castra 'TypeError: data type not understood' when storing categoricals

```
dd = df.from_pandas(d, npartitions=2)

dd.dtypes
Out[49]:
a int64
b datetime64[ns]
c object
dtype: object

c = dd.to_castra('delme0.castra')

c = None

c = dd.to_castra('delme1.castra', categories=True)

c
Out[54]:

ee = df.from_castra('delme1.castra')

ee
Out[56]: dd.DataFrame

ee.dtypes
Out[57]:
a int64
b datetime64[ns]
c category
dtype: object

c = ee.to_castra('delme2.castra')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
in ()
----> 1 c = ee.to_castra('delme2.castra')

/Applications/anaconda/envs/python3/lib/python3.5/site-packages/dask/dataframe/core.py in to_castra(self, fn, categories, sorted_index_column, compute)
1409 from .io import to_castra
1410 return to_castra(self, fn, categories, sorted_index_column,
-> 1411 compute=compute)
1412
1413 def to_bag(self, index=False):

/Applications/anaconda/envs/python3/lib/python3.5/site-packages/dask/dataframe/io.py in to_castra(df, fn, categories, sorted_index_column, compute)
769 keys = [(name, -1), (name, df.npartitions - 1)]
770 if compute:
--> 771 c, _ = DataFrame._get(dsk, keys, get=get_sync)
772 return c
773 else:

/Applications/anaconda/envs/python3/lib/python3.5/site-packages/dask/base.py in _get(cls, dsk, keys, get, **kwargs)
41 get = get or _globals['get'] or cls._default_get
42 dsk2 = cls._optimize(dsk, keys, **kwargs)
---> 43 return get(dsk2, keys, **kwargs)
44
45 @classmethod

/Applications/anaconda/envs/python3/lib/python3.5/site-packages/dask/async.py in get_sync(dsk, keys, **kwargs)
514 queue = Queue()
515 return get_async(apply_sync, 1, dsk, keys, queue=queue,
--> 516 raise_on_exception=True, **kwargs)
517
518

/Applications/anaconda/envs/python3/lib/python3.5/site-packages/dask/async.py in get_async(apply_async, num_workers, dsk, result, cache, queue, get_id, raise_on_exception, rerun_exceptions_locally, callbacks, **kwargs)
485 f(key, res, dsk, state, worker_id)
486 while state['ready'] and len(state['running']) < num_workers:
--> 487 fire_task()
488
489 # Final reporting

/Applications/anaconda/envs/python3/lib/python3.5/site-packages/dask/async.py in fire_task()
456 # Submit
457 apply_async(execute_task, args=[key, dsk[key], data, queue,
--> 458 get_id, raise_on_exception])
459
460 # Seed initial tasks into the thread pool

/Applications/anaconda/envs/python3/lib/python3.5/site-packages/dask/async.py in apply_sync(func, args, kwds)
506 def apply_sync(func, args=(), kwds={}):
507 """ A naive synchronous version of apply_async """
--> 508 return func(*args, **kwds)
509
510

/Applications/anaconda/envs/python3/lib/python3.5/site-packages/dask/async.py in execute_task(key, task, data, queue, get_id, raise_on_exception)
262 """
263 try:
--> 264 result = _execute_task(task, data)
265 id = get_id()
266 result = key, result, None, id

/Applications/anaconda/envs/python3/lib/python3.5/site-packages/dask/async.py in _execute_task(arg, cache, dsk)
243 elif istask(arg):
244 func, args = arg[0], arg[1:]
--> 245 args2 = [_execute_task(a, cache) for a in args]
246 return func(*args2)
247 elif not ishashable(arg):

/Applications/anaconda/envs/python3/lib/python3.5/site-packages/dask/async.py in (.0)
243 elif istask(arg):
244 func, args = arg[0], arg[1:]
--> 245 args2 = [_execute_task(a, cache) for a in args]
246 return func(*args2)
247 elif not ishashable(arg):

/Applications/anaconda/envs/python3/lib/python3.5/site-packages/dask/async.py in _execute_task(arg, cache, dsk)
244 func, args = arg[0], arg[1:]
245 args2 = [_execute_task(a, cache) for a in args]
--> 246 return func(*args2)
247 elif not ishashable(arg):
248 return arg

/Applications/anaconda/envs/python3/lib/python3.5/site-packages/castra/core.py in extend(self, df)
217 # Store columns
218 for col in df.columns:
--> 219 pack_file(df[col].values, self.dirname(partition_name, col))
220
221 # Store index

/Applications/anaconda/envs/python3/lib/python3.5/site-packages/castra/core.py in pack_file(x, fn, encoding)
390 if x.dtype != 'O':
391 bloscpack.pack_ndarray_file(x, fn, bloscpack_args=bp_args,
--> 392 blosc_args=blosc_args(x.dtype))
393 else:
394 bytes = blosc.compress(msgpack.packb(x.tolist(), encoding=encoding), 1)

/Applications/anaconda/envs/python3/lib/python3.5/site-packages/castra/core.py in blosc_args(dt)
28
29 def blosc_args(dt):
---> 30 if np.issubdtype(dt, int):
31 return bloscpack.BloscArgs(dt.itemsize, clevel=3, shuffle=True)
32 if np.issubdtype(dt, np.datetime64):

/Applications/anaconda/envs/python3/lib/python3.5/site-packages/numpy/core/numerictypes.py in issubdtype(arg1, arg2)
759 else:
760 val = mro[0]
--> 761 return issubclass(dtype(arg1).type, val)
762
763

TypeError: data type not understood
```

Start with castra/core.py at blosc_args and pack_file, then reproduce the shown to_castra round trip with categories=True. Confirm the categorical column triggers the failure, and consider the issue done when storing the restored dataframe completes without the TypeError and the categorical data remains usable.

Written by the indexing model from the issue text.