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  • jhamman · 2 ✖

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  • Support multi-dimensional grouped operations and group_over · 2 ✖

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  • MEMBER · 2 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
131878081 https://github.com/pydata/xarray/issues/324#issuecomment-131878081 https://api.github.com/repos/pydata/xarray/issues/324 MDEyOklzc3VlQ29tbWVudDEzMTg3ODA4MQ== jhamman 2443309 2015-08-17T16:20:14Z 2015-08-17T16:20:14Z MEMBER

Agreed, we have two use cases here.

For (1), can we just use the pandas grouping infrastructure. We just need to allow xray.DataArray.groupby to support an iterable and pandas.Grouper objects. I personally don't like the MultiIndex format and prefer to unstack the grouper operations when possible. In xray, I think we can justify going that route since we support N-D labeled dimensions much better than pandas.

For (2), I'll need to think a bit more about how this would work. Do we add a groupby method to DataArrayGroupBy? That sounds messy. Maybe we need to write a N-D grouper object?

 
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   Support multi-dimensional grouped operations and group_over 58117200
131599877 https://github.com/pydata/xarray/issues/324#issuecomment-131599877 https://api.github.com/repos/pydata/xarray/issues/324 MDEyOklzc3VlQ29tbWVudDEzMTU5OTg3Nw== jhamman 2443309 2015-08-16T18:51:05Z 2015-08-17T16:07:41Z MEMBER

@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:

Python 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:

Python 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.

 
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   Support multi-dimensional grouped operations and group_over 58117200

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