In this chapter, you practiced NumPy, SciPy, Pandas, and scikit-learn, using various examples, mainly for machine learning tasks. When you use Python data science libraries, there is usually more than one way of performing given task, and it usually helps to know more than one method.
You can either use alternatives for better implementations or for the sake of comparison. While trying different methods for a given task, you may either find different options that will allow you to further customize the implementation or simply observe some performance improvements.
The aim of this chapter was to show you these different options, and how flexible the Python language is because of its rich ecosystem of analytics libraries. In the next chapter, you will learn more about NumPy internals, such as how numpy manages data structures and memory, code profiling, and also tips for...