dask_expr._groupby.SeriesGroupBy.ffill
dask_expr._groupby.SeriesGroupBy.ffill¶
- SeriesGroupBy.ffill(limit=None, shuffle_method=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