Summary

This chapter has shown us a sample of the most commonly used and generic scientific Python libraries. While it covered a lot of libraries, there are many more available, especially when you start looking for domain-specific libraries. With regard to plotting alone, there are at least several other very big libraries that could be useful for your use cases but would be superfluous for this chapter.

To recap, we have covered the basics of working with NumPy matrices and Pandas data objects, both of which are important for the next chapter. We have also seen a few libraries that focus on mathematics and really precise calculations. Lastly, we have covered several plotting libraries, some of which will be used in the next chapter as well.

Next up is the chapter about artificial intelligence and machine learning in Python. As is the case with this chapter, we cannot go into too much depth, but we can cover the most important technologies and libraries so you know where...