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/557#issuecomment-427335183,https://api.github.com/repos/pydata/xarray/issues/557,427335183,MDEyOklzc3VlQ29tbWVudDQyNzMzNTE4Mw==,1217238,2018-10-05T11:33:45Z,2018-10-05T11:33:45Z,MEMBER,`dataset.time.dt.year.isin(elnino_years)` should work already with.,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,104484316
https://github.com/pydata/xarray/issues/557#issuecomment-427279439,https://api.github.com/repos/pydata/xarray/issues/557,427279439,MDEyOklzc3VlQ29tbWVudDQyNzI3OTQzOQ==,1217238,2018-10-05T07:59:47Z,2018-10-05T08:00:28Z,MEMBER,"I don’t think there’s an easy way to this with vectorized indexing, but if we supported multidimensional indexing with boolean keys as proposed in https://github.com/pydata/xarray/issues/1887 (equivalent to where with drop=True) we could write `pr_dataset[pr_dataset['time.year'].isin(elnino_years)]`","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,104484316
https://github.com/pydata/xarray/issues/557#issuecomment-427078606,https://api.github.com/repos/pydata/xarray/issues/557,427078606,MDEyOklzc3VlQ29tbWVudDQyNzA3ODYwNg==,1197350,2018-10-04T16:12:43Z,2018-10-04T16:12:43Z,MEMBER,Is there a way to do this easily now with vectorized indexing?,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,104484316
https://github.com/pydata/xarray/issues/557#issuecomment-137257727,https://api.github.com/repos/pydata/xarray/issues/557,137257727,MDEyOklzc3VlQ29tbWVudDEzNzI1NzcyNw==,2443309,2015-09-02T21:57:54Z,2015-09-02T21:57:54Z,MEMBER,"I'd also like to see this come into being.  

My two cents on the syntax is that your second idea is best:

``` Python
pr_dataset.sel('time.year', elnino_years)
```

I don't care much for option 3 since the variable name is being used as an attribute (what if my time variable is called ""Times""?).
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,104484316
https://github.com/pydata/xarray/issues/557#issuecomment-137182504,https://api.github.com/repos/pydata/xarray/issues/557,137182504,MDEyOklzc3VlQ29tbWVudDEzNzE4MjUwNA==,1217238,2015-09-02T17:37:56Z,2015-09-02T17:37:56Z,MEMBER,"Currently, the way to do this is to create a boolean indexer, with something like the following:

```
pr_dataset.sel(time=np.in1d(pr_dataset['time.year'], elnino_years))
```

I agree that this is overly verbose and we can come up with something better. I'm not quite happy with `selyear`, though:
- It's ambiguous which datetime variable the year refers do (there can be more than one time variable on some datasets).
- I'm also not a big fan of adding a bunch of new API methods that are datetime specific -- it creates a lot of noise (pandas has an issue with this).

Something like `pr_dataset.sel(year=elnino_years)` would be the ideal fix for this second concern (we've discussed this in another issue, can't remember which one now), but it's still ambiguous which time variable it refers to.

So, some other possible ways to spell this:
1. `pr_dataset.sel('time', year=elnino_years)`
2. `pr_dataset.sel('time.year', elnino_years)`
3. `pr_dataset.sel.time.year(elnino_years)`
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,104484316
https://github.com/pydata/xarray/issues/557#issuecomment-137134794,https://api.github.com/repos/pydata/xarray/issues/557,137134794,MDEyOklzc3VlQ29tbWVudDEzNzEzNDc5NA==,5356122,2015-09-02T15:32:17Z,2015-09-02T15:32:17Z,MEMBER,"Just curious- how would we currently do this?
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,104484316