--description--

You will be <a href="https://app.ona.com/?autostart=true#https://github.com/freeCodeCamp/boilerplate-demographic-data-analyzer/" target="_blank" rel="noopener noreferrer nofollow">working on this project with our Ona starter code</a>.

We are still developing the interactive instructional part of the Python curriculum. For now, here are some videos on the freeCodeCamp.org YouTube channel that will teach you everything you need to know to complete this project:

• <a href="https://www.freecodecamp.org/news/python-for-everybody/" target="_blank" rel="noopener noreferrer nofollow">Python for Everybody Video Course</a> (14 hours)

• <a href="https://www.freecodecamp.org/news/how-to-analyze-data-with-python-pandas/" target="_blank" rel="noopener noreferrer nofollow">How to Analyze Data with Python Pandas</a> (10 hours)

--instructions--

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    |

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.

Use the starter code in the file demographic_data_analyzer.py. Update the code so all variables set to None are set to the appropriate calculation or code. Round all decimals to the nearest tenth.

Development

Write your code in demographic_data_analyzer.py. For development, you can use main.py to test your code.

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.

Submitting

Copy your project's URL and submit it to freeCodeCamp.

Dataset Source

Dua, D. and Graff, C. (2019). <a href="http://archive.ics.uci.edu/ml" target="_blank" rel="noopener noreferrer nofollow">UCI Machine Learning Repository</a>. Irvine, CA: University of California, School of Information and Computer Science.

--hints--

It should pass all Python tests.

--solutions--

  # Python challenges don't need solutions,
  # because they would need to be tested against a full working project.
  # Please check our contributing guidelines to learn more.