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 introduces ten powerful Python modules—including Pillow, moviepy, requests, Plotly, Selenium, progressbar2, PyPDF2, dis, pyttsx3, and Turtle—explaining their key features, installation commands, and providing ready-to-run code examples to help developers work more efficiently.
This article introduces several lesser‑known Python standard‑library modules—including difflib, sched, binascii, tty, and weakref—explaining their key functions, typical use cases, and providing ready‑to‑run code examples so developers can quickly expand their toolkit.
This article analyzes a JD e‑commerce dataset using Python and MySQL to calculate key metrics such as PV, UV, conversion rates, attrition, daily activity, hourly trends, user‑behavior funnels, purchase intervals, retention rates, product sales, and RFM segmentation, and then offers data‑driven recommendations to improve traffic, conversion, and user loyalty.
Python’s async features, introduced in 3.5, excel at network I/O but face limitations from the GIL, lack of async file I/O, and fragmented library support, while upcoming 3.14 enhancements like free threads and sub‑interpreters promise broader concurrency, yet raise new integration challenges.
Python developers often create simple queues with lists using append and pop(0), but this approach has O(n) complexity and thread‑safety issues; the article explains how the built‑in queue module provides efficient, thread‑safe FIFO, LIFO, and priority queues, with code examples for single‑thread and multithreaded task processing.
This article introduces fifteen essential Python packages, explaining their core features and use cases—from data visualization with Dash to web services with Flask—providing beginners with a concise guide to the most useful libraries in the Python ecosystem.
This article reveals ten often‑overlooked Python performance techniques—from using enumerate and array structures to leveraging Numba, Polars, and generators—showing how careful coding, profiling, and modern libraries can turn sluggish scripts into lightning‑fast production workloads.
This article presents twelve practical Python code snippets—from regular expressions and list slicing to multithreading and CSV handling—that let you solve common programming tasks quickly and efficiently without writing lengthy code.
This tutorial walks you through using Python libraries such as PyPDF2, Tabula, PyMuPDF, Pillow, and fpdf2 to extract text, tables, and images from PDF files and to write new PDFs, complete with code examples, step‑by‑step explanations, and sample outputs.
This article presents twelve practical Python tricks—from retrieving string indices and adding progress bars to creating simple GUIs, converting strings to snake_case, flattening lists, sorting by index, generating UUIDs, initializing 2D lists, transposing iterables, using f‑strings, concatenating lists, and applying the walrus operator.
