xorbits.pandas.Index.sort_values#
- 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.
- Parameters
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 anIndex
and return anIndex
of the same shape.
- Returns
sorted_index (pandas.Index) – Sorted copy of the index.
indexer (numpy.ndarray, optional) – The indices that the index itself was sorted by.
See also
Series.sort_values
Sort values of a Series.
DataFrame.sort_values
Sort values in a DataFrame.
Examples
>>> 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]))
Warning
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.