Case Study 1: Summarzing Data Using Pandas Pivot Table¶

The goal of this section is to be able to make a comparison of data in our data set. In the previous sections, we learned how to extract, visualize, and save data. Now, we will focus on summarizing the collected data and group them in a meaningful way.

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 . To accomplish this, you should know how to do some web scraping to get data. In this section, we will learn about “pivot_table “and “pivot “methods.

If you don’t remember web scraping, you should review the example of ref:screenscrape. This will show you the basics of reading and grabbing information out of a page.

Let’s learn how to create a pivot table. We will use pivot_table to summarize and analyze a large amount of data. We also use it to compare different elements in our data set. To illustrate this, we will do an example where we explore how climate affects the economy. For this example, we will use the data that was scraped from the CIA World Factbook website and was saved as a CSV file.

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.

We have a column for the region, we have a column for climate, and we have information on the economy. We want to 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.

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 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 may be Agriculture, Service and Industry. How does that change your table?

Project¶

The goal of this project is to make some comparison of the different forms of government, and how the form of government might have an impact on some of our other variables.

You can dig into getting the information from this page.

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.

Lesson Feedback

1. Comfort Zone
2. Learning Zone
3. Panic Zone

1. Very little time
2. A reasonable amount of time
3. More time than is reasonable

1. Don't seem worth learning
2. May be worth learning
3. Are definitely worth learning

1. Definitely within reach
2. Within reach if I try my hardest
3. Out of reach no matter how hard I try

You have attempted of activities on this page