NumPy stack or append array to array

Question

I am starting with NumPy.

Given two np.arrays, queu and new_path:

queu = [ [[0 0]
          [0 1]]
       ]

new_path = [ [[0 0]
              [1 0]
              [2 0]]
           ]

My goal is to get the following queu:

queu = [ [[0 0]
          [0 1]]
         [[0 0]
          [1 0]
          [2 0]]
       ]

I've tried:

np.append(queu, new_path, 0)

and

np.vstack((queu, new_path))

But both are raising

all the input array dimensions except for the concatenation axis must match exactly

I didn't get the NumPy philosophy. What am I doing wrong?

Answer 1

In [741]: queu = np.array([[[0,0],[0,1]]])
In [742]: new_path = np.array([[[0,0],[1,0],[2,0]]])
In [743]: queu
Out[743]: 
array([[[0, 0],
        [0, 1]]])
In [744]: queu.shape
Out[744]: (1, 2, 2)
In [745]: new_path
Out[745]: 
array([[[0, 0],
        [1, 0],
        [2, 0]]])
In [746]: new_path.shape
Out[746]: (1, 3, 2)

You have defined 2 arrays, with shape (1,2,2) and (1,3,2). If you are puzzled about those shapes you need to reread some of the basic numpy introduction.

hstack, vstack and append all call concatenate. With 3d arrays using them will just confuse matters.

Joining on the 2nd axis, which is size 2 for one and 3 for the other, works, producing a (1,5,2) array. (This is equivalent to hstack)

In [747]: np.concatenate((queu, new_path),axis=1)
Out[747]: 
array([[[0, 0],
        [0, 1],
        [0, 0],
        [1, 0],
        [2, 0]]])

Trying to join on axis 0 (vstack) produces your error:

In [748]: np.concatenate((queu, new_path),axis=0)
....
ValueError: all the input array dimensions except for the concatenation axis must match exactly

The concatenation axis is 0, but dimensions of axis 1 differ. Hence the error.

Your target is not a valid numpy array. You could collect them together in a list:

In [759]: alist=[queu[0], new_path[0]]
In [760]: alist
Out[760]: 
[array([[0, 0],
        [0, 1]]), 
 array([[0, 0],
        [1, 0],
        [2, 0]])]

Or an object dtype array - but that's more advanced numpy.

Answer 2

It's not completely clear to me how you've set up your arrays, but from the sound of it, np.vstack should indeed do what you are look for:

In [30]: queue = np.array([0, 0, 0, 1]).reshape(2, 2)

In [31]: queue
Out[31]:
array([[0, 0],
       [0, 1]])

In [32]: new_path = np.array([0, 0, 1, 0, 2, 0]).reshape(3, 2)

In [33]: new_path
Out[33]:
array([[0, 0],
       [1, 0],
       [2, 0]])

In [35]: np.vstack((queue, new_path))
Out[35]:
array([[0, 0],
       [0, 1],
       [0, 0],
       [1, 0],
       [2, 0]])

Answer 3

what you need is np.hstack

In [73]: queu = np.array([[[0, 0],
                            [0, 1]]
                         ])
In [74]: queu.shape
Out[74]: (1, 2, 2)

In [75]: new_path = np.array([ [[0, 0],
                                [1, 0],
                                [2, 0]]
                             ])

In [76]: new_path.shape
Out[76]: (1, 3, 2)

In [81]: np.hstack((queu, new_path))
Out[81]: 
array([[[0, 0],
        [0, 1],
        [0, 0],
        [1, 0],
        [2, 0]]])