Save arbitrary Python objects to netCDF

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.

No files or tests are named. Start by examining xarray's netCDF serialization path and the netCDF4 backend boundary, then compare the proposed pickle-based variable-length uint8 approach with the stated security, accidental object-dtype, and performance concerns. Done would require an agreed scope and a supported save-and-retrieve behavior for arbitrary object arrays.