pandas.Series.sort_values¶
-
Series.
sort_values
(axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')[source]¶ Sort by the values.
Sort a Series in ascending or descending order by some criterion.
Parameters: axis : {0 or ‘index’}, default 0
Axis to direct sorting. The value ‘index’ is accepted for compatibility with DataFrame.sort_values.
ascending : bool, default True
If True, sort values in ascending order, otherwise descending.
inplace : bool, default False
If True, perform operation in-place.
kind : {‘quicksort’, ‘mergesort’ or ‘heapsort’}, default ‘quicksort’
Choice of sorting algorithm. See also
numpy.sort()
for more information. ‘mergesort’ is the only stable algorithm.na_position : {‘first’ or ‘last’}, default ‘last’
Argument ‘first’ puts NaNs at the beginning, ‘last’ puts NaNs at the end.
Returns: Series
Series ordered by values.
See also
Series.sort_index
- Sort by the Series indices.
DataFrame.sort_values
- Sort DataFrame by the values along either axis.
DataFrame.sort_index
- Sort DataFrame by indices.
Examples
>>> s = pd.Series([np.nan, 1, 3, 10, 5]) >>> s 0 NaN 1 1.0 2 3.0 3 10.0 4 5.0 dtype: float64
Sort values ascending order (default behaviour)
>>> s.sort_values(ascending=True) 1 1.0 2 3.0 4 5.0 3 10.0 0 NaN dtype: float64
Sort values descending order
>>> s.sort_values(ascending=False) 3 10.0 4 5.0 2 3.0 1 1.0 0 NaN dtype: float64
Sort values inplace
>>> s.sort_values(ascending=False, inplace=True) >>> s 3 10.0 4 5.0 2 3.0 1 1.0 0 NaN dtype: float64
Sort values putting NAs first
>>> s.sort_values(na_position='first') 0 NaN 1 1.0 2 3.0 4 5.0 3 10.0 dtype: float64
Sort a series of strings
>>> s = pd.Series(['z', 'b', 'd', 'a', 'c']) >>> s 0 z 1 b 2 d 3 a 4 c dtype: object
>>> s.sort_values() 3 a 1 b 4 c 2 d 0 z dtype: object