mars.dataframe.DataFrame.sort_values#

DataFrame.sort_values(by, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last', ignore_index=False, parallel_kind='PSRS', psrs_kinds=None)#

Sort by the values along either axis.

Parameters

  • df (Mars DataFrame) – Input dataframe.

  • by (str) – Name or list of names to sort by.

  • axis (%(axes_single_arg)s, default 0) – Axis to be sorted.

  • ascending (bool or list of bool, default True) – Sort ascending vs. descending. Specify list for multiple sort orders. If this is a list of bools, must match the length of the by.

  • inplace (bool, default False) – If True, perform operation in-place.

  • kind ({'quicksort', 'mergesort', 'heapsort'}, default 'quicksort') – Choice of sorting algorithm. See also ndarray.np.sort for more information. mergesort is the only stable algorithm. For DataFrames, this option is only applied when sorting on a single column or label.

  • na_position ({'first', 'last'}, default 'last') – Puts NaNs at the beginning if first; last puts NaNs at the end.

  • ignore_index (bool, default False) – If True, the resulting axis will be labeled 0, 1, …, n - 1.

  • parallel_kind ({'PSRS'}, default 'PSRS') – Parallel sorting algorithm, for the details, refer to: http://csweb.cs.wfu.edu/bigiron/LittleFE-PSRS/build/html/PSRSalgorithm.html

Returns

sorted_obj – DataFrame with sorted values if inplace=False, None otherwise.

Return type

DataFrame or None

Examples

>>> import mars.dataframe as md
>>> df = md.DataFrame({
...     'col1': ['A', 'A', 'B', np.nan, 'D', 'C'],
...     'col2': [2, 1, 9, 8, 7, 4],
...     'col3': [0, 1, 9, 4, 2, 3],
... })
>>> df.execute()
    col1 col2 col3
0   A    2    0
1   A    1    1
2   B    9    9
3   NaN  8    4
4   D    7    2
5   C    4    3

Sort by col1

>>> df.sort_values(by=['col1']).execute()
    col1 col2 col3
0   A    2    0
1   A    1    1
2   B    9    9
5   C    4    3
4   D    7    2
3   NaN  8    4

Sort by multiple columns

>>> df.sort_values(by=['col1', 'col2']).execute()
    col1 col2 col3
1   A    1    1
0   A    2    0
2   B    9    9
5   C    4    3
4   D    7    2
3   NaN  8    4

Sort Descending

>>> df.sort_values(by='col1', ascending=False).execute()
    col1 col2 col3
4   D    7    2
5   C    4    3
2   B    9    9
0   A    2    0
1   A    1    1
3   NaN  8    4

Putting NAs first

>>> df.sort_values(by='col1', ascending=False, na_position='first').execute()
    col1 col2 col3
3   NaN  8    4
4   D    7    2
5   C    4    3
2   B    9    9
0   A    2    0
1   A    1    1