This article demonstrates how to use Python's requests, lxml, and pandas libraries to crawl GDP data from a website, parse the HTML tables, and efficiently write the extracted rankings, regions, GDP values, and years into a CSV file, providing a complete, runnable example for web scraping beginners.
This article provides a comprehensive tutorial on building, configuring, testing, publishing, and listing custom Coze plugins for AI bots, covering both creation from existing services and direct development in Coze IDE with code examples and practical tips.
This step‑by‑step guide shows how to claim your free Kimi API credit, create an API key, test the service with Postman, and integrate it via Python SDK, frontend projects, or no‑code tools like Dify, enabling anyone to build AI applications without writing code.
This tutorial walks you through creating a serverless Milvus vector search service on Alibaba Cloud, preparing a news corpus, generating embeddings with DashScope, storing them in Milvus, and performing vector‑based retrieval and question answering using a large language model.
agere is a lightweight, dependency‑free Python framework that lets developers build fully customizable agents by defining reusable Job and handler task nodes in an edge‑node model, supporting parallel execution, fine‑grained callbacks, direct parameter passing, and hierarchical orchestration through a root Commander node.
Using Python data visualization and geospatial analysis, this article compares the nationwide distribution of Starbucks and Luckin Coffee stores in China, revealing differences in regional concentration, proximity patterns, and statistical insights such as average Luckin stores within 500 m of each Starbucks location.
This article presents LeetCode problem 797, which asks for all possible paths from node 0 to node n‑1 in a directed acyclic graph, explains the DFS approach, lists constraints and examples, and provides complete implementations in Java, C++, C, and Python.
This guide introduces CozeIDE, explains its main features such as code editing, dependency management, AI programming assistant, testing and publishing, and walks you through two complete plugin development examples—including a stock‑price lookup plugin—showing how to create, test, and release plugins using Node.js or Python runtimes.
A 2024 survey of Go developers reveals strong interest in building AI applications but frustration over Python's dominant ecosystem, highlighting performance priorities, tooling gaps, and a desire for Go libraries comparable to Python's, while also showing cloud platform preferences and IDE usage trends.
By externalizing feature processing with the EzFeaFly tool and feeding a dense index/value tensor directly to the GPU, the DSP platform decouples feature transformation from model inference, cutting instance usage by ~40%, reducing inference latency 70‑80%, and achieving over 60% end‑to‑end latency improvement while lowering costs.
This tutorial demonstrates how to install and use Python libraries such as xlrd, xlwt, xlutils, XlsxWriter, OpenPyXL, python-docx, and the csv module to read, write, modify, and format Excel, Word, and CSV files, including code examples and feature comparisons.
This article provides a comprehensive, code‑rich walkthrough of the Transformer architecture using PaddlePaddle, covering the encoder and decoder components, residual connections, layer normalization, feed‑forward networks, scaled dot‑product and multi‑head attention, and shows how to assemble the full model with training and inference functions.
This article demonstrates how to perform fuzzy string matching in Python using three libraries—difflib, fuzzywuzzy (with python-Levenshtein), and TheFuzz—by defining matching functions, installing required packages, and providing example code that finds the best match for a given pattern among a list of candidate strings.
This article explains how Docker's new CLI tool docker init uses generative AI to automatically generate Dockerfiles, docker‑compose files, and .dockerignore for a project, walking through a Flask example, showing commands, generated code, and the benefits of faster, safer container setup.
This article showcases twenty practical Python one‑liner examples that demonstrate how to write loops, conditionals, data‑structure operations, functions, recursion, file handling, and other common tasks in a single, compact line of code, complete with explanations and ready‑to‑run snippets.
This guide introduces Python fundamentals, covering variable declaration, common data types, conditional statements, loops, functions, list comprehensions, exception handling, classes, generators, decorators, and module imports, each illustrated with clear code examples for beginners.
This article introduces the concept and various methods of sensitivity analysis—including one‑factor, multi‑factor, variance‑based, and Monte Carlo approaches—explains Sobol indices, outlines step‑by‑step procedures, and demonstrates their application with a Python case study on urban air‑quality modeling.
This article presents ten practical Python examples demonstrating how to create and parse JSON data, perform GET and POST requests, handle parameters, timeouts, cookies, custom headers, file downloads, authentication, and retry mechanisms using the Requests library, enhancing web development and API integration skills.
This article demonstrates how to use Python's APScheduler to automate various WeChat subscription account tasks—such as publishing articles, sending group messages, analyzing data, clearing caches, and updating menus—through detailed code examples and scheduling configurations.
