重塑多个numpy数组

Question

我有以下numpy数组：

X = [[1],
     [2],
     [3],
     [4]]

Y = [[5],
     [6],
     [7],
     [8]]

Z = [[9],
     [10],
     [11],
     [12]]

我想得到以下输出：

H = [[1,5,9],
     [2,6,10],
     [3,7,11]
     [4,8,12]]

有没有办法使用numpy.reshape获得此结果？

Answer 1

您可以使用np.column_stack

np.column_stack((X,Y,Z))

或者np.concatenate沿axis=1

np.concatenate((X,Y,Z),axis=1)

或np.hstack

np.hstack((X,Y,Z))

或沿axis=0 np.stack然后进行多np.stack转置-

np.stack((X,Y,Z),axis=0).T

整形应用于数组，而不是将数组堆叠或连接在一起。 因此，仅reshape在这里没有意义。

有人可能会争辩说使用np.reshape为我们提供所需的输出，就像这样-

np.reshape((X,Y,Z),(3,4)).T

但是，在np.asarray进行了堆叠操作，而AFAIK可以通过np.asarray转换为np.asarray

In [453]: np.asarray((X,Y,Z))
Out[453]: 
array([[[ 1],
        [ 2],
        [ 3],
        [ 4]],

       [[ 5],
        [ 6],
        [ 7],
        [ 8]],

       [[ 9],
        [10],
        [11],
        [12]]])

我们只需要在其上使用multi-dim transpose即可为我们提供预期输出的3D阵列版本-

In [454]: np.asarray((X,Y,Z)).T
Out[454]: 
array([[[ 1,  5,  9],
        [ 2,  6, 10],
        [ 3,  7, 11],
        [ 4,  8, 12]]])

Answer 2

这个（更快的）解决方案怎么样？

In [16]: np.array([x.squeeze(), y.squeeze(), z.squeeze()]).T
Out[16]: 
array([[ 1,  5,  9],
       [ 2,  6, 10],
       [ 3,  7, 11],
       [ 4,  8, 12]])

---

效率 （降序）

# proposed (faster) solution
In [17]: %timeit np.array([x.squeeze(), y.squeeze(), z.squeeze()]).T
The slowest run took 7.40 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 7.36 µs per loop

# Other solutions
In [18]: %timeit np.column_stack((x, y, z))
The slowest run took 5.18 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 9.18 µs per loop

In [19]: %timeit np.hstack((x, y, z))
The slowest run took 4.49 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 16 µs per loop

In [20]: %timeit np.reshape((x,y,z),(3,4)).T
10000 loops, best of 3: 21.6 µs per loop

In [20]: %timeit np.c_[x, y, z]
10000 loops, best of 3: 55.9 µs per loop

Answer 3

并且不要忘记np.c_ （我看不到需要np.reshape ）：

np.c_[X,Y,Z]
# array([[ 1,  5,  9],
#        [ 2,  6, 10],
#        [ 3,  7, 11],
#        [ 4,  8, 12]])