dask.dataframe.api.SeriesGroupBy.bfill

SeriesGroupBy.bfill(limit=None, shuffle_method=None)

Backward fill the values.

This docstring was copied from pandas.core.groupby.groupby.GroupBy.bfill.

Some inconsistencies with the Dask version may exist.

Fill missing values within each group by propagating the next valid observation backward.

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