Sizes

Right now, the scatter plot is just a cloud of blue dots, indistinguishable from each other. Let's change this. Wouldn't it be nice if the size of the dots corresponds to the population?

To accomplish this, there is a list pop loaded in your workspace. It contains population numbers for each country expressed in millions. You can see that this list is added to the scatter method, as the argument s, for size.

Diese Übung ist Teil des Kurses

Intermediate Python

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Anleitung zur Übung

Run the script to see how the plot changes.
Looks good, but increasing the size of the bubbles will make things stand out more.
- Import the numpy package as np.
- Use np.array() to create a numpy array from the list pop. Call this NumPy array np_pop.
- Double the values in np_pop setting the value of np_pop equal to np_pop * 2. Because np_pop is a NumPy array, each array element will be doubled.
- Change the s argument inside plt.scatter() to be np_pop instead of pop.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# Import numpy as np


# Store pop as a numpy array: np_pop


# Double np_pop


# Update: set s argument to np_pop
plt.scatter(gdp_cap, life_exp, s = pop)

# 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'])

# Display the plot
plt.show()

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