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🕸️ Web Scraping in Python: A Practical Guide for Data Scientists
- Web scraping is a powerful tool for extracting data from websites automatically.
- Web scraping involves extracting data from websites using automated processes.
- Legal considerations for web scraping include checking robots.txt files, reading Terms of Service, and avoiding overloading servers.
- Popular Python libraries for web scraping include requests, BeautifulSoup, pandas, lxml, Selenium, and playwright.
- A step-by-step example of web scraping involves sending requests, parsing HTML, extracting quotes and authors, and storing data using pandas.
- Scraping multiple pages can involve iterating over pages and storing data in a structured format.
- Bonus: Scraping JavaScript-rendered sites using Selenium may require installing Selenium, WebDriver, and utilizing appropriate drivers.
- Best practices for web scraping include using headers, adding delays, handling exceptions, respecting terms of use, and using proxies for large-scale scraping.
- Real-world use cases for web scraping include news monitoring, e-commerce price tracking, competitor research, NLP/ML projects, job listings, and market analysis.
- Web scraping is a foundational tool for data scientists with endless possibilities for custom datasets and AI model empowerment.
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