import pandas as pd def calculate_demographic_data(print_data=True): # Read data from file df = None # How many of each race are represented in this dataset? This should be a Pandas series with race names as the index labels. race_count = None # What is the average age of men? average_age_men = None # What is the percentage of people who have a Bachelor's degree? percentage_bachelors = None # What percentage of people with advanced education (`Bachelors`, `Masters`, or `Doctorate`) make more than 50K? # What percentage of people without advanced education make more than 50K? # with and without `Bachelors`, `Masters`, or `Doctorate` higher_education = None lower_education = None # percentage with salary >50K higher_education_rich = None lower_education_rich = None # What is the minimum number of hours a person works per week (hours-per-week feature)? min_work_hours = None # What percentage of the people who work the minimum number of hours per week have a salary of >50K? num_min_workers = None rich_percentage = None # What country has the highest percentage of people that earn >50K? highest_earning_country = None highest_earning_country_percentage = None # Identify the most popular occupation for those who earn >50K in India. top_IN_occupation = None # DO NOT MODIFY BELOW THIS LINE if print_data: print("Number of each race:\n", race_count) print("Average age of men:", average_age_men) print(f"Percentage with Bachelors degrees: {percentage_bachelors}%") print(f"Percentage with higher education that earn >50K: {higher_education_rich}%") print(f"Percentage without higher education that earn >50K: {lower_education_rich}%") print(f"Min work time: {min_work_hours} hours/week") print(f"Percentage of rich among those who work fewest hours: {rich_percentage}%") print("Country with highest percentage of rich:", highest_earning_country) print(f"Highest percentage of rich people in country: {highest_earning_country_percentage}%") print("Top occupations in India:", top_IN_occupation) return { 'race_count': race_count, 'average_age_men': average_age_men, 'percentage_bachelors': percentage_bachelors, 'higher_education_rich': higher_education_rich, 'lower_education_rich': lower_education_rich, 'min_work_hours': min_work_hours, 'rich_percentage': rich_percentage, 'highest_earning_country': highest_earning_country, 'highest_earning_country_percentage': highest_earning_country_percentage, 'top_IN_occupation': top_IN_occupation }