Colors

The code you've written up to now is available in the script.

The next step is making the plot more colorful! To do this, a list col has been created for you. It's a list with a color for each corresponding country, depending on the continent the country is part of.

How did we make the list col you ask? The Gapminder data contains a list continent with the continent each country belongs to. A dictionary is constructed that maps continents onto colors:

dict = {
    'Asia':'red',
    'Europe':'green',
    'Africa':'blue',
    'Americas':'yellow',
    'Oceania':'black'
}

Nothing to worry about now; you will learn about dictionaries in the next chapter.

This exercise is part of the course

Intermediate Python

View Course

Exercise instructions

Add c = col to the arguments of the plt.scatter() function.
Change the opacity of the bubbles by setting the alpha argument to 0.8 inside plt.scatter(). Alpha can be set from zero to one, where zero is totally transparent, and one is not at all transparent.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Specify c and alpha inside plt.scatter()
plt.scatter(x = gdp_cap, y = life_exp, s = np.array(pop) * 2)

# Previous customizations
plt.xscale('log') 
plt.xlabel('GDP per Capita [in USD]')
plt.ylabel('Life Expectancy [in years]')
plt.title('World Development in 2007')
plt.xticks([1000,10000,100000], ['1k','10k','100k'])

# Show the plot
plt.show()

Edit and Run Code