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# pandas.Series.ffill

Series.ffill(*, axis=None, inplace=False, limit=None, limit_area=None)

  Fill NA/NaN values by propagating the last valid observation to next valid.

  This method fills missing values using forward fill, where the last valid observation is propagated forward to fill the gaps.

  Parameters:
    axis{0 or ‘index’} for Series, {0 or ‘index’, 1 or ‘columns’} for DataFrame

      Axis along which to fill missing values. For Series this parameter is unused and defaults to 0.

    inplace bool, default False

      If True, fill in-place. Note: this will modify any other views on this object (e.g., a no-copy slice for a column in a DataFrame).

    limit int, default None

      If method is specified, this is the maximum 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.

    limit_area{None, ‘inside’, ‘outside’}, default None

      If limit is specified, consecutive NaNs will be filled with this restriction.

      - `None`: No fill restriction.

      - ‘inside’: Only fill NaNs surrounded by valid values (interpolate).

      - ‘outside’: Only fill NaNs outside valid values (extrapolate).

      Added in version 2.2.0.

  Returns:
    Series/DataFrame

      Object with missing values filled.

  See also

  `DataFrame.bfill`

    Fill NA/NaN values by using the next valid observation to fill the gap.

  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