pythonnumpyarray-broadcastingcross-product# Element-wise Cross Product of 2D arrays of Coordinates

I'm working with a dataset that stores an array of unit-vectors as arrays of the vectors' components.

How would I use vectorised code / broadcasting to write clean and compact code to give the cross product of the vectors element-wise?

For example, here's a brute force method for looping through the length of the arrays, picking out the coordinates, re-composing the two vectors, then calculating the cross product.

```
x = [0,0,1,1]
y = [0,1,0,1]
z = [1,0,0,1]
v1 = np.array([x,y,z])
x = [1,1,0,1]
y = [1,0,1,1]
z = [0,1,1,1]
v2 = np.array([x,y,z])
result = []
for i in range(0, len(x)):
a = [v1[0][i], v1[1][i], v1[2][i]]
b = [v2[0][i], v2[1][i], v2[2][i]]
result.append(np.cross(a,b))
result
>>>
[
array([-1, 1, 0]),
array([ 1, 0, -1]),
array([ 0, -1, 1]),
array([ 0, 0, 0])
]
```

I've tried to understand this question and answer to generalise it, but failed:

- Element wise cross product of vectors contained in 2 arrays with Python

Solution

`np.cross`

can work with 2D arrays too, you just need to specify the right axes:

```
np.cross(v1,v2, axisa=0, axisb=0)
array([[-1, 1, 0],
[ 1, 0, -1],
[ 0, -1, 1],
[ 0, 0, 0]])
```

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