pandas.Series.sort_values   Series.sort_values(axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last', ignore_index=False, key=None)[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 or list of bools, default True


If True, sort values in ascending order, otherwise descending.  
inplace:bool, default False


If True, perform operation in-place.  
kind:{‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}, default ‘quicksort’


Choice of sorting algorithm. See also numpy.sort() for more information. ‘mergesort’ and ‘stable’ are the only stable algorithms.  
na_position:{‘first’ or ‘last’}, default ‘last’


Argument ‘first’ puts NaNs at the beginning, ‘last’ puts NaNs at the end.  
ignore_index:bool, default False


If True, the resulting axis will be labeled 0, 1, …, n - 1.  New in version 1.0.0.   
key:callable, optional


If not None, apply the key function to the series values before sorting. This is similar to the key argument in the builtin sorted() function, with the notable difference that this key function should be vectorized. It should expect a Series and return an array-like.  New in version 1.1.0.     Returns 
 Series or None

Series ordered by values or None if inplace=True.      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
  Sort using a key function. Your key function will be given the Series of values and should return an array-like. 
>>> s = pd.Series(['a', 'B', 'c', 'D', 'e'])
>>> s.sort_values()
1    B
3    D
0    a
2    c
4    e
dtype: object
>>> s.sort_values(key=lambda x: x.str.lower())
0    a
1    B
2    c
3    D
4    e
dtype: object
  NumPy ufuncs work well here. For example, we can sort by the sin of the value 
>>> s = pd.Series([-4, -2, 0, 2, 4])
>>> s.sort_values(key=np.sin)
1   -2
4    4
2    0
0   -4
3    2
dtype: int64
  More complicated user-defined functions can be used, as long as they expect a Series and return an array-like 
>>> s.sort_values(key=lambda x: (np.tan(x.cumsum())))
0   -4
3    2
4    4
1   -2
2    0
dtype: int64