Data Visualization with Python and JavaScript, 2nd Edition

Chapter 1. Development Setup

This chapter covers the downloads and software installations needed to use this book, and sketches out a recommended development environment. As you’ll see, this isn’t as onerous as it might once have been. I’ll cover Python and JavaScript dependencies separately and give a brief overview of cross-language IDEs.

The Accompanying Code

There’s a GitHub repository for the bulk of the code covered in this book, including the full Nobel Prize visualization. To get hold of it, just perform a git clone to a suitable local directory:

$ git clone https://github.com/Kyrand/dataviz-with-python-and-js-ed-2.git

This should create a local dataviz-with-python-and-js-v2 directory with the key source code covered by the book.

Python

The bulk of the libraries covered in the book are Python-based, but what might have been a challenging attempt to provide comprehensive installation instructions for the various operating systems and their quirks is made much easier by the existence of Anaconda, a Python platform that bundles together most of the popular analytics libraries in a convenient package. The book assumes you are using Python 3, which was released in 2008 and is now firmly established.

Anaconda

Installing some of the bigger Python libraries used to be a challenge all in itself, particularly those such as NumPy that depend on complex low-level C and Fortran packages. That’s a great deal easier now and most will happily install using Python’s easy_install with ...