Groups based on a time value (or index value of type Int32, Int64). Time windows are calculated and rows are assigned to windows. Different from a normal groupby is that a row can be member of multiple groups. The time/index window could be seen as a rolling window, with a window size determined by dates/times/values instead of slots in the DataFrame.
A window is defined by:
The every, period and offset arguments are created with
the following string language:
Or combine them: "3d12h4m25s" # 3 days, 12 hours, 4 minutes, and 25 seconds
In case of a groupbyDynamic on an integer column, the windows are defined by:
Optionalby?: ColumnsOrExprAlso group by this column/these columns
Optionalclosed?: "none" | "left" | "right" | "both"Defines if the window interval is closed or not. Any of {"left", "right", "both" "none"}
interval of the window
OptionalincludeBoundaries?: booleanadd the lower and upper bound of the window to the "_lower_bound" and "_upper_bound" columns. This will impact performance because it's harder to parallelize
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 dynamic 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.
Optionallabel?: stringDefine which label to use for the window: Any if {'left', 'right', 'datapoint'}
Optionaloffset?: stringoffset of the window if None and period is None it will be equal to negative every
Optionalperiod?: stringlength of the window, if None it is equal to 'every'
OptionalstartBy?: StartByThe strategy to determine the start of the first window by. Any of {'window', 'datapoint', 'monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday'}
Create rolling groups based on a time column (or index value of type Int32, Int64).
Different from a rolling groupby the windows are now determined by the individual values and are not of constant intervals. For constant intervals use groupByDynamic
The period and offset arguments are created with
the following string language:
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:
Optionalby?: ColumnsOrExprAlso group by this column/these columns
Optionalclosed?: "none" | "left" | "right" | "both"Defines if the window interval is closed or not. Any of {"left", "right", "both" "none"}
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.
Optionaloffset?: stringoffset of the window. Default is -period
length of the window
>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]}).withColumn(
... pl.col("dt").str.strptime(pl.Datetime)
... )
>out = df.groupbyRolling({indexColumn:"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"].toArray() === [3, 10, 15, 24, 11, 1])
>assert(out["max_a"].toArray() === [3, 7, 7, 9, 9, 1])
>assert(out["min_a"].toArray() === [3, 3, 3, 3, 2, 1])
>out
shape: (6, 4)
┌─────────────────────┬───────┬───────┬───────┐
│ dt ┆ a_sum ┆ a_max ┆ a_min │
│ --- ┆ --- ┆ --- ┆ --- │
│ datetime[ms] ┆ i64 ┆ i64 ┆ i64 │
╞═════════════════════╪═══════╪═══════╪═══════╡
│ 2020-01-01 13:45:48 ┆ 3 ┆ 3 ┆ 3 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ 2020-01-01 16:42:13 ┆ 10 ┆ 7 ┆ 3 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ 2020-01-01 16:45:09 ┆ 15 ┆ 7 ┆ 3 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ 2020-01-02 18:12:48 ┆ 24 ┆ 9 ┆ 3 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ 2020-01-03 19:45:32 ┆ 11 ┆ 9 ┆ 2 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ 2020-01-08 23:16:43 ┆ 1 ┆ 1 ┆ 1 │
└─────────────────────┴───────┴───────┴───────┘
GroupBy operations that can be applied to a DataFrame or LazyFrame.