When to use .shape and when to use .reshape?

I ran into a memory issue while trying to use .reshape

numpy in an array and figured out if I could somehow modify the array in place, which would be great.

I figured out that I can resize the array just by changing the value .shape

. Unfortunately, when I tried to use .shape

, I got a memory error again, which made me think it hadn't changed in place.

I was wondering when do I use it, when do I use another one?

Any help is appreciated.

If you would like more information, please let me know.

EDIT:

I added my code and how the matrix I want to change is generated in case it matters.

Change the N value depending on your memory.

import numpy as np
N = 100
a = np.random.rand(N, N)
b = np.random.rand(N, N)
c = a[:, np.newaxis, :, np.newaxis] * b[np.newaxis, :, np.newaxis, :]
c = c.reshape([N*N, N*N])
c.shape = ([N, N, N, N])

      

        
        
        
      

    

EDIT2: This is the best view. Apparently, transposition is important because it changes arrays from C-adjacent to F-adjacent, and the resulting multiplication is adjacent in the above case, while it is not in the following.

import numpy as np
N = 100
a = np.random.rand(N, N).T
b = np.random.rand(N, N).T
c = a[:, np.newaxis, :, np.newaxis] * b[np.newaxis, :, np.newaxis, :]
c = c.reshape([N*N, N*N])
c.shape = ([N, N, N, N])