issues

For a DataArray, when reusing a coordinate for multiple dimensions (is this expected usage?), it only shows once in the repr:

```python

x = xr.IndexVariable(data=range(5), dims='x') da = xr.DataArray(data=np.random.randn(5, 5), coords={'x': x}, dims=('x', 'x')) da <xarray.DataArray (x: 5)> array([[ 0.704139, 0.135638, -0.84717 , -0.580167, 0.95755 ], [ 0.966196, -0.126107, 0.547461, 1.075547, -0.477495], [-0.507956, -0.671571, 1.271085, 0.007741, -0.37878 ], [-0.969021, -0.440854, 0.062914, -0.3337 , -0.775898], [ 0.86893 , 0.227861, 1.831021, 0.702769, 0.868767]]) Coordinates: * x (x) int64 0 1 2 3 4 ```

I think it should be

python <xarray.DataArray (x: 5, x: 5)> array([[ ... ]]) Coordinates: * x (x) int64 0 1 2 3 4

Otherwise, everything appears to work exactly as I would expect.

This isn't an issue for Datasets:

```python

xr.Dataset({'da': da}) <xarray.Dataset> Dimensions: (x: 5) Coordinates: * x (x) int64 0 1 2 3 4 Data variables: da (x, x) float64 0.08976 0.1049 -1.291 -0.4605 -0.005165 -0.3259 ... ```

Thanks!

At the moment, bool and dates aren't supported as attributes when serializing to netcdf4:

```python

da = xr.DataArray(range(5), attrs={'test': True}) da <xarray.DataArray (dim_0: 5)> array([0, 1, 2, 3, 4]) Dimensions without coordinates: dim_0 Attributes: test: True

da.to_netcdf('test_bool.nc') ... TypeError: illegal data type for attribute, must be one of dict_keys(['S1', 'i1', 'u1', 'i2', 'u2', 'i4', 'u4', 'i8', 'u8', 'f4', 'f8']), got b1

da = xr.DataArray(range(5), attrs={'test': pd.to_datetime('now')}) da <xarray.DataArray (dim_0: 5)> array([0, 1, 2, 3, 4]) Dimensions without coordinates: dim_0 Attributes: test: 2017-08-03 13:02:29

da.to_netcdf('test_dt.nc') ... TypeError: Invalid value for attr: 2017-08-03 13:02:29 must be a number string, ndarray or a list/tuple of numbers/strings for serialization to netCDF files ```

I assume bool attributes aren't supported by netcdf4-python and dates are difficult (could always just write these as a string), but this would be really nice to have if possible.

As an aside, using h5netcdf works for bools, but coerces them to int64.

I am looking to transition from pandas to xarray, and the only feature that I am really missing is the ability to seamlessly save arrays of python objects to hdf5 (or netCDF). This might be an issue for the backend netCDF4 libraries instead, but I thought I would post it here first to see what the opinions were about this functionality.

For context, Pandas allows this by using pytables' ObjectAtom to serialize the object using pickle, then saves as a variable length bytes data type. It is already possible to do this using netCDF4, by applying to each object in the array np.fromstring(pickle.dumps(obj), dtype=np.uint8), and saving these using a uint8 VLType. Then retrieving is simply pickle.reads(obj.tostring()) for each array.

I know pickle can be a security problem, it can cause an problem if you try to save a numerical array that accidently has dtype=object (pandas gives a warning), and that this is probably quite slow (I think pandas pickles a list containing all the objects for speed), but it would be incredibly convenient.