Cars per capita (1)

Let's stick to the cars data some more. This time you want to find out which countries have a high cars per capita figure. In other words, in which countries do many people have a car, or maybe multiple cars.

Similar to the previous example, you'll want to build up a boolean Series, that you can then use to subset the cars DataFrame to select certain observations. If you want to do this in a one-liner, that's perfectly fine!

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Intermediate Python

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Anleitung zur Übung

Select the cars_per_cap column from cars as a Pandas Series and store it as cpc.
Use cpc in combination with a comparison operator and 500. You want to end up with a boolean Series that's True if the corresponding country has a cars_per_cap of more than 500 and False otherwise. Store this boolean Series as many_cars.
Use many_cars to subset cars, similar to what you did before. Store the result as car_maniac.
Print out car_maniac to see if you got it right.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Create car_maniac: observations that have a cars_per_cap over 500




# Print car_maniac

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