Demographic Data Analyzer

A comprehensive Python data analysis project that examines demographic patterns from the 1994 Census database using Pandas. This project answers 9 key demographic questions through statistical analysis.

Project Overview

This analyzer processes 32,561 demographic records to provide insights into:

• Race distribution across the population
• Income patterns by education level
• Work hour statistics and earnings correlation
• Geographic income distribution
• Occupation trends by country

Project Structure

demographic-data-analyzer/
├── src/
│   ├── demographic_data_analyzer.py    # Main analysis code
│   ├── test_module.py                  # Unit tests (9/9 passing)
│   ├── main.py                         # Development runner
│   └── demographic_data.csv            # Dataset (32,561 records)
├── requirements.txt                    # Dependencies
└── README.md                          # This file

Quick Start

1. Clone and navigate to the project:

   cd src/

2. Install dependencies:

   pip install pandas

3. Run the analysis:

   python main.py

4. Run tests:

   python test_module.py

Key Results

Metric	Result
Average age of men	39.4 years
Bachelor's degree holders	16.4%
Advanced education earning >50K	46.5%
No advanced education earning >50K	17.4%
Minimum work hours/week	1 hour
Highest earning country	Iran (41.9%)
Top India occupation (>50K)	Prof-specialty

Analysis Questions Answered

1. Race distribution - Pandas Series with race counts
2. Average age of men - Mean age calculation
3. Bachelor's degree percentage - Education level analysis
4. Advanced vs. non-advanced education income - Comparative analysis
5. Minimum work hours - Labor statistics
6. Income correlation with minimal hours - Work-income relationship
7. Highest earning country - Geographic income analysis
8. Popular occupation in India - Country-specific career trends

Testing

All functions include comprehensive unit tests:

• 9/9 tests passing
• Automated validation
• Error handling verification

Technical Details

• Language: Python 3.12+
• Primary Library: Pandas for data manipulation
• Data Format: CSV (no headers, 15 columns)
• Precision: All decimals rounded to nearest tenth
• Dataset Size: 32,561 records

Dataset Source

UCI Machine Learning Repository: Adult Data Set
Dua, D. and Graff, C. (2019). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science.

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