尝试连接两个不同维度的数组

Question

I'm learning python and I have 2 arrays:

a = [[ 1 , 2 ]
      [3,  4]]
b = [ 6,7]

when I print the shapes I get:

a.shape = (2,2)
b.shape = (2,)

want result to be:

c = [[ 1, 2 , 6]
     [3, 4, 7]]

I've tried

c = a + b

and

c = np.concatenate((a, b),axis=None) #tried axis=0, axis=1

I keep getting errors like

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

Answer 1

May be you can try as shown in numpy example but b needs to be of shape (1, 2) by just adding array as a inner element of array: np.array([[6,7]])

a = np.array([[1, 2 ],
              [3, 4]])
b = np.array([[6,7]])

c = np.concatenate((a, b.T), axis=1)

Output:

[[1 2 6]
 [3 4 7]]

Answer 2

You can use numpy.vstack

In [22]: import numpy as np

In [23]: a = np.array([[1,2], [3,4]])

In [24]: b = np.array([6,7])

In [25]: np.vstack((a.T, b)).T
Out[25]:
array([[1, 2, 6],
       [3, 4, 7]])

Answer 3

In [868]: a = np.array([[1,2],[3,4]]); b = np.array([6,7])
In [869]: a.shape, b.shape
Out[869]: ((2, 2), (2,))

b has 1 dimension, it needs 2 to match a :

In [870]: np.reshape(b,(2,1))
Out[870]: 
array([[6],
       [7]])

Now concatenate works:

In [871]: np.concatenate((a, np.reshape(b,(2,1))), axis=1)
Out[871]: 
array([[1, 2, 6],
       [3, 4, 7]])

np.vstack works because it adds a new initial dimension if needed. I added a trailing dimension.

In the long run to use concatenate effectively you must learn about dimensions, and how to adjust them if needed.