pandas.core.groupby.DataFrameGroupBy.value_counts   DataFrameGroupBy.value_counts(subset=None, normalize=False, sort=True, ascending=False, dropna=True)[source]
 
Return a Series or DataFrame containing counts of unique rows.  New in version 1.4.0.   Parameters 
 
subset:list-like, optional


Columns to use when counting unique combinations.  
normalize:bool, default False


Return proportions rather than frequencies.  
sort:bool, default True


Sort by frequencies.  
ascending:bool, default False


Sort in ascending order.  
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.  SeriesGroupBy.value_counts

Equivalent method on SeriesGroupBy.    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 
>>> df = pd.DataFrame({
...    'gender': ['male', 'male', 'female', 'male', 'female', 'male'],
...    'education': ['low', 'medium', 'high', 'low', 'high', 'low'],
...    'country': ['US', 'FR', 'US', 'FR', 'FR', 'FR']
... })
  
>>> df
    gender      education       country
0       male    low         US
1       male    medium      FR
2       female  high        US
3       male    low         FR
4       female  high        FR
5       male    low         FR
  
>>> df.groupby('gender').value_counts()
gender  education  country
female  high       FR         1
                   US         1
male    low        FR         2
                   US         1
        medium     FR         1
dtype: int64
  
>>> df.groupby('gender').value_counts(ascending=True)
gender  education  country
female  high       FR         1
                   US         1
male    low        US         1
        medium     FR         1
        low        FR         2
dtype: int64
  
>>> df.groupby('gender').value_counts(normalize=True)
gender  education  country
female  high       FR         0.50
                   US         0.50
male    low        FR         0.50
                   US         0.25
        medium     FR         0.25
dtype: float64
  
>>> df.groupby('gender', as_index=False).value_counts()
   gender education country  count
0  female      high      FR      1
1  female      high      US      1
2    male       low      FR      2
3    male       low      US      1
4    male    medium      FR      1
  
>>> df.groupby('gender', as_index=False).value_counts(normalize=True)
   gender education country  proportion
0  female      high      FR        0.50
1  female      high      US        0.50
2    male       low      FR        0.50
3    male       low      US        0.25
4    male    medium      FR        0.25