Data Visualization with Python and JavaScript

Preface

The chief ambition of this book is to describe a data visualization (dataviz) toolchain that, in the era of the Internet, is starting to predominate. The guiding principle of this toolchain is that whatever insightful nuggets you have managed to mine from your data deserve a home on the web browser. Being on the Web means you can easily choose to distribute your dataviz to a select few (using authentication or restricting to a local network) or the whole world. This is the big idea of the Internet and one that dataviz is embracing at a rapid pace. And that means that the future of dataviz involves JavaScript, the only first-class language of the web browser. But JavaScript does not yet have the data-processing stack needed to refine raw data, which means data visualization is inevitably a multi-language affair. I hope this book provides ammunition for my belief that Python is the natural complementary language to JavaScript’s monopoly of browser visualizations.

Although this book is a big one (that fact is felt most keenly by the author right now), it has had to be very selective, leaving out a lot of very cool Python and JavaScript dataviz tools and focusing on the ones I think provide the best building blocks. The number of cool libraries I couldn’t cover reflects the enormous vitality of the Python and JavaScript data science ecosystems. Even while the book was being written, brilliant new Python and JavaScript libraries were being introduced, and the pace continues.

I wanted to give the book some narrative structure by setting a data transformation challenge. All data visualization is essentially transformative, and showing the journey from one reflection of a dataset (HTML tables and lists) to a more modern, engaging, interactive, and, fundamentally, browser-based one seemed a good way to introduce key data visualization tools in a working context. The challenge I set was to transform a basic Wikipedia list of Nobel Prize winners into a modern, interactive, browser-based visualization. Thus the same dataset is presented in a more accessible, engaging form. But while the creation of the Nobel visualization lent the book a backbone, there were calculated redundancies. For example, although the book uses Flask and the MongoDB-based Python-EVE API to deliver the Nobel data to the browser, I also show how to do it with the SQL-based Flask-RESTless. If you work in the field of dataviz, you will need to be able to engage with both SQL and NoSQL databases, and this book aims to be impartial. Not every library demonstrated was used in transforming the Nobel dataset, but all are ones I have found most useful personally and think you will, too.

So the book is a collection of tools forming a chain, with the creation of the Nobel visualization providing a guiding narrative. You should be able to dip into relevant chapters when and if the need arises; the different parts of the book are self-contained so you can quickly review what you’ve learned when required.

Conventions Used in This Book

The following typographical conventions are used in this book:

Italic

Indicates new terms, URLs, email addresses, filenames, and file extensions.

Constant width

Used for program listings, as well as within paragraphs to refer to program elements such as variable or function names, databases, datatypes, environment variables, statements, and keywords.

Constant width bold

Shows commands or other text that should be typed literally by the user.

Constant width italic

Shows text that should be replaced with user-supplied values or by values determined by context.

Tip

This element signifies a tip or suggestion.

Note

This element signifies a general note.

Warning

This element indicates a warning or caution.

Using Code Examples

Supplemental material (code examples, exercises, etc.) is available for download at https://github.com/Kyrand/dataviz-with-python-and-js.

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We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: “Data Visualization with Python and JavaScript by Kyran Dale (O’Reilly). Copyright 2016 Kyran Dale, 978-1-491-92051-0.”

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Acknowledgments

Thanks first to Meghan Blanchette, who set the ball rolling and steered that ball through its first very rough chapters. Dawn Schanafelt then took the helm and did the bulk of the very necessary editing. Kristen Brown did a brilliant job taking the book through production, aided by Gillian McGarvey’s impressively tenacious copy editing. Working with such talented, dedicated professionals has been an honor and a privilege—and an education: the book would have been so much easier to write if I’d known then what I know now. Isn’t that always the way?

Many thanks to Amy Zielinski for making the author look better than he deserves.

The book benefited from some very helpful feedback. So much thanks to Christophe Viau, Tom Parslow, Peter Cook, Ian Macinnes, and Ian Ozsvald.

I’d also like to thank the valiant bug hunters who answered my appeal during Early Release. At time of writing, these are Douglas Kelley, Pavel Suk, Brigham Hausman, Marco Hemken, Noble Kennamer, Manfredi Biasutti, Matthew Maldonado, and Geert Bauwens.