To make things concrete, the solution that I have in mind is as simple as adding this function to DataArray:

python def __format__(self, format_spec): return self.values.__format__(format_spec)

Here's one use case I have encountered: python ds=xr.Dataset({'A':(['x','y','z'], np.random.rand(40,40,3)), 'B':(['z'], np.random.randn(3))}, coords={'z':[31,42,45]}) fg=ds.A.plot(col='z') for ax, d in zip(fg.axes.flat, fg.name_dicts.flat): t=ax.get_title() ax.set_title('{} and B(z)={:1.2}'.format(t, ds.sel(**d).B))

This way, if you want to vectorize a __format__ on an array can you not simply do ```python ar = xr.DataArray([39, 103, id(xr)]) print('{:3.3f} {:3.3e} {:x}'.format(*ar))

prints 39.000 1.030e+02 10e5bb548

```

I tend towards the former, to coerce singleton arrays to behave as scalars of their dytpe. I think it makes more sense in terms of use cases (at least everything that I needed). I don't mind implementing it if there is agreement as to which of the two to do.

These behaviors would definitely conflict for scalar objects -- in the second case, we would still want to include some indication that it's an xarray.DataArray. NumPy doesn't have a conflict because indexing an array results in a NumPy scalars, which prints like Python builtin scalars.

@shoyer I don't see why would that be the case. If I format something as '{:04d} {:3.5e} {:2.3E}'.format(dataarray) or whatnot, I would expect that the average user would expect to get '0043 4.35000e+02 2.450E+02' in return, without any indication that these are data arrays.

On the one hand I agree, but note that the same behavior works for numpy arrays

```python import numpy as np a=np.array([1,2,3,4]) ' '.join('{:d}'.format(v) for v in a)

prints '1 2 3 4'

```