Support multi-dimensional grouped operations and group_over

Multi-dimensional grouped operations should be relatively straightforward – the main complexity will be writing an N-dimensional concat that doesn’t involve repetitively copying data.

The idea with group_over would be to support groupby operations that act on a single element from each of the given groups, rather than the unique values. For example, ds.group_over(['lat', 'lon']) would let you iterate over or apply to 2D slices of ds, no matter how many dimensions it has.

Roughly speaking (it’s a little more complex for the case of non-dimension variables), ds.group_over(dims) would get translated into ds.groupby([d for d in ds.dims if d not in dims]).

Related: #266

[jhamman]

@shoyer -

I want to look into putting a PR together for this. I’m looking for the same functionality that you get with a pandas Series or DataFrame:

data.groupby([lambda x: x.hour, lambda x: x.timetuple().tm_yday]).mean()

The motivation comes in making a Hovmoller diagram. What we need is this functionality:

da.groupby(['time.hour', 'time.dayofyear']).mean().plot()

If you can point me in the right direction, I’ll see if I can put something together.

[clarkfitzg]

For (2) I think it makes sense to extend the existing groupby to deal with multiple dimensions. Ie, let it take an iterable of dimension names.

>>> darray.groupby(['lat', 'lon'])

Then we’d have something similar to the SQL groupby, which is a good thing.

By the way, in #527 we were considering using this approach to make the faceted plots on both rows and columns.