An introductory guide to Python's arithmetic, relational, logical, assignment operators and core data types, illustrated with over fifteen practical code snippets to reinforce understanding and application for beginners seeking to solidify foundational programming skills.
This article introduces data visualization concepts and demonstrates how to create line charts, scatter plots, histograms, pie charts, and box plots using Pandas, while applying these techniques to real-world datasets such as the 2013 American Community Survey and the Boston Housing dataset.
This article explains Python type hints, covering their syntax, benefits for readability and debugging, advanced usage with the typing module, and practical code examples that help developers write more robust and maintainable Python applications.
The April TIOBE Index shows Python gaining market share while C and C++ lose ground due to memory‑safety concerns, PHP slipping to historic lows, and a broader shift in developer preferences driven by AI, web frameworks, and security considerations.
This article presents a collection of useful Python scripts—including file sorting, empty‑folder removal, batch renaming, web‑scraping, bulk image downloading, form automation, and various text‑processing utilities—each accompanied by clear explanations and ready‑to‑run code examples.
This article introduces various Python-based data backup techniques, covering standard library modules such as shutil, zipfile, and tarfile, as well as database dump tools like pg_dump and mysqldump, and cloud storage options using awscli or boto3, with example code snippets for each method.
The article shares a tester’s personal journey from functional testing to automation, explains why programming skills are essential, and provides a detailed, step‑by‑step learning roadmap covering Python, Selenium, Appium, API testing, CI/CD and framework development to help newcomers advance their careers.
Allure-Python is a reporting tool for Python test frameworks such as unittest, pytest, and nose, providing customizable, interactive HTML reports with support for decorators, steps, attachments, environment data, and advanced features to enhance test visibility and analysis.
This article compiles a comprehensive set of Python algorithm templates—including syntax shortcuts, knapsack solutions, backtracking, union‑find, topological sorting, monotonic stacks, binary search, dynamic programming, prefix sums, two‑pointer techniques, tree traversals, and graph algorithms—providing clear code snippets and explanations to help developers ace LeetCode interview problems.
This article explains the motivation behind Self‑RAG, describes its core workflow—including conditional retrieval, enhanced generation, and self‑evaluation tokens—details the four evaluation metrics (Retrieve, IsRel, IsSup, IsUse), and provides a Python scoring example using log‑probabilities.
This article explains how to overcome JavaScript-rendered web pages when scraping with Python, comparing tools like Selenium, Playwright, and the DrissionPage library, and provides a concrete code example that fetches and prints page HTML, along with practical tips for handling JSON data sources.
This guide walks AI engineers through a rapid 10‑minute boot‑strap of LangChain, explaining its purpose, core concepts, design questions, environment setup, and step‑by‑step code examples that cover APIs, chains, memory, retrieval‑augmented generation, tools, agents, and the overall architecture.
This article provides a thorough overview of essential Python concepts, covering numeric operations, string manipulation, function definitions, data structures, class and object usage, as well as useful built‑in tools and utilities, illustrated with clear code examples for each topic.
This tutorial walks through creating a Tetris game in Python using the built‑in Tkinter library, covering window setup, drawing the board, implementing block movement, rotation, line clearing, scoring, and full source code with explanations.
This article presents a collection of concise Python one‑line code snippets—including ternary operators, multiple variable assignments, list swapping, list comprehensions, dictionary and set comprehensions, file handling, and command‑line one‑liners—demonstrating how to write more compact and readable code.
This article introduces Python fundamentals by explaining problem‑solving techniques such as the XY problem, demonstrating string and list operations, showing how to write functions and classes, and emphasizing the importance of following PEP‑8 style conventions for clean code.
This article explains why Python code uses self.name = name, how attributes like self.x and self.y can be accessed without explicit parameters, and what self.num = x does, providing clear examples and a concise summary for beginners.
This article explains how to perform UI automation testing across various computer screen resolutions using Python, covering compatible frameworks like Selenium and Appium, writing resolution‑agnostic scripts, creating resolution matrices, dynamic resizing, visual verification, and integrating tests into CI pipelines.
This article introduces Python's Turtle graphics library, explains its educational and creative use cases, and provides five detailed code examples—including drawing a square, a colorful spiral, a flower, a heart, and a Fibonacci spiral—to help beginners learn programming concepts through visual output.
This article provides a comprehensive overview of linear regression, covering its definition, purpose, algorithmic steps, data preparation, feature scaling, parameter initialization, cost function computation, gradient descent optimization, visualization, normal equation solution, and regularization, accompanied by detailed Python code examples.
This article shows Python enthusiasts how a few lines of code can automate everyday tasks—from email handling and web scraping to file management and data processing—helping you work smarter, save time, and focus on higher‑level problems.
