pandas.core.groupby.DataFrameGroupBy.bfill#

DataFrameGroupBy.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