id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
98587746,MDU6SXNzdWU5ODU4Nzc0Ng==,508,Ignore missing variables when concatenating datasets?,1217238,closed,0,,,8,2015-08-02T06:03:57Z,2023-01-20T16:04:28Z,2023-01-20T16:04:28Z,MEMBER,,,,"Several users (@raj-kesavan, @richardotis, now myself) have wondered about how to concatenate xray Datasets with different variables.

With the current `xray.concat`, you need to awkwardly create dummy variables filled with `NaN` in datasets that don't have them (or drop mismatched variables entirely). Neither of these are great options -- `concat` should have an option (the default?) to take care of this for the user.

This would also be more consistent with `pd.concat`, which takes a more relaxed approach to matching dataframes with different variables (it does an outer join).
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/508/reactions"", ""total_count"": 6, ""+1"": 6, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
711626733,MDU6SXNzdWU3MTE2MjY3MzM=,4473,Wrap numpy-groupies to speed up Xarray's groupby aggregations,1217238,closed,0,,,8,2020-09-30T04:43:04Z,2022-05-15T02:38:29Z,2022-05-15T02:38:29Z,MEMBER,,,,"<!-- Please do a quick search of existing issues to make sure that this has not been asked before. -->

**Is your feature request related to a problem? Please describe.**

Xarray's groupby aggregations (e.g., `groupby(..).sum()`) are very slow compared to pandas, as described in https://github.com/pydata/xarray/issues/659.

**Describe the solution you'd like**

We could speed things up considerably (easily 100x) by wrapping the [numpy-groupies](https://github.com/ml31415/numpy-groupies) package.

**Additional context**

One challenge is how to handle dask arrays (and other duck arrays). In some cases it might make sense to apply the numpy-groupies function (using apply_ufunc), but in other cases it might be better to stick with the current indexing + concatenate solution. We could either pick some simple heuristics for choosing the algorithm to use on dask arrays, or could just stick with the current algorithm for now.

In particular, it might make sense to stick with the current algorithm if there are a many chunks in the arrays to aggregated along the ""grouped"" dimension (depending on the size of the unique group values).","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4473/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
269700511,MDU6SXNzdWUyNjk3MDA1MTE=,1672,Append along an unlimited dimension to an existing netCDF file,1217238,open,0,,,8,2017-10-30T18:09:54Z,2020-11-29T17:35:04Z,,MEMBER,,,,"This would be a nice feature to have for some use cases, e.g., for writing simulation time-steps:
https://stackoverflow.com/questions/46951981/create-and-write-xarray-dataarray-to-netcdf-in-chunks

It should be relatively straightforward to add, too, building on support for writing files with unlimited dimensions. User facing API would probably be a new keyword argument to `to_netcdf()`, e.g., `extend='time'` to indicate the extended dimension.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1672/reactions"", ""total_count"": 21, ""+1"": 21, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue
169274464,MDU6SXNzdWUxNjkyNzQ0NjQ=,939,Consider how to deal with the proliferation of decoder options on open_dataset,1217238,closed,0,,,8,2016-08-04T01:57:26Z,2020-10-06T15:39:11Z,2020-10-06T15:39:11Z,MEMBER,,,,"There are already lots of keyword arguments, and users want even more! (#843)

Maybe we should use some sort of object to encapsulate desired options?
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/939/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
454168102,MDU6SXNzdWU0NTQxNjgxMDI=,3009,Xarray test suite failing with dask-master,1217238,closed,0,,,8,2019-06-10T13:21:50Z,2019-06-23T16:49:23Z,2019-06-23T16:49:23Z,MEMBER,,,,"There are a [wide variety of failures](https://travis-ci.org/pydata/xarray/jobs/543540695), mostly related to backends and indexing, e.g., `AttributeError: 'tuple' object has no attribute 'tuple'`. By the looks of it, something is going wrong with xarray's internal `ExplicitIndexer` objects, which are getting converted into something else.

I'm pretty sure this is due to the recent merge of the `Array._meta` pull request: https://github.com/dask/dask/pull/4543

There are 81 test failures, but my guess is that there that probably only a handful (at most) of underlying causes.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3009/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
171077425,MDU6SXNzdWUxNzEwNzc0MjU=,967,sortby() or sort_index() method for Dataset and DataArray,1217238,closed,0,,741199,8,2016-08-14T20:40:13Z,2017-05-12T00:29:12Z,2017-05-12T00:29:12Z,MEMBER,,,,"They should function like the pandas methods of the same name.

Under the covers, I believe it would suffice to simply remap `ds.sort_index('time')` -> `ds.isel(time=ds.indexes['time'].argsort())`.
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/967/reactions"", ""total_count"": 2, ""+1"": 2, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
40225000,MDU6SXNzdWU0MDIyNTAwMA==,212,"Get ride of ""noncoordinates"" as a name?",1217238,closed,0,,740776,8,2014-08-14T05:52:30Z,2014-09-22T00:55:22Z,2014-09-22T00:55:22Z,MEMBER,,,,"As @ToddSmall has pointed out (in #202), ""noncoordinates"" is a confusing name -- it's something defined by what it isn't, not what it is.

Unfortunately, our best alternative is ""variables"", which already has a lot of meaning from the netCDF world (and which we already use).

Related: #211
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