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\[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?
q1 = df\['education'\].isin(\['Bachelors', 'Masters', 'Doctorate'\])
q2 = df\['salary'\] == '>50K'
higher_education_rich = round((q1 & q2).sum() / q1.sum() \* 100, 1)
lower_education_rich = round((\~q1 & q2).sum() / (\~q1).sum() \* 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?
q1 = df\['hours-per-week'\] == min_work_hours
rich_percentage = round((q1 & q2).sum() / q1.sum() \* 100, 1)
\# What country has the highest percentage of people that earn >50K?
p = (df\[q2\]\['native-country'\].value_counts() \\
/ df\['native-country'\].value_counts() \* 100).sort_values(ascending=False)
highest_earning_country = p.index\[0\]
highest_earning_country_percentage = round(p.iloc\[0\], 1)
\# Identify the most popular occupation for those who earn >50K in India.
top_IN_occupation = df\[(df\['native-country'\] == 'India') & q2\] \\
\['occupation'\].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
}