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# pandas.api.typing.SeriesGroupBy.ffill

SeriesGroupBy.ffill(limit=None)

  Forward fill the values.

  Propagates the last valid observation forward within each group to fill missing values.

  Parameters:
    limit int, optional

      Limit of how many values to fill.

  Returns:
    Series or DataFrame

      Object with missing values filled.

  See also

  `Series.ffill`

    Forward fill missing values in a Series.

  `DataFrame.ffill`

    Forward fill missing values in a DataFrame.

  `Series.fillna`

    Fill NaN values of a Series.

  `DataFrame.fillna`

    Fill NaN values of a DataFrame.

  Examples

  For SeriesGroupBy:

      >>> key = [0, 0, 1, 1]
      >>> ser = pd.Series([np.nan, 2, 3, np.nan], index=key)
      >>> ser
      0    NaN
      0    2.0
      1    3.0
      1    NaN
      dtype: float64
      >>> ser.groupby(level=0).ffill()
      0    NaN
      0    2.0
      1    3.0
      1    3.0
      dtype: float64

  For DataFrameGroupBy:

      >>> df = pd.DataFrame(
      ...     {
      ...         "key": [0, 0, 1, 1, 1],
      ...         "A": [np.nan, 2, np.nan, 3, np.nan],
      ...         "B": [2, 3, np.nan, np.nan, np.nan],
      ...         "C": [np.nan, np.nan, 2, np.nan, np.nan],
      ...     }
      ... )
      >>> df
         key    A    B   C
      0    0  NaN  2.0 NaN
      1    0  2.0  3.0 NaN
      2    1  NaN  NaN 2.0
      3    1  3.0  NaN NaN
      4    1  NaN  NaN NaN

  Propagate non-null values forward or backward within each group along columns.

      >>> df.groupby("key").ffill()
           A    B   C
      0  NaN  2.0 NaN
      1  2.0  3.0 NaN
      2  NaN  NaN 2.0
      3  3.0  NaN 2.0
      4  3.0  NaN 2.0

  Propagate non-null values forward or backward within each group along rows.

      >>> df.T.groupby(np.array([0, 0, 1, 1])).ffill().T
         key    A    B    C
      0  0.0  0.0  2.0  2.0
      1  0.0  2.0  3.0  3.0
      2  1.0  1.0  NaN  2.0
      3  1.0  3.0  NaN  NaN
      4  1.0  1.0  NaN  NaN

  Only replace the first NaN element within a group along columns.

      >>> df.groupby("key").ffill(limit=1)
           A    B    C
      0  NaN  2.0  NaN
      1  2.0  3.0  NaN
      2  NaN  NaN  2.0
      3  3.0  NaN  2.0
      4  3.0  NaN  NaN