[FIXED] Numpy stack with unequal shapes

Issue

I've noticed that the solution to combining 2D arrays to 3D arrays through np.stack, np.dstack, or simply passing a list of arrays only works when the arrays have same .shape[0].

For instance, say I have:

print(arr)
[[0 1]
 [2 3]
 [4 5]
 [6 7]
 [8 9]]

it easy easy to get to:

print(np.array([arr[2:4], arr[3:5]])) # same shape
[[[4 5]
  [6 7]]

 [[6 7]
  [8 9]]]

However, if I pass a list of arrays of unequal length, I get:

print(np.array([arr[:2], arr[:3]]))
[array([[0, 1],
       [2, 3]])
 array([[0, 1],
       [2, 3],
       [4, 5]])]

How can I get to simply:

[[[0, 1]
  [2, 3]]
 [[0, 1]
  [2, 3]
  [4, 5]]]

What I've tried: a number of other Array manipulation routines.

Note: ultimately want to do this for more than 2 arrays, so np.append is probably not ideal.

Solution

Numpy arrays have to be rectangular, so what you are trying to get is not possible with a numpy array.

You need a different data structure. Which one is suitable depends on what you want to do with that data.

Answered By - Johannes