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Classe « Index »

Méthode pandas.Index.sort_values

Signature de la méthode sort_values

def sort_values(self, *, return_indexer: 'bool' = False, ascending: 'bool' = True, na_position: 'NaPosition' = 'last', key: 'Callable | None' = None) -> 'Self | tuple[Self, np.ndarray]' 

Description

help(Index.sort_values)

Return a sorted copy of the index.

Return a sorted copy of the index, and optionally return the indices
that sorted the index itself.

Parameters
----------
return_indexer : bool, default False
    Should the indices that would sort the index be returned.
ascending : bool, default True
    Should the index values be sorted in an ascending order.
na_position : {'first' or 'last'}, default 'last'
    Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at
    the end.
key : callable, optional
    If not None, apply the key function to the index 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 an
    ``Index`` and return an ``Index`` of the same shape.

Returns
-------
sorted_index : pandas.Index
    Sorted copy of the index.
indexer : numpy.ndarray, optional
    The indices that the index itself was sorted by.

See Also
--------
Series.sort_values : Sort values of a Series.
DataFrame.sort_values : Sort values in a DataFrame.

Examples
--------
>>> idx = pd.Index([10, 100, 1, 1000])
>>> idx
Index([10, 100, 1, 1000], dtype='int64')

Sort values in ascending order (default behavior).

>>> idx.sort_values()
Index([1, 10, 100, 1000], dtype='int64')

Sort values in descending order, and also get the indices `idx` was
sorted by.

>>> idx.sort_values(ascending=False, return_indexer=True)
(Index([1000, 100, 10, 1], dtype='int64'), array([3, 1, 0, 2]))

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