Tell us what’s happening:
I can’t seem to get this part right. My results end up being 0 percent.
Percentage with higher education that earn >50K: 0.0%
Percentage without higher education that earn >50K: 0.0%
Your code so far
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.loc[df['sex'] == 'Male', 'age'].mean(), 1)
# What is the percentage of people who have a Bachelor's degree?
percentage_bachelors = round(len(df[df['education'] == 'Bachelors']) / len(df) * 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 = df[df['education'].isin('Bachelors', 'Masters', 'Doctorate')]
lower_education = df[~df['education'].isin('Bachelors', 'Masters', 'Doctorate')]
# percentage with salary >50K
higher_education_rich = round(len(higher_education[higher_education['salary'] == '>50k']) / len(higher_education) * 100, 1)
lower_education_rich = round(len(lower_education[lower_education['salary'] == '>50k']) / len(lower_education) * 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 = len(df[df['hours-per-week'] == min_work_hours])
rich_percentage = round(len(df[(df['hours-per-week'] == min_work_hours) & (df['salary'] == '>50K')]) / num_min_workers * 100, 1)
# What country has the highest percentage of people that earn >50K?
highest_earning_country = (df.loc[df['salary'] == ">50K", 'native-country'].value_counts() / df['native-country'].value_counts()).fillna(0).sort_values(ascending=False).index[0]
highest_earning_country_percentage = round(len(df[(df['native-country'] == higher_earning_country) & (df['salary'] == '>50K')]) / len(df[df['native-country'] == highest_earning_country]) * 100, 1)
# Identify the most popular occupation for those who earn >50K in India.
top_IN_occupation = df[(df['salary'] == ">50K") & (df['native-country'] == "India")]["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
}
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Challenge: Demographic Data Analyzer
Link to the challenge: