Avoid These Common Web Scraping Mistakes (And Save Your Project!)

Introduction: A Costly Lesson in Web Scraping

A few years ago, a friend of mine—let’s call him Alex—decided to scrape an e-commerce website for product prices. He wrote a quick script, fired it up, and… got his IP banned within minutes. Turns out, he was sending hundreds of requests per second, triggering the site’s anti-bot defenses. His project stalled, and he had to switch to a new IP (and learn some humility).

If you're new to web scraping, mistakes like these can derail your project fast. The good news? Most are avoidable. Whether you're gathering data for research, business intelligence, or automation, steering clear of these common pitfalls will save you time, frustration, and potential legal trouble.

Let’s break down the top web scraping mistakes and how to avoid them.

1. Ignoring robots.txt (The Silent Rulebook)

Every website has a robots.txt file (e.g., example.com/robots.txt) that tells bots which pages they can or can’t scrape. Ignoring it is like barging into a private party—you might get kicked out.

What to Do Instead:

✔ Check robots.txt first – Use Python’s urllib or a simple browser visit.
✔ Respect Disallow rules – If a site blocks scraping certain pages, avoid them.
✔ Stay ethical – Scraping against a site’s terms can lead to legal issues.

💡 Example: Google’s robots.txt blocks scraping its search results—attempting it risks an IP ban.*

2. Sending Too Many Requests (Hello, IP Ban!)

Servers don’t like being bombarded with rapid requests. Too many too fast = IP ban or even a CAPTCHA wall.

How to Scrape Politely:

✔ Use delays – Add time.sleep(2) between requests (Python).
✔ Limit concurrent requests – Tools like Scrapy have built-in throttling.
✔ Rotate user agents & IPs – Services like ScraperAPI or proxies help avoid detection.

🚨 True Story: A startup once got their entire AWS IP range blacklisted by aggressively scraping—don’t be that person.

3. Not Using Headers (Looking Like a Bot)

Sending requests without headers (like User-Agent) is like walking into a bank wearing a ski mask—suspicious. Websites block "naked" requests.

Essential Headers to Include:

headers = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)",  
    "Accept-Language": "en-US,en;q=0.9",  
}

✔ Mimic a real browser – Use realistic User-Agent strings (list here).
✔ Add Referer and Accept – Makes requests seem more natural.

4. Not Handling Errors (When the Site Fights Back)

Websites change structure, go down, or block you. If your script crashes at the first error, you’ll lose data.

Bulletproof Your Scraper:

✔ Use try-except blocks – Catch connection timeouts, 404s, etc.
✔ Log errors – Track what went wrong for debugging.
✔ Retry failed requests – Libraries like retrying (Python) help.

try:
    response = requests.get(url, timeout=10)
except requests.exceptions.RequestException as e:
    print(f"Failed: {e}")

5. Scraping Without Testing (Going Big Too Soon)

Jumping straight to scraping 10,000 pages? Bad idea. Always test on a small scale first.

Smart Testing Strategy:

1. Scrape 5-10 pages manually to check structure.
2. Verify data quality – Are you extracting the right fields?
3. Monitor for bans – Run short tests before scaling up.

🔍 Pro Tip: Use browser tools (Right-Click → Inspect) to study a site’s HTML before writing code.

6. Storing Data Unstructured (A Mess to Clean Later)

Dumping raw HTML or inconsistent JSON? You’ll waste hours cleaning it later.

Keep Data Tidy:

✔ Use structured formats – CSV, SQLite, or Pandas DataFrames.
✔ Normalize early – Ensure consistent date/price formats.
✔ Add metadata – Timestamp when data was scraped.

Conclusion: Scrape Smarter, Not Harder

Web scraping is powerful, but reckless scraping gets you blocked—or worse. Follow these rules:
✅ Respect robots.txt and terms of service.
✅ Slow down (use delays + rotation).
✅ Mimic human behavior (headers, error handling).
✅ Test small before scaling.

Got a scraping horror story? Share your lessons in the comments!

Next Steps:

• Try a simple project (e.g., scrape weather data).
• Explore frameworks like Scrapy or BeautifulSoup.
• Need help? Ask in forums like r/webscraping or Stack Overflow.

Happy (and ethical) scraping! 🚀