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 124573556,MDU6SXNzdWUxMjQ1NzM1NTY=,697,TST: add test for converting 0-len Series,953992,closed,0,,,7,2016-01-02T03:48:48Z,2019-02-02T07:47:58Z,2019-02-02T07:47:58Z,MEMBER,,,,"are these supported? ``` In [3]: xray.DataArray.from_series(Series([])) ValueError: dimensions () must have the same length as the number of data dimensions, ndim=1 In [5]: xray.Dataset.from_dataframe(DataFrame({'A' : []})) ValueError: dimensions () must have the same length as the number of data dimensions, ndim=1 ``` ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/697/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 124685682,MDU6SXNzdWUxMjQ2ODU2ODI=,701,BUG: not converting datetime64[ns] with tz from pandas.Series,953992,closed,0,,,3,2016-01-04T02:04:27Z,2019-01-22T19:36:41Z,2019-01-22T19:36:41Z,MEMBER,,,,"``` In [5]: s = Series(pd.date_range('20130101',periods=3,tz='US/Eastern')) In [6]: s Out[6]: 0 2013-01-01 00:00:00-05:00 1 2013-01-02 00:00:00-05:00 2 2013-01-03 00:00:00-05:00 dtype: datetime64[ns, US/Eastern] In [7]: xray.DataArray.from_series(s) Out[7]: array(['2013-01-01T00:00:00.000000000-0500', '2013-01-02T00:00:00.000000000-0500', '2013-01-03T00:00:00.000000000-0500'], dtype='datetime64[ns]') Coordinates: * index (index) int64 0 1 2 In [8]: xray.DataArray.from_series(s).to_pandas() Out[8]: index 0 2013-01-01 05:00:00 1 2013-01-02 05:00:00 2 2013-01-03 05:00:00 dtype: datetime64[ns] ``` ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/701/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 288866396,MDU6SXNzdWUyODg4NjYzOTY=,1833,COMPAT: MultiIndex checking is fragile,953992,closed,0,,,1,2018-01-16T11:02:38Z,2018-02-16T21:02:58Z,2018-02-16T21:02:58Z,MEMBER,,,,"https://github.com/pydata/xarray/blob/master/xarray/core/dataset.py#L2755 checking for a MI via ``` if hasattr(idx, 'levels'): # is a MI ``` should be changed to ``` if idx.nlevels > 1: # is a MI ``` see [here](https://github.com/pandas-dev/pandas/issues/3268#issuecomment-357925936). This is not currently implemented but good to get ahead of this hard to detect usage of the API.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1833/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 288867371,MDU6SXNzdWUyODg4NjczNzE=,1834,DEPR: .asobject,953992,closed,0,,,1,2018-01-16T11:06:04Z,2018-01-16T15:01:27Z,2018-01-16T15:01:27Z,MEMBER,,,,"xarray 0.10.0. instead of ``.asobject``, use ``.astype(object)`` ``` pandas/tests/generic/test_series.py::TestSeries::()::test_to_xarray_index_types[PeriodIndex] /Users/jreback/miniconda3/envs/pandas/lib/python3.6/site-packages/xarray/core/indexing.py:728: FutureWarning: 'asobject' is deprecated. Use 'astype(object)' instead array = array.asobject -- Docs: http://doc.pytest.org/en/latest/warnings.html ```","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1834/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 124665607,MDU6SXNzdWUxMjQ2NjU2MDc=,700,BUG: not converting series with CategoricalIndex,953992,closed,0,,741199,2,2016-01-03T19:05:59Z,2017-02-01T16:56:56Z,2017-02-01T16:56:56Z,MEMBER,,,,"xray 0.6.1 ``` In [1]: s = Series(range(5),index=pd.CategoricalIndex(list('aabbc'),name='foo')) In [4]: xray.DataArray.from_series(s) ValueError: object __array__ method not producing an array ``` ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/700/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 124664101,MDU6SXNzdWUxMjQ2NjQxMDE=,699,BUG: Dataset.from_dataframe() losing dims?,953992,closed,0,,,4,2016-01-03T18:42:52Z,2016-01-04T02:05:33Z,2016-01-04T02:05:33Z,MEMBER,,,,"``` In [16]: df = DataFrame({'a': list('abc'), 'b': list(range(1, 4)), 'c': np.arange(3, 6).astype('u1'), 'd': np.arange(4.0, 7.0, dtype='float64'), 'e': [True, False, True], 'f': pd.Categorical(list('abc')), 'g': pd.date_range('20130101', periods=3), 'h': pd.date_range('20130101', periods=3, tz='US/Eastern')} ) In [17]: df.index.name = 'foo' In [18]: df.columns.name = 'bar' In [19]: df Out[19]: bar a b c d e f g h foo 0 a 1 3 4 True a 2013-01-01 2013-01-01 00:00:00-05:00 1 b 2 4 5 False b 2013-01-02 2013-01-02 00:00:00-05:00 2 c 3 5 6 True c 2013-01-03 2013-01-03 00:00:00-05:00 In [20]: df.dtypes Out[20]: bar a object b int64 c uint8 d float64 e bool f category g datetime64[ns] h datetime64[ns, US/Eastern] dtype: object ``` ``` In [21]: import xray In [22]: xray.__version__ Out[22]: '0.6.1' In [23]: xray.Data xray.DataArray xray.Dataset In [23]: xray.DataArray(df) Out[23]: array([['a', 1, 3, 4.0, True, 'a', Timestamp('2013-01-01 00:00:00'), Timestamp('2013-01-01 00:00:00-0500', tz='US/Eastern')], ['b', 2, 4, 5.0, False, 'b', Timestamp('2013-01-02 00:00:00'), Timestamp('2013-01-02 00:00:00-0500', tz='US/Eastern')], ['c', 3, 5, 6.0, True, 'c', Timestamp('2013-01-03 00:00:00'), Timestamp('2013-01-03 00:00:00-0500', tz='US/Eastern')]], dtype=object) Coordinates: * foo (foo) int64 0 1 2 * bar (bar) object 'a' 'b' 'c' 'd' 'e' 'f' 'g' 'h' In [24]: xray.Dat xray.DataArray xray.Dataset In [24]: xray.Dataset.from_dataframe(df) Out[24]: Dimensions: (foo: 3) Coordinates: * foo (foo) int64 0 1 2 Data variables: a (foo) object 'a' 'b' 'c' b (foo) int64 1 2 3 c (foo) uint8 3 4 5 d (foo) float64 4.0 5.0 6.0 e (foo) bool True False True f (foo) category 'a' 'b' 'c' g (foo) datetime64[ns] 2013-01-01 2013-01-02 2013-01-03 h (foo) datetime64[ns] 2013-01-01T05:00:00 2013-01-02T05:00:00 ... ``` so [23] is a bit unsatisfying as it basically gloms together things. But [24] doesn't appear correct as its now 3 dims? what happened to bar? this in the context of `.to_xray()` (from pandas). coming shortly. see [here](https://github.com/jreback/pandas/commit/7d581db564c334707eb51fee070275902ad63ed2) ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/699/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 106053954,MDU6SXNzdWUxMDYwNTM5NTQ=,571,pandas v0.17.0rc1,953992,closed,0,,,1,2015-09-11T17:08:08Z,2015-09-11T17:31:11Z,2015-09-11T17:31:11Z,MEMBER,,,,"we have some fairly significant changes and would like you to test and provide any feedback: https://github.com/pydata/pandas/releases/tag/v0.17.0rc1 thanks! ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/571/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 28262599,MDU6SXNzdWUyODI2MjU5OQ==,18,ENH: NETCDF4 in pandas,953992,closed,0,,,2,2014-02-25T16:48:17Z,2014-04-28T07:02:43Z,2014-04-28T07:02:43Z,MEMBER,,,,"see this related issue: https://github.com/pydata/pandas/issues/5487 this is actually not hard to do, and might allow you to push some of your backends to pandas. ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/18/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue