You can get the same with:

import numpy as np
a = np.arange(7, 13).reshape(2, 3)
b = np.arange(1, 7).reshape(2, 3)
z = np.stack((a, b), axis=1)
print(z)
# [[[ 7  8  9]
#   [ 1  2  3]]
# 
#  [[10 11 12]
#   [ 4  5  6]]]

One way would be to use np.vstack on 3D extended versions of those arrays -

np.vstack((A[None],B[None]))

Another way with np.row_stack (functionally same as np.vstack) -

np.row_stack((A[None],B[None]))

And similarly with np.concatenate -

np.concatenate((A[None],B[None])) # By default stacks along axis=0

Another way would be with np.stack and specifying the axis of stacking i.e. axis=0 or skip it as that's the default axis of stacking -

np.stack((A,B))

You should not vstack while iterating, as it will artificially increase memory usage, as explained in this similar question but related to pandas.

Secondly, assuming fn(i) returns a new array that you want to append to A, then that line should be A = np.vstack((A, fn(i))

Considering all this, a better option would be to create and collect all your arrays into a list that you can later stack.

A = np.zeros((1, 14), dtype=float)
arrays = [A] + [fn(i) for i in arr] # assuming `arr` is an iterable
A = np.vstack(tuple(arrays))

You can read more in the numpy.vstack docs