numpy array concatenate: "ValueError: all the input arrays must have same number of dimensions"

To use np.concatenate, we need to extend the second array to 2D and then concatenate along axis=1 -

np.concatenate((a,b[:,None]),axis=1)

Alternatively, we can use np.column_stack that takes care of it -

np.column_stack((a,b))

Sample run -

In [84]: a
Out[84]: 
array([[54, 30, 55, 12],
       [64, 94, 50, 72],
       [67, 31, 56, 43],
       [26, 58, 35, 14],
       [97, 76, 84, 52]])

In [85]: b
Out[85]: array([56, 70, 43, 19, 16])

In [86]: np.concatenate((a,b[:,None]),axis=1)
Out[86]: 
array([[54, 30, 55, 12, 56],
       [64, 94, 50, 72, 70],
       [67, 31, 56, 43, 43],
       [26, 58, 35, 14, 19],
       [97, 76, 84, 52, 16]])

If b is such that its a 1D array of dtype=object with a shape of (1,), most probably all of the data is contained in the only element in it, we need to flatten it out before concatenating. For that purpose, we can use np.concatenate on it too. Here's a sample run to make the point clear -

In [118]: a
Out[118]: 
array([[54, 30, 55, 12],
       [64, 94, 50, 72],
       [67, 31, 56, 43],
       [26, 58, 35, 14],
       [97, 76, 84, 52]])

In [119]: b
Out[119]: array([array([30, 41, 76, 13, 69])], dtype=object)

In [120]: b.shape
Out[120]: (1,)

In [121]: np.concatenate((a,np.concatenate(b)[:,None]),axis=1)
Out[121]: 
array([[54, 30, 55, 12, 30],
       [64, 94, 50, 72, 41],
       [67, 31, 56, 43, 76],
       [26, 58, 35, 14, 13],
       [97, 76, 84, 52, 69]])

There's also np.c_

>>> a = np.arange(20).reshape(5, 4)
>>> b = np.arange(-1, -6, -1)
>>> a
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11],
       [12, 13, 14, 15],
       [16, 17, 18, 19]])                                                                                                                                   
>>> b                                                                                                                                                       
array([-1, -2, -3, -4, -5])                                                                                                                                 
>>> np.c_[a, b]
array([[ 0,  1,  2,  3, -1],          
       [ 4,  5,  6,  7, -2],                       
       [ 8,  9, 10, 11, -3],                      
       [12, 13, 14, 15, -4],                                
       [16, 17, 18, 19, -5]])

You can do something like this.

import numpy as np

x = np.random.randint(100, size=(5, 4))
y = [16, 15, 12, 12, 17]

print(x)

val = np.concatenate((x,np.reshape(y,(x.shape[0],1))),axis=1)
print(val)

This outputs:

[[32 37 35 53]
 [64 23 95 76]
 [17 76 11 30]
 [35 42  6 80]
 [61 88  7 56]]

[[32 37 35 53 16]
 [64 23 95 76 15]
 [17 76 11 30 12]
 [35 42  6 80 12]
 [61 88  7 56 17]]