70 lines
3.7 KiB
Python
70 lines
3.7 KiB
Python
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import pandas as pd
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def calculate_demographic_data(print_data=True):
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# Read data from file
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df = pd.read_csv("adult.data.csv")
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# How many of each race are represented in this dataset? This should be a Pandas series with race names as the index labels.
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race_count = df["race"].value_counts()
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# What is the average age of men?
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average_age_men = df.loc[df['sex'] == 'Male']['age'].mean()
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# What is the percentage of people who have a Bachelor's degree?
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percentage_bachelors = df.loc[df['education'] == 'Bachelors'].size / df.size * 100
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# What percentage of people with advanced education (`Bachelors`, `Masters`, or `Doctorate`) make more than 50K?
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# What percentage of people without advanced education make more than 50K?
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# with and without `Bachelors`, `Masters`, or `Doctorate`
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higher_education = df.loc[(df['education'] == 'Bachelors') | (df['education'] == 'Masters') | (df['education'] == 'Doctorate')]
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lower_education = df.loc[(df['education'] != 'Bachelors') & (df['education'] != 'Masters') & (df['education'] != 'Doctorate')]
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# percentage with salary >50K
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higher_education_rich = higher_education.loc[higher_education['salary'] == ">50K"].size / higher_education.size * 100
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lower_education_rich = lower_education.loc[lower_education['salary'] == ">50K"].size / lower_education.size * 100
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# What is the minimum number of hours a person works per week (hours-per-week feature)?
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min_work_hours = df['hours-per-week'].min()
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# What percentage of the people who work the minimum number of hours per week have a salary of >50K?
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num_min_workers = df.loc[df['hours-per-week'] == min_work_hours]
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rich_percentage = num_min_workers.loc[num_min_workers['salary'] == ">50K"].size / num_min_workers.size * 100
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# What country has the highest percentage of people that earn >50K?
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highest_earning_country = df.loc[df['salary'] == ">50K"]['native-country'].value_counts().idxmax()
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highest_earning_country_percentage = df.loc[(df['salary'] == ">50K") & (df['native-country'] == highest_earning_country)].size / df.loc[df['native-country'] == highest_earning_country].size * 100
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# Identify the most popular occupation for those who earn >50K in India.
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top_IN_occupation = df.loc[(df['salary'] == ">50K") & (df['native-country'] == 'India')]['occupation'].value_counts().idxmax()
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# DO NOT MODIFY BELOW THIS LINE
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if print_data:
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print("Number of each race:\n", race_count)
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print("Average age of men:", average_age_men)
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print(f"Percentage with Bachelors degrees: {percentage_bachelors}%")
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print(f"Percentage with higher education that earn >50K: {higher_education_rich}%")
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print(f"Percentage without higher education that earn >50K: {lower_education_rich}%")
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print(f"Min work time: {min_work_hours} hours/week")
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print(f"Percentage of rich among those who work fewest hours: {rich_percentage}%")
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print("Country with highest percentage of rich:", highest_earning_country)
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print(f"Highest percentage of rich people in country: {highest_earning_country_percentage}%")
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print("Top occupations in India:", top_IN_occupation)
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return {
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'race_count': race_count,
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'average_age_men': average_age_men,
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'percentage_bachelors': percentage_bachelors,
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'higher_education_rich': higher_education_rich,
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'lower_education_rich': lower_education_rich,
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'min_work_hours': min_work_hours,
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'rich_percentage': rich_percentage,
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'highest_earning_country': highest_earning_country,
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'highest_earning_country_percentage':
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highest_earning_country_percentage,
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'top_IN_occupation': top_IN_occupation
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}
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