pyspark.pandas.DataFrame.sort_index¶

DataFrame.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.frame.DataFrame][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_objDataFrame

Examples

>>> df = ps.DataFrame({'A': [2, 1, np.nan]}, index=['b', 'a', np.nan])

>>> df.sort_index()
       A
a    1.0
b    2.0
NaN  NaN

>>> df.sort_index(ascending=False)
       A
b    2.0
a    1.0
NaN  NaN

>>> df.sort_index(na_position='first')
       A
NaN  NaN
a    1.0
b    2.0

>>> df.sort_index(inplace=True)
>>> df
       A
a    1.0
b    2.0
NaN  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
a 0  3  0
b 0  1  2
a 1  2  1
b 1  0  3

>>> df.sort_index(level=[1, 0])
     A  B
a 0  3  0
b 0  1  2
a 1  2  1
b 1  0  3

pyspark.pandas.DataFrame.pivot pyspark.pandas.DataFrame.sort_values