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

ci/code_checks.sh

@@ -175,8 +175,6 @@ 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\

pandas/core/groupby/generic.py

@@ -1176,16 +1176,124 @@ def nsmallest(
         result = self._python_apply_general(f, data, not_indexed_same=True)
         return result
 
-    @doc(Series.idxmin.__doc__)
     def idxmin(
         self, axis: Axis | lib.NoDefault = lib.no_default, 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
+        ----------
+        axis : {0 or 'index'}
+            Unused. Parameter needed for compatibility with DataFrame.
+        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.
+
+        Notes
+        -----
+        This method is the Series version of ``ndarray.argmin``. This method
+        returns the label of the minimum, while ``ndarray.argmin`` returns
+        the position. To get the position, use ``series.values.argmin()``.
+
+        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", axis=axis, skipna=skipna)
 
-    @doc(Series.idxmax.__doc__)
     def idxmax(
         self, axis: Axis | lib.NoDefault = lib.no_default, 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
+        ----------
+        axis : {0 or 'index'}
+            Unused. Parameter needed for compatibility with DataFrame.
+        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.
+
+        Notes
+        -----
+        This method is the Series version of ``ndarray.argmax``. This method
+        returns the label of the maximum, while ``ndarray.argmax`` returns
+        the position. To get the position, use ``series.values.argmax()``.
+
+        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", axis=axis, skipna=skipna)
 
     @doc(Series.corr.__doc__)