issue_comments: 531511177

Some additional information on the topic:

Combining named 1D data arrays works. ``` da1 = xr.DataArray(name='foo', data=np.random.randn(3), coords=[('x', [1, 2, 3])]) da2 = xr.DataArray(name='foo', data=np.random.randn(3), coords=[('x', [5, 6, 7])]) xr.combine_by_coords([da1, da2])

<xarray.Dataset> Dimensions: (x: 6) Coordinates: * x (x) int64 1 2 3 5 6 7 Data variables: foo (x) float64 1.443 0.4889 0.9233 0.1946 -1.639 -1.455 ```

However, when combining 2D gridded data...

``` da1 = xr.DataArray(name='foo', data=np.random.rand(3,3), coords=[('x', [1, 2, 3]), ('y', [1, 2, 3])])

da2 = xr.DataArray(name='foo', data=np.random.rand(3,3), coords=[('x', [5, 6, 7]), ('y', [5, 6, 7])])

xr.combine_by_coords([da1, da2]) ...the method fails, despite passing a data variable name.

ValueError Traceback (most recent call last) <ipython-input-145-77ae89136c1f> in <module> 9 ('y', [5, 6, 7])]) 10 ---> 11 xr.combine_by_coords([da1, da2])

~/xarray/xarray/core/combine.py in combine_by_coords(datasets, compat, data_vars, coords, fill_value, join) 580 581 # Group by data vars --> 582 sorted_datasets = sorted(datasets, key=vars_as_keys) 583 grouped_by_vars = itertools.groupby(sorted_datasets, key=vars_as_keys) 584

~/xarray/xarray/core/combine.py in vars_as_keys(ds) 465 466 def vars_as_keys(ds): --> 467 return tuple(sorted(ds)) 468 469

~/xarray/xarray/core/common.py in bool(self) 119 120 def bool(self: Any) -> bool: --> 121 return bool(self.values) 122 123 def float(self: Any) -> float:

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() ``` Again, converting to a dataset bypasses the issue.

``` ds1 = da1.to_dataset() ds2 = da2.to_dataset() xr.combine_by_coords([ds1, ds2])

<xarray.Dataset> Dimensions: (x: 6, y: 6) Coordinates: * x (x) int64 1 2 3 5 6 7 * y (y) int64 1 2 3 5 6 7 Data variables: foo (x, y) float64 0.5078 0.8981 0.8707 nan ... 0.4172 0.7259 0.8431 ```