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DOC: fix PR02 errors in docstrings - pandas.core.groupby.SeriesGroupBy.idxmax, pandas.core.groupby.SeriesGroupBy.idxmin #57145

Merged
merged 8 commits into from
Feb 1, 2024
2 changes: 0 additions & 2 deletions ci/code_checks.sh
Original file line number Diff line number Diff line change
Expand Up @@ -173,8 +173,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
pandas.core.groupby.DataFrameGroupBy.transform\
pandas.core.groupby.DataFrameGroupBy.nth\
pandas.core.groupby.DataFrameGroupBy.rolling\
pandas.core.groupby.SeriesGroupBy.idxmax\
pandas.core.groupby.SeriesGroupBy.idxmin\
pandas.core.groupby.SeriesGroupBy.nth\
pandas.core.groupby.SeriesGroupBy.rolling\
pandas.core.groupby.DataFrameGroupBy.hist\
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96 changes: 94 additions & 2 deletions pandas/core/groupby/generic.py
Original file line number Diff line number Diff line change
Expand Up @@ -1042,12 +1042,104 @@ def nsmallest(
result = self._python_apply_general(f, data, not_indexed_same=True)
return result

@doc(Series.idxmin.__doc__)
def idxmin(self, skipna: bool = True) -> Series:
"""
Return the row label of the minimum value.

If multiple values equal the minimum, the first row label with that
value is returned.

Parameters
----------
skipna : bool, default True
Exclude NA/null values. If the entire Series is NA, the result
will be NA.

Returns
-------
Index
Label of the minimum value.

Raises
------
ValueError
If the Series is empty.

See Also
--------
numpy.argmin : Return indices of the minimum values
along the given axis.
DataFrame.idxmin : Return index of first occurrence of minimum
over requested axis.
Series.idxmax : Return index *label* of the first occurrence
of maximum of values.

Examples
--------
>>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
... ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
>>> ser
2023-01-01 1
2023-01-15 2
2023-02-01 3
2023-02-15 4
dtype: int64

>>> ser.groupby(['a', 'a', 'b', 'b']).idxmin()
a 2023-01-01
b 2023-02-01
dtype: datetime64[ns]
"""
return self._idxmax_idxmin("idxmin", skipna=skipna)

@doc(Series.idxmax.__doc__)
def idxmax(self, skipna: bool = True) -> Series:
"""
Return the row label of the maximum value.

If multiple values equal the maximum, the first row label with that
value is returned.

Parameters
----------
skipna : bool, default True
Exclude NA/null values. If the entire Series is NA, the result
will be NA.

Returns
-------
Index
Label of the maximum value.

Raises
------
ValueError
If the Series is empty.

See Also
--------
numpy.argmax : Return indices of the maximum values
along the given axis.
DataFrame.idxmax : Return index of first occurrence of maximum
over requested axis.
Series.idxmin : Return index *label* of the first occurrence
of minimum of values.

Examples
--------
>>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
... ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
>>> ser
2023-01-01 1
2023-01-15 2
2023-02-01 3
2023-02-15 4
dtype: int64

>>> ser.groupby(['a', 'a', 'b', 'b']).idxmax()
a 2023-01-15
b 2023-02-15
dtype: datetime64[ns]
"""
return self._idxmax_idxmin("idxmax", skipna=skipna)

@doc(Series.corr.__doc__)
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