Just checking, even though you likely know. The numpy arrays really only pay off when you vectorise your calculations. Don't expect any speed up if you're still using list comprehensions.
A vector operation is just where instead of having two lists, U and V which add to make W = [x+y for x,y in zip(U,V)] you directly operate on the arrays, W = U + V.
This allows the inner loop to be completely in native code. Sometimes some curious things happen where it's easier to do more vector operations resulting in more looping just because each of those operations is a vector operation and so is faster than the loop. For example, increment a number every time you see a particular element (time series breaking out subsequences) might look like np.cumsum(U == value) which is two loops in practice, but much faster than the iterative approach.
