Doc: resolve GL08 for pandas.core.groupby.SeriesGroupBy.value_counts

ci/code_checks.sh

@@ -179,7 +179,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         pandas.Timestamp.tzinfo\
         pandas.Timestamp.value\
         pandas.Timestamp.year\
-        pandas.core.groupby.SeriesGroupBy.value_counts\
         pandas.tseries.offsets.BQuarterBegin.is_anchored\
         pandas.tseries.offsets.BQuarterBegin.is_on_offset\
         pandas.tseries.offsets.BQuarterBegin.n\

pandas/core/groupby/generic.py

@@ -801,6 +801,85 @@ def value_counts(
         bins=None,
         dropna: bool = True,
     ) -> Series | DataFrame:
+        """
+        Return a Series or DataFrame containing counts of unique rows.
+
+        .. versionadded:: 1.4.0
+
+        Parameters
+        ----------
+        normalize : bool, default False
+            Return proportions rather than frequencies.
+        sort : bool, default True
+            Sort by frequencies.
+        ascending : bool, default False
+            Sort in ascending order.
+        bins : int or list of ints, optional
+            Rather than count values, group them into half-open bins,
+            a convenience for pd.cut, only works with numeric data.
+        dropna : bool, default True
+            Don't include counts of rows that contain NA values.
+
+        Returns
+        -------
+        Series or DataFrame
+            Series if the groupby as_index is True, otherwise DataFrame.
+
+        See Also
+        --------
+        Series.value_counts: Equivalent method on Series.
+        DataFrame.value_counts: Equivalent method on DataFrame.
+        DataFrameGroupBy.value_counts: Equivalent method on DataFrameGroupBy.
+
+        Notes
+        -----
+        - If the groupby as_index is True then the returned Series will have a
+          MultiIndex with one level per input column.
+        - If the groupby as_index is False then the returned DataFrame will have an
+          additional column with the value_counts. The column is labelled 'count' or
+          'proportion', depending on the ``normalize`` parameter.
+
+        By default, rows that contain any NA values are omitted from
+        the result.
+
+        By default, the result will be in descending order so that the
+        first element of each group is the most frequently-occurring row.
+
+        Examples
+        --------
+        >>> s = pd.Series(
+        ...     [1, 1, 2, 3, 2, 3, 3, 1, 1, 3, 3, 3],
+        ...     index=["A", "A", "A", "A", "A", "A", "B", "B", "B", "B", "B", "B"],
+        ... )
+        >>> s
+        A    1
+        A    1
+        A    2
+        A    3
+        A    2
+        A    3
+        B    3
+        B    1
+        B    1
+        B    3
+        B    3
+        B    3
+        dtype: int64
+        >>> g1 = s.groupby(s.index)
+        >>> g1.value_counts(bins=2)
+        A  (0.997, 2.0]    4
+           (2.0, 3.0]      2
+        B  (2.0, 3.0]      4
+           (0.997, 2.0]    2
+        Name: count, dtype: int64
+        >>> g1.value_counts(normalize=True)
+        A  1    0.333333
+           2    0.333333
+           3    0.333333
+        B  3    0.666667
+           1    0.333333
+        Name: proportion, dtype: float64
+        """
         name = "proportion" if normalize else "count"
 
         if bins is None: