xorbits.pandas.Series.sort_values#

Series.sort_values(axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last', ignore_index=False, parallel_kind='PSRS', psrs_kinds=None)#

Sort by the values.

Sort a Series in ascending or descending order by some criterion.

Parameters

  • axis ({0 or 'index'}) – Unused. Parameter needed for compatibility with DataFrame.

  • 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 (Not supported yet)) –

    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 ordered by values or None if inplace=True.

Return type

Series or None

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

series : input Series.

This docstring was copied from pandas.core.series.Series.