Demographic Data Analyzer

Context and references:

• 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

---

• 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.

Hint ⛑

It can be easier to develop and debug data using jupyter notebook, it allows to have visual feedback faster than checking data in terminal.

• Jupyter Oficial
• Anaconda also allows to run jupyter notebooks locale