Life's short, I code in Python. This channel shares Python web crawling, data mining, analysis, processing, visualization, automated testing, DevOps, big data, AI, cloud computing, machine learning tools, resources, news, technical articles, tutorial videos and learning materials. Join us!
This tutorial shows how to encode a JD.com search URL, fetch the page, and use XPath expressions in Python to reliably extract product names, links, images, and prices, offering a clearer alternative to regular‑expression or BeautifulSoup scraping.
This tutorial shows how to build a JD.com search URL, fetch the page with urllib, parse the HTML using BeautifulSoup, and reliably extract each product's name, link, image and price while handling missing image URLs and avoiding regex complexity.
This tutorial shows how to build a JD.com search URL, encode keywords, fetch the page with Python's urllib, and extract product details using regular expressions, providing code snippets, regex explanations, and sample output for beginners.
This article walks you through building modern Python web applications, starting with Flask fundamentals, advancing to FastAPI’s async capabilities, covering RESTful API design, database integration with SQLAlchemy and Tortoise ORM, testing, deployment, and performance comparisons to help you choose the right framework.
This comprehensive guide explores Python’s memory management internals, covering allocation, reference counting, garbage collection, profiling tools, optimization strategies such as slots, generators, array usage, memory views, custom allocators, and practical case studies for big data and web applications, helping developers write faster, more memory‑efficient code.
This comprehensive guide explores Python concurrency and parallelism, covering threading, multiprocessing, async/await, the GIL, thread safety, thread pools, process pools, producer‑consumer patterns, performance testing, and practical best‑practice recommendations for choosing the right concurrency model.
This comprehensive guide explores Python debugging and error handling techniques—from simple print statements and logging to pdb, structured logging, retry mechanisms, circuit breakers, testing strategies, and remote debugging—providing a complete toolkit for building robust and maintainable Python applications.
Discover a comprehensive guide to dramatically improve Python program speed and memory usage through algorithmic refinements, profiling tools, concurrency models, and advanced techniques like generators, slots, and third‑party libraries, complete with real‑world code examples and performance benchmarks.
This comprehensive guide explains why Python type annotations act like a safety net for your code, covering their benefits, basic and advanced syntax, static analysis with mypy, IDE integration, best‑practice strategies, and how to adopt them in existing projects for improved reliability and collaboration.
Explore the evolution of Python asynchronous programming—from basic generators and yield expressions to advanced async/await syntax, including practical examples like network requests, file I/O, producer-consumer patterns, performance comparisons, error handling, and best-practice recommendations for efficient, scalable code.
