dask.dataframe.groupby.SeriesGroupBy.ffill

dask.dataframe.groupby.SeriesGroupBy.ffill

SeriesGroupBy.ffill(limit=None)

Forward fill the values.

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

Some inconsistencies with the Dask version may exist.

Parameters
limitint, optional

Limit of how many values to fill.

Returns
Series or DataFrame

Object with missing values filled.

See also

Series.ffill

Returns Series with minimum number of char in object.

DataFrame.ffill

Object with missing values filled or None if inplace=True.

Series.fillna

Fill NaN values of a Series.

DataFrame.fillna

Fill NaN values of a DataFrame.

Examples

For SeriesGroupBy:

>>> key = [0, 0, 1, 1]  
>>> ser = pd.Series([np.nan, 2, 3, np.nan], index=key)  
>>> ser  
0    NaN
0    2.0
1    3.0
1    NaN
dtype: float64
>>> ser.groupby(level=0).ffill()  
0    NaN
0    2.0
1    3.0
1    3.0
dtype: float64

For DataFrameGroupBy:

>>> df = pd.DataFrame(  
...     {
...         "key": [0, 0, 1, 1, 1],
...         "A": [np.nan, 2, np.nan, 3, np.nan],
...         "B": [2, 3, np.nan, np.nan, np.nan],
...         "C": [np.nan, np.nan, 2, np.nan, np.nan],
...     }
... )
>>> df  
   key    A    B   C
0    0  NaN  2.0 NaN
1    0  2.0  3.0 NaN
2    1  NaN  NaN 2.0
3    1  3.0  NaN NaN
4    1  NaN  NaN NaN

Propagate non-null values forward or backward within each group along columns.

>>> df.groupby("key").ffill()  
     A    B   C
0  NaN  2.0 NaN
1  2.0  3.0 NaN
2  NaN  NaN 2.0
3  3.0  NaN 2.0
4  3.0  NaN 2.0

Propagate non-null values forward or backward within each group along rows.

>>> df.T.groupby(np.array([0, 0, 1, 1])).ffill().T  
   key    A    B    C
0  0.0  0.0  2.0  2.0
1  0.0  2.0  3.0  3.0
2  1.0  1.0  NaN  2.0
3  1.0  3.0  NaN  NaN
4  1.0  1.0  NaN  NaN

Only replace the first NaN element within a group along rows.

>>> df.groupby("key").ffill(limit=1)  
     A    B    C
0  NaN  2.0  NaN
1  2.0  3.0  NaN
2  NaN  NaN  2.0
3  3.0  NaN  2.0
4  3.0  NaN  NaN

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