pyspark.pandas.DataFrame.sort_index#

DataFrame.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

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
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