polars.DataFrame.groupby_dynamic#

DataFrame.groupby_dynamic(
index_column: IntoExpr,
*,
every: str | timedelta,
period: str | timedelta | None = None,
offset: str | timedelta | None = None,
truncate: bool = True,
include_boundaries: bool = False,
closed: ClosedInterval = 'left',
by: IntoExpr | Iterable[IntoExpr] | None = None,
start_by: StartBy = 'window',
check_sorted: bool | None = None,
) → DynamicGroupBy[source]#

Group based on a time value (or index value of type Int32, Int64).

Deprecated since version 0.19.0: This method has been renamed to DataFrame.group_by_dynamic().

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 by is specified, then it must be sorted in ascending order within each group).

In case of a dynamic 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.

every

interval of the window

period

length of the window, if None it will equal ‘every’

offset

offset of the window, only takes effect if start_by is 'window'. Defaults to negative every.

truncate

truncate the time value to the window lower bound

include_boundaries

Add 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

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

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

by

Also group by this column/these columns

start_by{‘window’, ‘datapoint’, ‘monday’, ‘tuesday’, ‘wednesday’, ‘thursday’, ‘friday’, ‘saturday’, ‘sunday’}

The strategy to determine the start of the first window by.

  • ‘window’: Start by taking the earliest timestamp, truncating it with every, and then adding offset. Note that weekly windows start on Monday.

  • ‘datapoint’: Start from the first encountered data point.

  • a day of the week (only takes effect if every contains 'w'):

    • ‘monday’: Start the window on the Monday before the first data point.

    • ‘tuesday’: Start the window on the Tuesday before the first data point.

    • …

    • ‘sunday’: Start the window on the Sunday before the first data point.

    The resulting window is then shifted back until the earliest datapoint is in or in front of it.

check_sorted

Check whether index_column is sorted (or, if by is given, check whether it’s sorted within each group). When the by argument 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 groups is sorted, you can set this to False. Doing so incorrectly will lead to incorrect output

Returns:
DynamicGroupBy

Object you can call .agg on to aggregate by groups, the result of which will be sorted by index_column (but note that if by columns are passed, it will only be sorted within each by group).