Return a Series containing counts of unique values.

        The resulting object will be in descending order so that the
        first element is the most frequently-occurring element.
        Excludes NA values by default.

        Parameters
        ----------
        normalize : boolean, default False
            If True then the object returned will contain the relative
            frequencies of the unique values.
        sort : boolean, default True
            Sort by frequencies.
        ascending : boolean, default False
            Sort in ascending order.
        bins : integer, optional
            Rather than count values, group them into half-open bins,
            a convenience for ``pd.cut``, only works with numeric data.
        dropna : boolean, default True
            Don't include counts of NaN.

        Returns
        -------
        Series

        See Also
        --------
        Series.count: Number of non-NA elements in a Series.
        DataFrame.count: Number of non-NA elements in a DataFrame.

        Examples
        --------
        >>> index = pd.Index([3, 1, 2, 3, 4, np.nan])
        >>> index.value_counts()
        3.0    2
        4.0    1
        2.0    1
        1.0    1
        dtype: int64

        With `normalize` set to `True`, returns the relative frequency by
        dividing all values by the sum of values.

        >>> s = pd.Series([3, 1, 2, 3, 4, np.nan])
        >>> s.value_counts(normalize=True)
        3.0    0.4
        4.0    0.2
        2.0    0.2
        1.0    0.2
        dtype: float64

        **bins**

        Bins can be useful for going from a continuous variable to a
        categorical variable; instead of counting unique
        apparitions of values, divide the index in the specified
        number of half-open bins.

        >>> s.value_counts(bins=3)
        (2.0, 3.0]      2
        (0.996, 2.0]    2
        (3.0, 4.0]      1
        dtype: int64

        **dropna**

        With `dropna` set to `False` we can also see NaN index values.

        >>> s.value_counts(dropna=False)
        3.0    2
        NaN    1
        4.0    1
        2.0    1
        1.0    1
        dtype: int64

def value_counts(self, normalize=False, sort=True, ascending=False,
                     bins=None, dropna=True):
        """
        Return a Series containing counts of unique values.

        The resulting object will be in descending order so that the
        first element is the most frequently-occurring element.
        Excludes NA values by default.

        Parameters
        ----------
        normalize : boolean, default False
            If True then the object returned will contain the relative
            frequencies of the unique values.
        sort : boolean, default True
            Sort by frequencies.
        ascending : boolean, default False
            Sort in ascending order.
        bins : integer, optional
            Rather than count values, group them into half-open bins,
            a convenience for ``pd.cut``, only works with numeric data.
        dropna : boolean, default True
            Don't include counts of NaN.

        Returns
        -------
        Series

        See Also
        --------
        Series.count: Number of non-NA elements in a Series.
        DataFrame.count: Number of non-NA elements in a DataFrame.

        Examples
        --------
        >>> index = pd.Index([3, 1, 2, 3, 4, np.nan])
        >>> index.value_counts()
        3.0    2
        4.0    1
        2.0    1
        1.0    1
        dtype: int64

        With `normalize` set to `True`, returns the relative frequency by
        dividing all values by the sum of values.

        >>> s = pd.Series([3, 1, 2, 3, 4, np.nan])
        >>> s.value_counts(normalize=True)
        3.0    0.4
        4.0    0.2
        2.0    0.2
        1.0    0.2
        dtype: float64

        **bins**

        Bins can be useful for going from a continuous variable to a
        categorical variable; instead of counting unique
        apparitions of values, divide the index in the specified
        number of half-open bins.

        >>> s.value_counts(bins=3)
        (2.0, 3.0]      2
        (0.996, 2.0]    2
        (3.0, 4.0]      1
        dtype: int64

        **dropna**

        With `dropna` set to `False` we can also see NaN index values.

        >>> s.value_counts(dropna=False)
        3.0    2
        NaN    1
        4.0    1
        2.0    1
        1.0    1
        dtype: int64
        """
        from pandas.core.algorithms import value_counts
        result = value_counts(self, sort=sort, ascending=ascending,
                              normalize=normalize, bins=bins, dropna=dropna)
        return result