xorbits.pandas.DataFrame.sort_index#
- DataFrame.sort_index(axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True, ignore_index: bool = False, parallel_kind='PSRS', psrs_kinds=None)#
Sort object by labels (along an axis).
Returns a new DataFrame sorted by label if 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) – Whether to modify the DataFrame rather than creating a new one.
kind ({'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort') – 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.na_position ({'first', 'last'}, default 'last') – Puts NaNs at the beginning if first; 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.
key (callable, optional (Not supported yet)) – 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 anIndex
and return anIndex
of the same shape. For MultiIndex inputs, the key is applied per level.
- Returns
The original DataFrame sorted by the labels or None if
inplace=True
.- Return type
DataFrame or None
See also
Series.sort_index
Sort Series by the index.
DataFrame.sort_values
Sort DataFrame by the value.
Series.sort_values
Sort Series by the value.
Examples
>>> 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
>>> 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.>>> 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
a : Input DataFrame or Series. parallel_kind: {‘PSRS’}, optional.
Parallel sorting algorithm, for the details, refer to: http://csweb.cs.wfu.edu/bigiron/LittleFE-PSRS/build/html/PSRSalgorithm.html
psrs_kinds: Sorting algorithms during PSRS algorithm.
This docstring was copied from pandas.core.frame.DataFrame.