issues

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 ```

``` 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] ```

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. This is not currently implemented but good to get ahead of this hard to detect usage of the API.

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 ```

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 ```

``` 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

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!

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.