A global community of Chinese Python developers offering technical articles, columns, original video tutorials, and problem sets. Topics include web full‑stack development, web scraping, data analysis, natural language processing, image processing, machine learning, automated testing, DevOps automation, and big data.
This article benchmarks memory consumption of concurrent programs across Rust, Go, Java, C#, Node.js, Python, Elixir and others, revealing surprising differences in how threads, async runtimes, and virtual threads scale from a single task up to one million concurrent tasks.
Python 3.14 introduces a collection of subtle yet useful enhancements—such as TypedDict's NotRequired, improved static analysis, lazy imports, clearer error messages, a new contextlib.chdir manager, refined async task cancellation, better subprocess isolation, richer pattern‑matching errors, and import‑time profiling—that together make scripts run smoother, easier to debug, and more maintainable.
Learn how to create a modular Python script that batch processes Excel files—merging, cleaning, renaming, and more—using pandas and openpyxl, with a clear project structure and extensible design for real-world office automation.
This guide explains how to combine a master XLS file with a detailed XLSX file by matching the "Name" column, handling non‑standard headers, skipping index columns, inserting new fields at a specific position, and exporting the merged result using Python, pandas, xlrd, and openpyxl.
This guide explains how to create a non‑intrusive Python bot that automatically compensates weapon recoil in PUBG by capturing the screen, recognizing equipped gear with OpenCV and SSIM, and moving the mouse via pydirectinput based on weapon‑specific recoil tables.
This article compiles the most commonly used Python libraries for computer vision, covering basic image handling with Pillow, high‑performance processing with OpenCV and Mahotas, advanced tools like Scikit‑Image, TensorFlow Image, PyTorch Vision, SimpleCV, Imageio, Albumentations, and the model zoo timm, each with concise descriptions and practical code snippets.
This article introduces the Scrapeasy Python library, shows how to install it, initialize website and page objects, retrieve links, images, videos, and other file types, and download them with simple one‑line commands, providing a fast way to extract data from any site.
This guide introduces seven powerful Python libraries—Pandas, Selenium, Flask, Scrapy, Requests, Faker, and Pillow—explaining their core uses, installation commands, and example code snippets to help developers automate tasks, streamline workflows, and accelerate development.
Upgrading a Python project from 3.11 to 3.12 can cause macOS App Store rejections because the new standard library embeds an "itms-services" URL string that Apple’s automated review flags, prompting CPython core developers to propose patches, distribution‑tool options, and a new compliance flag to resolve the issue.
This article explains how using pandas loc and iloc inside Python loops can be extremely slow, demonstrates the performance difference with concrete timing examples, and shows that replacing them with the scalar accessors at and iat can accelerate the operation by up to sixty times.
