Behavior of dataarray with no dimensions

[0x0L]

Consider

type(np.array([1.0]).mean())
# -> numpy.float64

type(pd.Series([1.0]).mean())
# -> float

type(xr.DataArray([1.0]).mean())
# -> xarray.core.dataarray.DataArray

The issue is that this dimensionless data array won't be cast into float by numpy/pandas when constructing a new ndarray/dataframe. You'll have to do it explicitly. Not a big deal but it feels weird.

I'm sure there's a real technical reason (keeping metadata ?) behind this behavior but I couldn't find any discussion about it.

[shoyer]

Yes, this was an intentional choice, though not without occasional regrets!

This is nice from an consistency perspective: every operation on an xarray object returns another xarray object. Also, as you point out, this allows us to preserve metadata such as attributes or scalar coordinates.

I'm going to mark this as a documentation issue: this would be a great thing to add our docs somewhere (maybe the FAQ page?).

[0x0L]

Thanks for your answer.
I'll try to find a good spot in the doc to write a line or two about this.

[0x0L]

I dug it a bit further. Contrary to what I said numpy is fine. If x is a DataArray

np.array(x.mean())
# -> array(2.0)
np.array([x.mean()])
# -> array([2.0])
np.array(x)
# -> ok

pandas behavior is more surprising

pd.Series(2)
# ok
pd.Series(x.mean())
# -> TypeError: len() of unsized object
pd.Series([x.mean()])
# -> dtype=object
# however for 1D x
pd.Series(x)
# dtype is fine

Something looks a bit odd to me. But it looks more like a pandas issue