### Question :

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

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
```

##
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
```