Introduction

After going through a refresher on the Python language in the previous chapter, we are now ready to tackle the main topics of this book: mathematics and statistics.

Among others, the general fields of computational mathematics and statistics can be broken up into three main tool-centric components: representation and engineering; analysis and computation; and finally, visualization. In the ecosystem of the Python programming language, specific libraries are dedicated to each of these components (namely, pandas, NumPy, Matplotlib, and Seaborn), making the process modular.

While there might be other similar packages and tools, the libraries that we will be discussing have been proven to possess a wide range of functionalities and support powerful options in terms of computation, data processing, and visualization, making them some of a Python programmer's preferred tools over the years.

In this chapter, we will be introduced to each of these libraries and learn about their main API. Using a hands-on approach, we will see how these tools allow great freedom and flexibility in terms of creating, manipulating, analyzing, and visualizing data in Python. Knowing how to use these tools will also equip us for more complicated topics in the later chapters of this workshop.