Python Web Scraping & Data Extraction Projects

A comprehensive collection of web scraping projects using Python, focusing on data extraction, automation, and practical real-world applications.

🎯 Repository Focus

This repository demonstrates web scraping techniques from beginner to advanced levels, including:

• HTML parsing with BeautifulSoup
• API interactions and JSON handling
• Data cleaning and storage
• Rate limiting and ethical scraping
• Error handling and robust code

📚 Projects

1. News Headlines Scraper

Difficulty: Beginner Concepts: Basic HTML parsing, CSS selectors, data extraction

Scrapes latest news headlines from multiple news websites and saves them to a CSV file.

Features:

• Extract headlines, links, and timestamps
• Multiple news sources support
• CSV export functionality
• Clean and formatted output

Dependencies: requests, beautifulsoup4, pandas

---

2. Product Price Tracker

Difficulty: Intermediate Concepts: Dynamic scraping, price monitoring, data persistence

Track product prices from e-commerce sites and get alerts on price drops.

Features:

• Monitor multiple products simultaneously
• Price history tracking
• JSON data storage
• Price drop notifications
• Historical price charts

Dependencies: requests, beautifulsoup4, matplotlib

---

3. Job Listings Aggregator

Difficulty: Intermediate Concepts: Multi-page scraping, data filtering, advanced parsing

Aggregate job listings from multiple job boards based on search criteria.

Features:

• Search by job title and location
• Filter by salary range and experience
• Export to CSV/JSON
• Duplicate removal
• Sort by date posted

Dependencies: requests, beautifulsoup4, pandas

---

4. Weather Data Collector

Difficulty: Beginner Concepts: API integration, JSON parsing, data visualization

Collect weather data using public APIs and create visual reports.

Features:

• Current weather conditions
• 7-day forecast
• Historical data tracking
• Temperature charts
• Export to various formats

Dependencies: requests, matplotlib, pandas

---

5. GitHub Repository Analyzer

Difficulty: Advanced Concepts: API authentication, rate limiting, complex data structures

Analyze GitHub repositories for statistics, contributors, and trends.

Features:

• Repository statistics
• Contributor analysis
• Commit history visualization
• Language breakdown
• Star/fork trends over time

Dependencies: requests, matplotlib, pandas

---

🚀 Getting Started

Prerequisites

Python 3.8 or higher

Installation

1. Clone the repository:

git clone https://github.com/b5119/python-web-scraping-projects.git
cd python-web-scraping-projects

1. Install dependencies:

pip install -r requirements.txt

Running Projects

Each project has its own directory with a main script:

# Example: Run the news scraper
python 01-news-scraper/scraper.py

# Example: Run the price tracker
python 02-price-tracker/tracker.py

📦 Dependencies

Create a requirements.txt file:

requests>=2.31.0
beautifulsoup4>=4.12.0
pandas>=2.0.0
matplotlib>=3.7.0
lxml>=4.9.0

Install all dependencies:

pip install -r requirements.txt

🛡️ Ethical Scraping Guidelines

This repository follows ethical web scraping practices:

1. Respect robots.txt - Always check and follow website scraping policies
2. Rate Limiting - Implement delays between requests
3. User Agent - Identify your scraper appropriately
4. Terms of Service - Comply with website terms
5. Personal Use - Use scraped data responsibly

📁 Project Structure

python-web-scraping-projects/
├── README.md
├── requirements.txt
├── 01-news-scraper/
│   ├── scraper.py
│   └── output/
├── 02-price-tracker/
│   ├── tracker.py
│   ├── products.json
│   └── data/
├── 03-job-aggregator/
│   ├── aggregator.py
│   └── output/
├── 04-weather-collector/
│   ├── collector.py
│   └── data/
└── 05-github-analyzer/
├── analyzer.py
└── output/

🎓 Learning Objectives

• HTTP Requests: Understanding GET/POST requests
• HTML Parsing: Using BeautifulSoup for DOM navigation
• CSS Selectors: Targeting specific elements
• API Integration: Working with RESTful APIs
• Data Storage: CSV, JSON, and database operations
• Error Handling: Robust exception management
• Rate Limiting: Preventing server overload
• Data Cleaning: Preprocessing scraped data

🔧 Common Issues & Solutions

Issue: “Connection Refused”

• Add delays between requests
• Use proper User-Agent headers
• Check website’s robots.txt

Issue: “Empty Results”

• Website structure may have changed
• Check CSS selectors
• Verify the page has loaded completely

Issue: “Rate Limited”

• Increase delay between requests
• Use exponential backoff
• Consider using proxies (ethically)

📊 Sample Outputs

Each project generates structured data:

• CSV files - For spreadsheet analysis
• JSON files - For programmatic access
• Charts/Graphs - Visual data representation

🤝 Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Open a Pull Request

⚖️ Legal Notice

This repository is for educational purposes. Always:

• Check website Terms of Service before scraping
• Respect copyright and data privacy laws
• Use scraped data ethically and legally

📄 License

This project is licensed under the MIT License.

👤 Author

Frank Bwalya -https://github.com/b5119

🌟 Acknowledgments

• BeautifulSoup documentation
• Requests library
• Python community

---

⭐ If you find this repository helpful, please star it!