sort_index(self, axis: 'Axis' = 0, level: 'Level | None' = None, ascending: 'bool | int | Sequence[bool | int]' = True, inplace: 'bool' = False, kind: 'str' = 'quicksort', na_position: 'str' = 'last', sort_remaining: 'bool' = True, ignore_index: 'bool' = False, key: 'IndexKeyFunc' = None)

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

Parameters

axis : {0 or 'index', 1 or 'columns'}, default 0

The axis along which to sort. The value 0 identifies the rows, and 1 identifies the columns.

level : int or level name or list of ints or list of level names

If not None, sort on values in specified index level(s).

ascending : bool 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.

inplace : bool, default False

If True, perform operation in-place.

kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort'

Choice of sorting algorithm. See also numpy.sort for more information. :None:None:`mergesort` and :None:None:`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'

Puts NaNs at the beginning if :None:None:`first`; :None:None:`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.

versionadded

key : callable, optional

If not None, apply the key function to the index values before sorting. This is similar to the :None:None:`key` argument in the builtin sorted function, with the notable difference that this :None:None:`key` function should be vectorized. It should expect an Index and return an Index of the same shape. For MultiIndex inputs, the key is applied per level.

versionadded

Returns

DataFrame or None

The original DataFrame sorted by the labels or None if inplace=True .

Sort object by labels (along an axis).

See Also

DataFrame.sort_values

Sort DataFrame by the value.

Series.sort_index

Sort Series by the index.

Series.sort_values

Sort Series by the value.

Examples

This example is valid syntax, but we were not able to check execution

>>> df = pd.DataFrame([1, 2, 3, 4, 5], index=[100, 29, 234, 1, 150],
...                   columns=['A'])
... df.sort_index()
     A
1    4
29   2
100  1
150  5
234  3

By default, it sorts in ascending order, to sort in descending order, use ascending=False

This example is valid syntax, but we were not able to check execution

>>> df.sort_index(ascending=False)
     A
234  3
150  5
100  1
29   2
1    4

A key function can be specified which is applied to the index before sorting. For a MultiIndex this is applied to each level separately.

This example is valid syntax, but we were not able to check execution

>>> df = pd.DataFrame({"a": [1, 2, 3, 4]}, index=['A', 'b', 'C', 'd'])
... df.sort_index(key=lambda x: x.str.lower())
   a
A  1
b  2
C  3
d  4

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

pandas.core.series.Series.sort_values pandas.core.generic.NDFrame.sort_values pandas.core.series.Series.sort_index pandas.core.frame.DataFrame.sort_values

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