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# pandas.api.typing.SeriesGroupBy.value_counts

SeriesGroupBy.value_counts(normalize=False, sort=True, ascending=False, bins=None, dropna=True)

  Return a Series or DataFrame containing counts of unique rows.

  The resulting object will be in descending order by default so that the first element in each group is the most frequently-occurring value. NA values are excluded from the result by default.

  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