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
```
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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]:
<xray.DataArray (index: 3)>
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]
```
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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
```
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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]:
<xray.DataArray (foo: 3, bar: 8)>
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]:
<xray.Dataset>
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)
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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.
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