# 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. Series Series ordered by values.

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

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