sort_index(self, axis=0, level=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 Series sorted by label if :None:None:`inplace` argument is False
, otherwise updates the original series and returns None.
Axis to direct sorting. This can only be 0 for Series.
If not None, sort on values in specified index level(s).
Sort ascending vs. descending. When the index is a MultiIndex the sort direction can be controlled for each level individually.
If True, perform operation in-place.
Choice of sorting algorithm. See also 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.
If 'first' puts NaNs at the beginning, 'last' puts NaNs at the end. Not implemented for MultiIndex.
If True and sorting by level and index is multilevel, sort by other levels too (in order) after sorting by specified level.
If True, the resulting axis will be labeled 0, 1, …, n - 1.
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.
The original Series sorted by the labels or None if inplace=True
.
Sort Series by index labels.
DataFrame.sort_index
Sort DataFrame by the index.
DataFrame.sort_values
Sort DataFrame by the value.
Series.sort_values
Sort Series by the value.
>>> 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
This example is valid syntax, but we were not able to check execution>>> s.sort_index(ascending=False) 4 d 3 a 2 b 1 c dtype: object
Sort Inplace
This example is valid syntax, but we were not able to check execution>>> 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 :None:None:`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
This example is valid syntax, but we were not able to check execution>>> 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
This example is valid syntax, but we were not able to check execution>>> 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
This example is valid syntax, but we were not able to check execution>>> s = pd.Series([1, 2, 3, 4], index=['A', 'b', 'C', 'd'])See :
... s.sort_index(key=lambda x : x.str.lower()) A 1 b 2 C 3 d 4 dtype: int64
The following pages refer to to this document either explicitly or contain code examples using this.
pandas.core.frame.DataFrame.sort_index
pandas.core.series.Series.sort_values
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