demographic_data_analyzer.py

import pandas as pd


def calculate_demographic_data():
    # 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 = pd.Series([df[df['race'] == 'White'].shape[0] , df[df['race'] == 'Black'].shape[0] , df[df['race'] == 'Asian-Pac-Islander'].shape[0] , df[df['race'] == 'Amer-Indian-Eskimo'].shape[0] , df[df['race'] == 'Other'].shape[0]] ,
    index=['White', 'Black','Asian-Pac-Islander' ,'Amer-Indian-Eskimo' ,'Other'])

    # 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 (((df[df['education'] == 'Bachelors'].shape[0]) / (df.shape[0]) ) * 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'] == 'Bachelors')  |  (df['education'] == 'Masters')  | (df['education'] == 'Doctorate') ].shape[0])
    lower_education = (df.shape[0]) - higher_education

    # percentage with salary >50K
    higher_education_rich = round (((df[ ((df['education'] == 'Bachelors')  |  (df['education'] == 'Masters')  | (df['education'] == 'Doctorate')) & (df['salary'] == '>50K' ) ].shape[0]) / higher_education ) *100 , 1)
    lower_education_rich = round ((((df[ df['salary'] == '>50K'].shape[0]) - (df[ ((df['education'] == 'Bachelors')  |  (df['education'] == 'Masters')  | (df['education'] == 'Doctorate')) & (df['salary'] == '>50K' ) ].shape[0]) ) /   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 = df[ df['hours-per-week'] == min_work_hours ].shape[0]

    rich_percentage = ((df[ (df['hours-per-week'] == df['hours-per-week'].min()) & (df['salary'] == '>50K' ) ].shape[0] ) /  num_min_workers )*100

    # What country has the highest percentage of people that earn >50K?
    highest_earning_country = ((df[df['salary'] == '>50K']['native-country'].value_counts())/(df['native-country'].value_counts()) ).idxmax()
    highest_earning_country_percentage = round (((df[df['salary'] == '>50K']['native-country'].value_counts())/(df['native-country'].value_counts()) *100).max() ,1)

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

    # Print out the result 

    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 0
calculate_demographic_data()