Data Visualization with Python and JavaScript

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.git

This should create a local dataviz-with-python-and-js 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 Continuum Analytics’ Anaconda, a Python platform that bundles together most of the popular analytics libraries in a convenient package.

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 why the existence of Anaconda is such a godsend. It does all the dependency checking and binary installs so you don’t have to. It’s also a very convenient resource for a ...