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
46049691,MDU6SXNzdWU0NjA0OTY5MQ==,255,Add Dataset.to_pandas() method,1217238,closed,0,,987654,2,2014-10-17T00:01:36Z,2021-05-04T13:56:00Z,2021-05-04T13:56:00Z,MEMBER,,,,"This would be the complement of the DataArray constructor, converting an xray.DataArray into a 1D series, 2D DataFrame or 3D panel, whichever is appropriate.

`to_pandas` would also makes sense for Dataset, if it could convert 0d datasets to series, e.g., `pd.Series({k: v.item() for k, v in ds.items()})` (there is currently no direct way to do this), and revert to to_dataframe for higher dimensional input.
- [x] DataArray method
- [ ] Dataset method
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48301141,MDU6SXNzdWU0ODMwMTE0MQ==,277,Creation of an empty DataArray,358378,closed,0,,987654,11,2014-11-10T19:07:55Z,2020-03-06T12:38:08Z,2020-03-06T12:38:07Z,CONTRIBUTOR,,,,"I'd like to create an empty `DataArray`, i.e., one with only NA values.  The docstring of `DataArray` says that `data=None` is allowed, if a _dataset_ argument is provided.  However, the docstring doesn't say anything about a _dataset_ argument.
1. I think there's a bug in the docstring
2. I'd like to pass _data=None_ and get a `DataArray` with the coords/dims set up properly (as defined by the `coords` and `dims` kwargs), but with a `values` array of NA.
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/277/reactions"", ""total_count"": 10, ""+1"": 10, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
69216911,MDU6SXNzdWU2OTIxNjkxMQ==,394,Checklist for releasing a version of xray with dask support,1217238,closed,0,,987654,3,2015-04-17T21:02:10Z,2015-06-01T18:27:49Z,2015-06-01T18:27:49Z,MEMBER,,,,"For dask:
- [x] [default threadpool](https://github.com/ContinuumIO/dask/issues/154) for dask.array
- [x] fix [indexing bugs](https://github.com/ContinuumIO/dask/pull/149) for dask.array
- [x] make a decision on (and if necessary implement) [renaming ""block"" to ""chunk""](https://github.com/ContinuumIO/dask/issues/150)
- [x] fix [repeated use of `da.insert`](https://github.com/ContinuumIO/dask/pull/165)

For xray:
- [x] update xray for the updated dask (https://github.com/xray/xray/pull/395)
- [x] figure out how to handle caching with the `.load()` method on dask arrays
- [x] cleanup the xray documentation on dask array.
- [x] write an introductory blog post

Things we can add in an incremental release:
- make [non-aggregating grouped operations more useable](https://github.com/xray/xray/issues/392)
- automatic [lazy apply](https://github.com/ContinuumIO/dask/pull/122) for grouped operations on xray objects

CC @mrocklin
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/394/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
72145600,MDU6SXNzdWU3MjE0NTYwMA==,406,millisecond and microseconds support,4806877,closed,0,,987654,5,2015-04-30T12:38:27Z,2015-05-01T20:33:10Z,2015-05-01T20:33:10Z,CONTRIBUTOR,,,,"netcdf4python supports milliseconds and microseconds:

https://github.com/Unidata/netcdf4-python/commit/22d439d6d3602171dc2c23bca0ade31d3c49ad20

would it be possible to support in X-ray?
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/406/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
58310637,MDU6SXNzdWU1ODMxMDYzNw==,328,Support out-of-core computation using dask,1217238,closed,0,,987654,7,2015-02-20T05:02:22Z,2015-04-17T21:03:12Z,2015-04-17T21:03:12Z,MEMBER,,,,"[Dask](https://github.com/ContinuumIO/dask) is a library for out of core computation somewhat similar to [biggus](https://github.com/scitools/biggus) in conception, but with slightly grander aspirations. For examples of how Dask could be applied to weather data, see this blog post by @mrocklin: http://matthewrocklin.com/blog/work/2015/02/13/Towards-OOC-Slicing-and-Stacking/

It would be interesting to explore using dask internally in xray, so that we can implement lazy/out-of-core aggregations, concat and groupby to complement the existing lazy indexing. This functionality would be quite useful for xray, and even more so than merely supporting datasets-on-disk (#199).

A related issue is #79: we can easily imagine using Dask with groupby/apply to power out-of-core and multi-threaded computation.

Todos for xray:
- [x] refactor `Variable.concat` to make use of functions like `concatenate` and `stack` instead of in-place array modification (Dask arrays do not support mutation, for good reasons)
- [x] refactor `reindex_variables` to not make direct use of mutation (e.g., by using `da.insert` below)
- [x] add some sort of internal abstraction to represent ""computable"" arrays that are not necessarily `numpy.ndarray` objects (done: this is the `data` attribute)
- [x] expose `reblock` in the public API
- [x] load datasets into dask arrays from disk
- [x] load dataset from multiple files into dask
- [x] ~~some sort of API for user controlled lazy apply on dask arrays (using groupby, mostly likely)~~ (not necessary for initial release)
- [x] save from dask arrays
- [x] an API for lazy ufuncs like `sin` and `sqrt`
- [x] robustly handle indexing along orthogonal dimensions if dask can't handle it directly.

Todos for dask (to be clear, none of these are blockers for a proof of concept):
- [x] support for NaN skipping aggregations
- [x] ~~support for interleaved concatenation (necessary for transformations by group, which are quite common)~~ (turns out to be a one-liner with concatenate and take, see below)
- [x] ~~support for something like `take_nd` from pandas: like `np.take`, but with -1 as a sentinel value for ""missing"" (necessary for many alignment operations)~~ `da.insert`, modeled after `np.insert` would solve this problem.
- [x] ~~support ""orthogonal"" MATLAB-like array-based indexing along multiple dimensions~~ (taking along one axis at a time is close enough)
- [x] `broadcast_to`: see https://github.com/numpy/numpy/pull/5371
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58288666,MDU6SXNzdWU1ODI4ODY2Ng==,326,DataArray.groupby.apply with a generic ndarray function,2002703,closed,0,,987654,1,2015-02-19T23:37:34Z,2015-02-20T04:41:08Z,2015-02-20T04:41:08Z,CONTRIBUTOR,,,,"Need to apply a transformation function across one dimension of a DataArray, where that non-xray function speaks in ndarrays.  Currently the only ways to do this involve wrapping the function.  An example:

```
import numpy as np
import xray
from scipy.ndimage.morphology import binary_opening

da = xray.DataArray(np.random.random_integers(0, 1, (10, 10, 3)),
                    dims=['row', 'col', 'time'])

# I want to apply an operation the 2D image at each point in time
da.groupby('time').apply(binary_opening)
# AttributeError: 'numpy.ndarray' object has no attribute 'dims'

def wrap_binary_opening(da, **kwargs):
    return xray.DataArray(binary_opening(da.values, **kwargs), da.coords)

da.groupby('time').apply(wrap_binary_opening)
da.groupby('time').apply(wrap_binary_opening, iterations=2) # func may take custom args
```

My proposed solution is that `apply` would automatically coerce `func`'s return value to a DataArray.
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