pandas.api.typing.SeriesGroupBy.value_counts — pandas 3.0.0rc0+33.g1fd184de2a documentation (original) (raw)

SeriesGroupBy.value_counts(normalize=False, sort=True, ascending=False, bins=None, dropna=True)[source]#

Return a Series or DataFrame containing counts of unique rows.

Parameters:

normalizebool, default False

Return proportions rather than frequencies.

sortbool, default True

Sort by frequencies.

ascendingbool, default False

Sort in ascending order.

binsint 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.

dropnabool, 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