issue_comments: 511210149

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https://github.com/pydata/xarray/issues/2064#issuecomment-511210149 https://api.github.com/repos/pydata/xarray/issues/2064 511210149 MDEyOklzc3VlQ29tbWVudDUxMTIxMDE0OQ== 10638475 2019-07-14T15:03:29Z 2019-07-14T21:36:03Z NONE

So there are some units tests that assert the behavior for open_mfdataset() is identical to the behavior for concat(). This implies that if we change the default data_vars value from "all" to "minimal" for one function, we need to change it for both functions.

@shoyer I think you suggested that concat() default behavior should change in #2145, in the same way it will change for open_mfdataset. So I am going to add this change to the pull request.

UPDATE: There is a problem with changing concat() away from having data_ vars='all'. This breaks many unit tests that check for compatibility with Pandas. What I've been told is that Pandas concat() will include all unique variables from each dataframe. This is what data_vars='all' will also do. By changing to data_vars='minimal', only data variables with the specified concatenation dimension will be included. So it seems that in order to stay compatible with Pandas, we need to include all data variables, but not add the concatenation dimension to data variables that do not already have that dimension. The problem, however, is what to do when both datasets have a variable x without the concatenation dimension? What should the resulting concatenation look like? I think in this case, the concat dimension should be added, to preserve information. It's just the unique variables that should be left alone.

Please, could someone confirm that I have understood the problem correctly? Thank you in advance.

 
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