concatenating two multidimensional arrays in numpy

Question

I have two arrays A and B ,

>> np.shape(A)
>> (7, 6, 2)
>> np.shape(B)
>> (6,2)

Now, I want to concatenate the two arrays such that A is extended to (8,6,2) with A[8] = B

I tried np.concatenate()

>> np.concatenate((A,B),axis = 0)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-40-d614e94cfc50> in <module>()
----> 1 np.concatenate((A,B),axis = 0)

ValueError: all the input arrays must have same number of dimensions  

and np.vstack()

>> np.vstack((A,B))
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-41-7c091695f277> in <module>()
----> 1 np.vstack((A,B))
//anaconda/lib/python2.7/site-packages/numpy/core/shape_base.pyc in vstack(tup)
    228 
    229     """
--> 230     return _nx.concatenate([atleast_2d(_m) for _m in tup], 0)
    231 
    232 def hstack(tup):

ValueError: all the input arrays must have same number of dimensions

Answer 1

Likely the simplest way is to use numpy newaxis like this:

import numpy as np

A = np.zeros((7, 6, 2))
B = np.zeros((6,2))
C = np.concatenate((A,B[np.newaxis,:,:]),axis=0)
print(A.shape,B.shape,C.shape)

, which results in this:

(7, 6, 2) (6, 2) (8, 6, 2)

As @sascha mentioned you can use vstack (also see hstack , dstack ) to perform direct concatenation operations with an implicit axis (respectively axis = 0 , axis = 1 , axis =2 ):

D = np.vstack((A,B[np.newaxis,:,:]))
print(D.shape)

, result:

(8, 6, 2)