Number crunching is a topic specific to the programming world. However, given that Python is so often used for scientific research and data science problems, number crunching ends up being a very common topic in the Python world.

That being said, we could just as easily implement our algorithms using the information from the earlier six chapters, and we would most likely end up with pretty fast and performant code. Again, that information is meant to be for generic use cases. There will always be something to say about optimizing for a particular case.

In this chapter, we'll cover three options that will help us write faster and more optimized code focused on scientific problems. For each one, we'll go over the basic installation instructions. We will also look at some code samples showing the benefits of each option.

The tools we'll review in this chapter are as follows: