Index.reindex(target, method: ReindexMethod | None = None, level=None, limit: int | None = None, tolerance: float | None = None) tuple[Index, npt.NDArray[np.intp] | None][source]#

Create index with target’s values.

  • 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.


  • new_index (pd.Index) – Resulting index.

  • indexer (np.ndarray[np.intp] or None) – Indices of output values in original index.

  • 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


Conform Series to new index with optional filling logic.


Conform DataFrame to new index with optional filling logic.


>>> 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]))


This method has not been implemented yet. Xorbits will try to execute it with pandas.

This docstring was copied from pandas.core.indexes.base.Index.