在numpy数组中添加一个额外的列:

Numpy np。append方法有三个参数，前两个是2D numpy数组，第三个是一个轴参数，指示沿哪个轴追加:

import numpy as np  
x = np.array([[1,2,3], [4,5,6]]) 
print("Original x:") 
print(x) 

y = np.array([[1], [1]]) 
print("Original y:") 
print(y) 

print("x appended to y on axis of 1:") 
print(np.append(x, y, axis=1)) 

打印:

Original x:
[[1 2 3]
 [4 5 6]]
Original y:
[[1]
 [1]]
y appended to x on axis of 1:
[[1 2 3 1]
 [4 5 6 1]]

我觉得下面这些最优雅:

b = np.insert(a, 3, values=0, axis=1) # Insert values before column 3

插入的一个优点是它还允许您在数组中的其他位置插入列(或行)。此外，您可以轻松地插入整个向量，而不是插入单个值，例如复制最后一列:

b = np.insert(a, insert_index, values=a[:,2], axis=1)

这就导致:

array([[1, 2, 3, 3],
       [2, 3, 4, 4]])

对于时间，insert可能比JoshAdel的解决方案慢:

In [1]: N = 10

In [2]: a = np.random.rand(N,N)

In [3]: %timeit b = np.hstack((a, np.zeros((a.shape[0], 1))))
100000 loops, best of 3: 7.5 µs per loop

In [4]: %timeit b = np.zeros((a.shape[0], a.shape[1]+1)); b[:,:-1] = a
100000 loops, best of 3: 2.17 µs per loop

In [5]: %timeit b = np.insert(a, 3, values=0, axis=1)
100000 loops, best of 3: 10.2 µs per loop

使用hstack的一种方法是:

b = np.hstack((a, np.zeros((a.shape[0], 1), dtype=a.dtype)))

在我的例子中，我必须向NumPy数组中添加一列1

X = array([ 6.1101, 5.5277, ... ])
X.shape => (97,)
X = np.concatenate((np.ones((m,1), dtype=np.int), X.reshape(m,1)), axis=1)

后 X.shape => (97,2)

array([[ 1. , 6.1101],
       [ 1. , 5.5277],
...

我喜欢这个:

new_column = np.zeros((len(a), 1))
b = np.block([a, new_column])

我认为一个更直接的解决方案和更快的启动是做以下工作:

import numpy as np
N = 10
a = np.random.rand(N,N)
b = np.zeros((N,N+1))
b[:,:-1] = a

和时间:

In [23]: N = 10

In [24]: a = np.random.rand(N,N)

In [25]: %timeit b = np.hstack((a,np.zeros((a.shape[0],1))))
10000 loops, best of 3: 19.6 us per loop

In [27]: %timeit b = np.zeros((a.shape[0],a.shape[1]+1)); b[:,:-1] = a
100000 loops, best of 3: 5.62 us per loop