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/1041#issuecomment-252193599,https://api.github.com/repos/pydata/xarray/issues/1041,252193599,MDEyOklzc3VlQ29tbWVudDI1MjE5MzU5OQ==,900941,2016-10-07T09:26:28Z,2016-10-07T09:26:59Z,CONTRIBUTOR,"Hi @shoyer:

> Does a2.resample(""D"", ""time"") work in your second example?

with `a2.resample(""D"", ""time"")` I get the following exception:

```
ValueError: 'time' not found in array dimensions ('t',)
```

> but I think this is working as intended -- if you only supply the name of a dimension with integer labels, xarray has no way to know how what times to use for resampling.

I am not sure how it is done now or whether it is possible to implement, but I feel like it searches for the coordinate with the same name as the supplied dimension. Probably searching for a coordinate containing only the dimension specified in resample could do the trick.

Maybe there is a better way to do what I am trying to do?:
I needed it to calculate daily means.  But I have a hole in my data in the middle of the year and resample is masking this hole, by adding masked fields. So I've done it with groupby as follows, but it seems there should be an easier way to achieve this:

``` python
  day_dates = [datetime(d.year, d.month, d.day) for d in pd.to_datetime(snfl.coords[""t""].values)]
  day_dates = DataArray(day_dates, name=""time"", dims=""t"")
  snfl = snfl.groupby(day_dates).mean(dim=""t"")
```
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