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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(r'C:\Users\BlueworksADM1\OneDrive\Documents\PYTHON\Data-project-1\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 = average_age_men=df[df['sex']=='Male']['age'].mean()
# What is the percentage of people who have a Bachelor's degree?
bachelor =( df['education']=='Bachelors').sum()
total = len(df)
percentage_bachelors =( bachelor/total)*100
# 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`
advanced_education= df['education'].isin(['Bachelors','Masters' ,'Doctorate'])
higher_education = df[advanced_education]
lower_education = df[~advanced_education]
# percentage with salary >50K for educated
sum_rich_high=( higher_education['salary']=='>50K').sum()
total_rich_high = len(higher_education)
higher_education_rich = (sum_rich_high/total_rich_high)*100
#percentage with salary >50k for not educated
sum_rich_low=( lower_education['salary']=='>50K').sum()
total_rich_low = len(lower_education)
lower_education_rich = (sum_rich_low/total_rich_low)*100
# What is the minimum number of hours a person works per week (hours-per-week feature)?
min_work_hours = df['hours-per-week'].min()
#dataframe of those who work low hours
lowhours= df[df['hours-per-week']== min_work_hours]
# What percentage of the people who work the minimum number of hours per week have a salary of >50K?
num_min_workers = len(lowhours)
richminworkerssum= (lowhours['salary']=='>50K').sum()
rich_percentage = (richminworkerssum/num_min_workers)*100
# What country has the highest percentage of people that earn >50K?
#First look at all the data for individuals earning more than 50k
high_salary= df[df['salary']=='>50K']
country_high_salary_counts = high_salary['native-country'].value_counts()
country_counts = df['native-country'].value_counts()
percentages = ( country_high_salary_counts / country_counts) * 100
highest_earning_country = percentages.idxmax()
#
highest_earning_country_percentage = percentages.max()
# Identify the most popular occupation for those who earn >50K in India.
high_india=high_salary[high_salary['native-country']=='India']
counts= high_india['occupation'].value_counts()
top_IN_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}