How can I iterate over an array of numpy arrays row by row efficiently?

I have a large list of lists that I'm converting to a numpy array, and then iterating through, something like this:

a = [ [0,1,2], [3,4,5], [6,7,8] ]
np_a = np.array(a)

for i in np_a:
    print i # prints [0,1,2], then [3,4,5], then [6,7,8] as expected
    # do a bunch of stuff to each row

Of course this works, but since I'm dealing with a large body of data, this can take up to a minute to run. I've been looking into various ways to speed up this code, with the number one recommendation being to vectorize the operations I'm performing within the loop. However, the operations are not trivial to vectorize (think a bunch of dot products and transforms and such on each row...). One method I've found is to use the np.nditer method, like this:

a = [ [0,1,2], [3,4,5], [6,7,8] ]
np_a = np.array(a)

for i in np.nditer(np_a):
    print i # prints 0, then 1, then 2, etc...
    # do a bunch of stuff to each rowon 

But this seems to flatten the array into a 1-D list. I've looked through the documentation pretty extensively, and haven't found what I'm looking for here: https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.nditer.html#iterating-as-a-specific-data-type

Does anyone have any specific recommendations on how to make this work as I expect with np.nditer, or some other implementation that's more efficient than the standard 'for r in a' method?