Method apply

pandas/core/frame.py:12202–12494

Apply a function along an axis of the DataFrame. Objects passed to the function are Series objects whose index is either the DataFrame's index (``axis=0``) or the DataFrame's columns (``axis=1``). By default (``result_type=None``), the final return type is i

(
        self,
        func: AggFuncType,
        axis: Axis = 0,
        raw: bool = False,
        result_type: Literal["expand", "reduce", "broadcast"] | None = None,
        args=(),
        by_row: Literal[False, "compat"] = "compat",
        engine: Callable | None | Literal["python", "numba"] = None,
        engine_kwargs: dict[str, bool] | None = None,
        **kwargs,
    )

        return result

    def apply(
        self,
        func: AggFuncType,
        axis: Axis = 0,
        raw: bool = False,
        result_type: Literal["expand", "reduce", "broadcast"] | None = None,
        args=(),
        by_row: Literal[False, "compat"] = "compat",
        engine: Callable | None | Literal["python", "numba"] = None,
        engine_kwargs: dict[str, bool] | None = None,
        **kwargs,
    ):
        """
        Apply a function along an axis of the DataFrame.

        Objects passed to the function are Series objects whose index is
        either the DataFrame's index (``axis=0``) or the DataFrame's columns
        (``axis=1``). By default (``result_type=None``), the final return type
        is inferred from the return type of the applied function. Otherwise,
        it depends on the `result_type` argument. The return type of the applied
        function is inferred based on the first computed result obtained after
        applying the function to a Series object.

        Parameters
        ----------
        func : function
            Function to apply to each column or row.
        axis : {0 or 'index', 1 or 'columns'}, default 0
            Axis along which the function is applied:

            * 0 or 'index': apply function to each column.
            * 1 or 'columns': apply function to each row.

        raw : bool, default False
            Determines if row or column is passed as a Series or ndarray object:

            * ``False`` : passes each row or column as a Series to the
              function.
            * ``True`` : the passed function will receive ndarray objects
              instead.
              If you are just applying a NumPy reduction function this will
              achieve much better performance.

         .. note::

                When ``raw=True``, the result dtype is inferred from the **first**
                returned value.

        result_type : {'expand', 'reduce', 'broadcast', None}, default None
            These only act when ``axis=1`` (columns):

            * 'expand' : list-like results will be turned into columns.
            * 'reduce' : returns a Series if possible rather than expanding
              list-like results. This is the opposite of 'expand'.
            * 'broadcast' : results will be broadcast to the original shape
              of the DataFrame, the original index and columns will be
              retained.