For elementwise multiplication of matrix objects, you can use numpy.multiply:

import numpy as np
a = np.array([[1,2],[3,4]])
b = np.array([[5,6],[7,8]])
np.multiply(a,b)

Result

array([[ 5, 12],
       [21, 32]])

However, you should really use array instead of matrix. matrix objects have all sorts of horrible incompatibilities with regular ndarrays. With ndarrays, you can just use * for elementwise multiplication:

a * b

If you're on Python 3.5+, you don't even lose the ability to perform matrix multiplication with an operator, because @ does matrix multiplication now:

a @ b  # matrix multiplication

Are you using arrays? If yes, you're in luck because the '*' is element by element multiplication and not matrix multiplication. Therefore:

import numpy as np

A = np.array([5, 4, 3, 2, 1])
B = np.array([[ 123.,    4.,    0.,    0.,    0.],
                    [   0.,    0.,    0.,   45.,    0.],
                    [   0.,    0.,  100.,   -5.,    0.]])

C = np.tile(A, (M, 1)) * B

D = A * B

And as result you get:

>>> C
array([[ 615.,   16.,    0.,    0.,    0.],
       [   0.,    0.,    0.,   90.,    0.],
       [   0.,    0.,  300.,  -10.,    0.]])
>>> D
array([[ 615.,   16.,    0.,    0.,    0.],
       [   0.,    0.,    0.,   90.,    0.],
       [   0.,    0.,  300.,  -10.,    0.]])

And if you're working with matrices, use multiply() to have element-wise multiplication (which is what you're trying to do)

https://docs.scipy.org/doc/numpy-dev/user/numpy-for-matlab-users.html#array-or-matrix-which-should-i-use

Element-wise multiplication of two vectors (also known as the Hadamard product) involves multiplying corresponding elements from each vector to produce a new vector of the same length. In NumPy, this can be easily achieved using either the * operator or the numpy.multiply function. Here's how you can perform element-wise multiplication with NumPy: 1. Using the * Operator The simplest way to perform element-wise multiplication is by using the * operator between two NumPy arrays (vectors). Example: import numpy as np # Define two vectors vector_a = np.array([1, 2, 3, 4]) vector_b = np.array([5, 6, 7, 8]) # Element-wise multiplication result = vector_a * vector_b print("Vector A:", vector_a) print("Vector B:", vector_b) print("Element-wise multiplication:", result) Output: Vector A: [1 2 3 4] Vector B: [5 6 7 8] Element-wise multiplication: [ 5 12 21 32] 2. Using numpy.multiply Alternatively, you can use the numpy.multiply function to achieve the same result. Example: import numpy as np # Define two vectors vector_a = np.array([1, 2, 3, 4]) vector_b = np.array([5, 6, 7, 8]) # Element-wise multiplication using numpy.multiply result = np.multiply(vector_a, vector_b) print("Element-wise multiplication:", result) Output: Element-wise multiplication: [ 5 12 21 32] Important Considerations Same Shape or Broadcastable Shapes:For element-wise multiplication to work, the two vectors must have the same shape. Alternatively, NumPy's broadcasting rules can apply if the shapes are compatible. Broadcasting allows NumPy to perform operations on arrays of different shapes under certain conditions.