Why does numpy reshape mess up my data pattern?

Let’s say I have the following array A –

import numpy as np
batch_size, seq_len = 3, 5
A = np.zeros((batch_size, seq_len))
A[0,0:] = 1
A[1,0:] = 2
A[2,0:] = 3

A has the following value –

array([[1., 1., 1., 1., 1.],
       [2., 2., 2., 2., 2.],
       [3., 3., 3., 3., 3.]])

Now, if I reshape it in the following way –

A4 = A.reshape(seq_len, -1)

array([[1., 1., 1.],
       [1., 1., 2.],
       [2., 2., 2.],
       [2., 3., 3.],
       [3., 3., 3.]])

However, I expected it to be –

array([[1., 2., 3.],
       [1., 2., 3.],
       [1., 2., 3.],
       [1., 2., 3.],
       [1., 2., 3.]])

Kudos to this awesome blog post bringing my attention to this problem – https://discuss.pytorch.org/t/for-beginners-do-not-use-view-or-reshape-to-swap-dimensions-of-tensors/75524

>Solution :

From the np.reshape docs

You can think of reshaping as first raveling the array (using the given index order), then inserting the elements from the raveled array into the new array using the same kind of index ordering as was used for the raveling.

a4 is (5,3) with the elements in the same order [1,1,1,1,1,2,2,...]