Data Visualization with Python and JavaScript, 2nd Edition

Chapter 22. Conclusion

Although this book had a guiding narrative—the transformation of some basic Wikipedia HTML pages into a modern, interactive JavaScript web visualization—it is meant to be dipped into as and when required. The different parts are self-contained, allowing for the existence of the dataset in its various stages, and can be used independently. Let’s have a short recap of what was covered before moving on to a few ideas for future visualization work.

Recap

This book was divided into five parts. The first part introduced a basic Python and JavaScript dataviz toolkit, while the next four showed how to retrieve raw data, clean it, explore it, and finally transform it into a modern web visualization. This process of refinement and transformation used as its backbone a dataviz challenge: to take a fairly basic Wikipedia Nobel Prize list and transform the dataset contained into something more engaging and informative. Let’s summarize the key lessons of each part now.

Part I: Basic Toolkit

Our basic toolkit consisted of:

• A language-learning bridge between Python and JavaScript. This was designed to smooth the transition between the two languages, highlighting their many similarities and setting the scene for the bilingual process of modern dataviz. Python and JavaScript share even more in common, making switching between them that much less stressful.

• Being able to read from and write to the key data formats (e.g., JSON and CSV) and databases (both SQL and NoSQL) ...