# 6.5. Case Study 1: Comparing Forms of Government¶

The goal of this section is to be able to do some comparison of the different forms of government, and how the form of government might have an impact on some of our other variables. We’ll do this by building a pivot table in Pandas. You have already done this in a spreadsheet, so it’s good to see how to do it in Pandas as well. In order to accomplish this, we are going to have to do the following.

1. Learn or review how you can do some screen scraping to get the form of government.

2. Learn about the pivot_table and pivot methods

3. Practice adding new data to a data frame

If you haven’t already, you should review the example of Case Study 1: Screen Scraping the CIA. This will show you the basics of reading and grabbing information out of a page. When you are comfortable with that, you can dig into getting the information from this page.

Now, let’s look at making a pivot table. We will leave pivoting on the form of government up to you. Instead, we will do an example to see how climate, region of the world, and parts of the economy might be related. We have a column for region, we have a column for climate, and we have information on the economy. What we want to do is summarize that information in a table where we have a row for each region and a column for each classification of climate. Then in each cell, we would like to summarize the fraction of the economy that comes from agriculture.

Why analyze the relationship between Climate and Economy? Climate affects the economy in more ways than we realize. According to the article Can Civilization Survive What’s Coming?, extreme weather costs the U.S \$306 billion in damages in 2017. If climate denial continues, these costs will only increase. Therefore, we will do an example to see how climate, a region of the world, and parts of the economy might be related.

 Climate 1.0 1.5 2.0 2.5 3.0 4.0 Region ASIA (EX. NEAR EAST) 0.229500 0.1250 0.167500 0.186 0.116667 NaN BALTICS NaN NaN NaN NaN 0.040000 NaN C.W. OF IND. STATES 0.230667 NaN 0.234000 0.353 0.179500 0.133 EASTERN EUROPE NaN NaN NaN NaN 0.087500 0.142 LATIN AMER. & CARIB NaN 0.0820 0.094722 NaN 0.082667 NaN NEAR EAST 0.060100 NaN NaN NaN 0.060000 NaN NORTHERN AFRICA 0.125000 NaN NaN NaN 0.132000 NaN NORTHERN AMERICA NaN NaN 0.010000 NaN 0.010000 NaN OCEANIA 0.038000 NaN 0.194357 NaN 0.043000 NaN SUB-SAHARAN AFRICA 0.230714 0.2455 0.311406 0.119 0.228333 NaN WESTERN EUROPE NaN NaN NaN NaN 0.029389 0.041

The first thing we really want to do is change those headings. Climate values of 1.0, 2.0 etc are not very useful, but we can translate that into a more human-friendly form.

The climate numbers are as follows:

1. Dry tropical or Tundra 1.5 Mixed tropical 2. Wet tropical 2.5 Mixed 3. Temperate 4. Dry summers and wet winters.

Let’s change our climate classification from numeric to nominal. We can do this using the map method, a lambda function, and a dictionary that maps from the climate number to a label.

Now, let’s pivot the table. The pivot table method takes three parameters: index, columns, and values. The index parameter asks “what values from the original table should I use as the new row index?”. The columns parameter asks “what values from the original table should I use as the column headings?”. The values parameter says what values to include in the cells. In most cases, these values will need to be aggregated in some way, and by default the aggregation is to take the mean.

wd.pivot_table(index='Region', columns='Climate', values='Agriculture')

 Climate Dry Tropical or Tundra Mixed Mixed Tropical Mountain Temperate Unknown Wet Tropical Region ASIA (EX. NEAR EAST) 0.229500 0.186 0.1250 NaN 0.116667 0.3800 0.167500 BALTICS NaN NaN NaN NaN 0.040000 0.0550 NaN C.W. OF IND. STATES 0.230667 0.353 NaN 0.133 0.179500 0.1335 0.234000 EASTERN EUROPE NaN NaN NaN 0.142 0.087500 0.0880 NaN LATIN AMER. & CARIB NaN NaN 0.0820 NaN 0.082667 NaN 0.094722 NEAR EAST 0.060100 NaN NaN NaN 0.060000 0.1200 NaN NORTHERN AFRICA 0.125000 NaN NaN NaN 0.132000 0.1465 NaN NORTHERN AMERICA NaN NaN NaN NaN 0.010000 0.0220 0.010000 OCEANIA 0.038000 NaN NaN NaN 0.043000 NaN 0.194357 SUB-SAHARAN AFRICA 0.230714 0.119 0.2455 NaN 0.228333 0.2640 0.311406 WESTERN EUROPE NaN NaN NaN 0.041 0.029389 0.1002 NaN

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.

Try changing the values parameter to be a list of columns maybe Agriculture, Service, and Industry. How does that change your table?

## 6.5.1. Project¶

Add a “form of government” column to your data frame. There may be other alternatives for finding the data besides the web page presented earlier to scrape. If you don’t want to do screen scraping, there may be a different easier route.

Then, create a pivot table using the region as the rows, form of government as the columns, and summarize the GDP in tabular form.

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