Skip to main content
`Ctrl`+`K`

# pandas.api.typing.DataFrameGroupBy.value_counts

DataFrameGroupBy.value_counts(subset=None, normalize=False, sort=True, ascending=False, dropna=True)

  Return a Series or DataFrame containing counts of unique rows.

  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

      Stable sort by frequencies when True. When False, non-grouping columns will appear in the order they occur in within groups.

      Changed in version 3.0.0: In prior versions, `sort=False` would sort the non-grouping columns by label.

    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       US         1
                         FR         1
      male    low        FR         2
                         US         1
              medium     FR         1
      Name: count, dtype: int64

      >>> df.groupby("gender").value_counts(ascending=True)
      gender  education  country
      female  high       US         1
                         FR         1
      male    low        US         1
              medium     FR         1
              low        FR         2
      Name: count, dtype: int64

      >>> df.groupby("gender").value_counts(normalize=True)
      gender  education  country
      female  high       US         0.50
                         FR         0.50
      male    low        FR         0.50
                         US         0.25
              medium     FR         0.25
      Name: proportion, dtype: float64

      >>> df.groupby("gender", as_index=False).value_counts()
         gender education country  count
      0  female      high      US      1
      1  female      high      FR      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      US        0.50
      1  female      high      FR        0.50
      2    male       low      FR        0.50
      3    male       low      US        0.25
      4    male    medium      FR        0.25