pandas.DataFrame.sort_values — pandas 0.24.0rc1 documentation (original) (raw)
DataFrame. sort_values(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’ first puts NaNs at the beginning, last puts NaNs at the end |
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| Returns: | sorted_obj : DataFrame |
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