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Classe « Series »

Méthode pandas.Series.sort_values

Signature de la méthode sort_values

def sort_values(self, *, axis: 'Axis' = 0, ascending: 'bool | Sequence[bool]' = True, inplace: 'bool' = False, kind: 'SortKind' = 'quicksort', na_position: 'NaPosition' = 'last', ignore_index: 'bool' = False, key: 'ValueKeyFunc | None' = None) -> 'Series | None' 

Description

help(Series.sort_values)

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

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 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

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