Practical Linear Algebra for Data Science

Chapter 16. Python Tutorial

As I explained in Chapter 1, this chapter is a crash course on Python programming. It is designed to get you up to speed quickly to follow along with the code in the rest of the book, but it is not designed to be a complete source for Python mastery. If you’re looking for a dedicated Python book, then I recommend Learning Python by Mark Lutz (O’Reilly).

While working through this chapter, please have a Python session open. (I’ll explain how to do that later.) You are not going to learn Python just by reading the chapter; you need to read, type into Python, change and test the code, and so on.

Also, in this chapter you should manually key in all the code you see printed here. The code for all other chapters in this book is available online, but I want you to type in the code for this chapter manually. Once you are more familiar with Python coding, then manually typing in lots of code is a tedious waste of time. But when you are first learning to code, you need to code—that is, use your fingers to type in everything. Don’t just look at code on a page.

Why Python, and What Are the Alternatives?

Python is designed to be a general-purpose programming language. You can use Python to do text analysis, process web forms, create algorithms, and myriad other applications. Python is also widely used in data science and machine learning; for these applications, Python is basically just a calculator. Well, it’s an extremely powerful and versatile calculator, but ...