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/2230#issuecomment-398045641,https://api.github.com/repos/pydata/xarray/issues/2230,398045641,MDEyOklzc3VlQ29tbWVudDM5ODA0NTY0MQ==,6815844,2018-06-18T12:59:48Z,2018-06-18T12:59:48Z,MEMBER,"@rpnaut, thanks for lookng inside the code.
See #2236.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,331981984
https://github.com/pydata/xarray/issues/2230#issuecomment-397092870,https://api.github.com/repos/pydata/xarray/issues/2230,397092870,MDEyOklzc3VlQ29tbWVudDM5NzA5Mjg3MA==,1217238,2018-06-13T21:27:33Z,2018-06-13T21:27:33Z,MEMBER,"OK, I see you already saw the pandas issues :).

> For earth science it would be nice to have an option telling xarray what to do in case of a sum over values being all NA. Do you see a chance to have a fast fix for that issue in the model code?

Yes, I would be very open to adding a `min_count` argument.

We could probably copy the implementation of `sum` with `min_count` largely from pandas:
https://github.com/pandas-dev/pandas/blob/0c4e611927772af44b02204192b29282341a5716/pandas/core/nanops.py#L329

In xarray this would go into `_create_nan_agg_method` in https://github.com/pydata/xarray/blob/master/xarray/core/duck_array_ops.py (sorry, this has gotten a little messy!)","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,331981984
https://github.com/pydata/xarray/issues/2230#issuecomment-397090519,https://api.github.com/repos/pydata/xarray/issues/2230,397090519,MDEyOklzc3VlQ29tbWVudDM5NzA5MDUxOQ==,1217238,2018-06-13T21:19:55Z,2018-06-13T21:19:55Z,MEMBER,"The difference between `mean` and `sum` here isn't resample specific. Xarray consistently interprets a ""NA skipping sum"" consistently as returning 0 in the case of all NaN inputs:
```
>>> float(xarray.DataArray([np.nan]).sum())
0.0
```
This is consistent with the sum of an empty set being 0, e.g.,
```
>>> float(xarray.DataArray([]).sum())
0.0
```

The reason why a ""NA skipping mean"" is different in the case of all NaN inputs is that the mean simply isn't well defined on an empty set. The mean would literally be a sum of zero divided by a count of zero, which is not a valid number: the literal meaning of NaN as ""not a number"".

There was a long discussion/debate about this recently in pandas. See https://github.com/pandas-dev/pandas/issues/18678 and links there-in. There are certainly use-cases where it is nicer for the sum of all NaN outputs to be NaN (exactly as you mention here), but ultimately pandas decided that the answer for this operation should be zero. The decisive considerations were simplicity and consistency with other tools (including NumPy and R).

What pandas added to solve this use-case is an optional `min_count` argument (see [pandas.DataFrame.sum](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sum.html) for an example). We could definitely copy this behavior in xarray if someone is interested in implementing it.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,331981984
https://github.com/pydata/xarray/issues/2230#issuecomment-396928537,https://api.github.com/repos/pydata/xarray/issues/2230,396928537,MDEyOklzc3VlQ29tbWVudDM5NjkyODUzNw==,6815844,2018-06-13T13:00:45Z,2018-06-13T13:01:13Z,MEMBER,"Thank you for raising an issue.
Could you try using `.sum(skipna=False)` for resampled data?

As similar to `pandas.DataFrame.sum`, our `.sum` (and other reduction methods) assumes `skipna=True` unless explicitly specified.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,331981984