So I have two arrays that have x, y, z coordinates. I'm just trying to apply the 3D distance formula. Problem is, that I can't find a post that constitutes arrays with multiple values in each column and spits out an array.
print MW_FirstsubPos1
[[ 51618.7265625 106197.7578125 69647.6484375 ]
[ 33864.1953125 11757.29882812 11849.90332031]
[ 12750.09863281 58954.91015625 38067.0859375 ]
...,
[ 99002.6640625 96021.0546875 18798.44726562]
[ 27180.83984375 74350.421875 78075.78125 ]
[ 19297.88476562 82161.140625 1204.53503418]]
print MW_SecondsubPos1
[[ 51850.9140625 106004.0078125 69536.5234375 ]
[ 33989.9375 11847.11425781 12255.80859375]
[ 12526.203125 58372.3046875 37641.34765625]
...,
[ 98823.2734375 95837.1796875 18758.7734375 ]
[ 27047.19140625 74242.859375 78166.703125 ]
[ 19353.97851562 82375.8515625 1147.07556152]]
import numpy as np
xs1,ys1,zs1 = zip(*MW_FirstsubPos1)
xs11,ys11,zs11 = zip(*MW_SecondsubPos1)
squared_dist1 = (xs11  xs1)**2 + (ys11  ys1)**2 + (zs11  zs1)**2
dist1 = np.sqrt(squared_dist1)
print dist1
TypeError: unsupported operand type(s) for : 'tuple' and 'tuple'
Distance1 = []
for Fir1, Sec1 in zip(MW_FirstsubVel1, MW_SecondsubPos1):
dist1 = 0
for i in range(3):
dist1 += (Fir1[i]Sec1[i])**2
Distance1.append(dist1**0.5)
squared_dist1 = (xs11[0]  xs1[0])**2 + (ys11[0]  ys1[0])**2 + (zs11[0]  zs1[0])**2
dist1 = np.sqrt(squared_dist1)
print dist1
322.178309762
result = []
for a, b in zip(MW_FirstsubVel1, MW_SecondsubPos1):
dist = 0
for i in range(3):
dist += (a[i]b[i])**2
result.append(dist**0.5)
print result[0]
137163.203004
Here's a vectorized approach using np.einsum

diffs = MW_FirstsubPos1  MW_SecondsubPos1
dists = np.sqrt(np.einsum('ij,ij>i',diffs,diffs))
Sample run 
In [233]: MW_FirstsubPos1
Out[233]:
array([[2, 0, 0],
[8, 6, 1],
[0, 2, 8],
[7, 6, 3],
[3, 1, 7]])
In [234]: MW_SecondsubPos1
Out[234]:
array([[3, 4, 7],
[0, 8, 4],
[4, 7, 4],
[2, 5, 6],
[5, 0, 6]])
In [235]: diffs = MW_FirstsubPos1  MW_SecondsubPos1
In [236]: np.sqrt(np.einsum('ij,ij>i',diffs,diffs))
Out[236]: array([ 8.1240384 , 8.77496439, 7.54983444, 5.91607978, 2.44948974])