Divide ndarray by scalar - Numpy / Python

Question

I'm just wondering how could I do such thing without using loops.

I made a simple test trying to call a division as we do with a numpy.array, but I got the same ndarray.

N = 2
M = 3

matrix_a = np.array([[15., 27., 360.],
            [180., 265., 79.]])
matrix_b = np.array([[.5, 1., .3], 
            [.25, .7, .4]])

matrix_c = np.zeros((N, M), float)

n_size = 360./N
m_size = 1./M

for i in range(N):
    for j in range(M):
        n = int(matrix_a[i][j] / n_size) % N
        m = int(matrix_b[i][j] / m_size) % M
        matrix_c[n][m] += 1 

matrix_c / (N * M)
print matrix_c  

I guess this should be pretty simple. Any help would be appreciated.

Answer 1

I think that you want to modify matrix_c in-place:

matrix_c /= (N * M)

Or probably less effective:

matrix_c = matrix_c / (N * M) 

Expression matrix_c / (N * M) doesn't change matrix_c - it creates a new matrix.

Answer 2

Another solution would be to use numpy.divide

matric_c = np.divide(matrix_c, N*M)

Just make sure N*M is a float in case your looking for precision.