sort_values(self, return_indexer: 'bool' = False, ascending: 'bool' = True, na_position: 'str_t' = 'last', key: 'Callable | None' = None)
Return a sorted copy of the index, and optionally return the indices that sorted the index itself.
Should the indices that would sort the index be returned.
Should the index values be sorted in an ascending order.
Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at the end.
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.
Sorted copy of the index.
The indices that the index itself was sorted by.
Return a sorted copy of the index.
DataFrame.sort_values
Sort values in a DataFrame.
Series.sort_values
Sort values of a Series.
>>> idx = pd.Index([10, 100, 1, 1000])
... idx Int64Index([10, 100, 1, 1000], dtype='int64')
Sort values in ascending order (default behavior).
This example is valid syntax, but we were not able to check execution>>> idx.sort_values() Int64Index([1, 10, 100, 1000], dtype='int64')
Sort values in descending order, and also get the indices :None:None:`idx` was sorted by.
>>> idx.sort_values(ascending=False, return_indexer=True) (Int64Index([1000, 100, 10, 1], dtype='int64'), array([3, 1, 0, 2]))See :
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pandas.core.indexes.base.Index.argsort
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