def value_counts(self, normalize: bool=False, sort: bool=True, ascending: bool=False, bins=None, dropna: bool=True):
    "\n        Return a Series containing counts of unique values.\n\n        The resulting object will be in descending order so that the\n        first element is the most frequently-occurring element.\n        Excludes NA values by default.\n\n        Parameters\n        ----------\n        normalize : bool, default False\n            If True then the object returned will contain the relative\n            frequencies of the unique values.\n        sort : bool, default True\n            Sort by frequencies.\n        ascending : bool, default False\n            Sort in ascending order.\n        bins : int, optional\n            Rather than count values, group them into half-open bins,\n            a convenience for ``pd.cut``, only works with numeric data.\n        dropna : bool, default True\n            Don't include counts of NaN.\n\n        Returns\n        -------\n        Series\n\n        See Also\n        --------\n        Series.count: Number of non-NA elements in a Series.\n        DataFrame.count: Number of non-NA elements in a DataFrame.\n        DataFrame.value_counts: Equivalent method on DataFrames.\n\n        Examples\n        --------\n        >>> index = pd.Index([3, 1, 2, 3, 4, np.nan])\n        >>> index.value_counts()\n        3.0    2\n        1.0    1\n        2.0    1\n        4.0    1\n        dtype: int64\n\n        With `normalize` set to `True`, returns the relative frequency by\n        dividing all values by the sum of values.\n\n        >>> s = pd.Series([3, 1, 2, 3, 4, np.nan])\n        >>> s.value_counts(normalize=True)\n        3.0    0.4\n        1.0    0.2\n        2.0    0.2\n        4.0    0.2\n        dtype: float64\n\n        **bins**\n\n        Bins can be useful for going from a continuous variable to a\n        categorical variable; instead of counting unique\n        apparitions of values, divide the index in the specified\n        number of half-open bins.\n\n        >>> s.value_counts(bins=3)\n        (0.996, 2.0]    2\n        (2.0, 3.0]      2\n        (3.0, 4.0]      1\n        dtype: int64\n\n        **dropna**\n\n        With `dropna` set to `False` we can also see NaN index values.\n\n        >>> s.value_counts(dropna=False)\n        3.0    2\n        1.0    1\n        2.0    1\n        4.0    1\n        NaN    1\n        dtype: int64\n        "
    return value_counts(self, sort=sort, ascending=ascending, normalize=normalize, bins=bins, dropna=dropna)
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 : bool, default False
    If True then the object returned will contain the relative
    frequencies of the unique values.
sort : bool, default True
    Sort by frequencies.
ascending : bool, default False
    Sort in ascending order.
bins : int, 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 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.
DataFrame.value_counts: Equivalent method on DataFrames.

Examples
--------
>>> index = pd.Index([3, 1, 2, 3, 4, np.nan])
>>> index.value_counts()
3.0    2
1.0    1
2.0    1
4.0    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
1.0    0.2
2.0    0.2
4.0    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)
(0.996, 2.0]    2
(2.0, 3.0]      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
1.0    1
2.0    1
4.0    1
NaN    1
dtype: int64
def value_counts(self, normalize: bool=False, sort: bool=True, ascending: bool=False, bins=None, dropna: bool=True):
    "\n        Return a Series containing counts of unique values.\n\n        The resulting object will be in descending order so that the\n        first element is the most frequently-occurring element.\n        Excludes NA values by default.\n\n        Parameters\n        ----------\n        normalize : bool, default False\n            If True then the object returned will contain the relative\n            frequencies of the unique values.\n        sort : bool, default True\n            Sort by frequencies.\n        ascending : bool, default False\n            Sort in ascending order.\n        bins : int, optional\n            Rather than count values, group them into half-open bins,\n            a convenience for ``pd.cut``, only works with numeric data.\n        dropna : bool, default True\n            Don't include counts of NaN.\n\n        Returns\n        -------\n        Series\n\n        See Also\n        --------\n        Series.count: Number of non-NA elements in a Series.\n        DataFrame.count: Number of non-NA elements in a DataFrame.\n        DataFrame.value_counts: Equivalent method on DataFrames.\n\n        Examples\n        --------\n        >>> index = pd.Index([3, 1, 2, 3, 4, np.nan])\n        >>> index.value_counts()\n        3.0    2\n        1.0    1\n        2.0    1\n        4.0    1\n        dtype: int64\n\n        With `normalize` set to `True`, returns the relative frequency by\n        dividing all values by the sum of values.\n\n        >>> s = pd.Series([3, 1, 2, 3, 4, np.nan])\n        >>> s.value_counts(normalize=True)\n        3.0    0.4\n        1.0    0.2\n        2.0    0.2\n        4.0    0.2\n        dtype: float64\n\n        **bins**\n\n        Bins can be useful for going from a continuous variable to a\n        categorical variable; instead of counting unique\n        apparitions of values, divide the index in the specified\n        number of half-open bins.\n\n        >>> s.value_counts(bins=3)\n        (0.996, 2.0]    2\n        (2.0, 3.0]      2\n        (3.0, 4.0]      1\n        dtype: int64\n\n        **dropna**\n\n        With `dropna` set to `False` we can also see NaN index values.\n\n        >>> s.value_counts(dropna=False)\n        3.0    2\n        1.0    1\n        2.0    1\n        4.0    1\n        NaN    1\n        dtype: int64\n        "
    return value_counts(self, sort=sort, ascending=ascending, normalize=normalize, bins=bins, dropna=dropna)