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Classe « DataFrame »

Méthode pandas.DataFrame.sort_index

Signature de la méthode sort_index

def sort_index(self, *, axis: 'Axis' = 0, level: 'IndexLabel | None' = None, ascending: 'bool | Sequence[bool]' = True, inplace: 'bool' = False, kind: 'SortKind' = 'quicksort', na_position: 'NaPosition' = 'last', sort_remaining: 'bool' = True, ignore_index: 'bool' = False, key: 'IndexKeyFunc | None' = None) -> 'DataFrame | None' 

Description

help(DataFrame.sort_index)

Sort object by labels (along an axis).

Returns a new DataFrame sorted by label if `inplace` argument is
``False``, otherwise updates the original DataFrame and returns None.

Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
    The axis along which to sort.  The value 0 identifies the rows,
    and 1 identifies the columns.
level : int or level name or list of ints or list of level names
    If not None, sort on values in specified index level(s).
ascending : bool or list-like of bools, default True
    Sort ascending vs. descending. When the index is a MultiIndex the
    sort direction can be controlled for each level individually.
inplace : bool, default False
    Whether to modify the DataFrame rather than creating a new one.
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. For
    DataFrames, this option is only applied when sorting on a single
    column or label.
na_position : {'first', 'last'}, default 'last'
    Puts NaNs at the beginning if `first`; `last` puts NaNs at the end.
    Not implemented for MultiIndex.
sort_remaining : bool, default True
    If True and sorting by level and index is multilevel, sort by other
    levels too (in order) after sorting by specified level.
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 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. For MultiIndex
    inputs, the key is applied *per level*.

Returns
-------
DataFrame or None
    The original DataFrame sorted by the labels or None if ``inplace=True``.

See Also
--------
Series.sort_index : Sort Series by the index.
DataFrame.sort_values : Sort DataFrame by the value.
Series.sort_values : Sort Series by the value.

Examples
--------
>>> df = pd.DataFrame([1, 2, 3, 4, 5], index=[100, 29, 234, 1, 150],
...                   columns=['A'])
>>> df.sort_index()
     A
1    4
29   2
100  1
150  5
234  3

By default, it sorts in ascending order, to sort in descending order,
use ``ascending=False``

>>> df.sort_index(ascending=False)
     A
234  3
150  5
100  1
29   2
1    4

A key function can be specified which is applied to the index before
sorting. For a ``MultiIndex`` this is applied to each level separately.

>>> df = pd.DataFrame({"a": [1, 2, 3, 4]}, index=['A', 'b', 'C', 'd'])
>>> df.sort_index(key=lambda x: x.str.lower())
   a
A  1
b  2
C  3
d  4

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