This article introduces Taichi, a Python‑embedded DSL that compiles kernel functions for CPU and GPU execution, and demonstrates through three practical examples how importing the library and adding decorators can accelerate Python code by up to a hundredfold, with detailed performance numbers and installation instructions.
This article provides a comprehensive guide on creating a Python-based ChatGPT chat interface using Flask, covering web application setup, HTML interface creation, backend logic implementation, and frontend JavaScript enhancement.
This article provides a comprehensive introduction to popular Python libraries for artificial intelligence, computer vision, data analysis, and machine learning—such as NumPy, OpenCV, scikit‑image, Pillow, TensorFlow, PyTorch, and many others—accompanied by concise code snippets and performance comparisons to help beginners select suitable tools.
This article presents a collection of 17 practical Python scripts that automate tasks such as file management, web scraping, email handling, Excel processing, database interaction, system administration, image editing, and more, providing code examples and explanations to help developers and analysts improve productivity through automation.
This article explains the role of Python's __init__ constructor in object‑oriented programming, demonstrates how it initializes object attributes with example code, and shows how other methods can access those initialized values.
This article introduces three popular Python text‑based UI frameworks—Curses, npyscreen, and Urwid—explaining their features, installation steps, and providing runnable code examples that demonstrate creating windows, forms, and interactive command‑line applications for developers seeking lightweight terminal interfaces.
This tutorial explains how to build a Python‑based Minesweeper bot using OpenCV for image processing, win32gui for window handling, and a rule‑based solving algorithm, covering environment setup, window capture, block segmentation, block classification, and automated mouse actions.
jws-cli is a Python‑based, extensible one‑click information collection and scanning tool that automates subdomain discovery, CDN and WAF detection, port and C‑segment scanning, and integrates third‑party vulnerability scanners, offering visual reports and email delivery for rapid penetration testing workflows.
This article explains why Chinese characters often appear garbled when using Python web crawlers and presents three practical solutions—switching to response.content, manually setting the response encoding, and applying a universal encode‑decode trick—to reliably decode GBK‑encoded text.
This guide explains how to install Ollama, explore its supported open‑source LLMs, use its REST API for generation, chat, and embeddings, and build a Streamlit‑based web chat application that runs locally on your machine.
This article introduces Apache Airflow, explains its core concepts such as DAGs, tasks, operators, executors, and the web UI, and provides multiple practical Python code examples for Bash commands, Python functions, SQL queries, task dependencies, sensors, dynamic DAGs, SubDAGs, XCom, email alerts, and error handling.
This guide explains how to set up Kivy on macOS and Linux, create a simple Python hello‑world mobile app, test it, install and use Buildozer to package the app into an Android APK, and troubleshoot common build issues, providing full command‑line examples and code snippets.
This guide explains why and how to monitor CPU, memory, and disk I/O on macOS or Windows using Python's psutil, matplotlib, and numpy libraries, covering performance analysis, troubleshooting, capacity planning, automated alerts, and includes a complete example script that visualizes resource usage over time.
This tutorial walks through using Python's Pillow library to load an image, overlay or replace text with custom fonts and colors, and save or display the edited result, providing step‑by‑step code and practical tips for image manipulation.
This article presents a detailed overview of Python's 66 built-in functions, explaining each function's purpose and providing concise example code snippets that demonstrate typical usage, making it a valuable resource for beginners and intermediate programmers seeking to master fundamental Python capabilities.
This article explains why a Python web crawler often fails to retrieve JavaScript‑rendered page content, outlines common causes such as dynamic loading, anti‑scraping measures, and server‑side rendering, and offers practical techniques to capture the fully rendered HTML.
This article introduces the Python library librosa, outlines its main audio processing features such as loading, visualization, MFCC, pitch detection, chromagram, and rhythm analysis, and provides complete code examples for each operation.
This article explains how to install the gym and highway‑env packages, configure the environment for various driving scenarios, define observations, actions and rewards, build a DQN network in PyTorch, run the training loop, and analyze the resulting performance metrics.
This tutorial walks through building a Retrieval‑Augmented Generation application by setting up Baidu’s Vector Database and Qianfan embedding service, configuring credentials, creating a document database and vector table, loading and chunking PDFs, generating embeddings, storing them, and performing scalar, vector and hybrid similarity searches, ready for integration with Wenxin LLM for answer generation.
This article presents nine practical Python automation scenarios—including file management, web scraping, text processing, email handling, Excel manipulation, database interaction, social media posting, system task scheduling, and image editing—each accompanied by clear script examples and explanations to help readers streamline repetitive tasks efficiently.
