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demographic_data_analyzer.py
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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 = df.loc[df['sex']=='Male', 'age'].mean().round(1)
# What is the percentage of people who have a Bachelor's degree?
percentage_bachelors = round(df['education'].value_counts(normalize=True)['Bachelors']*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 = round((df[df['education'].isin(['Bachelors', 'Masters', 'Doctorate'])].shape[0]) / df['education'].shape[0] *100, 1)
lower_education = round((df[~df['education'].isin(['Bachelors', 'Masters', 'Doctorate'])].shape[0]) / df['education'].shape[0] *100, 1)
# percentage with salary >50K
higher_education_rich = round(df[(df['education'].isin(['Bachelors', 'Masters', 'Doctorate'])) & (df['salary'] == '>50K')].shape[0] / df[(df['education'].isin(['Bachelors', 'Masters', 'Doctorate']))].shape[0] *100 , 1)
lower_education_rich = round(df[(~df['education'].isin(['Bachelors', 'Masters', 'Doctorate'])) & (df['salary'] == '>50K')].shape[0] / df[(~df['education'].isin(['Bachelors', 'Masters', 'Doctorate']))].shape[0] *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.loc[df['hours-per-week'] == min_work_hours].shape[0]
rich_percentage = round(df[(df['hours-per-week'] == min_work_hours) & (df['salary'] == '>50K')].shape[0] / num_min_workers, 1)*100
# What country has the highest percentage of people that earn >50K?
highest_earning_country = df.groupby('native-country')['salary'].apply(lambda x: (x == '>50K').mean()).idxmax()
highest_earning_country_percentage = round(df.groupby('native-country')['salary'].apply(lambda x: (x == '>50K').mean()).max() * 100, 1)
# Identify the most popular occupation for those who earn >50K in India.
top_IN_occupation = df.loc[(df['native-country'] =='India') & (df['salary'] == '>50K')]['occupation'].value_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
}