html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/1553#issuecomment-748491929,https://api.github.com/repos/pydata/xarray/issues/1553,748491929,MDEyOklzc3VlQ29tbWVudDc0ODQ5MTkyOQ==,18488,2020-12-19T16:00:00Z,2020-12-19T16:00:00Z,NONE,"For the case of a simple vectorized `reindex` you can work around the lack of a multi-dimensional `DataArray.reindex` by falling back on `isel` as follows:

```
def reindex_vectorized(da, indexers, method=None, tolerance=None, dim=None, fill_value=None):
    # Reindex does not presently support vectorized lookups: https://github.com/pydata/xarray/issues/1553
    # Sel does (e.g. https://github.com/pydata/xarray/issues/4630) but can't handle missing keys
    
    if dim is None:
        dim = 'dim_0'

    if fill_value is None:
        fill_value = {'i': np.nan, 'f': np.nan}[da.dtype.kind]
    dtype = np.result_type(fill_value, da.dtype)
    
    if method is None:
        method = {}
    elif not isinstance(method, dict):
        method = {dim: method for dim in da.dims}
        
    if tolerance is None:
        tolerance = {}
    elif not isinstance(tolerance, dict):
        tolerance = {dim: tolerance for dim in da.dims}
    
    ixs = {}
    masks = []
    any_empty = False
    for index_dim, index in indexers.items():
        ix = da.indexes[index_dim].get_indexer(index, method=method.get(index_dim), tolerance=tolerance.get(index_dim))
        ixs[index_dim] = xr.DataArray(np.fmax(0, ix), dims=[dim])
        masks.append(ix >= 0)
        any_empty = any_empty or (len(da.indexes[index_dim]) == 0)
    
    mask = functools.reduce(lambda x, y: x & y, masks)
    
    if any_empty and len(mask):
        # Unfortunately can't just isel with `ixs` in this special case, because we'll go out of bounds accessing index 0
        new_coords = {
            name: coord
            for name, coord in da.coords.items()
            # XXX: to match the other case we should really include coords with name in ixs too, but it's fiddly
            if name not in ixs
        }
        new_dims = [name for name in da.dims if name not in ixs] + [dim]
        result = xr.DataArray(
            data=np.broadcast_to(
                fill_value,
                tuple(n for name, n in da.sizes.items() if name not in ixs) + (len(mask),)
            ),
            coords=new_coords, dims=new_dims,
            name=da.name, attrs=da.attrs
        )
    else:
        result = da[ixs]

        if not mask.all():
            result = result.astype(dtype, copy=False)
            result[{dim: ~mask}] = fill_value
    
    return result
```

Example:

```
sensor_data = xr.DataArray(np.arange(6).reshape((3, 2)), coords=[
    ('time', [0, 2, 3]),
    ('sensor', ['A', 'C']),
])

reindex_vectorized(sensor_data, {
    'sensor': ['A', 'A', 'A', 'B', 'C'],
    'time': [0, 1, 2, 0, 0],
}, method={'time': 'ffill'})
# [0, 0, 2, nan, 1]

reindex_vectorized(xr.DataArray(coords=[
    ('sensor', []),
    ('time', [0, 2])
]), {
    'sensor': ['A', 'A', 'A', 'B', 'C'],
    'time': [0, 1, 2, 0, 0],
}, method={'time': 'ffill'})
# [nan, nan, nan, nan, nan]
```","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,254927382