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

Conform this object onto a new set of indexes, filling in missing values with NaN.


indexers : dict. 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.

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)

tolerance : optional

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.

copy : bool, 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.

**kw_indexers : optional

Keyword arguments in the same form as indexers.


reindexed : Dataset

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