Method transform

dask/dataframe/dask_expr/_groupby.py:2045–2080

Parallel version of pandas GroupBy.transform 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.

(self, func, meta=no_default, shuffle_method=None, *args, **kwargs)

    @insert_meta_param_description(pad=12)
    def transform(self, func, meta=no_default, shuffle_method=None, *args, **kwargs):
        """Parallel version of pandas GroupBy.transform

        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.transform is not appropriate for aggregations. For custom
            aggregations, use :class:`dask.dataframe.groupby.Aggregation`.

        .. warning::

           Pandas' groupby-transform can be used to apply arbitrary functions,
           including aggregations that result in one row per group. Dask's
           groupby-transform 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 :class:`dask.dataframe.groupby.Aggregation`.

        Parameters
        ----------
        func: function
            Function to apply
        args, kwargs : Scalar, Delayed or object
            Arguments and keywords to pass to the function.
        $META

        Returns
        -------
        applied : Series or DataFrame depending on columns keyword
        """
        self._warn_if_no_meta(meta, method="transform")
        return self._transform_like_op(
            GroupByTransform, func, meta, shuffle_method, *args, **kwargs
        )

    @insert_meta_param_description(pad=12)
    def shift(self, periods=1, meta=no_default, shuffle_method=None, *args, **kwargs):