issue_comments

I see. This is because coordinates are just DataArray objects. So

```python

arr.coords['y'] = range(3) ```

is equivalent to

```python

new_coords = xr.DataArray(data=range(3)) arr.coords['y'] = new_coords ```

And the reason this is a ValueError is that new_coords has a default dimension dim_0 that is not on arr. However, this

```python

arr.coords['y'] = 1 ```

is equivalent to...

```python

new_coords = xr.DataArray(data=1, dims=[]) arr.coords['y'] = new_coords ```

And new_coords has no dimensions that are not on arr.

In that example, y has no dimensions that aren't on the array. So while it is a bit unusual, the resulting object doesn't break any rules / invariants. Does that make sense?

@shoyer, maybe I am missing a nuance in your answer, but I think I already understand this bit about Xarray. My question was why is it is allowed to add a scalar:

```python

arr = xr.DataArray(range(3), dims=['x'], coords={'x': list('abc')}) arr.coords Coordinates: * x (x) <U1 'a' 'b' 'c' arr.coords['y'] = 1 # <----------- Why does this work? arr.coords Coordinates: * x (x) <U1 'a' 'b' 'c' y int64 1 ```

In this case, y are coordinates that do not share an existing dimension.

This was intentional. array.assign_coords(name=value) should be equivalent to array = array.copy(deep=False); array.coords[name] = value.

You are allowed to add new coordinates to a DataArray if they share existing dimensions. You are not allowed to add coordinates with new dimensions, because it is enforced as an invariant of the DataArray data model that all coordinate dimensions are found on the DataArray variable as well.

Interesting case, thanks for posting.

There's some logic that this could be allowed - it's a coord along no dimensions (whereas in the previous case it had to be on a dimension, unspecified).

But that's what attributes are, so I'm not sure whether this is something we should support.

Does anyone know whether this is a deliberate decision?