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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
data = pd.read_csv('adult.data.csv', delimiter = ',')
# 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 = data['race'].value_counts()
# What is the average age of men?
average_age_men = round(data[data.sex == 'Male']['age'].mean(), 1)
# What is the percentage of people who have a Bachelor's degree?
len_bsc = len(data[data.education == 'Bachelors'])
len_data = len(data)
percentage_bachelors = round(len_bsc/len_data *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 = data[data['education'].isin(['Bachelors', 'Masters', 'Doctorate'])]
lower_education =data[~data['education'].isin(['Bachelors', 'Masters', 'Doctorate'])]
# percentage with salary >50K
no_higherEdu_earning = len(higher_education[higher_education.salary == '>50K'])
no_lowerEdu_earning = len(lower_education[lower_education.salary == '>50K'])
higher_education_rich = round(no_higherEdu_earning / len(higher_education) * 100, 1 )
lower_education_rich = round(no_lowerEdu_earning / len(lower_education) * 100, 1 )
# What is the minimum number of hours a person works per week (hours-per-week feature)?
min_work_hours = data['hours-per-week'].min()
# What percentage of the people who work the minimum number of hours per week have a salary of >50K?
min_hours = data[data['hours-per-week']== min_work_hours]
#min_hours
num_min_workers =min_hours[min_hours['salary'] == '>50K']
rich_percentage = round(len(num_min_workers) /len(min_hours) * 100, 1)
# What country has the highest percentage of people that earn >50K?
country_count = data['native-country'].value_counts()
country_rich_count = data[data['salary'] == '>50K']['native-country'].value_counts()
highest_earning_country = (country_rich_count/ country_count * 100).idxmax()
highest_earning_country_percentage = round((country_rich_count/ country_count * 100).max(), 1)
# Identify the most popular occupation for those who earn >50K in India
indians= data[(data['native-country'] =='India') & (data['salary']=='>50K')]
occupation_counts = indians['occupation'].value_counts()
top_IN_occupation = occupation_counts.idxmax()
top_IN_occupation
# 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
}