Python is the most widely used language for data scraping and processing. Requests, Beautiful Soup, LXML, and the Scrapy framework are all excellent utilities for web scraping in the Python ecosystem.
Scraping e-commerce sites employs all of the standard web scraping techniques. When it comes to online scraping, the e-commerce industry isn’t particularly unique. The specific implementation details vary on every website, however here are some general guidelines:
• For simple scripts, use requests+lxml (or requests+Beautiful soup). Scrapy framework can be used for scraping on a wider scale.
• If the sites have automated traffic countermeasures and/or rate-limiting depending on source IP, use a proxy pool. Also, ensure that HTTP headers are as close as feasible to those used by the appropriate browsers.
• Examine the HTTP requests in the Network tab of various scraping service providers to determine if there’s a method to retrieve pre-structured data via a hidden API. Many Shopify stores, for example, publish product information at /products.json.
• Scraped data can be saved to a CSV file, an Excel file (using openpyxl), or a database.