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
124700322,MDExOlB1bGxSZXF1ZXN0NTQ5NDUxNzE=,702,Basic multiIndex support and stack/unstack methods,1217238,closed,0,,,13,2016-01-04T05:48:49Z,2016-06-01T16:48:54Z,2016-01-18T00:11:11Z,MEMBER,,0,pydata/xarray/pulls/702,"Fixes #164, #700

Example usage:

```
In [3]: df = pd.DataFrame({'foo': range(3),
   ...:                    'x': ['a', 'b', 'b'],
   ...:                    'y': [0, 0, 1]})
   ...: 

In [4]: s = df.set_index(['x', 'y'])['foo']

In [5]: arr = xray.DataArray(s, dims='z')

In [6]: arr
Out[6]: 
<xray.DataArray 'foo' (z: 3)>
array([0, 1, 2])
Coordinates:
  * z        (z) object ('a', 0) ('b', 0) ('b', 1)

In [7]: arr.indexes['z']
Out[7]: 
MultiIndex(levels=[[u'a', u'b'], [0, 1]],
           labels=[[0, 1, 1], [0, 0, 1]],
           names=[u'x', u'y'])

In [8]: arr.unstack('z')
Out[8]: 
<xray.DataArray 'foo' (x: 2, y: 2)>
array([[  0.,  nan],
       [  1.,   2.]])
Coordinates:
  * x        (x) object 'a' 'b'
  * y        (y) int64 0 1

In [9]: arr.unstack('z').stack(z=('x', 'y'))
Out[9]: 
<xray.DataArray 'foo' (z: 4)>
array([  0.,  nan,   1.,   2.])
Coordinates:
  * z        (z) object ('a', 0) ('a', 1) ('b', 0) ('b', 1)
```

TODO (maybe not necessary yet, but eventually):
- [x] Multi-index support working with `.loc` and `.sel()`
- [x] Multi-dimensional `stack`/`unstack`
- [ ] Serialization to NetCDF
- [ ] Better repr, showing level names/dtypes?
- [ ] Make levels accessible as coordinate variables (e.g., `ds['time']` can pull out the `'time'` level of a multi-index)
- [ ] Make `isel_points`/`sel_points` return objects with a MultiIndex? (probably after the previous TODO, so we can preserve basic backwards compatibility)
- [ ] Add `set_index`/`reset_index`/`swaplevel` to make it easier to create and manipulate multi-indexes

It would be nice to eventually build a full example showing how `stack` can be combined with lazy loading / dask to do out-of-core PCA on a large geophysical dataset (e.g., identify El Nino).

cc @MaximilianR @jreback @jhamman 
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