pandas.core.groupby.SeriesGroupBy.bfill#

SeriesGroupBy.bfill(limit=None)[source] #

Backward fill the values.

Parameters:

limitint, optional

Limit of how many values to fill.

Returns:

Series or DataFrame

Object with missing values filled.

See also

Series.bfill

Backward fill the missing values in the dataset.

DataFrame.bfill

Backward fill the missing values in the dataset.

Series.fillna

Fill NaN values of a Series.

DataFrame.fillna

Fill NaN values of a DataFrame.

Examples

With Series:

>>> index = ['Falcon', 'Falcon', 'Parrot', 'Parrot', 'Parrot']
>>> s = pd.Series([None, 1, None, None, 3], index=index)
>>> s
Falcon NaN
Falcon 1.0
Parrot NaN
Parrot NaN
Parrot 3.0
dtype: float64
>>> s.groupby(level=0).bfill()
Falcon 1.0
Falcon 1.0
Parrot 3.0
Parrot 3.0
Parrot 3.0
dtype: float64
>>> s.groupby(level=0).bfill(limit=1)
Falcon 1.0
Falcon 1.0
Parrot NaN
Parrot 3.0
Parrot 3.0
dtype: float64

With DataFrame:

>>> df = pd.DataFrame({'A': [1, None, None, None, 4],
...  'B': [None, None, 5, None, 7]}, index=index)
>>> df
 A B
Falcon 1.0 NaN
Falcon NaN NaN
Parrot NaN 5.0
Parrot NaN NaN
Parrot 4.0 7.0
>>> df.groupby(level=0).bfill()
 A B
Falcon 1.0 NaN
Falcon NaN NaN
Parrot 4.0 5.0
Parrot 4.0 7.0
Parrot 4.0 7.0
>>> df.groupby(level=0).bfill(limit=1)
 A B
Falcon 1.0 NaN
Falcon NaN NaN
Parrot NaN 5.0
Parrot 4.0 7.0
Parrot 4.0 7.0