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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
col_names =['age','workclas','fnlwgt','education','education-num','marital-status','occupation','relationship','race','sex','capital-gain','hours-per-week','native-country','salary']
df = pd.read_csv('adult.data.csv',names = col_names)
# 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.str.split(expand=True).stack().value_counts()
# What is the average age of men?
average_age_men = df.loc[df['sex'] == 'Male','age'].mean()
# What is the percentage of people who have a Bachelor's degree?
percentage_bachelors = ((df['native_country'] == 'United States').sum())/df.shape[0]*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`
higher_education = df['Education'].isin(['Bachelors','Masters','Doctorate'])
lower_education = df['Education'].isin(~['Bachelors','Masters','Doctorate'])
# percentage with salary >50K
higher_education_rich = float(higher_education['higher_education'['salary']== '>50K']['salary'].count() / higher_education.shape[0])*100.0
lower_education_rich = float(lower_education['lower_education'['salary']=='>50K']['salary'].count() / lower_education.shape[0])*100.0
# 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].shape[0]
rich_percentage = float(df[(df['hours-per-week'] == min_work) & (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'].groupby('native-country')['native-country'].count().max()
highest_earning_country_percentage = float((highest_earning_country / df.groupby('native-country')['native-country'].count()).max())*100
# Identify the most popular occupation for those who earn >50K in India.
top_IN_occupation = []
for (country, salary), sub_df in df.groupby(['native-country', 'salary']):
top_IN_occupation.append(f"{country} {salary} {sub_df['occupation'].value_counts().keys()[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
}