pandas.Series.sort_index¶

Series.sort_index(self, axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True, ignore_index: bool = False)[source]¶

Sort Series by index labels.

Returns a new Series sorted by label if inplace argument is False, otherwise updates the original series and returns None.

Parameters

axisint, default 0: Axis to direct sorting. This can only be 0 for Series.
levelint, optional: If not None, sort on values in specified index level(s).
ascendingbool, default true: Sort ascending vs. descending.
inplacebool, default False: If True, perform operation in-place.
kind{‘quicksort’, ‘mergesort’, ‘heapsort’}, default ‘quicksort’: Choice of sorting algorithm. See also numpy.sort() for more information. ‘mergesort’ is the only stable algorithm. 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_remainingbool, 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_indexbool, default False: If True, the resulting axis will be labeled 0, 1, …, n - 1.

New in version 1.0.0.

Returns

Series: The original Series sorted by the labels.

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

Sort Inplace

>>> s.sort_index(inplace=True)
>>> s
1    c
2    b
3    a
4    d
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

pandas.Series.slice_shift pandas.Series.sort_values