In this part of the book, we’ll see how to deliver our select Nobel Prize dataset, recently cleaned and explored, to the browser, wherein JavaScript and D3 will turn it into an engaging, interactive visualization (see Figure IV-1).
The great thing about using a general-purpose library like Python is that you can as easily roll a web server in a few, impressively succinct lines, as mine your data with powerful data-processing libraries.
The key server tool in our toolchain is Flask, Python’s powerful but lightweight web framework. In Chapter 12 we’ll see how to serve your data statically (serving system files) and dynamically, usually as a database selection specified in the request. In Chapter 13 we’ll see how two Flask-based libraries make creating a RESTful web API the work of a few lines of Python.
Figure IV-1. Delivering the data
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