Merging rows in numpy to form new array

Question

This is a sample of what I am trying to accomplish. I am very new to python and have searched for hours to find out what I am doing wrong. I haven't been able to find what my issue is. I am still new enough that I may be searching for the wrong phrases. If so, could you please point me in the right direction?

I want to combine n mumber of arrays to make one array. I want to have the first row from x as the first row in the combined the first row from y as the second row in combined, the first row from z as the third row in combined the the second row in x as the fourth row in combined, etc. so I would look something like this.

x = [x1 x2 x3]
    [x4 x5 x6]
    [x7 x8 x9]

y = [y1 y2 y3]
    [y4 y5 y6]
    [y7 y8 y9]

x = [z1 z2 z3]
    [z4 z5 z6]
    [z7 z8 z9]

combined = [x1 x2 x3]
           [y1 y2 y3]
           [z1 z2 z3]
           [x4 x5 x6]
           [...]
           [z7 z8 z9]

The best I can come up with is the

    import numpy as np

x = np.random.rand(6,3)
y = np.random.rand(6,3)
z = np.random.rand(6,3)

combined = np.zeros((9,3))

for rows in range(len(x)):        
    combined[0::3] = x[rows,:] 
    combined[1::3] = y[rows,:]
    combined[2::3] = z[rows,:]


print(combined)

All this does is write the last value of the input array to every third row in the output array instead of what I wanted. I am not sure if this is even the best way to do this. Any advice would help out.

*I just figure out this works but if someone knows a higher performance method, *please let me know.

import numpy as np

x = np.random.rand(6,3) 
y = np.random.rand(6,3) 
z = np.random.rand(6,3)

combined = np.zeros((18,3))

for rows in range(6):        
  combined[rows*3,:] = x[rows,:] 
  combined[rows*3+1,:] = y[rows,:]
  combined[rows*3+2,:] = z[rows,:]

  print(combined)

Answer 1

You can do this using a list comprehension and zip:

combined = np.array([row for row_group in zip(x, y, z) for row in row_group])

Answer 2

Using vectorised operations only:

A = np.vstack((x, y, z))
idx = np.arange(A.shape[0]).reshape(-1, x.shape[0]).T.flatten()

A = A[idx]

Here's a demo:

import numpy as np

x, y, z = np.random.rand(3,3), np.random.rand(3,3), np.random.rand(3,3)

print(x, y, z)

[[ 0.88259564  0.17609363  0.01067734]
 [ 0.50299357  0.35075811  0.47230915]
 [ 0.751129    0.81839586  0.80554345]]
[[ 0.09469396  0.33848691  0.51550685]
 [ 0.38233976  0.05280427  0.37778962]
 [ 0.7169351   0.17752571  0.49581777]]
[[ 0.06056544  0.70273453  0.60681583]
 [ 0.57830566  0.71375038  0.14446909]
 [ 0.23799775  0.03571076  0.26917939]]

A = np.vstack((x, y, z))
idx = np.arange(A.shape[0]).reshape(-1, x.shape[0]).T.flatten()

print(idx)  # [0 3 6 1 4 7 2 5 8]

A = A[idx]

print(A)

[[ 0.88259564  0.17609363  0.01067734]
 [ 0.09469396  0.33848691  0.51550685]
 [ 0.06056544  0.70273453  0.60681583]
 [ 0.50299357  0.35075811  0.47230915]
 [ 0.38233976  0.05280427  0.37778962]
 [ 0.57830566  0.71375038  0.14446909]
 [ 0.751129    0.81839586  0.80554345]
 [ 0.7169351   0.17752571  0.49581777]
 [ 0.23799775  0.03571076  0.26917939]]