polars.LazyFrame.groupby_rolling#
- LazyFrame.groupby_rolling(index_column: str, *, period: str | timedelta, offset: str | timedelta | None = None, closed: ClosedInterval = 'right', by: IntoExpr | Iterable[IntoExpr] | None = None) LazyGroupBy[Self] [source]#
Create rolling groups based on a time column.
Also works for index values of type Int32 or Int64.
Different from a
dynamic_groupby
the windows are now determined by the individual values and are not of constant intervals. For constant intervals use groupby_dynamic.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 day)
1w (1 week)
1mo (1 calendar month)
1y (1 calendar year)
1i (1 index count)
Or combine them: “3d12h4m25s” # 3 days, 12 hours, 4 minutes, and 25 seconds
In case of a groupby_rolling on an integer column, the windows are defined by:
“1i” # length 1
“10i” # length 10
- Parameters:
- index_column
Column used to group based on the time window. Often to type Date/Datetime This column must be sorted in ascending order. If not the output will not make sense.
In case of a rolling groupby on indices, dtype needs to be one of {Int32, Int64}. Note that Int32 gets temporarily cast to Int64, so if performance matters use an Int64 column.
- period
length of the window
- offset
offset of the window. Default is -period
- closed{‘right’, ‘left’, ‘both’, ‘none’}
Define which sides of the temporal interval are closed (inclusive).
- by
Also group by this column/these columns
See also
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) ... ) >>> out = df.groupby_rolling(index_column="dt", period="2d").agg( ... [ ... pl.sum("a").alias("sum_a"), ... pl.min("a").alias("min_a"), ... pl.max("a").alias("max_a"), ... ] ... ) >>> assert out["sum_a"].to_list() == [3, 10, 15, 24, 11, 1] >>> assert out["max_a"].to_list() == [3, 7, 7, 9, 9, 1] >>> assert out["min_a"].to_list() == [3, 3, 3, 3, 2, 1] >>> out shape: (6, 4) ┌─────────────────────┬───────┬───────┬───────┐ │ dt ┆ sum_a ┆ min_a ┆ max_a │ │ --- ┆ --- ┆ --- ┆ --- │ │ datetime[μs] ┆ i64 ┆ i64 ┆ i64 │ ╞═════════════════════╪═══════╪═══════╪═══════╡ │ 2020-01-01 13:45:48 ┆ 3 ┆ 3 ┆ 3 │ │ 2020-01-01 16:42:13 ┆ 10 ┆ 3 ┆ 7 │ │ 2020-01-01 16:45:09 ┆ 15 ┆ 3 ┆ 7 │ │ 2020-01-02 18:12:48 ┆ 24 ┆ 3 ┆ 9 │ │ 2020-01-03 19:45:32 ┆ 11 ┆ 2 ┆ 9 │ │ 2020-01-08 23:16:43 ┆ 1 ┆ 1 ┆ 1 │ └─────────────────────┴───────┴───────┴───────┘