dask_expr._groupby.SeriesGroupBy.apply

dask_expr._groupby.SeriesGroupBy.apply

SeriesGroupBy.apply(func, *args, meta=_NoDefault.no_default, shuffle_method=None, **kwargs)

Parallel version of pandas GroupBy.apply

This mimics the pandas version except for the following:

  1. If the grouper does not align with the index then this causes a full shuffle. The order of rows within each group may not be preserved.

  2. Dask’s GroupBy.apply is not appropriate for aggregations. For custom aggregations, use dask.dataframe.groupby.Aggregation.

Warning

Pandas’ groupby-apply can be used to to apply arbitrary functions, including aggregations that result in one row per group. Dask’s groupby-apply will apply func once on each group, doing a shuffle if needed, such that each group is contained in one partition. When func is a reduction, e.g., you’ll end up with one row per group. To apply a custom aggregation with Dask, use dask.dataframe.groupby.Aggregation.

Parameters
func: function

Function to apply

args, kwargsScalar, Delayed or object

Arguments and keywords to pass to the function.

metapd.DataFrame, pd.Series, dict, iterable, tuple, optional

An empty pd.DataFrame or pd.Series that matches the dtypes and column names of the output. This metadata is necessary for many algorithms in dask dataframe to work. For ease of use, some alternative inputs are also available. Instead of a DataFrame, a dict of {name: dtype} or iterable of (name, dtype) can be provided (note that the order of the names should match the order of the columns). Instead of a series, a tuple of (name, dtype) can be used. If not provided, dask will try to infer the metadata. This may lead to unexpected results, so providing meta is recommended. For more information, see dask.dataframe.utils.make_meta.

Returns
appliedSeries or DataFrame depending on columns keyword

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