In this chapter, we only scratched the surface of what is possible with the scientific Python ecosystem. We used some of the libraries that are considered, if not part of the common stack, then at least fundamental. We used interpolation and numerical integration provided by SciPy. Two of the dozens of algorithms in scikit-learn were demonstrated. We also saw Cython in action, which is technically a programming language in its own right. Finally, we had a look at Blaze, a library supposed to generalize and extend the principles of NumPy. This is in light of recent developments such as Big Data and Cloud Computing. Blaze and related projects are still in the incubation phase, but we can expect stable software to be produced in the near future. You can refer to http://continuum.io/developer-resources for some of these projects.
Unfortunately, we have come to the end of this book. Because of this book's format, that is the number of pages, you should have essential NumPy knowledge and...
