Python Web Scraping Tutorial

Web scraping is a technology that automates the extraction of publicly available data from web pages. It is widely used in fields such as price monitoring, public opinion analysis, and academic research. Its core value lies in transforming unstructured web page content into standardized, analyzable data.

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Environment setup and tool selection

Basic library installation

Request libraries: requests is used to send HTTP requests, while aiohttp supports asynchronous concurrency.

Parsing libraries: BeautifulSoup is suitable for parsing HTML tags, while lxml improves XPath processing efficiency.

Dynamic rendering tools: Selenium or Playwright for handling JavaScript loading content.

Development environment configuration

It is recommended to use Jupyter Notebook to quickly validate code snippets, or PyCharm to build a complete project. Virtual environments (such as venv) can isolate dependency conflicts.

Web scraping practical steps

Target website analysis

View network requests and element structure using the browser's developer tools (F12).

Identify data storage format: HTML tags, JSON interface, or dynamically rendered content.

Request sending and response processing

Add headers to simulate a browser (fields such as User-proxy and Referer).

Handle HTTP status codes (such as 301 redirect, 403 forbid).

Using PYPROXY static proxy to maintain IP stability (Example scenario: When you need to continuously access the same target website).

Data analysis and cleaning

HTML parsing: Locating div containers or class attributes using CSS selectors

JSON Extraction: Directly parse key-value pairs returned by the API

Noise reduction: Filtering out irrelevant symbols or whitespace characters using regular expressions

Persistent storage design

Small-scale data: CSV files (quick export using pandas.to_csv)

Structured storage: SQLite or PostgreSQL database

Advanced Techniques and Problem Solving

Anti-scraping mechanism countermeasures

IP blocking avoidance: Implement automatic IP rotation using the PyPROXY dynamic proxy pool.

Request frequency control: Randomize the request interval (0.5-3 seconds) and access period.

CAPTCHA cracking: Integrate Tesseract OCR or third-party recognition services

Performance optimization strategies

Asynchronous concurrency: asyncio coroutines handle 50+ requests simultaneously

Caching mechanism: Store static page content locally (e.g., using the requests-cache library).

Resuming breakpoint capture: Records identifiers of already collected data to avoid repetitive work.

Building an enterprise-level web crawling system

Distributed architecture design

Implement multi-node task distribution using Scrapy-Redis

Message queue (such as RabbitMQ) management fetch priority

Operation and maintenance monitoring system

Log recording: Real-time anomaly analysis using ELK (Elasticsearch + Logstash + Kibana)

Proxy IP Health Check: Regularly verify IP availability and response speed

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