DataFrame.
sort_index
Sort object by labels (along an axis)
if not None, sort on values in specified index level(s)
Sort ascending vs. descending
if True, perform operation in-place
pandas-on-Spark does not allow specifying the sorting algorithm now, default None
first puts NaNs at the beginning, last puts NaNs at the end. Not implemented for MultiIndex.
If True, the resulting axis will be labeled 0, 1, …, n - 1.
New in version 3.4.0.
Examples
>>> df = ps.DataFrame({'A': [2, 1, np.nan]}, index=['b', 'a', np.nan])
>>> df.sort_index() A a 1.0 b 2.0 None NaN
>>> df.sort_index(ascending=False) A b 2.0 a 1.0 None NaN
>>> df.sort_index(na_position='first') A None NaN a 1.0 b 2.0
>>> df.sort_index(ignore_index=True) A 0 1.0 1 2.0 2 NaN
>>> df.sort_index(inplace=True) >>> df A a 1.0 b 2.0 None NaN
>>> df = ps.DataFrame({'A': range(4), 'B': range(4)[::-1]}, ... index=[['b', 'b', 'a', 'a'], [1, 0, 1, 0]], ... columns=['A', 'B'])
>>> df.sort_index() A B a 0 3 0 1 2 1 b 0 1 2 1 0 3
>>> df.sort_index(level=1) A B b 0 1 2 a 0 3 0 b 1 0 3 a 1 2 1
>>> df.sort_index(level=[1, 0]) A B a 0 3 0 b 0 1 2 a 1 2 1 b 1 0 3
>>> df.sort_index(ignore_index=True) A B 0 3 0 1 2 1 2 1 2 3 0 3