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. Update the code so all variables set to "None" are set to the appropriate calculation or code. Round all decimals to the nearest tenth.

Unit tests are written for you under test_module.py.

For development, you can use main.py to test your functions. Click the "run" button and main.py will run.

We imported the tests from test_module.py to main.py for your convenience. The tests will run automatically whenever you hit the "run" button.

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

Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.