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demographic_data_analyzer.py
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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 = 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[(df['education'] == 'Bachelors') | (df['education'] == 'Masters') | (df['education'] == 'Doctorate')]
lower_education =df[(df['education'] != 'Bachelors') & (df['education'] != 'Masters') & (df['education'] != 'Doctorate')]
# percentage with salary >50K
higher_education_rich = round((higher_education[higher_education['salary'] == '>50K']['education'].count() / higher_education['salary'].count() )* 100, 1)
lower_education_rich = round((lower_education[lower_education['salary'] == '>50K']['education'].count() / lower_education['salary'].count() )* 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['salary'] == '>50K') & (df['hours-per-week'] == min_work_hours)].count())['hours-per-week']
rich_percentage = ((num_min_workers / df.loc[df['hours-per-week'] == 1].count()) * 100)['salary']
# What country has the highest percentage of people that earn >50K?
highest_earning_country = (df[df['salary'] == '>50K' ]['native-country'].value_counts()/df.groupby(['native-country']).size()).sort_values(ascending=False).index[0]
highest_earning_country_percentage = round((df[df['salary'] == '>50K' ]['native-country'].value_counts()/df.groupby(['native-country']).size()).sort_values(ascending=False).max() * 100, 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().index[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
}