# xarray.Dataset.reindex¶

Dataset.reindex(indexers=None, method=None, tolerance=None, copy=True, fill_value=<NA>, **indexers_kwargs)

Conform this object onto a new set of indexes, filling in missing values with fill_value. The default fill value is NaN.

Parameters
indexersdict. optional

Dictionary with keys given by dimension names and values given by arrays of coordinates tick labels. Any mis-matched coordinate values will be filled in with NaN, and any mis-matched dimension names will simply be ignored. One of indexers or indexers_kwargs must be provided.

Method to use for filling index values in indexers not found in this dataset:

• None (default): don’t fill gaps

• pad / ffill: propagate last valid index value forward

• backfill / bfill: propagate next valid index value backward

• nearest: use nearest valid index value (requires pandas>=0.16)

toleranceoptional

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. Requires pandas>=0.17.

copybool, optional

If copy=True, data in the return value is always copied. If copy=False and reindexing is unnecessary, or can be performed with only slice operations, then the output may share memory with the input. In either case, a new xarray object is always returned.

fill_valuescalar, optional

Value to use for newly missing values

**indexers_kwarg{dim: indexer, …}, optional

Keyword arguments in the same form as indexers. One of indexers or indexers_kwargs must be provided.

Returns
reindexedDataset

Another dataset, with this dataset’s data but replaced coordinates.