pyspark.pandas.MultiIndex.sort_values¶

MultiIndex.sort_values(ascending: bool = True) → pyspark.pandas.indexes.base.Index¶

Return a sorted copy of the index.

Note

This method is not supported for pandas when index has NaN value. pandas raises unexpected TypeError, but we support treating NaN as the smallest value.

Parameters

ascendingbool, default True: Should the index values be sorted in an ascending order.

Returns

sorted_indexps.Index or ps.MultiIndex: Sorted copy of the index.

See also

Series.sort_values: Sort values of a Series.
DataFrame.sort_values: Sort values in a DataFrame.

Examples

>>> idx = ps.Index([10, 100, 1, 1000])
>>> idx
Int64Index([10, 100, 1, 1000], dtype='int64')

Sort values in ascending order (default behavior).

>>> idx.sort_values()
Int64Index([1, 10, 100, 1000], dtype='int64')

Sort values in descending order.

>>> idx.sort_values(ascending=False)
Int64Index([1000, 100, 10, 1], dtype='int64')

Support for MultiIndex.

>>> psidx = ps.MultiIndex.from_tuples([('a', 'x', 1), ('c', 'y', 2), ('b', 'z', 3)])
>>> psidx  
MultiIndex([('a', 'x', 1),
            ('c', 'y', 2),
            ('b', 'z', 3)],
           )

>>> psidx.sort_values()  
MultiIndex([('a', 'x', 1),
            ('b', 'z', 3),
            ('c', 'y', 2)],
           )

>>> psidx.sort_values(ascending=False)  
MultiIndex([('c', 'y', 2),
            ('b', 'z', 3),
            ('a', 'x', 1)],
           )

pyspark.pandas.MultiIndex.spark.transform pyspark.pandas.DatetimeIndex