pandas.Index.reindex#

Index.reindex(target, method=None, level=None, limit=None, tolerance=None)[source]#

Create index with target’s values.

Parameters:
targetan iterable

An iterable containing the values to be used for creating the new index.

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.

levelint, optional

Level of multiindex.

limitint, optional

Maximum number of consecutive labels in target to match for inexact matches.

toleranceint 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_indexpd.Index

Resulting index.

indexernp.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]))