pandas.core.resample.Resampler.ffill#

final Resampler.ffill(limit=None)[source]#

Forward fill the values.

Parameters:

limitint, optional: Limit of how many values to fill.

Returns:

An upsampled Series.

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