I’m writing code which does matrix operations in functions and I’m using numpy arrays to store my matrices. From reading books/ discussions on here I have got as far as understanding that scalar arguments of functions are passed by value and arrays are passed in C-like style (in C we would say a pointer to the array is being passed and an equivalent thing is happening in Python).

Here are two simple Python codes, the first successfully multiplies 2 matrices together and passes the matrix back to the calling code. I achieved this by hand-coding the matrix multiplication following a hint I saw on here (or a similar forum). In the 2nd I replace the hand-coded matrix multiplication by c=np.dot(a,b) and the matrix product doesn’t get passed back.

How do I get the 2nd approach to work?

Example 1:

import numpy as np

def my_func(a,b,c):

for i in range (0,2):

for j in range (0,2):

c[i,j]=0.

for k in range(0,2):

c[i,j]=c[i,j]+a[i,k]*b[k,j]

print(“c”)

print(c)

d = np.array([[1,2],[3,4]])

e = np.array([[-1,3],[2,-1]])

f = np.zeros((2,2))

my_func(d,e,f)

print(“f”)

print(f)

Output from example 1:

c

[[3. 1.]

[5. 5.]]

f

[[3. 1.]

[5. 5.]]

Example 2:

import numpy as np

def my_func(a,b,c):

c=np.dot(a,b)

print(“c”)

print(c)

d = np.array([[1,2],[3,4]])

e = np.array([[-1,3],[2,-1]])

f = np.zeros((2,2))

my_func(d,e,f)

print(“f”)

print(f)

Output from example 2:

c

[[3 1]

[5 5]]

f

[[0. 0.]

[0. 0.]]