issue_comments: 433952128

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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!

 
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