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Classe « Index »

Méthode pandas.Index.reindex

Signature de la méthode reindex

def reindex(self, target, method: 'ReindexMethod | None' = None, level=None, limit: 'int | None' = None, tolerance: 'float | None' = None) -> 'tuple[Index, npt.NDArray[np.intp] | None]' 

Description

help(Index.reindex)

Create index with target's values.

Parameters
----------
target : an iterable
method : {None, 'pad'/'ffill', 'backfill'/'bfill', 'nearest'}, optional
    * default: exact matches only.
    * pad / ffill: find the PREVIOUS index value if no exact match.
    * backfill / bfill: use NEXT index value if no exact match
    * nearest: use the NEAREST index value if no exact match. Tied
      distances are broken by preferring the larger index value.
level : int, optional
    Level of multiindex.
limit : int, optional
    Maximum number of consecutive labels in ``target`` to match for
    inexact matches.
tolerance : int or float, optional
    Maximum distance between original and new labels for inexact
    matches. The values of the index at the matching locations must
    satisfy the equation ``abs(index[indexer] - target) <= tolerance``.

    Tolerance may be a scalar value, which applies the same tolerance
    to all values, or list-like, which applies variable tolerance per
    element. List-like includes list, tuple, array, Series, and must be
    the same size as the index and its dtype must exactly match the
    index's type.

Returns
-------
new_index : pd.Index
    Resulting index.
indexer : np.ndarray[np.intp] or None
    Indices of output values in original index.

Raises
------
TypeError
    If ``method`` passed along with ``level``.
ValueError
    If non-unique multi-index
ValueError
    If non-unique index and ``method`` or ``limit`` passed.

See Also
--------
Series.reindex : Conform Series to new index with optional filling logic.
DataFrame.reindex : Conform DataFrame to new index with optional filling logic.

Examples
--------
>>> idx = pd.Index(['car', 'bike', 'train', 'tractor'])
>>> idx
Index(['car', 'bike', 'train', 'tractor'], dtype='object')
>>> idx.reindex(['car', 'bike'])
(Index(['car', 'bike'], dtype='object'), array([0, 1]))

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