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.groupby("race")["race"].count().sort_values(ascending = False)

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

    # What is the percentage of people who have a Bachelor's degree?
    percentage_bachelors = round(((df[(df["education"] == "Bachelors")]["education"].count() / df["education"].count()) * 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.where((df["education"].isin(["Bachelors","Masters","Doctorate"])) & (df["salary"] == ">50K"))["education"].count()
    lower_education = df.where((~(df["education"].isin(["Bachelors","Masters","Doctorate"]))) & (df["salary"] == ">50K"))["education"].count()

    # percentage with salary >50K
    higher_education_rich = round((higher_education * 100 / (df.where(df["education"].isin(["Bachelors","Masters","Doctorate"]))["education"].count())),1)
    lower_education_rich = round((lower_education * 100 / (df.where(~(df["education"].isin(["Bachelors","Masters","Doctorate"])))["education"].count())),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 = df.where((df["hours-per-week"] == min_work_hours) & (df["salary"] == ">50K"))["hours-per-week"].count()

    rich_percentage = round((num_min_workers * 100 / df.where(df["hours-per-week"] == min_work_hours)["hours-per-week"].count()),1)

    # What country has the highest percentage of people that earn >50K?
    coun = dict()
    countries = df["native-country"].unique().tolist()
    for country in countries:
        rich = df.where((df["native-country"] == country) & (df["salary"] == ">50K"))["native-country"].count()
        total =  df.where(df["native-country"] == country)["native-country"].count()
        percent = round((rich * 100 / total),1)
        coun[country] = percent
    highest_earning_country = max(coun, key = coun.get)
    highest_earning_country_percentage = coun[highest_earning_country]

    # Identify the most popular occupation for those who earn >50K in India.
    f1 = df["salary"] == ">50K"
    f2 = df["native-country"] == "India"
    top_IN_occupation = df.where(f1 & f2)["occupation"].unique()[1]

    # 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
    }
