Function call changing its argument python

Question

I am trying to reduce a matrix to its echlon form using a function but upon the calling the function, it is also changing its arguments. Here is the code

def GaussE(E,r):
    N = np.size(r)
    for i in range(0,N-1):
        for j in range(i+1,N):
            f=(E[j,i]/E[i,i])
            r[j]= r[j] -(f)*r[i]             
            for k in range(i,N):                          
                E[j,k] = E[j,k]-(f*E[i,k])  
    return(E,r)
A = np.array([[5.10,8.70],[2.40,4.10]])
b = np.array([9.48,4.48])
print(A,b)

output: [[ 5.1 8.7] [ 2.4 4.1]] [ 9.48 4.48]

X = GaussE(A[:],b[:])
print(A,b) # Why is A and b changing? they should not change

output:[[ 5.10000000e+00 8.70000000e+00] [ 0.00000000e+00 5.88235294e-03]] [ 9.48 0.01882353]

Answer 1

In python every variable refers to an object. It refers to the object pointer so it is using the object itself.
In that function you are referencing E like E[j,k] and r like r[j] and assigning to it which means you are using that same object and means you are manipulating it.
Try to use copy at the first of your function (from copy import copy)

from copy import copy

def GaussE(E,r):
    E = copy(E)
    r = copy(r)
    N = np.size(r)
    for i in range(0,N-1):
        for j in range(i+1,N):
            f=(E[j,i]/E[i,i])
            r[j]= r[j] -(f)*r[i]             
            for k in range(i,N):                          
                E[j,k] = E[j,k]-(f*E[i,k])  
    return(E,r)
A = np.array([[5.10,8.70],[2.40,4.10]])
b = np.array([9.48,4.48])
print(A,b)