pandas.core.groupby.DataFrameGroupBy.ffill#

DataFrameGroupBy.ffill(limit=None)[source] #

Forward 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.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