With itertools.chain() on the values
This could be faster:
from itertools import chain
categories = list(chain.from_iterable(categories.values))
Performance
from functools import reduce
from itertools import chain
categories = pd.Series([['a', 'b'], ['c', 'd', 'e']] * 1000)
%timeit list(chain.from_iterable(categories.values))
1000 loops, best of 3: 231 µs per loop
%timeit list(chain(*categories.values.flat))
1000 loops, best of 3: 237 µs per loop
%timeit reduce(lambda l1, l2: l1 + l2, categories)
100 loops, best of 3: 15.8 ms per loop
For this data set the chaining is about 68x faster.
Vectorization?
Vectorization works when you have native NumPy data types (pandas uses NumPy for its data after all). Since we have lists in the Series already and want a list as result, it is rather unlikely that vectorization will speed things up. The conversion between standard Python objects and pandas/NumPy data types will likely eat up all the performance you might get from the vectorization. I made one attempt to vectorize the algorithm in another answer.