Index.sort_values(return_indexer: bool = False, ascending: bool = True, na_position: NaPosition = 'last', key: Callable | None = None)[source]#

Return a sorted copy of the index.

Return a sorted copy of the index, and optionally return the indices that sorted the index itself.

  • return_indexer (bool, default False) – Should the indices that would sort the index be returned.

  • ascending (bool, default True) – Should the index values be sorted in an ascending order.

  • na_position ({'first' or 'last'}, default 'last') –

    Argument ‘first’ puts NaNs at the beginning, ‘last’ puts NaNs at the end.

    New in version 1.2.0(pandas).

  • key (callable, 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.


  • sorted_index (pandas.Index) – Sorted copy of the index.

  • indexer (numpy.ndarray, optional) – The indices that the index itself was sorted by.

See also


Sort values of a Series.


Sort values in a DataFrame.


>>> idx = pd.Index([10, 100, 1, 1000])  
>>> idx  
Index([10, 100, 1, 1000], dtype='int64')

Sort values in ascending order (default behavior).

>>> idx.sort_values()  
Index([1, 10, 100, 1000], dtype='int64')

Sort values in descending order, and also get the indices idx was sorted by.

>>> idx.sort_values(ascending=False, return_indexer=True)  
(Index([1000, 100, 10, 1], dtype='int64'), array([3, 1, 0, 2]))


This method has not been implemented yet. Xorbits will try to execute it with pandas.

This docstring was copied from pandas.core.indexes.base.Index.