issue_comments: 949484507

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https://github.com/pydata/xarray/issues/1603#issuecomment-949484507 https://api.github.com/repos/pydata/xarray/issues/1603 949484507 IC_kwDOAMm_X844l_vb 38346144 2021-10-22T10:14:01Z 2021-10-22T10:14:01Z NONE

For such case you could already do ds.stack(z=("t", "x")).set_index(z="C2").sel(z=["a", "e", "h"]).

After the explicit index refactor, we could imagine a custom index that supports multi-dimension coordinates such that you would only need to do something like

```python

S_res = S4.sel(C2=("z", ["a", "e", "h"])) S_res <xarray.Dataset> Dimensions: (z: 3) Coordinates: * C2 (z) <U1 'a' 'e' 'h' Data variables: A1 (z) float64 4 3 3 ```

or without explicitly providing the name of the packed dimension:

```python

S_res = S4.sel(C2=["a", "e", "h"]) S_res <xarray.Dataset> Dimensions: (C2: 3) Coordinates: * C2 (C2) <U1 'a' 'e' 'h' Data variables: A1 (C2) float64 4 3 3 ```

well, both "contain the origin dims" or just "generate another one" have its benefit. if we contain origin dims, we can ensure that: - less difference between 1d coordinate and multi dims ones, both can run like S1.sel(C1=["a", "e", "h"]) S4.sel(C2=["a", "e", "h"]) and return a new data set with origin dims ( that's why I highly not recommended the implicit one ) - return a new data set have original dims which means if you change C1 to C2, and the rest code have S_res.sel(x=[1,2,3]) still work.

 
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