pyspark.pandas.Series.sort_index#

Series.sort_index(axis=0, level=None, ascending=True, inplace=False, kind=None, na_position='last', ignore_index=False)[source]#

Sort object by labels (along an axis)

Parameters

axisindex, columns to direct sorting. Currently, only axis = 0 is supported.
levelint or level name or list of ints or list of level names: if not None, sort on values in specified index level(s)
ascendingboolean, default True: Sort ascending vs. descending
inplacebool, default False: if True, perform operation in-place
kindstr, default None: pandas-on-Spark does not allow specifying the sorting algorithm now, default None
na_position{‘first’, ‘last’}, default ‘last’: first puts NaNs at the beginning, last puts NaNs at the end. Not implemented for MultiIndex.
ignore_indexbool, default False: If True, the resulting axis will be labeled 0, 1, …, n - 1.

New in version 3.4.0.

Returns

Examples

>>> s = ps.Series([2, 1, np.nan], index=['b', 'a', np.nan])

>>> s.sort_index()  
a       1.0
b       2.0
None    NaN
dtype: float64

>>> s.sort_index(ignore_index=True)
0    1.0
1    2.0
2    NaN
dtype: float64

>>> s.sort_index(ascending=False)  
b       2.0
a       1.0
None    NaN
dtype: float64

>>> s.sort_index(na_position='first')  
None    NaN
a       1.0
b       2.0
dtype: float64

>>> s.sort_index(inplace=True)
>>> s  
a       1.0
b       2.0
None    NaN
dtype: float64

Multi-index series.

>>> s = ps.Series(range(4), index=[['b', 'b', 'a', 'a'], [1, 0, 1, 0]], name='0')

>>> s.sort_index()
a  0    3
   1    2
b  0    1
   1    0
Name: 0, dtype: int64

>>> s.sort_index(level=1)  
a  0    3
b  0    1
a  1    2
b  1    0
Name: 0, dtype: int64

>>> s.sort_index(level=[1, 0])
a  0    3
b  0    1
a  1    2
b  1    0
Name: 0, dtype: int64