Email of Cryptocurrency Data Scraper
This application scrapes cryptocurrency data from CoinMarketCap (CMC) and websites, storing it in a Google Sheet. It fetches new coins, extracts metadata from the CMC API, and then performs a deep web crawl of each coin's official website. The web scraping uses a Breadth-First Search (BFS) algorithm to systematically navigate the site, extracting emails from HTML content and even parsing PDF documents found on the site.
Features
- Comprehensive cryptocurrency data extraction from CoinMarketCap
- Breadth-First Search (BFS) web crawling of official project websites
- Email extraction from both HTML content and PDF documents
- Cloudflare email protection decoding
- Smart URL filtering to avoid unnecessary crawling
- Domain-level caching for improved efficiency
- Rate limiting to respect API restrictions
- Google Sheets integration for data storage
Technical Stack
- Python
- Requests library for API interactions
- BeautifulSoup for HTML parsing
- PyPDF2 for PDF document extraction
- Google API for spreadsheet integration
- Regular expressions for email pattern matching
- Multi-level caching system
Lessons Learned
The Email of Cryptocurrency Data Scraper project represents my most sophisticated web scraping implementation to date. This system tackles the complex challenge of extracting contact information from cryptocurrency projects through a multi-layered approach that combines API data collection with deep web crawling.
The technical implementation incorporates several advanced features that significantly enhance its effectiveness. The Breadth-First Search algorithm ensures systematic exploration of project websites, while the ability to extract emails from both HTML content and PDF documents maximizes data collection opportunities. Handling Cloudflare-protected emails demanded particularly creative solutions, requiring decoding of obfuscated email addresses.
Efficiency was a major focus in this project. The implementation of domain-level caching dramatically reduced redundant crawling, while smart URL filtering—using skip patterns, regular expressions, and path prioritization—ensured the crawler focused on the most promising content areas. These optimizations were essential given the scale of data being processed.
Error handling proved to be another critical component. The system incorporates SSL verification fallback and retry mechanisms to maintain continuous operation even when encountering problematic websites. This robust approach ensures that temporary issues don't derail the entire data collection process.
The comprehensive logging and configuration options added significantly to the system's usability and maintainability. These features make it possible to monitor performance, diagnose issues, and adjust parameters without code modifications.
One of the most interesting aspects of this project was developing platform-specific handling for services like Linktree that are commonly used by cryptocurrency projects. This adaptability to different website structures demonstrates how web scraping often requires tailored approaches rather than one-size-fits-all solutions.
This project has proven highly valuable not just for its immediate data collection capabilities but also as a framework that can be adapted for other web scraping needs with similar requirements for depth, efficiency, and robustness.