pandas.core.groupby.DataFrameGroupBy.fillna#

DataFrameGroupBy.fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None)[source]#

Fill NA/NaN values using the specified method within groups.

Parameters
valuescalar, dict, Series, or DataFrame

Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame). Values not in the dict/Series/DataFrame will not be filled. This value cannot be a list. Users wanting to use the value argument and not method should prefer DataFrame.fillna() as this will produce the same result and be more performant.

method{{‘bfill’, ‘ffill’, None}}, default None

Method to use for filling holes. 'ffill' will propagate the last valid observation forward within a group. 'bfill' will use next valid observation to fill the gap.

axis{0 or ‘index’, 1 or ‘columns’}

Axis along which to fill missing values. When the DataFrameGroupBy axis argument is 0, using axis=1 here will produce the same results as DataFrame.fillna(). When the DataFrameGroupBy axis argument is 1, using axis=0 or axis=1 here will produce the same results.

inplacebool, default False

Broken. Do not set to True.

limitint, default None

If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill within a group. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.

downcastdict, default is None

A dict of item->dtype of what to downcast if possible, or the string ‘infer’ which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible).

Returns
DataFrame

Object with missing values filled.

See also

ffill

Forward fill values within a group.

bfill

Backward fill values within a group.

Examples

>>> 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").fillna(method="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

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

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

>>> df.groupby([0, 0, 1, 1], axis=1).fillna(method="ffill")
   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

>>> df.groupby([0, 0, 1, 1], axis=1).fillna(method="bfill")
   key    A    B    C
0  0.0  NaN  2.0  NaN
1  0.0  2.0  3.0  NaN
2  1.0  NaN  2.0  2.0
3  1.0  3.0  NaN  NaN
4  1.0  NaN  NaN  NaN

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

>>> df.groupby("key").fillna(method="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