using numpy reshape for column major in multi-dimensional array in python

I am trying to convert a string into n-dimensioned numpy array (x, 4, 4). Basic requirement is 4x4 array with column major filling of values. We will use as many 4x4 arrays as per the length of the input string. For example if my string is:

'A quick brown fox jumps over dog'

The resultant array should look like this:

[[['A' 'i' 'b' 'n']
  [' ' 'c' 'r' ' ']
  ['q' 'k' 'o' 'f']
  ['u' ' ' 'w' 'o']]

 [['x' 'm' 'o' ' ']
  [' ' 'p' 'v' 'd']
  ['j' 's' 'e' 'o']
  ['u' ' ' 'r' 'g']]]

Note that instead of the conventional row-first filling of values requirement is for the filling to be column first within the 4x4 subarray.

I understand the conventional code for achieving this would be something like below with a triple-nested loop:

string = 'A quick brown fox jumps over dog'
dim1 = len(string) // 16
matrix1 = np.empty((dim1, 4, 4), str)
position = 0
for z in range(dim1):
    for y in range(4):
        for x in range(4):
            matrix1[z, x, y] = string[position]
            position += 1
print(matrix1)

I wanted to use functional power of Python, so after some research I found the following method:

string = 'A quick brown fox jumps over dog'
dim1 = len(string) // 16
matrix2 = np.array(list(string))
matrix2 = np.reshape(matrix2, (dim1, 4, 4))

But when I do the reshape, it gives me the following output:

[[['A' ' ' 'q' 'u']
  ['i' 'c' 'k' ' ']
  ['b' 'r' 'o' 'w']
  ['n' ' ' 'f' 'o']]

 [['x' ' ' 'j' 'u']
  ['m' 'p' ' ' 'o']
  ['v' 'e' 'r' ' ']
  ['d' 'o' 'g' 's']]]

which is completing the string row by row. I want it column by column within the sub (4x4) array and then move on to the next (4x4) one.

Further research showed that I can use the swapaxes function to achieve this, as below:

matrix2 = np.swapaxes(matrix2, 1, 2)

The above gives me the desired result.

So, my final code becomes:

string = 'A quick brown fox jumps over dog'
dim1 = len(string) // 16
matrix2 = np.array(list(string))
matrix2 = np.reshape(matrix2, (dim1, 4, 4))
matrix2 = np.swapaxes(matrix2, 1, 2)
print(matrix2)

Just wanted your expert opinions if there is a way to achieve the reshape and swapaxes using a single method/function. Or if there is a totally better, more convenient method without using loops or without using so many functions.