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I am trying to use NumPy and vectorization operations to make a section of code run faster. I appear to have a misunderstanding of how to vectorize this code, however (probably due to an incomplete understanding of vectorization). Here's the working code with loops (A and B are 2D arrays of a set size, already initialized): for k in range(num_v): B[:] = A[:] for i in range(num_v): for j in range(num_v): A[i][j] = min(B[i][j], B[i][k] + B[k][j]) return A And here is my attempt at vectorizing the above code: for k in range(num_v): B = numpy.copy(A) A = numpy.minimum(B, B[:,k] + B[k,:]) return A For testing these, I used the following, with the code above wrapped in a function called 'algorithm': def setup_array(edges, num_v): r = range(1, num_v + 1) A = [[None for x in r] for y in r] # or (numpy.ones((num_v, num_v)) * 1e10) for numpy for i in r: for j in r: val = 1e10 if i == j: val = 0 elif (i,j) in edges: val = edges[(i,j)] A[i-1][j-1] = val return A A = setup_array({(1, 2): 2, (6, 4): 1, (3, 2): -3, (1, 3): 5, (3, 6): 5, (4, 5): 2, (3, 1): 4, (4, 3): 8, (3, 4): 6, (2, 4): -4, (6, 5): -5}, 6) B = [] algorithm(A, B, 6) The expected outcome, and what I get with the first code is: [[0, 2, 5, -2, 0, 10] [8, 0, 4, -4, -2, 9] [4, -3, 0, -7, -5, 5] [12, 5, 8, 0, 2, 13] [10000000000.0, 9999999997.0, 10000000000.0, 9999999993.0, 0, 10000000000.0] [13, 6, 9, 1, -5, 0]] The second (vectorized) function instead returns: [[ 0. -4. 0. 0. 0. 0.] [ 0. -4. 0. -4. 0. 0.] [ 0. -4. 0. 0. 0. 0.] [ 0. -4. 0. 0. 0. 0.] [ 0. -4. 0. 0. 0. 0.] [ 0. -4. 0. 0. -5. 0.]] What am I missing?

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