This tutorial walks through implementing a basic Denoising Diffusion Probabilistic Model in Python, explaining the forward noise schedule, reverse denoising training, and providing complete code for noise schedules, diffusion functions, residual and attention blocks, a UNet architecture, loss computation, and a training loop.
This article explains a real‑world question about grouping 5,000 nationwide records by province to compute counts, totals, averages and ratios, compares using Excel pivot tables, SQL, and demonstrates a concise Python pandas pivot_table solution.
This article introduces PyTorch as a leading open‑source deep‑learning framework, outlines its key components such as dynamic computation graphs, tensors, autograd, modules, optimizers, data loading, distributed training and TorchScript, and provides a complete Python example that defines a simple CNN and trains it on the CIFAR‑10 dataset.
An in‑depth comparison of popular Docker base images—including Ubuntu, Alpine, Debian, CentOS, Amazon Linux 2, and official Python images—evaluates stability, security updates, dependency freshness, size, and build time to guide developers in selecting the optimal Linux distribution for Python container deployments.
A Python community member struggled with a pandas null check that produced incorrect results, and the discussion reveals why the diff_qty calculation can't be null and offers concise code fixes to correctly handle zero and null values in data processing.
pdoc is a lightweight Python command‑line tool that automatically extracts docstrings to produce clean HTML API documentation, offering features such as module and package generation, custom templates, markdown export, and a built‑in preview server for rapid documentation workflows.
This article introduces the ModelScope Python library, explains its support for PyTorch and TensorFlow, and provides step‑by‑step code examples for loading models, running inference pipelines on text and images, handling batch inputs, and customizing preprocessors in both Python and PHP.
This article outlines the key programming languages—Python, Julia, and C++—that are essential for quantum computing development, explains their strengths, and offers practical advice for developers eager to enter the field.
This article presents a collection of practical Python techniques—such as list comprehensions, pre‑computing lengths, using sets, skipping irrelevant iterations, inlining functions, generators, map, memoization, vectorization, filterfalse, and join—that together can boost for‑loop performance anywhere from 1.3‑fold up to nearly a thousand times, with concrete code examples and benchmark results.
This article explains the mathematics and step‑by‑step Python pygame implementation of an automatic missile‑tracking algorithm for shooting games, covering time‑slice calculations, trigonometric direction computation, and image rotation handling to keep the missile tip aligned with the target.
This article demonstrates how to use the simple schedule library and the more powerful APScheduler library—including blocking, background, and async schedulers—to create, run, log, handle errors, and manage concurrency and dependencies for Python scheduled tasks.
This tutorial walks you through installing the Python requests library on Windows and Linux, demonstrates how to perform GET, POST, PUT requests, pass parameters, bypass anti‑scraping measures, and save fetched HTML or images to local files with clear, runnable code examples.
This article introduces thirteen hidden or underused Python features—including list stepping, the find method, iterators, doctest, yield, dictionary get, for/else loops, named string formatting, recursion limits, conditional expressions, argument unpacking, the __hello__ module, and multi‑line strings—each illustrated with code examples.
This article introduces eight Python libraries—including Optuna, ITMO_FS, shap‑hypetune, PyCaret, floWeaver, Gradio, Terality, and Torch‑Handle—that streamline data‑science tasks such as hyperparameter optimization, feature selection, model building, visualization, and deployment, helping users save coding time and improve productivity.
This tutorial demonstrates how to use the tika-python library to extract textual content from PDF and Excel files, providing code examples and important notes about installation and potential formatting limitations, and suggestions for further processing to obtain readable or structured output.
Domain Admin is a lightweight Python‑Flask and Vue3 monitoring tool that tracks domain and SSL certificate expiration, supports multiple notification channels, and can be installed via pip, Docker, or source code, providing cross‑platform deployment for reliable service uptime.
This article reviews the fifteen most downloaded Python packages on PyPI, explaining each library's purpose, key features, typical use cases, and providing code examples to illustrate how they simplify tasks such as HTTP requests, compatibility, AWS interaction, date handling, security, and data processing.
This article walks through a real‑world Python group question about efficiently inserting a processed pandas DataFrame into a MySQL table, explains why the original pymysql approach fails, and demonstrates a working solution with SQLAlchemy and create_engine.
This article explains Python's four scope levels—global, local, enclosing (non‑local), and built‑in—describes how the LEGB lookup order works, and provides clear code examples demonstrating variable access and modification in each scope.
This article provides a detailed tutorial on the PyMySQL library, covering installation, connection setup, cursor operations, executing queries, transaction handling, advanced features like batch processing and connection pooling, as well as best practices for error handling and performance optimization in Python‑MySQL applications.
