id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
104484316,MDU6SXNzdWUxMDQ0ODQzMTY=,557,CDO-like convenience methods to select times,6883049,open,0,,,9,2015-09-02T13:42:48Z,2022-04-18T16:03:35Z,,CONTRIBUTOR,,,,"I feel like the time selecting features of xray can be improved. Currently, some common operations are too involved or verbose, like selecting the data in a group of months that are not a standard season (e.g. the monsoon season in india JJAS), or in non consecutive years (e.g. El Niño years). I think it would be great to implement (and easy), some methods inspired in the widely used Climate Data Operators https://code.zmaw.de/projects/cdo For example: selyear, selmon, selday and selhour.

Then we could easily do a composite of JJAS seasons in El Niño years like this:

```
pr_dataset = xray.open(my_precipitation_dataset)
elnino_years = [year list here...]
pr_dataset.selyear(elnino_years).selmon([6, 7, 8, 9]).mean('time')
```

This would make me very happy. The way to go would be to write methods that call grouby, then select the years/months, merge them, and return the corresponding dataset/dataarray, but I am not sure about what is the most efficient way to do this.
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/557/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue