freecodecamp-data-analysis/demographic-data-analyzer
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Manish 2023-06-06 10:07:13 +10:00
parent 44824de57f
commit 5143ac8801
1 changed files with 11 additions and 20 deletions
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demographic_data_analyzer.py
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@ -3,42 +3,33 @@ import pandas as pd
def calculate_demographic_data(print_data=True): def calculate_demographic_data(print_data=True):
# Read data from file # 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. # 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? # 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? # 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 with advanced education (`Bachelors`, `Masters`, or `Doctorate`) make more than 50K?
# What percentage of people without advanced education make more than 50K? 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)
# 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
# What is the minimum number of hours a person works per week (hours-per-week feature)? # 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? # 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 = round(df['salary'][df['hours-per-week'] == df['hours-per-week'].min()].value_counts(normalize=True).mul(100).loc[">50K"], 1)
rich_percentage = None
# What country has the highest percentage of people that earn >50K? # What country has the highest percentage of people that earn >50K?
highest_earning_country = None highest_earning_country = (df['native-country'][df['salary'] == '>50K'].value_counts()/df['native-country'].value_counts()).idxmax()
highest_earning_country_percentage = None 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. # 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 # DO NOT MODIFY BELOW THIS LINE