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.
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 aSeries
and return an array-like.
- 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 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.