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I think this could be done with minimal GroupBy subclasses to supply the default dimension argument for aggregation functions. All the machinery on groupby should already be there. On Wed, Feb 15, 2017 at 10:59 AM Daniel Rothenberg notifications@github.com wrote:

@MaximilianR https://github.com/MaximilianR Oh, the interface is easy enough to do, even maintaining backwards-compatibility (already have that working). I was considering going the route done with GroupBy https://github.com/pydata/xarray/blob/93d6963315026f87841c7cf39cc39bb78f555345/xarray/core/groupby.py#L165 and the classes that compose it, like DatasetGroupBy https://github.com/pydata/xarray/blob/93d6963315026f87841c7cf39cc39bb78f555345/xarray/core/groupby.py#L586... basically, we just record the wanted resampling dimension and inject the grouping/resampling operations we want. Also adds the ability to specialize methods like .first() and .last(), which is done under the current implementation.

But.... if there's a simpler way, that might be preferable!

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@MaximilianR Oh, the interface is easy enough to do, even maintaining backwards-compatibility (already have that working). I was considering going the route done with GroupBy and the classes that compose it, like DatasetGroupBy... basically, we just record the wanted resampling dimension and inject the grouping/resampling operations we want. Also adds the ability to specialize methods like .first() and .last(), which is done under the current implementation.

But.... if there's a simpler way, that might be preferable!

the only sticking point I've come across so far is how to have the resulting Data{Array,set}GroupBy object "remember" the resampling dimension

I think an interface like ds.resample(time='24H').mean() would be much better. We could do that with a wrapper of pd.TimeGrouper that also had a dim field. Or inheritance 😨

Would be great to test for these sorts of issues if we redo this: https://github.com/pydata/xarray/issues/1269

Assuming we want to stick with pd.TimeGrouper under the hood, the only sticking point I've come across so far is how to have the resulting Data{Array,set}GroupBy object "remember" the resampling dimension, e.g. if you have multi-dimensional data and want to compute time means you have to call

python ds.resample(time='24H').mean('time')

or else mean will operate across all dimensions. Any thoughts, @shoyer?

Let me dig into this a bit right now. My analysis project for this afternoon was already going to require digging into pandas' resampling in more depth anyways.