issue_comments: 43294717

As a note on your points (1) and (2): currently, we remove all dataset and array attributes when doing any operations other than (re)indexing. This includes when reduce operations like mean are applied, because it didn't seem safe to assume that the original attributes were still descriptive. In particular, I was worried about units.

I'm willing to reconsider this, but in general I would like to avoid any functionality that is metadata aware other than dimension and coordinate labels. In my experience, systems that rely on attributes become much more complex and harder to predict, so I would like to avoid that. I don't see a unit system as in scope for xray, at least not at this time.

Your solution 4(b) -- dropping coordinates rather than attempting to summarize them -- would also be my preferred approach. It is consistent with pandas (try df.mean(level='time')) and quite often labels can't be meaningfully reduced anyways (e.g., suppose a coordinate's ticks are labeled by datetimes or worse, strings).

Speaking of non-numerical data, we will need to take an approach like pandas to ignore non-numerical variables with taking the mean. It might be worth taking a look at how pandas handles this, but I imagine using a try/except clause would be the sensible way to do that.

In you're interested in taking a crack at implementation, take a look at DataArray.reduce and Variable.reduce. Once we have a generic reduce function that handles the labels, injecting the all numpy methods like mean and sum is trivial.