Sort by the values.

        Sort a Series in ascending or descending order by some
        criterion.

        Parameters
        ----------
        axis : {0 or 'index'}, default 0
            Axis to direct sorting. The value 'index' is accepted for
            compatibility with DataFrame.sort_values.
        ascending : bool, default True
            If True, sort values in ascending order, otherwise descending.
        inplace : bool, default False
            If True, perform operation in-place.
        kind : {'quicksort', 'mergesort' or 'heapsort'}, default 'quicksort'
            Choice of sorting algorithm. See also :func:`numpy.sort` for more
            information. 'mergesort' is the only stable  algorithm.
        na_position : {'first' or 'last'}, default 'last'
            Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at
            the end.

        Returns
        -------
        Series
            Series ordered by values.

        See Also
        --------
        Series.sort_index : Sort by the Series indices.
        DataFrame.sort_values : Sort DataFrame by the values along either axis.
        DataFrame.sort_index : Sort DataFrame by indices.

        Examples
        --------
        >>> s = pd.Series([np.nan, 1, 3, 10, 5])
        >>> s
        0     NaN
        1     1.0
        2     3.0
        3     10.0
        4     5.0
        dtype: float64

        Sort values ascending order (default behaviour)

        >>> s.sort_values(ascending=True)
        1     1.0
        2     3.0
        4     5.0
        3    10.0
        0     NaN
        dtype: float64

        Sort values descending order

        >>> s.sort_values(ascending=False)
        3    10.0
        4     5.0
        2     3.0
        1     1.0
        0     NaN
        dtype: float64

        Sort values inplace

        >>> s.sort_values(ascending=False, inplace=True)
        >>> s
        3    10.0
        4     5.0
        2     3.0
        1     1.0
        0     NaN
        dtype: float64

        Sort values putting NAs first

        >>> s.sort_values(na_position='first')
        0     NaN
        1     1.0
        2     3.0
        4     5.0
        3    10.0
        dtype: float64

        Sort a series of strings

        >>> s = pd.Series(['z', 'b', 'd', 'a', 'c'])
        >>> s
        0    z
        1    b
        2    d
        3    a
        4    c
        dtype: object

        >>> s.sort_values()
        3    a
        1    b
        4    c
        2    d
        0    z
        dtype: object

def sort_values(self, axis=0, ascending=True, inplace=False,
                    kind='quicksort', na_position='last'):
        """
        Sort by the values.

        Sort a Series in ascending or descending order by some
        criterion.

        Parameters
        ----------
        axis : {0 or 'index'}, default 0
            Axis to direct sorting. The value 'index' is accepted for
            compatibility with DataFrame.sort_values.
        ascending : bool, default True
            If True, sort values in ascending order, otherwise descending.
        inplace : bool, default False
            If True, perform operation in-place.
        kind : {'quicksort', 'mergesort' or 'heapsort'}, default 'quicksort'
            Choice of sorting algorithm. See also :func:`numpy.sort` for more
            information. 'mergesort' is the only stable  algorithm.
        na_position : {'first' or 'last'}, default 'last'
            Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at
            the end.

        Returns
        -------
        Series
            Series ordered by values.

        See Also
        --------
        Series.sort_index : Sort by the Series indices.
        DataFrame.sort_values : Sort DataFrame by the values along either axis.
        DataFrame.sort_index : Sort DataFrame by indices.

        Examples
        --------
        >>> s = pd.Series([np.nan, 1, 3, 10, 5])
        >>> s
        0     NaN
        1     1.0
        2     3.0
        3     10.0
        4     5.0
        dtype: float64

        Sort values ascending order (default behaviour)

        >>> s.sort_values(ascending=True)
        1     1.0
        2     3.0
        4     5.0
        3    10.0
        0     NaN
        dtype: float64

        Sort values descending order

        >>> s.sort_values(ascending=False)
        3    10.0
        4     5.0
        2     3.0
        1     1.0
        0     NaN
        dtype: float64

        Sort values inplace

        >>> s.sort_values(ascending=False, inplace=True)
        >>> s
        3    10.0
        4     5.0
        2     3.0
        1     1.0
        0     NaN
        dtype: float64

        Sort values putting NAs first

        >>> s.sort_values(na_position='first')
        0     NaN
        1     1.0
        2     3.0
        4     5.0
        3    10.0
        dtype: float64

        Sort a series of strings

        >>> s = pd.Series(['z', 'b', 'd', 'a', 'c'])
        >>> s
        0    z
        1    b
        2    d
        3    a
        4    c
        dtype: object

        >>> s.sort_values()
        3    a
        1    b
        4    c
        2    d
        0    z
        dtype: object
        """
        inplace = validate_bool_kwarg(inplace, 'inplace')
        # Validate the axis parameter
        self._get_axis_number(axis)

        # GH 5856/5853
        if inplace and self._is_cached:
            raise ValueError("This Series is a view of some other array, to "
                             "sort in-place you must create a copy")

        def _try_kind_sort(arr):
            # easier to ask forgiveness than permission
            try:
                # if kind==mergesort, it can fail for object dtype
                return arr.argsort(kind=kind)
            except TypeError:
                # stable sort not available for object dtype
                # uses the argsort default quicksort
                return arr.argsort(kind='quicksort')

        arr = self._values
        sortedIdx = np.empty(len(self), dtype=np.int32)

        bad = isna(arr)

        good = ~bad
        idx = ibase.default_index(len(self))

        argsorted = _try_kind_sort(arr[good])

        if is_list_like(ascending):
            if len(ascending) != 1:
                raise ValueError('Length of ascending (%d) must be 1 '
                                 'for Series' % (len(ascending)))
            ascending = ascending[0]

        if not is_bool(ascending):
            raise ValueError('ascending must be boolean')

        if not ascending:
            argsorted = argsorted[::-1]

        if na_position == 'last':
            n = good.sum()
            sortedIdx[:n] = idx[good][argsorted]
            sortedIdx[n:] = idx[bad]
        elif na_position == 'first':
            n = bad.sum()
            sortedIdx[n:] = idx[good][argsorted]
            sortedIdx[:n] = idx[bad]
        else:
            raise ValueError('invalid na_position: {!r}'.format(na_position))

        result = self._constructor(arr[sortedIdx], index=self.index[sortedIdx])

        if inplace:
            self._update_inplace(result)
        else:
            return result.__finalize__(self)