issue_comments

@aaronspring I had a quick look at your version - do you have an idea why it is is faster? Does yours also work for dask arrays?

• In a, b = xr.broadcast(a, b, exclude=dim) why can you exclude dim?
• I think you could also use a, b = xr.align(a, b, exclude=dim) (broadcast has join="outer" which fills it with NA which then get ignored; align uses join="inner")
• Does your version work if the weights contain NA?

Another possibility is to replace

https://github.com/pydata/xarray/blob/cc53a77ff0c8aaf8686f0b0bd7f75985b74e2054/xarray/core/computation.py#L1327

with xr.dot. However, to do so, you need to replace NA with 0 (and I am not sure if that's worth it). Also the min_count needs to be addressed (but that should not be too difficult).

Yes if not valid_values.all() is not lazy. That's the same problem as #4541 and therefore #4559 can be an inspiration how to tackle this. It would be good to test if the check also makes this slower for numpy arrays? Then it could also be removed entirely. That would be counter-intuitive for me, but it seems to be faster for dask arrays...

Other improvements * I am not sure if /= avoids a copy but if so, that's also a possibility to make it faster. * We could add a short-cut for skipna=False (would require adding this option) or dtypes that cannot have NA values as follows:

```python if skipna: # 2. Ignore the nans valid_values = da_a.notnull() & da_b.notnull()

if not valid_values.all():
    da_a = da_a.where(valid_values)
    da_b = da_b.where(valid_values)

valid_count = valid_values.sum(dim) - ddof

else: # shortcut for skipna=False # da_a and da_b are aligned, so the have the same dims and shape axis = da_a.get_axis_num(dim) valid_count = np.take(da_a.shape, axis).prod() - ddof

```