I am using Ubuntu 16, python 3.6, and xarray 0.9.1

A DataArray can be stacked along one dimension, but when unstack is called a ValueError is raised. It seems that either unstack should work, or calling stack should also raise a ValueError.

```python import xarray as xr

dims = ['a', 'b'] coords = {'a': range(2), 'b':range(2)} values = [[0, 0], [0, 0]]

dar = xr.DataArray(values, coords, dims)

stacked = dar.stack(z=('a',)) # this works

unstack = stacked.unstack('z') # this raises ValueError

```

I am using Ubuntu 16, python 3.6, and xarray 0.9.1

Consider the following code: ```python import xarray as xr import numpy as np

dims = ['a', 'b'] coords = {'a': range(2), 'b':range(2), 'group': (('a', 'b'), [[0, 0], [0, 1]])} values = [[0, 0], [0, 0]]

dar = xr.DataArray(values, coords, dims)

dar[dict(group=0)] = 1 # group is only 0 for three of the four elements

expected_values = np.array([[1, 1], [1, 0]])

yet this raises because all four values are set to 1

assert np.all(np.isclose(dar.values, expected_values)) ```

I suspect trying to assign values in this way using a multi-dimensional coordinate should raise a ValueError as this does:

python dar[dict(group=0)]

I am using Ubuntu 16, python 3.6, and xarary 0.9.5.

```python import numpy as np import xarray as xr

setup for a simple grid

DX = 50 X = np.arange(0, 2010, DX) Y = np.arange(0, 2010, DX) Z = np.arange(0, 2010, DX) grid_shape = (len(X), len(Y), len(Z))

Create data array

dims = 'X Y Z'.split() coords = {'X': X, 'Y': Y, 'Z': Z} dar = xr.DataArray(np.ones(grid_shape), dims=dims, coords=coords)

slice the data array so that all Z values are greater than 1000

dar2 = dar.loc[dar.Z > 1000] assert np.all(dar2.Z > 1000) # fails becase dar is sliced along X, not Z ``` Since the object returned from dar.Z > 1000 is a data array with "Z" as the only dim I would expect this to slice the "Z" dim rather than X.

I am using: Ubuntu 16.04 python 3.6.0 xarray 0.9.1 scipy 0.18.1

The documentation states that if no path argument is passed to the to_netcdf method of the DataArray class it returns a bytes object. However, it returns None.

python import xarray as xr dar = xr.DataArray([1, 2, 3]) output = dar.to_netcdf() assert isinstance(output, bytes) # fails because output is None

It appears that the DataArray is not using the bottleneck move_var when calling var on a rolling object but it does for std. I have attached some simple benchmarks to demonstrate.

python version 3.6 pandas version 0.19.2 xarray version 0.9.1 bottleneck version 1.2.0

```python import numpy as np import xarray as xr import bottleneck import pandas as pd

data = np.random.rand(10000)

xarray setup

ar = xr.DataArray(data, dims=['time']) xrol = ar.rolling(time=30)

pandas setup

df = ar.to_pandas() prol = df.rolling(30) ```

```python

xray std profile

%time _ = xrol.std() ```

CPU times: user 0 ns, sys: 0 ns, total: 0 ns
Wall time: 209 µs

```python

xray var profile

%time _ = xrol.var() ```

CPU times: user 8.69 s, sys: 132 ms, total: 8.82 s
Wall time: 8.52 s

```python

pandas std profile

%time _ = prol.std() ```

CPU times: user 0 ns, sys: 0 ns, total: 0 ns
Wall time: 1.13 ms

```python

pandas var profile

%time _ = prol.var() ```

CPU times: user 0 ns, sys: 0 ns, total: 0 ns
Wall time: 617 µs

```python

bottleneck std profile

%time _ = bottleneck.move_std(data, 30) ```

CPU times: user 0 ns, sys: 0 ns, total: 0 ns
Wall time: 138 µs

```python

bottleneck var profile

%time _ = bottleneck.move_var(data, 30) ```

CPU times: user 0 ns, sys: 0 ns, total: 0 ns
Wall time: 143 µs

fixed a small type in the docs

• [ ] closes #xxxx
• [ ] tests added / passed
• [ ] passes git diff upstream/master | flake8 --diff
• [ ] whatsnew entry