polars.DataFrame.groupby_rolling#
- DataFrame.groupby_rolling(
- index_column: IntoExpr,
- *,
- period: str | timedelta,
- offset: str | timedelta | None = None,
- closed: ClosedInterval = 'right',
- by: IntoExpr | Iterable[IntoExpr] | None = None,
- check_sorted: bool = True,
Create rolling groups based on a time, Int32, or Int64 column.
Deprecated since version 0.19.0: This method has been renamed to
DataFrame.rolling().- 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 (or, if
byis specified, then it must be sorted in ascending order within each group).In case of a rolling group by 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 - 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).
- by
Also group by this column/these columns
- check_sorted
When the
byargument is given, polars can not check sortedness by the metadata and has to do a full scan on the index column to verify data is sorted. This is expensive. If you are sure the data within the by groups is sorted, you can set this toFalse. Doing so incorrectly will lead to incorrect output