Method apply

pandas/core/groupby/groupby.py:1487–1650

Apply function ``func`` group-wise and combine the results together. The function passed to ``apply`` must take a dataframe as its first argument and return a DataFrame, Series or scalar. ``apply`` will then take care of combining the results back together into a si

(self, func, *args, include_groups: bool = False, **kwargs)

    # apply/agg/transform

    def apply(self, func, *args, include_groups: bool = False, **kwargs) -> NDFrameT:
        """
        Apply function ``func`` group-wise and combine the results together.

        The function passed to ``apply`` must take a dataframe as its first
        argument and return a DataFrame, Series or scalar. ``apply`` will
        then take care of combining the results back together into a single
        dataframe or series. ``apply`` is therefore a highly flexible
        grouping method.

        While ``apply`` is a very flexible method, its downside is that
        using it can be quite a bit slower than using more specific methods
        like ``agg`` or ``transform``. Pandas offers a wide range of method that will
        be much faster than using ``apply`` for their specific purposes, so try to
        use them before reaching for ``apply``.

        Parameters
        ----------
        func : callable
            A callable that takes a dataframe as its first argument, and
            returns a dataframe, a series or a scalar. In addition the
            callable may take positional and keyword arguments.

        *args : tuple
            Optional positional arguments to pass to ``func``.

        include_groups : bool, default False
            When True, will attempt to apply ``func`` to the groupings in
            the case that they are columns of the DataFrame. If this raises a
            TypeError, the result will be computed with the groupings excluded.
            When False, the groupings will be excluded when applying ``func``.

            .. versionadded:: 2.2.0

            .. versionchanged:: 3.0.0

            The default changed from True to False, and True is no longer allowed.

        **kwargs : dict
            Optional keyword arguments to pass to ``func``.

        Returns
        -------
        Series or DataFrame
            A pandas object with the result of applying ``func`` to each group.

        See Also
        --------
        pipe : Apply function to the full GroupBy object instead of to each
            group.
        aggregate : Apply aggregate function to the GroupBy object.
        transform : Apply function column-by-column to the GroupBy object.
        Series.apply : Apply a function to a Series.
        DataFrame.apply : Apply a function to each row or column of a DataFrame.

        Notes
        -----
        The resulting dtype will reflect the return value of the passed ``func``,