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/4630#issuecomment-740320785,https://api.github.com/repos/pydata/xarray/issues/4630,740320785,MDEyOklzc3VlQ29tbWVudDc0MDMyMDc4NQ==,44210245,2020-12-08T02:32:42Z,2020-12-08T02:32:42Z,CONTRIBUTOR,Thanks for sharing! I'll give this a first shot before the end of the year. ,"{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,753874419
https://github.com/pydata/xarray/issues/4630#issuecomment-736892959,https://api.github.com/repos/pydata/xarray/issues/4630,736892959,MDEyOklzc3VlQ29tbWVudDczNjg5Mjk1OQ==,14808389,2020-12-01T23:46:18Z,2020-12-01T23:46:18Z,MEMBER,"sure. #4621 added examples for `interp` / `interpolate_na`. For the narrative documentation I don't have a example PR but I think it's fine to just extend the `Vectorized indexing` section (thoughts, @dcherian?).","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,753874419
https://github.com/pydata/xarray/issues/4630#issuecomment-736869263,https://api.github.com/repos/pydata/xarray/issues/4630,736869263,MDEyOklzc3VlQ29tbWVudDczNjg2OTI2Mw==,44210245,2020-12-01T22:48:34Z,2020-12-01T22:48:34Z,CONTRIBUTOR,I'd be happy to give this a shot. But I'm not sure where to start... Can you point me to an example PR that has done something similar? ,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,753874419
https://github.com/pydata/xarray/issues/4630#issuecomment-736853065,https://api.github.com/repos/pydata/xarray/issues/4630,736853065,MDEyOklzc3VlQ29tbWVudDczNjg1MzA2NQ==,14808389,2020-12-01T22:12:37Z,2020-12-01T22:12:37Z,MEMBER,"this trick is not mentioned in the narrative documentation (or rather: I can't find it), and the docstrings of `isel` and `sel` don't contain any examples at all.
Since I believe it should be documented somewhere I'm reopening this to make sure we don't forget. Also, we would definitely welcome a PR adding this, if you're up for it.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,753874419
https://github.com/pydata/xarray/issues/4630#issuecomment-736758627,https://api.github.com/repos/pydata/xarray/issues/4630,736758627,MDEyOklzc3VlQ29tbWVudDczNjc1ODYyNw==,44210245,2020-12-01T19:09:52Z,2020-12-01T19:09:52Z,CONTRIBUTOR,👏 👍 I didn't realize I needed to do that. Thanks for letting me know. Problem solved - marking this as closed. ,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,753874419
https://github.com/pydata/xarray/issues/4630#issuecomment-736755070,https://api.github.com/repos/pydata/xarray/issues/4630,736755070,MDEyOklzc3VlQ29tbWVudDczNjc1NTA3MA==,2448579,2020-12-01T19:03:12Z,2020-12-01T19:03:12Z,MEMBER,"TO use ""vectorized indexing"", `tgt_lat` and `tgt_lon` need to be `DataArrays` with a common dimension name that is not a dimenion in `ds`
``` python
import xarray as xr
import numpy as np
ds = xr.tutorial.open_dataset(""air_temperature"")
# Define taget latitude and longitude
tgt_lat = xr.DataArray(np.linspace(0, 100, num=10), dims=""points"") # <---
tgt_lon = xr.DataArray(np.linspace(0, 100, num=10), dims=""points"") # <---
# Retrieve data at target latitude and longitude
tgt_data = ds['air'].sel(lon=tgt_lon, lat=tgt_lat, method='nearest')
tgt_data
```
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,753874419
https://github.com/pydata/xarray/issues/4630#issuecomment-736752768,https://api.github.com/repos/pydata/xarray/issues/4630,736752768,MDEyOklzc3VlQ29tbWVudDczNjc1Mjc2OA==,44210245,2020-12-01T18:59:18Z,2020-12-01T18:59:18Z,CONTRIBUTOR,"@dcherian Thanks for pointing me in the right direction. I'm trying to implement this with vectorized indexing, but it seems that my queries need to exactly match the xarray object lat/lon, which is why I tried `method='nearest'`. Am I missing something? ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,753874419
https://github.com/pydata/xarray/issues/4630#issuecomment-736117204,https://api.github.com/repos/pydata/xarray/issues/4630,736117204,MDEyOklzc3VlQ29tbWVudDczNjExNzIwNA==,2448579,2020-11-30T23:28:58Z,2020-11-30T23:28:58Z,MEMBER,"You should be able to do this with ""vectorized indexing"": https://xarray.pydata.org/en/stable/indexing.html#vectorized-indexing","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,753874419