ dt                  ┆ a   ┆ sum_a ┆ min_a ┆ max_a │
│ ---                 ┆ --- ┆ ---   ┆ ---   ┆ ---   │
│ datetime[μs]        ┆ i64 ┆ i64   ┆ i64   ┆ i64   │
╞═════════════════════╪═════╪═══════╪═══════╪═══════╡
│ 2020-01-01 13:45:48 ┆ 3   ┆ 3     ┆ 3     ┆ 3     │
│ 2020-01-01 16:42:13 ┆ 7   ┆ 10    ┆ 3     ┆ 7     │
│ 2020-01-01 16:45:09 ┆ 5   ┆ 15    ┆ 3     ┆ 7     │
│ 2020-01-02 18:12:48 ┆ 9   ┆ 24    ┆ 3     ┆ 9     │
│ 2020-01-03 19:45:32 ┆ 2   ┆ 11    ┆ 2     ┆ 9     │
│ 2020-01-08 23:16:43 ┆ 1   ┆ 1     ┆ 1     ┆ 1     │
└─────────────────────┴─────┴───────┴───────┴───────┘