o fill the gap.
        interpolate : Fill NaN values using interpolation.
        reindex : Conform object to new index.
        asfreq : Convert TimeSeries to specified frequency.

        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

        Replace all NaN elements with 0s.

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

        Replace all NaN elements in column 'A', 'B', 'C', and 'D', with 0, 1,
        2, and 3 respectively.

        >>> values = {{"A": 0, "B": 1, "C": 2, "D": 3}}
        >>> df.fillna(value=values)
             A    B    C    D
        0  0.0  2.0  2.0  0.0
        1  3.0  4.0  2.0  1.0
        2  0.0  1.0  2.0  3.0
        3  0.0  3.0  2.0  4.0

        Only replace the first NaN element.

        >>> df.fillna(value=values, limit=1)
             A    B    C    D
        0  0.0  2.0  2.0  0.0
        1  3.0  4.0  NaN  1.0
        2  NaN  1.0  NaN  3.0
        3  NaN  3.0  NaN  4.0

        When filling using a DataFrame, replacement happens along
        the same column names and same indices

        >>> df2 = pd.DataFrame(np.zeros((4, 4)), columns=list("ABCE"))
        >>> df.fillna(df2)
             A    B    C    D
        0  0.0  2.0  0.0  0.0
        1  3.0  4.0  0.0  1.0
        2  0.0  0.0  0.0  NaN
        3  0.0  3.0  0.0  4.0

        Note that column D is not affected since it is not present in df2.
        rŸ