You need

xr.DataArray([1], coords=[('onecoord', [2])]).sel(onecoord=[2]).to_dataframe(name='name')

The difference is using onecoord=2 gives a scalar

>>> xr.DataArray([1], coords=[('onecoord', [2])]).sel(onecoord=2)
<xarray.DataArray ()>
array(1)
Coordinates:
    onecoord  int64 2

while using onecoord=[2] gives a 1 element vector

>>> xr.DataArray([1], coords=[('onecoord', [2])]).sel(onecoord=[2])
<xarray.DataArray (onecoord: 1)>
array([1])
Coordinates:
  * onecoord  (onecoord) int64 2

And to_dataframe cannot handle scalars.

I am not sure that there is a sensible way to convert a scalar DataArray to a DataFrame but we should throw a more informative error in any case.

thanks for the very clear response. The behaviro make sense.

In fact, I should have explained what I'm trying to achieve, as this is kind of "take". I've a dict like this:

{'label1' : dict(coord1=1, coord2=4), 
'label2' : dict(coord1=5, coord2=6),
'label3' : dict(coord1=4, coord2=2),
}

and I want to build an xarray (and then a dataframe) with coord1 and coord2 replaced by a new dims with values 'label1', 'label2', 'label3'.

I've done that by iterating over the dict, selecting with sel using the dict values, convert to dataframe and then concat the dataframes. pd.concat([x.sel(**d[k]).to_dataframe() or k in d]

A better option would be to do this "sel" or "take" with xarray only.
Do you have an idea how to do it with existing xarray methods?

You could do it with "advanced indexing" by providing a dataarray to the .sel or .isel methods: https://xarray.pydata.org/en/stable/indexing.html#more-advanced-indexing

da = xr.DataArray([[1, 2, 3], [4,5,6]], dims=["coord1", "coord2"], coords={"coord2": [10, 20, 30], "coord1": [1,2]}) 

i1 = xr.DataArray([1, 0], dims=["z"], coords={"z": ["label1", "label2"]})
i2 = xr.DataArray([2, 1], dims=["z"], coords={"z": ["label1", "label2"]})  

da.isel(coord1=i1, coord2=i2, drop=True).to_dataframe(name="asd")

        asd
z          
label1    6
label2    2