要使用np.concatenate，我们需要将第二个数组扩展为2D，然后沿着axis=1连接-

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

或者，我们可以使用np.column_stack来处理它-

np.column_stack((a,b))

样品运行-

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]])

如果b是dtype=object的1D数组，形状为(1,)，很可能所有的数据都包含在其中唯一的元素中，我们需要在连接之前将其“扁平化”。为此，我们也可以在其上使用np.concatenate。下面是一个示例运行，以澄清这一点-

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]])

还有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]])

你可以做这样的事。

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)

这将输出：

[[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]]

它与原始问题无关，但在循环中垂直添加列时很有用。

a = np.empty([0,1], dtype=float) 
b = np.array([[6487, 400, 489.580],
              [6488, 401, 492.994],
              [6491, 408, 489.247],
              [6491, 408, 489.247],
              [6492, 402, 499.013]])

for c in range(0, 3):
    col = b[:, c]
    col = col.reshape(len(col),1)
    a = np.vstack((a, col))

print(a)

----------------------------------------------
Output:
[[6487.   ]
 [6488.   ]
 [6491.   ]
 [6491.   ]
 [6492.   ]
 [ 400.   ]
 [ 401.   ]
 [ 408.   ]
 [ 408.   ]
 [ 402.   ]
 [ 489.58 ]
 [ 492.994]
 [ 489.247]
 [ 489.247]
 [ 499.013]]