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:

limitint, 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