Data Visualization with Python and JavaScript, 2nd Edition

Part III. Cleaning and Exploring Data with pandas

In this part of the book, in the second phase of our toolchain (see Figure III-1), we take the Nobel Prize dataset we just scraped with Scrapy in Chapter 6 and first clean it up, then explore it for interesting nuggets. The principal tools we’ll be using are the large Python libraries Matplotlib and pandas.

Note

The second edition of this book is using the same Nobel dataset scraped in the first. It was felt that available time was better spent writing new material and updating all the libraries than changing the exploration and analysis. Dataviz usually involves working with older datasets, and the few additional Nobel winners don’t change the substance of the material at all.

pandas will be introduced in the next couple of chapters, along with its building block, NumPy. In Chapter 9, we’ll use pandas to clean the Nobel dataset. Then in Chapter 11, in conjunction with Python’s plotting library Matplotlib, we’ll use it to explore it.

In Part IV, we’ll see how to deliver the freshly cleaned Nobel Prize dataset to the browser, using Python’s Flask web server.

Figure III-1. Our dataviz toolchain: cleaning and exploring the data

Tip

You can find the code for this part of the book at the book’s GitHub repo.