Method value_counts

pandas/core/groupby/generic.py:1090–1338

Return a Series or DataFrame containing counts of unique rows. Parameters ---------- normalize : bool, default False Return proportions rather than frequencies. sort : bool, default True Sort by frequencies. ascending : bool,

(
        self,
        normalize: bool = False,
        sort: bool = True,
        ascending: bool = False,
        bins=None,
        dropna: bool = True,
    )

        )

    def value_counts(
        self,
        normalize: bool = False,
        sort: bool = True,
        ascending: bool = False,
        bins=None,
        dropna: bool = True,
    ) -> Series | DataFrame:
        """
        Return a Series or DataFrame containing counts of unique rows.

        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