pandas.Series.sort_index

Series.sort_index(axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True, ignore_index=False, key=None)

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 or list-like of bools, default True

Sort ascending vs. descending. When the index is a MultiIndex the sort direction can be controlled for each level individually.

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.

keycallable, optional

If not None, apply the key function to the index values before sorting. This is similar to the key argument in the builtin 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.

New in version 1.1.0.

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

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

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

pandas.Series.slice_shift pandas.Series.sort_values