aximum number of consecutive
            NaN values to forward/backward fill. In other words, if there is
            a gap with more than this number of consecutive NaNs, it will only
            be partially filled. If method is not specified, this is the
            maximum number of entries along the entire axis where NaNs will be
            filled. Must be greater than 0 if not None.
        downcast : dict, default is None
            A dict of item->dtype of what to downcast if possible,
            or the string 'infer' which will try to downcast to an appropriate
            equal type (e.g. float64 to int64 if possible).

        Returns
        -------
        {klass} or None
            Object with missing values filled or None if ``inplace=True``.

        Examples
        --------
        >>> df = pd.DataFrame([[np.nan, 2, np.nan, 0],
        ...                    [3, 4, np.nan, 1],
        ...                    [np.nan, np.nan, np.nan, np.nan],
        ...                    [np.nan, 3, np.nan, 4]],
        ...                   columns=list("ABCD"))
        >>> df
             A    B   C    D
        0  NaN  2.0 NaN  0.0
        1  3.0  4.0 NaN  1.0
        2  NaN  NaN NaN  NaN
        3  NaN  3.0 NaN  4.0

        >>> df.ffill()
             A    B   C    D
        0  NaN  2.0 NaN  0.0
        1  3.0  4.0 NaN  1.0
        2  3.0  4.0 NaN  1.0
        3  3.0  3.0 NaN  4.0

        >>> ser = pd.Series([1, np.nan, 2, 3])
        >>> ser.ffill()
        0   1.0
        1   1.0
        2   2.0
        3   3.0
        dtype: float64
        r\