pandas.DataFrame.sort_values

DataFrame.sort_values(self, by, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last') [source]

Sort by the values along either axis.

Parameters:

by : str or list of str Name or list of names to sort by. if axis is 0 or ‘index’ then by may contain index levels and/or column labels if axis is 1 or ‘columns’ then by may contain column levels and/or index labels Changed in version 0.23.0: Allow specifying index or column level names. axis : {0 or ‘index’, 1 or ‘columns’}, 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.

Returns:

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

Examples

>>> df = pd.DataFrame({
...     'col1': ['A', 'A', 'B', np.nan, 'D', 'C'],
...     'col2': [2, 1, 9, 8, 7, 4],
...     'col3': [0, 1, 9, 4, 2, 3],
... })
>>> df
    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'])
    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'])
    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)
    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')
    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