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 article introduces Python's regular expression engine, explains the special characters ^, ., and * with clear examples, and demonstrates how they can be used to match and extract specific patterns in web‑scraped text.
This article explains Python's built‑in object model, focusing on simple numeric types—int, long, float, complex, bool—and their literal forms, division behavior differences between Python 2 and Python 3, as well as fractions, boolean values, and complex number operations.
This guide shows how to use the virtualenv tool on Windows to create a Python 2 virtual environment by specifying the interpreter path, activate it, verify the Python version, and then deactivate it, with step‑by‑step commands and screenshots.
This guide walks you through installing virtualenv on Windows, creating a named virtual environment, locating its files, activating it with activate.bat, checking the Python version, and properly deactivating the environment to keep Python2 and Python3 projects isolated.
This guide shows how to launch Python's interactive environment on Windows via IDLE or the command prompt, explains executing statements, using the special '_' variable, handling errors, navigating command history, and switching to script editing for longer programs.
This article reviews the four main Python selectors used for web scraping—regular expressions, BeautifulSoup, lxml/XPath, and CSS—detailing their installation, parsing behavior, advantages, drawbacks, and a performance comparison table to help developers choose the most suitable tool for their projects.
The article argues that in 2026 AI engineering must shift from costly, uncertain experiments to measurable, data‑driven investments by exposing hidden AI expenses, adopting a "grower" mindset, and applying four control valves to turn AI spend into concrete business returns.
The article explains a real‑world requirement to map numeric business levels to Chinese strings in Excel and provides two Python solutions—one using openpyxl for modern .xlsx files and another using win32com for legacy .xls files—complete with runnable code and execution results.
The author recounts a Python‑based workflow that automatically adds watermarks to foreign‑trade .doc documents, details client validation, unexpected .docx conversion issues, and the iterative debugging steps taken to ensure reliable batch processing.
This article walks through a fan‑requested Python automation that replaces artistic watermark text in shipping‑bill DOCX files, detailing the initial python‑docx attempt, the shortcomings of win32com shape traversal, and the final reliable solution using zipfile‑based XML replacement.
