

Crawlee For Python
Overview :
Crawlee is a Python library for building reliable web crawlers. Developed by experienced web crawling professionals, it's used daily to crawl millions of pages. Crawlee supports JavaScript rendering, allowing you to easily switch to browser crawling without rewriting code. It also offers automatic proxy rotation and management, intelligently managing and cycling through proxies based on system resources and discarding those frequently encountering timeouts or network errors.
Target Users :
Crawlee for Python is designed for developers and data scientists who need to crawl large amounts of web data. It helps users efficiently acquire and process web data by providing a fast and reliable crawling framework, particularly suitable for scenarios requiring JavaScript rendering or highly customized crawler behavior.
Use Cases
Scraping social media data for market analysis and user behavior research.
Crawling product information from e-commerce websites for price comparison and inventory monitoring.
Extracting content from news websites for content aggregation and news analysis.
Features
Written in modern Python with type hints, providing code completion in your IDE.
Built on Playwright, allowing you to switch your crawler from HTTP to headless browser in just 3 lines of code.
Supports multiple browsers, including Chrome and Firefox.
Automatically manages and rotates proxies, intelligently discarding underperforming proxies.
Provides a CLI tool for quickly creating new projects and adding template code.
Supports data extraction and dataset export functionalities for easy data management and analysis.
How to Use
1. Install Crawlee and Playwright: Install Crawlee using pip and run `playwright install` to install the browser binaries.
2. Create a new project using CLI: Create a new crawler project using the command `pipx run crawlee create my-crawler`.
3. Write the crawler logic: Write the crawler logic in the project, including request handling, data extraction, and proxy management.
4. Run the crawler: Run the `main` function using asyncio to start crawling the specified URLs.
5. Data processing: After the crawler finishes running, you can export the dataset to a JSON file or use the data directly.
6. Optimization and maintenance: Adjust crawler parameters as needed, optimize proxy usage strategies, and maintain the stability and efficiency of the crawler.
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