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/1471#issuecomment-433952128,https://api.github.com/repos/pydata/xarray/issues/1471,433952128,MDEyOklzc3VlQ29tbWVudDQzMzk1MjEyOA==,21049064,2018-10-29T15:21:34Z,2018-10-29T15:21:34Z,NONE,"@smartass101 & @shoyer what would be the code for working with a `pandas.MultiIndex` object in this use case? Could you show how it would work related to your example above:

```
<xarray.Dataset>
Dimensions:  (num: 21, ar:2)   # <-- note that MB is still of dims {'num': 19} only
Coordinates:              # <-- mostly unions as done by concat
  * num      (num) int64 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
    B        <U1 'r'
  * ar       <U1 'A' 'B'    # <-- this is now a dim of the dataset, but not of MA or MB
Data variables:
    MA       (num) float64 0.5 1.0 1.5 2.0 2.5 3.0 ... 8.0 8.5 9.0 9.5 10.0 10.5
    MB       (num) float64 1.0 1.5 2.0 2.5 3.0 3.5 ... 7.5 8.0 8.5 9.0 9.5 10.0
```

I am working with land surface model outputs. I have lots of one-dimensional data for different lat/lon points, at different times. I want to join them all into one dataset to make plotting easier. E.g. plot the evapotranspiration estimates for all the stations at their x,y coordinates.

Thanks very much!","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,241290234