Pandas Series: sort_index() function

Last update on September 15 2022 12:55:24 (UTC/GMT +8 hours)

Sorts Pandas series by labels along the given axis

The sort_index() function is used to 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.

Syntax:

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

Parameters:

Name	Description	Type/Default Value	Required / Optional
axis	Axis to direct sorting. This can only be 0 for Series.	int Default Value: 0	Required
level	If not None, sort on values in specified index level(s).	int	Optional
ascending	Sort ascending vs. descending.	bool Default Value: True	Required
inplace	If True, perform operation in-place.	bool Default Value: False	Required
kind	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.	{‘quicksort’, ‘mergesort’, ‘heapsort’} Default Value: 'quicksort’	Required
na_position	If ‘first’ puts NaNs at the beginning, ‘last’ puts NaNs at the end. Not implemented for MultiIndex.	{‘first’, ‘last’} Default Value: ‘last’	Required
sort_remaining	If True and sorting by level and index is multilevel, sort by other levels too (in order) after sorting by specified level.	bool Default Value: True	Required

Returns: Series- The original Series sorted by the labels.

Example:

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series(['p', 'q', 'r', 's'], index=[3, 2, 4, 5])
s.sort_index()

Output:

2    q
3    p
4    r
5    s
dtype: object

Example - Sort Descending:

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series(['p', 'q', 'r', 's'], index=[3, 2, 4, 5])
s.sort_index(ascending=False)

Output:

5    s
4    r
3    p
2    q
dtype: object

Example - Sort Inplace:

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series(['p', 'q', 'r', 's'], index=[3, 2, 4, 5])
s.sort_index(inplace=True)
s

Output:

2    q
3    p
4    r
5    s
dtype: object

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

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series(['p', 'q', 'r', 's'], index=[3, 2, 4, np.nan])
s.sort_index(na_position='first')

Output:

NaN    s
2.0    q
3.0    p
4.0    r
dtype: object

Example - Specify index level to sort:

Python-Pandas Code:

import numpy as np
import pandas as pd
arrays = [np.array(['xx', 'xx', 'ff', 'ff',
                    'bb', 'bb', 'br', 'br']),
          np.array(['two', 'one', 'two', 'one',
                    'two', 'one', 'two', 'one'])]
s = pd.Series([2, 3, 4, 5, 6, 7, 8, 9], index=arrays)
s.sort_index(level=1)

Output:

bb  one    7
br  one    9
ff  one    5
xx  one    3
bb  two    6
br  two    8
ff  two    4
xx  two    2
dtype: int64

Example - Does not sort by remaining levels when sorting by levels:

Python-Pandas Code:

import numpy as np
import pandas as pd
arrays = [np.array(['xx', 'xx', 'ff', 'ff',
                    'bb', 'bb', 'br', 'br']),
          np.array(['two', 'one', 'two', 'one',
                    'two', 'one', 'two', 'one'])]
s = pd.Series([2, 3, 4, 5, 6, 7, 8, 9], index=arrays)
s.sort_index(level=1, sort_remaining=False)

Output:

xx  one    3
ff  one    5
bb  one    7
br  one    9
xx  two    2
ff  two    4
bb  two    6
br  two    8
dtype: int64

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