sort_values(self, axis=0, ascending: 'bool | int | Sequence[bool | int]' = True, inplace: 'bool' = False, kind: 'str' = 'quicksort', na_position: 'str' = 'last', ignore_index: 'bool' = False, key: 'ValueKeyFunc' = None)

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.

versionadded

key : callable, optional

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

versionadded

Returns

Series or None

Series ordered by values or None if inplace=True .

Sort by the values.

See Also

DataFrame.sort_index

Sort DataFrame by indices.

DataFrame.sort_values

Sort DataFrame by the values along either axis.

Series.sort_index

Sort by the Series indices.

Examples

This example is valid syntax, but we were not able to check execution

>>> 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)

This example is valid syntax, but we were not able to check execution

>>> 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

This example is valid syntax, but we were not able to check execution

>>> s.sort_values(ascending=False)
3    10.0
4     5.0
2     3.0
1     1.0
0     NaN
dtype: float64

Sort values inplace

This example is valid syntax, but we were not able to check execution

>>> 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

This example is valid syntax, but we were not able to check execution

>>> 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

This example is valid syntax, but we were not able to check execution

>>> s = pd.Series(['z', 'b', 'd', 'a', 'c'])
... s
0    z
1    b
2    d
3    a
4    c
dtype: object

This example is valid syntax, but we were not able to check execution

>>> s.sort_values()
3    a
1    b
4    c
2    d
0    z
dtype: object

Sort using a key function. Your :None:None:`key` function will be given the Series of values and should return an array-like.

This example is valid syntax, but we were not able to check execution

>>> s = pd.Series(['a', 'B', 'c', 'D', 'e'])
... s.sort_values()
1    B
3    D
0    a
2    c
4    e
dtype: object

This example is valid syntax, but we were not able to check execution

>>> 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

This example is valid syntax, but we were not able to check execution

>>> 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

This example is valid syntax, but we were not able to check execution

>>> s.sort_values(key=lambda x: (np.tan(x.cumsum())))
0   -4
3    2
4    4
1   -2
2    0
dtype: int64

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

pandas.core.indexes.base.Index.sort_values pandas.core.series.Series.sort_index pandas.core.frame.DataFrame.sort_values pandas.core.arrays.categorical.Categorical.sort_values pandas.core.series.Series.nlargest pandas.core.generic.NDFrame.sort_values pandas.core.groupby.generic.SeriesGroupBy.nsmallest pandas.core.series.Series.searchsorted pandas.core.groupby.generic.SeriesGroupBy.nlargest pandas.core.series.Series.nsmallest pandas.core.frame.DataFrame.sort_index

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