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/436#issuecomment-454533643,https://api.github.com/repos/pydata/xarray/issues/436,454533643,MDEyOklzc3VlQ29tbWVudDQ1NDUzMzY0Mw==,5635139,2019-01-15T20:10:37Z,2019-01-15T20:10:37Z,MEMBER,"Closing as stale, please reopen if still relevant","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,88897697
https://github.com/pydata/xarray/issues/436#issuecomment-113340207,https://api.github.com/repos/pydata/xarray/issues/436,113340207,MDEyOklzc3VlQ29tbWVudDExMzM0MDIwNw==,1217238,2015-06-19T02:00:27Z,2015-06-19T02:00:27Z,MEMBER,"@j08lue good idea! Right now that page is mostly targeted at non-climate scientists, but it would be nice to include an example for climate scientists, too.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,88897697
https://github.com/pydata/xarray/issues/436#issuecomment-113127892,https://api.github.com/repos/pydata/xarray/issues/436,113127892,MDEyOklzc3VlQ29tbWVudDExMzEyNzg5Mg==,3404817,2015-06-18T11:47:42Z,2015-06-18T11:47:42Z,CONTRIBUTOR,"That is actually an excellent demonstration of the power of `xray` for climate data analysis, @shoyer. 

Something like this (including the pandas bridge) should be included in the documentation somewhere, for example under [Why xray?](http://xray.readthedocs.org/en/stable/why-xray.html).

Just a thought...
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,88897697
https://github.com/pydata/xarray/issues/436#issuecomment-112651491,https://api.github.com/repos/pydata/xarray/issues/436,112651491,MDEyOklzc3VlQ29tbWVudDExMjY1MTQ5MQ==,1217238,2015-06-17T04:46:52Z,2015-06-17T04:46:52Z,MEMBER,"Have you tried `xray.open_mfdataset`? All of the examples should work the same with a dataset loaded with that function as from a single file.

In your case, something like the following should work:

``` python
# load data
ds = xray.open_mfdataset('path/to/my/files/*.nc')

# calculate anomalies
clim = ds.groupby('time.month').mean('time')
anom = ds.groupby('time.month') - clim

# plot anomalies over time
# (in practice, would probably want to use .sel here to do
# labeled lookups)
anom.temperature.isel(x=0, y=0).to_pandas().plot()
# plot anomalies over space
plt.imshow(anom.temperature.isel(time=0).values)
```

Plotting is currently not so easy as it should be with xray (hence why you see me exporting everything to pandas), but that's something we plan to start work on very soon.
","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,88897697