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
88339814,MDExOlB1bGxSZXF1ZXN0Mzc2NjMwMDc=,434,One less copy when reading big-endian data with engine='scipy',1217238,closed,0,,1143506,0,2015-06-15T06:59:55Z,2015-06-15T07:51:44Z,2015-06-15T07:51:41Z,MEMBER,,0,pydata/xarray/pulls/434,,"{""url"": ""https://api.github.com/repos/pydata/xarray/issues/434/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
88240870,MDExOlB1bGxSZXF1ZXN0Mzc2NDc1NDQ=,433,Assign order,1217238,closed,0,,1143506,0,2015-06-14T20:09:04Z,2015-06-15T01:16:45Z,2015-06-15T01:16:31Z,MEMBER,,0,pydata/xarray/pulls/433,"`xray.Dataset.assign` and `xray.Dataset.assign_coords` now assign new
variables in sorted (alphabetical) order, mirroring the behavior in pandas.
 Previously, the order was arbitrary.
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/433/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
87025092,MDExOlB1bGxSZXF1ZXN0MzczNzY5Njk=,429,Add pipe method copied from pandas,1217238,closed,0,,1143506,0,2015-06-10T16:19:52Z,2015-06-11T16:45:57Z,2015-06-11T16:45:56Z,MEMBER,,0,pydata/xarray/pulls/429,"The implementation here is directly copied from pandas:
https://github.com/pydata/pandas/pull/10253
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/429/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
83700033,MDU6SXNzdWU4MzcwMDAzMw==,416,Automatically decode netCDF data to native endianness,1217238,closed,0,,1143506,1,2015-06-01T21:23:52Z,2015-06-10T16:01:00Z,2015-06-06T03:51:13Z,MEMBER,,,,"Unfortunately, netCDF3 is big endian, but most modern CPUs are little endian.

Cython requires that data match native endianness in order to perform operations. This means that users can get strange errors when performing aggregations with bottleneck or after converting an xray dataset to pandas.

It would be nice to handle this automatically as part of the ""decoding"" process. I don't think there are any particular advantages to preserving non-native endianness (except, I suppose, for serialization back to another netCDF3 file). My understanding is that most calculations require native endianness, anyways.

CC @bareid
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/416/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
85692656,MDExOlB1bGxSZXF1ZXN0MzcwODQ0MjM=,427,Fix concat for identical index variables,1217238,closed,0,,1143506,0,2015-06-06T04:05:10Z,2015-06-07T06:03:23Z,2015-06-07T06:03:16Z,MEMBER,,0,pydata/xarray/pulls/427,"Fixes #425
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/427/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
85662203,MDU6SXNzdWU4NTY2MjIwMw==,425,xray.concat fails in an edge case involving identical coordinate variables,1217238,closed,0,,1143506,0,2015-06-06T00:22:45Z,2015-06-07T06:03:16Z,2015-06-07T06:03:16Z,MEMBER,,,,"``` python
ds1 = xray.Dataset({'foo': 1.5}, {'x': 0, 'y': 1})
ds2 = xray.Dataset({'foo': 2.5}, {'x': 1, 'y': 1})
xray.concat([ds1, ds2], 'y')
```

```
ValueError                                Traceback (most recent call last)
<ipython-input-235-69cea5440248> in <module>()
      1 ds1 = xray.Dataset({'foo': 1.5}, {'x': 0, 'y': 1})
      2 ds2 = xray.Dataset({'foo': 2.5}, {'x': 1, 'y': 1})
----> 3 xray.concat([ds1, ds2], 'y')

/Users/shoyer/dev/xray/xray/core/alignment.pyc in concat(objs, dim, indexers, mode, concat_over, compat)
    276         raise ValueError('must supply at least one object to concatenate')
    277     cls = type(first_obj)
--> 278     return cls._concat(objs, dim, indexers, mode, concat_over, compat)
    279 
    280 

/Users/shoyer/dev/xray/xray/core/dataset.pyc in _concat(cls, datasets, dim, indexers, mode, concat_over, compat)
   1732         for k in concat_over:
   1733             vars = ensure_common_dims([ds._variables[k] for ds in datasets])
-> 1734             concatenated[k] = Variable.concat(vars, dim, indexers)
   1735 
   1736         concatenated._coord_names.update(datasets[0].coords)

/Users/shoyer/dev/xray/xray/core/dataset.pyc in __setitem__(self, key, value)
    637             raise NotImplementedError('cannot yet use a dictionary as a key '
    638                                       'to set Dataset values')
--> 639         self.update({key: value})
    640 
    641     def __delitem__(self, key):

/Users/shoyer/dev/xray/xray/core/dataset.pyc in update(self, other, inplace)
   1224         """"""
   1225         return self.merge(
-> 1226             other, inplace=inplace, overwrite_vars=list(other), join='left')
   1227 
   1228     def merge(self, other, inplace=False, overwrite_vars=set(),

/Users/shoyer/dev/xray/xray/core/dataset.pyc in merge(self, other, inplace, overwrite_vars, compat, join)
   1291             raise ValueError('cannot merge: the following variables are '
   1292                              'coordinates on one dataset but not the other: %s'
-> 1293                              % list(ambiguous_coords))
   1294 
   1295         obj = self if inplace else self.copy()

ValueError: cannot merge: the following variables are coordinates on one dataset but not the other: ['y']
```
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/425/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
85670978,MDExOlB1bGxSZXF1ZXN0MzcwODIzNjk=,426,Decode non-native endianness,1217238,closed,0,,1143506,0,2015-06-06T01:31:14Z,2015-06-06T03:51:14Z,2015-06-06T03:51:13Z,MEMBER,,0,pydata/xarray/pulls/426,"Fixes #416

By the way, it turns out the simple work around for this was to install netCDF4 -- only scipy.io.netcdf returns the big-endian arrays directly.

CC @bareid
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/426/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull