n with ``by_row=False``
            (backward compatible).
            If False, the func will be passed the whole Series at once.

            ``by_row`` has no effect when ``func`` is a string.

            .. versionadded:: 2.1.0
        **kwargs
            Additional keyword arguments passed to func.

        Returns
        -------
        Series or DataFrame
            If func returns a Series object the result will be a DataFrame.

        See Also
        --------
        Series.map: For element-wise operations.
        Series.agg: Only perform aggregating type operations.
        Series.transform: Only perform transforming type operations.

        Notes
        -----
        Functions that mutate the passed object can produce unexpected
        behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
        for more details.

        Examples
        --------
        Create a series with typical summer temperatures for each city.

        >>> s = pd.Series([20, 21, 12],
        ...               index=['London', 'New York', 'Helsinki'])
        >>> s
        London      20
        New York    21
        Helsinki    12
        dtype: int64

        Square the values by defining a function and passing it as an
        argument to ``apply()``.

        >>> def square(x):
        ...     return x ** 2
        >>> s.apply(square)
        London      400
        New York    441
        Helsinki    144
        dtype: int64

        Square the values by passing an anonymous function as an
        argument to ``apply()``.

        >>> s.apply(lambda x: x ** 2)
        London      400
        New York    441
        Helsinki    144
        dtype: int64

        Define a custom function that needs additional positional
        arguments and pass these additional arguments using the
        ``args`` keyword.

        >>> def subtract_custom_value(x, custom_value):
        ...     return x - custom_value

        >>> s.apply(subtract_custom_value, args=(5,))
        London      15
        New York    16
        Helsinki     7
        dtype: int64

        Define a custom function that takes keyword arguments
        and pass these arguments to ``apply``.

        >>> def add_custom_values(x, **kwargs):
        ...     for month in kwargs:
        ...         x += kwargs[month]
        ...     return x

        >>> s.apply(add_custom_values, june=30, july=20, august=25)
        London      95
        New York    96
        Helsinki    87
        dtype: int64

        Use a function from the Numpy library.

        >>> s.apply(np.log)
        London      2.995732
        New York    3.044522
        Helsinki    2.484907
        dtype: float64
        )