pandas.core.groupby.DataFrameGroupBy.ffill — pandas 2.2.3 documentation (original) (raw)

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