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/1596#issuecomment-332619692,https://api.github.com/repos/pydata/xarray/issues/1596,332619692,MDEyOklzc3VlQ29tbWVudDMzMjYxOTY5Mg==,4992424,2017-09-27T18:49:34Z,2017-09-27T18:49:34Z,NONE,@willirath Never hurts! ,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,260912521
https://github.com/pydata/xarray/issues/1596#issuecomment-332519089,https://api.github.com/repos/pydata/xarray/issues/1596,332519089,MDEyOklzc3VlQ29tbWVudDMzMjUxOTA4OQ==,4992424,2017-09-27T13:23:38Z,2017-09-27T13:23:38Z,NONE,"@willirath is your time data equally spaced? If so, you should be able to use the new version of `DataArray.resample()` available on the master (and scheduled for the 0.10.0 release) which supports upsampling/infilling. 

Should work something like this, assuming each timestep is a daily value on the **time** axis:

``` python
ds = xr.open_mfdataset(""paths/to/my/data.nc"")

ds_infilled = ds.resample(time='1D').asfreq()
```

That should get you nans wherever your data is missing.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,260912521