The cython code is a bit messy. Changing from:

    cpdef float iterate_list(a_list):

        cdef double count = 0
        cdef int i, j
        for i in range(len(a_list)):
            internal_list = a_list[i]
            for j in range(len(internal_list)):
                count += internal_list[j]
        print(count)
        return count

To:

    cpdef float iterate_list(list a_list):

        cdef double count = 0
        cdef double val = 0
        cdef list ilist
        for ilist in a_list:
            for val in ilist:
                count += val
        print(count)
        return count

Speeds up the iterate_list function an order of magnitude. On my PC:

    In [9]: %timeit list_cy.iterate_list(a_list)
    1000000.0007792843
    1000000.0007792843
    1000000.0007792843
    1000000.0007792843
    1000000.0007792843
    1000000.0007792843
    1000000.0007792843
    1000000.0007792843
    385 ms ± 6.15 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

    In [10]: %timeit list_cyo.iterate_list(a_list)
    1000000.0007792843
    1000000.0007792843
    1000000.0007792843
    1000000.0007792843
    1000000.0007792843
    1000000.0007792843
    1000000.0007792843
    1000000.0007792843
    2.71 s ± 182 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

(yeah, I kept the prints that the code has)

Where list_cy is the fixed code and list_cyo is the original code. Even then, iterating over a list of lists is _definitely not_ the optimal way you'd face a problem of this kind. Numpy arrays and memoryviews would be the correct tool to use.