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DOC: Add docs for DataFrameGroupBy.fillna and SeriesGroupBy.fillna #48771

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DOC: Add docs for DataFrameGroupBy.fillna and SeriesGroupBy.fillna
rhshadrach Sep 25, 2022
779f41b
fixup
rhshadrach Sep 25, 2022
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4 changes: 2 additions & 2 deletions pandas/core/generic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6730,9 +6730,9 @@ def fillna(
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.
method : {{'backfill', 'bfill', 'pad', 'ffill', None}}, default None
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pad is deprecated, so I removed from the fillna docstrings. backfill is deprecated in groupby but not DataFrame or Series. I'll look into this for a followup.

method : {{'backfill', 'bfill', 'ffill', None}}, default None
Method to use for filling holes in reindexed Series
pad / ffill: propagate last valid observation forward to next valid
ffill: propagate last valid observation forward to next valid
backfill / bfill: use next valid observation to fill gap.
axis : {axes_single_arg}
Axis along which to fill missing values. For `Series`
Expand Down
200 changes: 198 additions & 2 deletions pandas/core/groupby/generic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -750,7 +750,6 @@ def build_codes(lev_codes: np.ndarray) -> np.ndarray:
out = ensure_int64(out)
return self.obj._constructor(out, index=mi, name=self.obj.name)

@doc(Series.fillna.__doc__)
def fillna(
self,
value: object | ArrayLike | None = None,
Expand All @@ -760,6 +759,92 @@ def fillna(
limit: int | None = None,
downcast: dict | None = None,
) -> Series | None:
"""
Fill NA/NaN values using the specified method within groups.

Parameters
----------
value : scalar, 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 :meth:`.Series.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'}
Unused, only for compatibility with :meth:`DataFrameGroupBy.fillna`.
inplace : bool, default False
Broken. Do not set to True.
Comment on lines +781 to +782
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For both DataFrameGroupBy and SeriesGroupBy, using inplace=True doesn't modify the source and returns an empty DataFrame or a Series filled with None respectively. I plan to deprecate this in a followup.

limit : int, 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.
downcast : dict, 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
-------
Series
Object with missing values filled within groups.

See Also
--------
ffill : Forward fill values within a group.
bfill : Backward fill values within a group.

Examples
--------
>>> ser = pd.Series([np.nan, np.nan, 2, 3, np.nan, np.nan])
>>> ser
0 NaN
1 NaN
2 2.0
3 3.0
4 NaN
5 NaN
dtype: float64

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

>>> ser.groupby([0, 0, 0, 1, 1, 1]).fillna(method="ffill")
0 NaN
1 NaN
2 2.0
3 3.0
4 3.0
5 3.0
dtype: float64

>>> ser.groupby([0, 0, 0, 1, 1, 1]).fillna(method="bfill")
0 2.0
1 2.0
2 2.0
3 3.0
4 NaN
5 NaN
dtype: float64

Only replace the first NaN element within a group.

>>> ser.groupby([0, 0, 0, 1, 1, 1]).fillna(method="ffill", limit=1)
0 NaN
1 NaN
2 2.0
3 3.0
4 3.0
5 NaN
dtype: float64
"""
result = self._op_via_apply(
"fillna",
value=value,
Expand Down Expand Up @@ -2071,7 +2156,6 @@ def value_counts(
result = result_frame
return result.__finalize__(self.obj, method="value_counts")

@doc(DataFrame.fillna.__doc__)
def fillna(
self,
value: Hashable | Mapping | Series | DataFrame = None,
Expand All @@ -2081,6 +2165,118 @@ def fillna(
limit=None,
downcast=None,
) -> DataFrame | None:
"""
Fill NA/NaN values using the specified method within groups.

Parameters
----------
value : scalar, 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 :meth:`.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 :class:`DataFrameGroupBy`
``axis`` argument is ``0``, using ``axis=1`` here will produce
the same results as :meth:`.DataFrame.fillna`. When the
:class:`DataFrameGroupBy` ``axis`` argument is ``1``, using ``axis=0``
or ``axis=1`` here will produce the same results.
inplace : bool, default False
Broken. Do not set to True.
limit : int, 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.
downcast : dict, 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
"""
result = self._op_via_apply(
"fillna",
value=value,
Expand Down