What happened: Conversion to a pandas DataFrame of a zero dimensional DataArray or Dataset fails.

What you expected to happen: I would expect it to return a trivial DataFrame with one row and the respective coordinate / data set columns. However, I am not sure if that conflicts with potential other round trips between xarray and pandas - e.g. for one-dimensional 1-sized data arrays.

Minimal Complete Verifiable Example:

```python da = DataArray([1, 2, 3], dims=("x",), coords=dict(x=[1, 2, 3]))

I don't know of a way to construct such data array without the isel.

Essentially, below also works for higher dimensional data arrays and

results in a zero dimensional data array with all the coordinates of

the found minimum.

da = da.isel(**da.argmin(dim=("x",))) ds = Dataset({'a': da})

fails with ValueError: cannot convert a scalar to a DataFrame

from xarray/core/dataarray.py", line 2664, in to_dataframe

da.to_dataframe(name="foo")

Expected: a DataFrame with two columns (x and foo) and one row

fails with ValueError: no valid index for a 0-dimensional object

from xarray/core/coordinates.py", line 106, in to_index

ds.to_dataframe()

Expected: a DataFrame with two columns (x and a) and one row

```

Anything else we need to know?: I tested a little bit and got what I want with simply removing the

python def to_dataframe(...): ... if self.ndim == 0: raise ValueError("cannot convert a scalar to a DataFrame")

block from dataarray.py and changing

python def to_index(self, ordered_dims: Sequence[Hashable] = None) -> pd.Index: ... if len(ordered_dims) == 0: return pd.Index([0]) # raise ValueError("no valid index for a 0-dimensional object") to not raise and instead return a trivial index in coordinates.py.

I that would be considered reasonable behavior I am happy to contribute the respective unit test and changes!

Environment: