Welcome to the last chapter of the book. If you've made it this far, you've gone over several optimization techniques, both specific to the Python programming language and generic ones applicable to other similar technologies.

You've also read about tools for profiling and visualizing those results. We also delved into one specific use case for Python, which is number crunching for scientific purposes. You learned about the tools that allow you to optimize the performance of your code.

In this final chapter, we'll go over one practical use case that covers all the technologies we covered in the earlier chapters (remember that some of the tools we've seen are alternatives, so using all of them is not really a good plan). We will write an initial version of the code, measure its performance, and then go through the optimization process to finally rewrite the code and measure the performance again.