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
(HTMLtables 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 SQLand NoSQLdatabases, 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.

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