demographic-data.py

import pandas as pd


def calculate_demographic_data(print_data=True):
    # Read data from file
    df = pd.read_csv("adult.data.csv")

    # 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 = df['race'].value_counts()

    # What is the average age of men?
    average_age_men = df[df["sex"] == "Male"]["age"].mean().round(1)

    # What is the percentage of people who have a Bachelor's degree?
    num_bachelors = len(df[df["education"] == "Bachelors"])
    total_num = len(df)
    percentage_bachelors = round(num_bachelors / total_num * 100, 1)

    # 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 = df[df["education"].isin(["Bachelors", "Masters", "Doctorate"])]
    lower_education = df[~df["education"].isin(["Bachelors", "Masters", "Doctorate"])]

    # percentage with salary >50K
    non_percentage_higher = len(higher_education[higher_education.salary == ">50K"])
  
    higher_education_rich = round(non_percentage_higher / len(higher_education) * 100, 1)

    non_percentage_lower = len(lower_education[lower_education.salary == ">50K"])

    lower_education_rich = round(non_percentage_lower / len(lower_education) * 100, 1)

    # What is the minimum number of hours a person works per week (hours-per-week feature)?
    min_work_hours = df["hours-per-week"].min()

    # What percentage of the people who work the minimum number of hours per week have a salary of >50K?
    num_min_workers = len(df[df["hours-per-week"] == min_work_hours])

    rich_percentage = round(len(num_min_workers[num_min_workers.salary == ">50K"] / len(num_min_workers) * 100, 1))

    # What country has the highest percentage of people that earn >50K?
    country_count = df['native-country'].value_counts()
    country_count = df[df['salary'] == '>50K'] ['native-country'].value_counts()
  
    highest_earning_country = (country_rich_count / country_count * 100).idxmax()

    highest_earning_country_percentage = round((country_rich_count / country_count * 100).max(), 1)

    # Identify the most popular occupation for those who earn >50K in India.
    people_of_india = df[(df["native-country"] == "India") & (df["salary"] == ">50K")]
    occupation_counts_ = people_of_india['occupation'].value_counts()
    top_IN_occupation = occupation_counts.idxmax()

    # 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
    }