Filter observations from a DataFrame based on boolean arrays: Part 2


Thu Nov 25 2021 10:29:37 GMT+0000 (Coordinated Universal Time)

Saved by @Sourabh #numpy #booleans_in_numpy

# 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
cpc = cars['cars_per_cap']
many_cars = cpc > 500
car_maniac = cars[many_cars]

# Print car_maniac

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.