为什么这些在Python中定义多个数组的方法给出不同的答案？

Question

I'm currently writing a programme in which two arrays are defined and then undergo singular value decomposition followed by some other processes to simulate some physics. The arrays should be of dimension D² x D². I initially tried:

Ma, Mb = ( np.zeros([ pow(D, 2), pow(D,2)]) for i in range(2))

followed by

Ma = np . zeros ([ pow(D,2), pow(D,2)])
Mb = np . zeros ([ pow(D,2), pow(D,2)])

and finally

Ma , Mb = [np . zeros ([ pow(D,2), pow(D,2)])]*2

I've found that defining the arrays in different ways affects my final answer, with only the final way giving me the answer I expect. The first two options give me nonsensical answers. Are these different methods actually equivalent or am I missing something?

Edit:

The code that follows is:

for i in D_array:
    for j in D_array:
        for k in D_array:
            for l in D_array:
                Ma[k + D * j][i + D * l] = T[i][j][k][l]
                Mb[k + D * l][i + D * j] = T[i][j][k][l]

where T and D_array were previously defined. After this Ma and Mb undergo SVD,

Answer 1

The last way will have Ma is Mb return true. I find it surprising that that'd be the case you desire

Essentially the last one is equivalent to Mb = Ma = np . zeros ([ pow(D,2), pow(D,2)]) Mb = Ma = np . zeros ([ pow(D,2), pow(D,2)])