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 walks through a real‑world Python web‑scraping issue where the retrieved map data mismatched the displayed results, explains why the original API call failed, and shows how switching to the correct API and adjusting parameters resolves the inconsistency.
This article walks through a Python web‑scraping problem where an incorrect XPath selector caused unexpected results, demonstrates the faulty code, explains why the XPath was wrong, and provides a corrected script with proper headers to reliably extract the desired text.
This article explains why Chinese characters become garbled when a CSV file is opened in Excel and provides a simple Python solution—using the UTF‑8‑SIG encoding—to ensure the text displays correctly, along with additional tips for handling CSV data.
This article walks through a real‑world Python web‑scraping problem, shows why the original XPath selector fails, provides corrected code with a working XPath expression, and highlights best practices such as adding request headers for reliable crawling.
This article walks through a Python web‑scraping problem, demonstrates why the original XPath extraction returns noisy or missing price data, and provides multiple refined code solutions—including filtering empty entries, correcting XPath selectors, and using map‑filter techniques—to produce clean, formatted price lists.
This guide walks you through common Python library installation issues—such as missing environment variables and absent dependencies—provides step‑by‑step screenshots, explains how to add the Python path to system variables, and shows how to use a reliable mirror with pip to install packages like netCDF4.
This article walks through a Python web‑scraping issue, shows how to clean scraped data using regular expressions, provides sample code for extracting both floating‑point and integer numbers from mixed strings, and offers a concise solution that readers can apply to their own projects.
This article walks through a real‑world Python dictionary processing challenge, demonstrates several solutions—from basic loops to itertools grouping and Pandas aggregation—provides complete code snippets, and explains how to obtain the desired structured output.
This article walks through a real‑world Python problem of extracting nested soccer odds from a JSON file, comparing pandas read_json, jsonpath, and regex approaches, and ultimately presenting a complete pandas‑based solution that normalizes the data, cleans it with a helper function, and exports it to CSV.
This article explains Python lambda syntax, default arguments, and the special None value, illustrating each concept with clear code examples and concise explanations to help beginners grasp function parameter nuances.
