NumPy: Cross Product in 3D Space

In this tutorial, we will learn how to find the cross product of two vectors using NumPy's numpy.cross() function.

Consider two vectors $\mathbf{u}$ and $\mathbf{v}$, where

$$ \mathbf{u} = a_{1}\mathbf{i} + a_{2}\mathbf{j} + a_{3}\mathbf{k} \\ \mathbf{v} = b_{1}\mathbf{i} + b_{2}\mathbf{j} + b_{3}\mathbf{k} $$

We may also represent them as linear arrays $\mathbf{u} = (a_{1}, a_{2}, a_{3})$ and $\mathbf{v} = (b_{1}, b_{2}, b_{3})$.

The cross product of two three-dimensional vectors $\mathbf{u}$ and $\mathbf{v}$ is denoted by

$$ \mathbf{u} \times \mathbf{v} $$

and is defined by

$$ \mathbf{u} \times \mathbf{v} = (a_{2}b_{3} - a_{3}b_{2})\mathbf{i} + (a_{3}b_{1} - a_{1}b_{3})\mathbf{j} + (a_{1}b_{2} - a_{2}b_{1})\mathbf{k} $$

which is

$$ \mathbf{u} \times \mathbf{v} = \begin{vmatrix} a_{2} & a_{3} \\ b_{2} & b_{3} \\ \end{vmatrix} \mathbf{i} - \begin{vmatrix} a_{1} & a_{3} \\ b_{1} & b_{3} \\ \end{vmatrix} \mathbf{j} + \begin{vmatrix} a_{1} & a_{2} \\ b_{1} & b_{2} \\ \end{vmatrix} \mathbf{k} $$

This whole cross product can actually be expressed as a single $3 \times 3$ determinant.

$$ \mathbf{u} \times \mathbf{v} = \begin{vmatrix} \mathbf{i} & \mathbf{j} & \mathbf{k} \\ a_{1} & a_{2} & a_{3} \\ b_{1} & b_{2} & b_{3} \\ \end{vmatrix} $$

Vector cross product is defined only in $R^{3}$.

Python has a numerical library called NumPy, which has a function called numpy.cross() to compute the cross product of two vectors.

Now we pick two vectors from an example in the book Linear Algebra (4^th Ed.) by Seymour Lipschutz and Marc Lipson¹.

$$ 4\mathbf{i} + 3\mathbf{j} + 6\mathbf{k} \\ 2\mathbf{i} + 5\mathbf{j} - 3\mathbf{k} $$

Below we compute their cross product using NumPy's numpy.cross() function.

				
					import numpy as np
					u = [4,3,6]	
					v = [2,5,-3]			
					crossproduct = np.cross(u,v)
					print(crossproduct)
				
			

On executing the above Python script, we get the resulting vector as

				
					[-39 24 14]
				
			

which is

$$ -39\mathbf{i} + 24\mathbf{j} + 14\mathbf{k} $$

Now one important note on array representation here. Sometimes it may seem like the cross product is being carried out on a vectors of dimension lower than 3, and even NumPy does not seem to have any problem processing it either. To see what I mean, even if you input vectors $\mathbf{u}$ and $\mathbf{v}$ as follows

				
					import numpy as np
					u = [1,2]	
					v = [4,5]			
					crossproduct = np.cross(u,v)
					print(crossproduct)
				
			

NumPy still evaluates and gives a result. That is because, if the third component of the input vector is missing, it is assumed to be zero. So, in the context of NumPy's numpy.cross() function, the vector $\mathbf{u} = (1,2)$ is assumed to be the vector $\mathbf{u} = (1,2,0)$ and the vector $\mathbf{v} = (4,5)$ is assumed to be the vector $\mathbf{v} = (4,5,0)$. So it is actually the cross product

$$ (\mathbf{i} + 2\mathbf{j} + 0\mathbf{k}) \times (4\mathbf{i} + 5\mathbf{j} + 0\mathbf{k}) $$

which results in

$$ -3\mathbf{k} $$