html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/4898#issuecomment-778341660,https://api.github.com/repos/pydata/xarray/issues/4898,778341660,MDEyOklzc3VlQ29tbWVudDc3ODM0MTY2MA==,367900,2021-02-12T17:44:55Z,2021-02-12T17:55:48Z,CONTRIBUTOR,"@dcherian it looks like that works. A better test script:

```python
import numpy as np
import xarray as xr
from xarray.tests import raise_if_dask_computes


def worker(da):
    if da.shape == (0, 0):
        return da

    return da.where(da > 1)


np.random.seed(1023)
da = xr.DataArray(
    np.random.normal(size=(20, 500)),
    dims=(""x"", ""y""),
    coords=(np.arange(20), np.arange(500)),
)

da = da.chunk(dict(x=5))
lazy = da.map_blocks(worker)

with raise_if_dask_computes():
    result = lazy.sum(""x"", skipna=True, min_count=5)

result.load()

assert np.isnan(result[0])
assert not np.isnan(result[6])
```

If I then remove the `if null_mask.any()` check and the following block, and replace it with

```python
dtype, fill_value = dtypes.maybe_promote(result.dtype)
result = result.astype(dtype)
result = np.where(null_mask, fill_value, result)
```
it passes. I can start working on a pull request with these tests and changes if that looks acceptable to you.

~~How would you suggest handling the possible type promotion from the current `dtype, fill_value = dtypes.maybe_promote(result.dtype)` line? Currently it only tries promoting if the mask is True anywhere. Always promote, or just use the fill value and hope it works out?~~","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,807089005
https://github.com/pydata/xarray/issues/4898#issuecomment-778329536,https://api.github.com/repos/pydata/xarray/issues/4898,778329536,MDEyOklzc3VlQ29tbWVudDc3ODMyOTUzNg==,367900,2021-02-12T17:23:51Z,2021-02-12T17:23:51Z,CONTRIBUTOR,"A quick check with the debugger and it is the `null_mask.any()` call that is causing it to compute.

I think I've found another problem with `_maybe_null_out` if it is reducing over all dimensions. With the altered MCVE

```python
import numpy as np
import xarray as xr

def worker(da):
    if da.shape == (0, 0):
        return da

    res = xr.full_like(da, np.nan)
    res[0, 0] = 1
    return res


da = xr.DataArray(
    np.random.normal(size=(20, 500)),
    dims=(""x"", ""y""),
    coords=(np.arange(20), np.arange(500)),
)

da = da.chunk(dict(x=5))
lazy = da.map_blocks(worker)
result_allaxes = lazy.sum(skipna=True, min_count=5)
result_allaxes.load()
```

I would expect `result_allaxes` to be nan since there are four blocks and therefore four non-nan values, less than min_count. Instead it is 4.

The problem seems to be the dtype check:

https://github.com/pydata/xarray/blob/5296ed18272a856d478fbbb3d3253205508d1c2d/xarray/core/nanops.py#L39

The test returns True for float64 and so the block isn't run. Another MCVE:

```python
import numpy as np
from xarray.core import dtypes

print(dtypes.NAT_TYPES)
print(np.dtype(""float64"") in dtypes.NAT_TYPES)
```

Output:
```console
(numpy.datetime64('NaT'), numpy.timedelta64('NaT'))
True
```
where I think False would be expected. Should I open a separate issue for this or can we track it here too?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,807089005
https://github.com/pydata/xarray/issues/4898#issuecomment-778312719,https://api.github.com/repos/pydata/xarray/issues/4898,778312719,MDEyOklzc3VlQ29tbWVudDc3ODMxMjcxOQ==,367900,2021-02-12T16:55:11Z,2021-02-12T16:55:11Z,CONTRIBUTOR,"grepping the code, the only other function that calls `_maybe_null_out` is prod, and I can confirm the problem also exists there. Updated the title, MCVE for prod:

```python
import numpy as np
import xarray as xr


def worker(da):
    if da.shape == (0, 0):
        return da

    raise RuntimeError(""I was evaluated"")


da = xr.DataArray(
    np.random.normal(size=(20, 500)),
    dims=(""x"", ""y""),
    coords=(np.arange(20), np.arange(500)),
)

da = da.chunk(dict(x=5))
lazy = da.map_blocks(worker)
result1 = lazy.prod(""x"", skipna=True)
result2 = lazy.prod(""x"", skipna=True, min_count=5)
```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,807089005