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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Scraping promotional data from Lidl.de using Python allows you to track weekly offers, discounts, and product availability across a wide range of grocery and household items. Lidl is a major supermarket chain in Europe, and its website often features a dedicated section for ongoing promotions. Extracting data from this section requires you…

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