    If True, perform operation in-place.
        kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort'
            Choice of sorting algorithm. See also :func:`numpy.sort` for more
            information. 'mergesort' and 'stable' are the only stable algorithms. For
            DataFrames, this option is only applied when sorting on a single
            column or label.
        na_position : {'first', 'last'}, default 'last'
            If 'first' puts NaNs at the beginning, 'last' puts NaNs at the end.
            Not implemented for MultiIndex.
        sort_remaining : bool, default True
            If True and sorting by level and index is multilevel, sort by other
            levels too (in order) after sorting by specified level.
        ignore_index : bool, default False
            If True, the resulting axis will be labeled 0, 1, …, n - 1.
        key : callable, optional
            If not None, apply the key function to the index values
            before sorting. This is similar to the `key` argument in the
            builtin :meth:`sorted` function, with the notable difference that
            this `key` function should be *vectorized*. It should expect an
            ``Index`` and return an ``Index`` of the same shape.

        Returns
        -------
        Series or None
            The original Series sorted by the labels or None if ``inplace=True``.

        See Also
        --------
        DataFrame.sort_index: Sort DataFrame by the index.
        DataFrame.sort_values: Sort DataFrame by the value.
        Series.sort_values : Sort Series by the value.

        Examples
        --------
        >>> s = pd.Series(['a', 'b', 'c', 'd'], index=[3, 2, 1, 4])
        >>> s.sort_index()
        1    c
        2    b
        3    a
        4    d
        dtype: object

        Sort Descending

        >>> s.sort_index(ascending=False)
        4    d
        3    a
        2    b
        1    c
        dtype: object

        By default NaNs are put at the end, but use `na_position` to place
        them at the beginning

        >>> s = pd.Series(['a', 'b', 'c', 'd'], index=[3, 2, 1, np.nan])
        >>> s.sort_index(na_position='first')
        NaN     d
         1.0    c
         2.0    b
         3.0    a
        dtype: object

        Specify index level to sort

        >>> arrays = [np.array(['qux', 'qux', 'foo', 'foo',
        ...                     'baz', 'baz', 'bar', 'bar']),
        ...           np.array(['two', 'one', 'two', 'one',
        ...                     'two', 'one', 'two', 'one'])]
        >>> s = pd.Series([1, 2, 3, 4, 5, 6, 7, 8], index=arrays)
        >>> s.sort_index(level=1)
        bar  one    8
        baz  one    6
        foo  one    4
        qux  one    2
        bar  two    7
        baz  two    5
        foo  two    3
        qux  two    1
        dtype: int64

        Does not sort by remaining levels when sorting by levels

        >>> s.sort_index(level=1, sort_remaining=False)
        qux  one    2
        foo  one    4
        baz  one    6
        bar  one    8
        qux  two    1
        foo  two    3
        baz  two    5
        bar  two    7
        dtype: int64

        Apply a key function before sorting

        >>> s = pd.Series([1, 2, 3, 4], index=['A', 'b', 'C', 'd'])
        >>> s.sort_index(key=lambda x : x.str.lower())
        A    1
        b    2
        C    3
        d    4
        dtype: int64
        r(