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  • Support multi-dimensional grouped operations and group_over · 4 ✖

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  • NONE · 4 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
781427391 https://github.com/pydata/xarray/issues/324#issuecomment-781427391 https://api.github.com/repos/pydata/xarray/issues/324 MDEyOklzc3VlQ29tbWVudDc4MTQyNzM5MQ== matthiasdemuzere 6926916 2021-02-18T15:33:06Z 2021-02-18T15:33:06Z NONE

still relevant, also for me ... I just wanted to group by half hours, for which I'd need access to.groupby(['time.hour','time.minutes'])

 
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   Support multi-dimensional grouped operations and group_over 58117200
531937119 https://github.com/pydata/xarray/issues/324#issuecomment-531937119 https://api.github.com/repos/pydata/xarray/issues/324 MDEyOklzc3VlQ29tbWVudDUzMTkzNzExOQ== stale[bot] 26384082 2019-09-16T20:08:04Z 2019-09-16T20:08:04Z NONE

In order to maintain a list of currently relevant issues, we mark issues as stale after a period of inactivity

If this issue remains relevant, please comment here or remove the stale label; otherwise it will be marked as closed automatically

 
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   Support multi-dimensional grouped operations and group_over 58117200
336925565 https://github.com/pydata/xarray/issues/324#issuecomment-336925565 https://api.github.com/repos/pydata/xarray/issues/324 MDEyOklzc3VlQ29tbWVudDMzNjkyNTU2NQ== jjpr-mit 25231875 2017-10-16T15:35:06Z 2017-10-16T15:35:06Z NONE

Is use case 1 (Multiple groupby arguments along a single dimension) being held back for use case 2 (Multiple groupby arguments along different dimensions)? Use case 1 would be very useful by itself. 

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   Support multi-dimensional grouped operations and group_over 58117200
265462343 https://github.com/pydata/xarray/issues/324#issuecomment-265462343 https://api.github.com/repos/pydata/xarray/issues/324 MDEyOklzc3VlQ29tbWVudDI2NTQ2MjM0Mw== hottwaj 5629061 2016-12-07T14:35:01Z 2016-12-07T14:35:01Z NONE

In case it is of interest to anyone, the snippet below is a temporary and quite dirty solution I've used to do a multi-dimensional groupby...

It runs nested groupby-apply operations over each given dimension until no further grouping needs to be done, then applies the given function "apply_fn"

def nested_groupby_apply(dataarray, groupby, apply_fn): if len(groupby) == 1: return dataarray.groupby(groupby[0]).apply(apply_fn) else: return dataarray.groupby(groupby[0]).apply(nested_groupby_apply, groupby = groupby[1:], apply_fn = apply_fn)

Obviously performance can potentially be quite poor. Passing the dimensions to group over in order of increasing length will reduce your cost a little.

 
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   Support multi-dimensional grouped operations and group_over 58117200

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