This tutorial demonstrates how to use Python and the Pygame library to create a real‑time visual simulation of the solar system, complete with background images, rotating planet sprites, optional music, and frame‑rate control, providing a hands‑on example of basic game‑style graphics programming.
This article presents twenty practical Python code snippets, covering topics such as a simple HTTP server, list comprehensions, dictionary updates, string manipulation, CSV reading, memory measurement, class creation, and more, to help programmers quickly solve common tasks.
Learn the core concepts of using proxies in Python web scraping, including what proxies are, common types like anonymous and high‑anonymity, how they protect your crawler, practical implementation with the requests library, and an overview of building a proxy pool for scalable data extraction.
This article walks through a five‑minute Python web‑scraping tutorial that fetches recipe pages, parses HTML with urllib and BeautifulSoup, extracts titles and images, and wraps the workflow into a simple console application for random meal selection.
Confucius watches two children argue about a brick’s sinking time, then uses physics—gravity, buoyancy, drag—and a mathematical model with Python code to estimate how long the brick takes to reach the bottom of various oceans, highlighting differences due to depth and terminal velocity.
This guide explains how to use QEMU on an x86 host to pull an ARM base image, create a Dockerfile with QEMU support, build the ARM-compatible Docker image, and test it, including a practical example of packaging a simple Python app for Raspberry Pi.
This reference lists Python's built‑in functions, providing concise English descriptions and example code snippets for each, covering topics from basic type conversions and arithmetic to object introspection and file handling and utility operations.
This tutorial explains how to create a simple Flask web site and RESTful API, then demonstrates retrieving the table data using Excel's web import feature and Python's requests, BeautifulSoup, and pandas libraries, while comparing the two approaches.
This article introduces 10 essential Python built-in functions and utilities including enumerate(), zip(), filter(), map(), sorted(), functools.partial(), itertools.combinations(), lambda, getattr()/setattr(), and reversed(), with practical code examples for each.
This guide walks you through creating a lightweight Python web scraper that fetches recipe data from a cooking website, covering HTTP requests, HTML parsing with BeautifulSoup, extracting titles and images, and wrapping the process into an interactive console application.
This article explains how Python's mutable list and dictionary types and immutable tuple type can be employed to organize test data, build request payloads, and parse JSON responses when automating API tests, providing clear code examples for each use case.
The article explains how Python's CPython interpreter is set to make the Global Interpreter Lock optional through PEP 703, detailing the recent merge that adds PYTHON_GIL=0 support, the expected Python 3.13 release date, and the potential impact on concurrency and AI workloads.
This step‑by‑step guide explains how to implement a lightweight Retrieval‑Augmented Generation system—Tiny‑RAG—by creating embedding classes, loading and chunking documents, building a simple vector store, performing similarity search, and integrating a large language model for answer generation, complete with runnable Python code.
This guide explains how to automatically collect all third‑party modules used in a Python project—by manually inspecting imports or using tools such as pipreqs, Poetry, or pipdeptree to generate a requirements.txt file—and provides a Python script for extracting the first‑line comments from each .py file and saving them to a text file.
This guide walks you through setting up a Conda Python environment, installing PyTorch and the ModelScope library, running NLP pipelines for tasks like word segmentation and text classification, and calling ModelScope models from PHP using the PHPY extension, complete with code examples and troubleshooting tips.
Discover the essential Python libraries for data science in 2024, from versatile tools like Taipy and Pandas to powerful machine‑learning frameworks such as TensorFlow, PyTorch, and Scikit‑Learn, each with key features, use‑cases, and GitHub links to boost your analytics career.
This article explains how to use LangGraph—a graph‑based extension of LangChain—to implement a corrective RAG (C‑RAG) pipeline that evaluates retrieved documents, rewrites queries when needed, performs web search, and generates accurate answers, complete with code snippets and a runnable example.
This tutorial provides a visual, step‑by‑step guide to NumPy, covering array creation, arithmetic and broadcasting, indexing, aggregation, matrix operations, reshaping, and practical examples such as computing mean‑squared error for machine‑learning models, illustrated with code snippets and diagrams.
A 12‑year‑old British boy created a series of pixel‑style whale drawings, used a Python script to generate NFT metadata, and sold the collection on the blockchain, earning more than 250 000 CNY in cryptocurrency while illustrating the growing popularity and speculative nature of NFTs.
An extensive Python tutorial covering numeric operations, string handling, built‑in functions, data structures, class and object concepts, as well as useful tools such as iterators, file I/O, serialization, and common utilities, illustrated with clear code examples.
