- FCC Data Analysis Challenge - Demographic Data Analyzer
- From FCC recomendations:
- In this challenge you must analyze demographic data using Pandas. You are given a dataset of demographic data that was extracted from the 1994 Census database. Here is a sample of what the data looks like:
| age | workclass | fnlwgt | education | education-num | marital-status | occupation | relationship | race | sex | capital-gain | capital-loss | hours-per-week | native-country | salary | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 39 | State-gov | 77516 | Bachelors | 13 | Never-married | Adm-clerical | Not-in-family | White | Male | 2174 | 0 | 40 | United-States | <=50K |
| 1 | 50 | Self-emp-not-inc | 83311 | Bachelors | 13 | Married-civ-spouse | Exec-managerial | Husband | White | Male | 0 | 0 | 13 | United-States | <=50K |
| 2 | 38 | Private | 215646 | HS-grad | 9 | Divorced | Handlers-cleaners | Not-in-family | White | Male | 0 | 0 | 40 | United-States | <=50K |
| 3 | 53 | Private | 234721 | 11th | 7 | Married-civ-spouse | Handlers-cleaners | Husband | Black | Male | 0 | 0 | 40 | United-States | <=50K |
| 4 | 28 | Private | 338409 | Bachelors | 13 | Married-civ-spouse | Prof-specialty | Wife | Black | Female | 0 | 0 | 40 | Cuba | <=50K |
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You must use Pandas to answer the following questions:
- How many people of each race are represented in this dataset? This should be a Pandas series with race names as the index labels. (race column)
- What is the average age of men?
- What is the percentage of people who have a Bachelor's degree?
- 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?
- What is the minimum number of hours a person works per week?
- What percentage of the people who work the minimum number of hours per week have a salary of more than 50K?
- What country has the highest percentage of people that earn >50K and what is that percentage?
- Identify the most popular occupation for those who earn >50K in India.
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Use the starter code in the file demographic_data_analyzer.py.
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Update the code so all variables set to None are set to the appropriate calculation or code. Round all decimals to the nearest tenth.
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Development Write your code in demographic_data_analyzer.py. For development, you can use main.py to test your code.
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Testing The unit tests for this project are in test_module.py. We imported the tests from test_module.py to main.py for your convenience.
It can be easier to develop and debug data using jupyter notebook, it allows to have visual feedback faster than checking data in terminal.