6.9. Case Study 2: Comparing CIA Government Forms

The goal of this section is to be able to summarize, sort, and organize data using pivot tables. In the previous case study project, you retrieved the government types from the “list of countries by system of government” website. However, in this case study, you will work with the data that you scraped from the CIA World Factbook website. You will compare different forms of government and how the form of government might impact the countries’ economic success. For this example, measure financial stability based on GDP. You can do this by building a pivot table in Pandas.

If you haven’t already, you should review the example of ref:screenscrape_Bus. This will show you the basics of reading and grabbing information out of a page.

1. If you scraped the whole data from CIA World Factbook 2017 in the previous exercise, you should be able to use the government type field from the CSV file you saved. If not, try and scrape the government type fields.

2. For this exercise, you should have a row for every region, a column for government forms; then, in each cell, we would like to summarize the fraction of the economy that comes from GDP.

3. Remember, if the information you choose is numeric, you can change it to nominal using the map method, a lambda function, and a dictionary that maps from that specific column number to a label.

4. Now, let’s pivot the table. Remember, the pivot table method takes three parameters: index, columns, and values.

If you recall from the previous case study, the pivot function works like the pivot_table function but does not do any aggregation. Therefore, it will throw an error if you have duplicate index rows.

6.9.1. Project

Try changing the values parameter to be a list of columns may be agriculture, inflation rate and, industry. How does that change your table?

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