Should I use Python or Node.js for web scraping?

Both Python and Node.js are excellent choices for web scraping, each with distinct strengths.

Python advantages:

• Mature ecosystem with battle-tested libraries (Requests, BeautifulSoup, Scrapy)
• Better data analysis tools (pandas, numpy)
• More tutorials and community support for scraping
• Simpler syntax for beginners
• Better for data science workflows (scraping → analysis → ML)

Node.js advantages:

• Native JavaScript execution (better for JS-heavy sites)
• Higher performance for concurrent requests
• Smaller memory footprint
• Better integration with modern web technologies
• Playwright and Puppeteer are first-class tools

Performance comparison:

For 1000 concurrent requests:

• Node.js: 10-20 seconds (async/await native)
• Python: 15-30 seconds (requires asyncio or threading)

Choose Python when:

• You're doing data analysis after scraping
• The team knows Python better
• You need Scrapy for large-scale crawling
• Scraping static HTML sites
• Rich ecosystem of data processing tools matters

Choose Node.js when:

• Target sites are JavaScript-heavy SPAs
• Performance and concurrency are critical
• You need tight integration with browser automation
• Team has strong JavaScript experience
• Real-time processing is needed

Recommendation:

For most beginners: Start with Python (easier learning curve, better tutorials). For JavaScript developers: Use Node.js (leverage existing knowledge). For enterprise crawling: Python with Scrapy. For JavaScript-heavy sites: Node.js with Playwright.