Chapter 12. Delivering the Data

Chapter 6 showed how to grab your data of interest from the Web with a web scraper. We used Scrapy to fetch a dataset of Nobel Prize winners and then in Chapters 9 and 11 we cleaned and explored the Nobel Prize dataset using Pandas.

This chapter will show you how to deliver data statically or dynamically from a Python server to JavaScript on the client/browser, using our Nobel Prize dataset as an example. This data is stored in the JSON format and consists of a list of Nobel Prize–winner objects like the one shown in Example 12-1.

Example 12-1. Our Nobel Prize JSON data, scraped and then cleaned
    "category": "Physiology or Medicine",
    "country": "Argentina",
    "date_of_birth": "1927-10-08T00:00:00.000Z",
    "date_of_death": "2002-03-24T00:00:00.000Z",
    "gender": "male",
    "link": "http:\/\/\/wiki\/C%C3%A9sar_Milstein",
    "name": "C\u00e9sar Milstein",
    "place_of_birth": "Bah\u00eda Blanca ,  Argentina",
    "place_of_death": "Cambridge , England",
    "text": "C\u00e9sar Milstein , Physiology or Medicine, 1984",
    "year": 1984,
    "award_age": 57

As with the rest of this book, the emphasis will be on minimizing the amount of web development so you can get down to the business of building the web visualization in JavaScript.


A good rule of thumb is to aim to do as much data manipulation as possible with Python—it’s much less painful than equivalent operations in JavaScript. Following from this, the data delivered should be as close as possible ...

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