In this chapter you’ll download data sets from online sources and create working visualizations of that data. An incredible variety of data can be found online, much of which hasn’t been examined thoroughly. The ability to analyze this data allows you to discover patterns and connections that no one else has found.
TRY IT YOURSELF #1
16-1. San Francisco: Are temperatures in San Francisco more like temperatures in Sitka or temperatures in Death Valley? Generate a high-low temperature plot for San Francisco and make a comparison. (You can download weather data for almost any location from http://www.wunderground.com/history/. Enter a location and date range, scroll to the bottom of the page, and find a link labeled Comma-Delimited File. Right-click this link, and save the data as a CSV file.)
16-2. Sitka-Death Valley Comparison: The temperature scales on the Sitka and Death Valley graphs reflect the different ranges of the data. To accurately compare the temperature range in Sitka to that of Death Valley, you need identical scales on the y-axis. Change the settings for the y-axis on one or both of the charts in Figures 16-5 and 16-6, and make a direct comparison between temperature ranges in Sitka and Death Valley (or any two places you want to compare). You can also try plotting the two data sets on the same chart.
16-3. Rainfall: Choose any location you’re interested in, and make a visualization that plots its rainfall. Start by focusing on one month’s data, and then once your code is working, run it for a full year’s data.
16-4. Explore: Generate a few more visualizations that examine any other weather aspect you’re interested in for any locations you’re curious about.
TRY IT YOURSELF #2
16-5. All Countries: On the population
maps we made in this section, our program couldn’t automatically find
two-letter codes for about 12 countries. Work out which countries are
missing codes, and look through the COUNTRIES dictionary for the
codes. Add an if-elif block to get_country_code() so it returns
the correct country code values for these specific countries:
if country_name == 'Yemen, Rep.'
    return 'ye'
elif --snip--
Place this code after the COUNTRIES loop but before the return None
statement. When you’re finished, you should see a more complete map.
16-6. Gross Domestic Product: The Open Knowledge Foundation maintains a data set containing the gross domestic product (GDP) for each country in the world, which you can find at https://index.okfn.org/dataset/statistics/. Download the JSON version of this data set, and plot the GDP of each country in the world for the most recent year in the data set.
16-7. Choose Your Own Data: The World
Bank maintains many data sets that are broken down for information on
each country worldwide. Go to http://data.worldbank.org/indicator/
and find a data set that looks interesting. Click the data set, click
the Download Data link, and choose CSV. You’ll receive three CSV
files, two of which are labeled Metadata; use the third CSV file.
Write a program that generates a dictionary with Pygal’s two-letter
country codes as its keys and your chosen data from the file as its
values. Plot the data on a Worldmap and style the map as you like.
16-8. Testing the country_codes
Module: When we wrote the country_codes module, we used print
statements to check whether the get_country_code() function worked.
Write a proper test for this function using what you learned in Chapter
11.