demographic_data_analyzer.py

import pandas as pd


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

    # How many of each race are represented in this dataset? This should be a Pandas series with race names as the index labels.
    race_counts = []
    races = df["race"].unique().tolist()
    for race in races:
        race_counts.append(len(df[df["race"] == race]))
    race_count = pd.Series(race_counts, index=races)

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

    # What is the percentage of people who have a Bachelor's degree?
    percentage_bachelors = round(len(df[df["education"] == "Bachelors"]) / len(df) * 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_mask = (df["education"] == "Bachelors") | (df["education"] == "Masters") | (df["education"] == "Doctorate")
    higher_education = df[higher_education_mask]
    
    lower_education_mask = ~(higher_education_mask)
    lower_education = df[lower_education_mask]

    # percentage with salary >50K
    higher_education_rich = round(len(higher_education[higher_education["salary"] == ">50K"]) / len(higher_education) * 100, 1)
    lower_education_rich = round(len(lower_education[lower_education["salary"] == ">50K"]) / 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(df[(df["hours-per-week"] == min_work_hours) & (df["salary"] == ">50K")]) / num_min_workers * 100, 1)

    # What country has the highest percentage of people that earn >50K?
    rich_df = df[df["salary"] == ">50K"]
    highest_earning_country = (rich_df["native-country"].value_counts().sort_index() / df["native-country"].value_counts().sort_index()).sort_values(ascending=False).index[0]
    highest_earning_country_percentage = round((rich_df["native-country"].value_counts().sort_index() / df["native-country"].value_counts().sort_index()).sort_values(ascending=False)[highest_earning_country] * 100, 1)

    # Identify the most popular occupation for those who earn >50K in India.
    top_IN_occupation = df[(df["native-country"] == "India") & (df["salary"] == ">50K")][["occupation"]].value_counts().index[0][0]

    # 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
    }