pyspark.pandas.Series.sort_index¶

Series.sort_index(axis: Union[int, str] = 0, level: Union[int, List[int], None] = None, ascending: bool = True, inplace: bool = False, kind: str = None, na_position: str = 'last') → Optional[pyspark.pandas.series.Series][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 at the moment, default None
na_position{‘first’, ‘last’}, default ‘last’: first puts NaNs at the beginning, last puts NaNs at the end. Not implemented for MultiIndex.

Returns

sorted_objSeries

Examples

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

>>> df.sort_index()
a      1.0
b      2.0
NaN    NaN
dtype: float64

>>> df.sort_index(ascending=False)
b      2.0
a      1.0
NaN    NaN
dtype: float64

>>> df.sort_index(na_position='first')
NaN    NaN
a      1.0
b      2.0
dtype: float64

>>> df.sort_index(inplace=True)
>>> df
a      1.0
b      2.0
NaN    NaN
dtype: float64

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

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

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

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

pyspark.pandas.Series.argmax pyspark.pandas.Series.sort_values