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

        Parameters
        ----------
        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.
            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)
        tolerance : 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``.
            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.
        **indexers_kwarg : {dim: indexer, ...}, optional
            Keyword arguments in the same form as ``indexers``.
            One of indexers or indexers_kwargs must be provided.

        Returns
        -------
        reindexed : Dataset
            Another dataset, with this dataset's data but replaced coordinates.

        See Also
        --------
        Dataset.reindex_like
        align
        pandas.Index.get_indexer

def reindex(self, 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
        ----------
        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.
            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)
        tolerance : 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``.
            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.
        **indexers_kwarg : {dim: indexer, ...}, optional
            Keyword arguments in the same form as ``indexers``.
            One of indexers or indexers_kwargs must be provided.

        Returns
        -------
        reindexed : Dataset
            Another dataset, with this dataset's data but replaced coordinates.

        See Also
        --------
        Dataset.reindex_like
        align
        pandas.Index.get_indexer
        """
        indexers = utils.either_dict_or_kwargs(indexers, indexers_kwargs,
                                               'reindex')

        bad_dims = [d for d in indexers if d not in self.dims]
        if bad_dims:
            raise ValueError('invalid reindex dimensions: %s' % bad_dims)

        variables, indexes = alignment.reindex_variables(
            self.variables, self.sizes, self.indexes, indexers, method,
            tolerance, copy=copy)
        coord_names = set(self._coord_names)
        coord_names.update(indexers)
        return self._replace_with_new_dims(
            variables, coord_names, indexes=indexes)