Web crawling in Python is an automated process that systematically navigates web pages to discover and collect URLs and relevant content, making it valuable for tasks like research, SEO audits, and e-commerce analysis. Python offers various libraries for building web crawlers, such as Requests, Beautiful Soup, Scrapy, and Selenium, each serving different purposes like static and dynamic content handling. A web crawler begins with a seed URL and works by fetching HTML content, parsing links, resolving URLs, managing a queue of URLs, and storing data in formats like JSON or CSV. It is crucial to differentiate between web crawling, which focuses on discovering and indexing pages, and web scraping, which involves extracting specific data from pages. Best practices for web crawling include respecting website guidelines, avoiding duplicate URLs, and handling dynamic content using tools like Selenium or Playwright. Troubleshooting common issues such as missing elements or blocked requests involves updating selectors, using browser headers, or rotating IPs. To avoid overwhelming servers or getting blocked, it is advisable to implement rate limits, respect robots.txt files, and capture errors and logs efficiently.