xarray.CFTimeIndex.reindex

CFTimeIndex.reindex(target, method=None, level=None, limit=None, tolerance=None)

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 (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]))