Skip to main content
`Ctrl`+`K`

# pandas.api.typing.Resampler.ffill

final Resampler.ffill(limit=None)

  Forward fill the values.

  This method fills missing values by propagating the last valid observation forward, up to the next valid observation. It is commonly used in time series analysis when resampling data to a higher frequency (upsampling) and filling gaps in the resampled output.

  Parameters:
    limit int, optional

      Limit of how many values to fill.

  Returns:
    Series

      The resampled data with missing values filled forward.

  See also

  `Series.fillna`

    Fill NA/NaN values using the specified method.

  `DataFrame.fillna`

    Fill NA/NaN values using the specified method.

  Examples

  Here we only create a `Series`.

      >>> ser = pd.Series(
      ...     [1, 2, 3, 4],
      ...     index=pd.DatetimeIndex(
      ...         ["2023-01-01", "2023-01-15", "2023-02-01", "2023-02-15"]
      ...     ),
      ... )
      >>> ser
      2023-01-01    1
      2023-01-15    2
      2023-02-01    3
      2023-02-15    4
      dtype: int64

  Example for `ffill` with downsampling (we have fewer dates after resampling):

      >>> ser.resample("MS").ffill()
      2023-01-01    1
      2023-02-01    3
      Freq: MS, dtype: int64

  Example for `ffill` with upsampling (fill the new dates with the previous value):

      >>> ser.resample("W").ffill()
      2023-01-01    1
      2023-01-08    1
      2023-01-15    2
      2023-01-22    2
      2023-01-29    2
      2023-02-05    3
      2023-02-12    3
      2023-02-19    4
      Freq: W-SUN, dtype: int64

  With upsampling and limiting (only fill the first new date with the previous value):

      >>> ser.resample("W").ffill(limit=1)
      2023-01-01    1.0
      2023-01-08    1.0
      2023-01-15    2.0
      2023-01-22    2.0
      2023-01-29    NaN
      2023-02-05    3.0
      2023-02-12    NaN
      2023-02-19    4.0
      Freq: W-SUN, dtype: float64