Solution

demographic_data_analyzer.py

@@ -3,42 +3,33 @@ import pandas as pd
 
 def calculate_demographic_data(print_data=True):
     # Read data from file
-    df = None
+    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 = None
+    race_count = df['race'].value_counts()
 
     # What is the average age of men?
-    average_age_men = None
+    average_age_men = round(df['age'][df['sex'] == 'Male'].mean(), 1)
 
     # What is the percentage of people who have a Bachelor's degree?
-    percentage_bachelors = None
+    percentage_bachelors = round(df['education'].value_counts(normalize=True).mul(100).loc['Bachelors'], 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 = None
-    lower_education = None
-
-    # percentage with salary >50K
-    higher_education_rich = None
-    lower_education_rich = None
+    higher_education_rich = round(df['salary'][(df['education'] == 'Bachelors') | (df['education'] == 'Masters') | (df['education'] == 'Doctorate')].value_counts(normalize=True).mul(100).loc['>50K'], 1)
+    lower_education_rich = round(df['salary'][(df['education'] != 'Bachelors') & (df['education'] != 'Masters') & (df['education'] != 'Doctorate')].value_counts(normalize=True).mul(100).loc[">50K"], 1)
 
     # What is the minimum number of hours a person works per week (hours-per-week feature)?
-    min_work_hours = None
+    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 = None
-
-    rich_percentage = None
+    rich_percentage = round(df['salary'][df['hours-per-week'] == df['hours-per-week'].min()].value_counts(normalize=True).mul(100).loc[">50K"], 1)
 
     # What country has the highest percentage of people that earn >50K?
-    highest_earning_country = None
-    highest_earning_country_percentage = None
+    highest_earning_country = (df['native-country'][df['salary'] == '>50K'].value_counts()/df['native-country'].value_counts()).idxmax()
+    highest_earning_country_percentage = round((df['native-country'][df['salary'] == '>50K'].value_counts()/df['native-country'].value_counts()).max() * 100, 1)
 
     # Identify the most popular occupation for those who earn >50K in India.
-    top_IN_occupation = None
+    top_IN_occupation = df['occupation'][(df['native-country'] == 'India') & (df['salary'] == '>50K')].value_counts().idxmax()
 
     # DO NOT MODIFY BELOW THIS LINE