A numpy array unexpectedly changes when changing another one despite being separate

Question

I found a bug in my large code, and I simplified the issue to the case below.

Although in each step I only change w2, but when at each step I print out w1, it is also changed, because end of the first loop I assign them to be equal. I read for this but there was written in case I make w1 = w2[:] it will solve the issue but it does not

import numpy as np
import math
w1=np.array([[1,2,3],[4,5,6],[7,8,9]])
w2=np.zeros_like(w1)
print 'w1=',w1
for n in range(0,3):
 for i in range(0,3):
 for j in range(0,3):
 print 'n=',n,'i=',i,'j=',j,'w1=',w1
 w2[i,j]=w1[i,j]*2
 w1=w2[:]
#Simple tests
# w=w2[:]
# w1=w[:]
# p=[1,2,3]
# q=p[:];
# q[1]=0;
# print p

• Brian Cain: You're assigning a copy of w2 to w1 each time, only after modifying w1, correct? But you don't expect w1 to remain [[1,2,3],[4,5,6],[7,8,9]], do you?
• Suever: Why not just do w2 = w1 * 2?
• Brian Cain: Ugh, yes, thanks @Suever -- let's take a step back: what are you trying to do?
• Soyol: I start with initial values for w1, then in loops at each step of n I assign values to w2, and when I did for its all elements then I feed it in to w1, and make w1 equal to that. but when for the first time I do this(i.e. w1=w2), whenever I change w2 during loop w1 changes as well immediately.
• Soyol: @BrianCain the reason for that is just I want to make an example of my code, there it is more complicated. okay. take w2[i,j]=w1[i,j]+(2*i+j) instead

Answer 1

The issue is that when you're assigning values back to w1 from w2 you aren't actually passing the values from w1 to w2, but rather you are actually pointing the two variables at the same object.

The issue you are having

w1 = np.array([1,2,3])
w2 = w1
w2[0] = 3
print(w2) # [3 2 3]
print(w1) # [3 2 3]
np.may_share_memory(w2, w1) # True

The Solution

Instead you will want to copy over the values. There are two common ways of doing this with numpy arrays.

w1 = numpy.copy(w2)
w1[:] = w2[:]

Demonstration

w1 = np.array([1,2,3])
w2 = np.zeros_like(w1)
w2[:] = w1[:]
w2[0] = 3
print(w2) # [3 2 3]
print(w1) # [1 2 3]
np.may_share_memory(w2, w1) # False

• Soyol: Thank you, that is my mistake
• ivan_pozdeev: Do you assert that w1=w2[:] makes them point to the same object? It's not like this with lists.
• Suever: @ivan_pozdeev Yes this is correct. Typically a numpy array created simply using an indexing operation (such as w2[:]) is simply a different "view" of the same underlying data in memory. This can be verified using numpy.may_share_memory(w1, w2)
• Suever: @ivan_pozdeev here is a great answer that goes over some of the finer points of memory management in numpy