5 Common Mistakes Data Scrapers Make (and How to Fix Them)

 

1. Ignoring Website Policies (robots.txt / TOS)

Mistake: Scraping without checking the website's robots.txt or Terms of Service.
Risk: Legal issues or getting permanently IP-banned.

Solution:

Always check https://example.com/robots.txt

Respect crawl delays, disallowed paths

Use public or scrape-friendly websites

Add disclaimers if you share the data

2. Scraping Too Fast = Getting Blocked

Mistake: Sending too many requests in a short time.
Result: Server blocks your IP (rate-limiting or 403 errors).

Solution:

Add randomized delays (time.sleep(random.uniform(1, 3)))

Use rotating proxies or services like ScraperAPI, BrightData

Use headers to mimic real browsers (User-Agent, Referer)

3. Not Handling JavaScript-Rendered Content

Mistake: Using basic scraping tools (like BeautifulSoup) on sites that load content with JavaScript.
Result: You get partial or no data.

Solution:

Use Selenium, Playwright, or Puppeteer for dynamic content

Check browser DevTools → Network → XHR for hidden APIs

4. Scraping Dirty or Irrelevant Data

Mistake: Grabbing everything from the page without cleaning or filtering.
Result: You end up with messy, unusable data.
Solution:

Use strip(), regex, and data validation checks

Plan what fields you need before scraping

Use Pandas or Excel to clean and format after scraping

5. No Error Handling or Logging

Mistake: Script crashes when one page fails, and you lose progress.
Result: Frustration and wasted time.

Solution:

Use try-except blocks.

Save progress in batches (e.g., every 50 rows to a CSV).

Log failed URLs and errors for later re-scraping