xarray.Dataset.reindex

Dataset.reindex(indexers=None, method=None, tolerance=None, copy=True, **indexers_kwargs)

Conform this object onto a new set of indexes, filling in missing values with 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{None, ‘nearest’, ‘pad’/’ffill’, ‘backfill’/’bfill’}, optional

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 most 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.

**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.