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Rotating user-agent headers and using proxies can improve the scraper’s robustness. Etsy may block repeated requests from the same IP address, so distributing traffic across multiple proxies can prevent detection. Libraries like fake_useragent or proxy rotation services can make your scraper appear more like a real user.

Adding error handling to manage missing or inconsistent data ensures the scraper runs smoothly. Products may lack ratings or prices, and failing to account for these cases can crash the scraper. Using try-except blocks or conditional checks prevents such issues and improves the script’s robustness.

Storing scraped product data in a database like MongoDB or SQLite allows for efficient querying and analysis. This approach makes it easier to track price trends, compare products, and analyze customer ratings over time. It’s also scalable for handling large datasets.

Handling missing data or unexpected changes in the site’s structure is crucial for building a robust scraper. Websites like RottenTomatoes frequently update their layouts, which can break hardcoded scripts. Using flexible selectors that rely on attributes or patterns rather than static class names can mitigate this issue. Regularly testing and…

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