Introduction

Our approach to learning in this book is highly applied since hands-on learning is the quickest way to understand abstract concepts. With this in mind, the focus of this chapter is to introduce Jupyter Notebooks—the data science tool that we will be using throughout this book.

Since Jupyter Notebooks have gained mainstream popularity, they have been one of the most important tools for data scientists who use Python. This is because they offer a great environment for a variety of tasks, such as performing quick and dirty analysis, researching model selection, and creating reproducible pipelines. They allow for data to be loaded, transformed, and modeled inside a single file, where it's quick and easy to test out code and explore ideas along the way. Furthermore, all of this can be documented inline using formatted text, which means you can make notes or even produce a structured report.

Other comparable platforms—for example, RStudio or Spyder—offer multiple panels to work between. Frequently, one of these panels will be a Read Eval Prompt Loop (REPL), where code is run on a Terminal session that has saved memory. Code written here may end up being copied and pasted into a different panel within the main codebase, and there may also be additional panels to see visualizations or other files. Such development environments are prone to efficiency issues and can promote bad practices for reproducibility if you're not careful.

Jupyter Notebooks work differently. Instead of having multiple panels for different components of your project, they offer the same functionality in a single component (that is, the Notebook), where the text is displayed along with code snippets, and code outputs are displayed inline. This lets you code efficiently and allows you to look back at previous work for reference, or even make alterations.

We'll start this chapter by explaining exactly what Jupyter Notebooks are and why they are so popular among data scientists. Then, we'll access a Notebook together and go through some exercises to learn how the platform is used.