Thanks both for the comments. I understand sparse's behaviour; to clarify, the bug (IMO) is that xarray doesn't handle this for the user. To condense my example: ```python

Same as above to ---

import numpy as np import pandas as pd import xarray as xr

foo = [f'foo{i}' for i in range(6)] bar = [f'bar{i}' for i in range(6)] raw = np.random.rand(len(foo) // 2, len(bar))

b_series = pd.DataFrame(raw, index=foo[3:], columns=bar) \ .stack() \ .rename_axis(index=['foo', 'bar'])

---

b = xr.DataArray.from_series(b_series, sparse=True) c = b.sum(dim='foo').expand_dims({'foo': ['total']}) d = xr.concat([b, c], dim='foo') ```

This succeeds when sparse=False and fails when sparse=True. - Shouldn't it succeed automatically? I feel like it should. - If it does, what should be the fill value on d? I'm not clear what the intended behaviour is.

I haven't touched xarray internals before, but if time allows I will try to add some tests.