cudf.Index.sort_values

Index.sort_values(return_indexer=False, ascending=True, na_position='last', key=None)

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

Parameters:

return_indexerbool, default False

Should the indices that would sort the index be returned.

ascendingbool, 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.

keyNone, optional

This parameter is NON-FUNCTIONAL.

Returns:

sorted_indexIndex

Sorted copy of the index.

indexercupy.ndarray, optional

The indices that the index itself was sorted by.

See also

cudf.Series.min

Sort values of a Series.

cudf.DataFrame.sort_values

Sort values in a DataFrame.

Examples

>>> import cudf
>>> idx = cudf.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],
                                                    dtype=int32))

Sorting values in a MultiIndex:

>>> midx = cudf.MultiIndex(
...      levels=[[1, 3, 4, -10], [1, 11, 5]],
...      codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
...      names=["x", "y"],
... )
>>> midx
MultiIndex([(  1,  1),
            (  1,  5),
            (  3, 11),
            (  4, 11),
            (-10,  1)],
           names=['x', 'y'])
>>> midx.sort_values()
MultiIndex([(-10,  1),
            (  1,  1),
            (  1,  5),
            (  3, 11),
            (  4, 11)],
           names=['x', 'y'])
>>> midx.sort_values(ascending=False)
MultiIndex([(  4, 11),
            (  3, 11),
            (  1,  5),
            (  1,  1),
            (-10,  1)],
           names=['x', 'y'])