This article introduces Ragent, Ant Group’s Ray‑powered distributed agent framework, covering its background, motivation, design, implementation details, multi‑agent capabilities, and future directions for large‑model AI applications.
This article provides comprehensive Python scripts for system administration tasks including CPU monitoring, memory usage tracking, log analysis, file backup, system updates, network monitoring, service management, user administration, and system information collection.
This article introduces twenty advanced Python concepts—including decorators, context managers, generators, metaclasses, multiple inheritance, coroutines, closures, magic methods, dynamic attributes, GIL, async IO, regular expressions, garbage collection, modules, virtual environments, SOLID principles, type annotations, IPC, memory management, and reflection—explaining their typical use cases and providing clear code examples for each.
The article shows how AI accelerated a chatroom project's development—cutting cycle time, lowering developer skill demands, and delivering over 8% efficiency gains—by auto‑generating boilerplate code, debugging suggestions, and routine components such as API, Redis, MySQL, and WebSocket snippets, while outlining future improvements and a potential additional 10% R&D boost through integrated AI tools.
This article demonstrates how to use Python to read and write Excel files, extract birth dates from Chinese ID numbers, and compute the time interval between dates, providing a practical example for automating HR data handling.
This article reviews the most popular programming languages that can serve as alternatives to PHP for web development, evaluating each option’s performance, ecosystem, and suitability for server‑side projects, and offers guidance on selecting the best replacement based on project requirements.
This article presents practical examples and code snippets for using Python libraries such as NumPy, Pandas, SciPy, Statsmodels, Dask, Vaex, Modin, CuPy, Scikit‑learn, TensorFlow, PyTorch, XGBoost, LightGBM, and various visualization tools to perform efficient numerical computation, data processing, machine‑learning modeling, and interactive visual analytics.
This tutorial demonstrates how to convert a JSON lyric database into Excel, filter Jay Chou songs, perform Chinese word segmentation with Jieba, compute word frequencies, and create visualizations such as word clouds using Python code and online tools.
The January 2025 TIOBE Index reveals Python as the 2024 programming language of the year with a 9.3% growth, while C declines, C++ and Java surge, PHP exits the top ten, and Rust, Kotlin, and Go show varied trajectories, underscoring Python’s dominance across data science, web development, and automation.
An analysis of 2024 startup‑released open‑source projects shows AI‑focused tools leading popularity, with Python used in 60% of the top 20, a median monthly growth of about 15%, and the United States contributing 65% of the most impactful releases.
This article explains how to create various parameter‑less decorators in Python, providing clear examples for logging, performance measurement, authentication checks, result caching, and log‑level control, along with complete code snippets and explanations of their behavior.
This article surveys twenty of the most talked‑about open‑source projects released by early‑2024 startups, spanning AI assistants, developer tools, Python package managers, blockchain platforms, and more, each highlighted with its GitHub URL, star count, and a brief functional description.
This article provides a comprehensive overview of Python's built-in functions, data types, numeric conversions, mathematical operations, data structures, string handling, scope utilities, iterator tools, dynamic code execution, and file and module operations, complete with practical code examples for each concept.
This tutorial explains how to capture Bilibili video streams with Fiddler, extract the necessary request headers and URLs, and use a concise Python script based on the requests library to download videos, handling header nuances and batch‑fetching video lists from an uploader’s page.
This article introduces Python's class and object fundamentals, covering class definitions, instance and class variables, inheritance, special (dunder) methods, and advanced examples such as encapsulation, operator overloading, and the use of class and static methods, all illustrated with clear code snippets.
This article presents a collection of practical Python techniques—including one‑line variable swapping, list, set and dictionary comprehensions, the Counter class for counting, pretty‑printing JSON, inline conditional expressions, list slicing, itertools combinations, and a quirky False‑equals‑True example—each illustrated with concise code snippets and explanations.
Python introspection allows programs to examine their own structure at runtime, enabling dynamic manipulation of objects, classes, and methods through built-in functions and modules.
This article provides a complete, illustrated walkthrough for downloading and installing Anaconda on Windows, configuring the environment, launching Jupyter Notebook, and troubleshooting common issues such as missing environment variables and permission errors.
This article walks through a step‑by‑step demonstration of how a Python beginner can use GitHub Copilot to build a command‑line tool that renders QR codes as text, illustrating interface familiarization, handling errors, iterative prompting, and final code refinement.
This guide presents 20 practical Linux, security, networking, and Python questions with step‑by‑step command examples, covering bulk image download, file sorting, port monitoring, header retrieval, user account facts, RAID misconceptions, load‑balancer tools, time measurement, rewrite flags, cookie vs session, HTTP versions, common web vulnerabilities, DoS attacks, incident response, TCP handshake, TIME_WAIT optimization, Python range differences, set intersections, and parallel remote execution.
This article details how Instagram migrated its massive Python 2/Django codebase to Python 3, covering the motivations, custom Django extensions, migration strategy, technical pitfalls such as Unicode handling, pickle compatibility, iterator behavior, and the resulting CPU and memory improvements.
This article explains how to build animated line, bar, and pie charts in Python using Matplotlib's FuncAnimation library, demonstrating data preparation, key parameters, code examples, and saving the resulting animations as GIF files.
This article introduces Kivy, an open‑source Python framework for building cross‑platform graphical user interfaces, outlines its core features, provides step‑by‑step installation instructions, and shares personal insights on why developers often choose Kivy over other UI toolkits.
This article explains the Orchestrator‑Workers pattern from Anthropic’s “Build effective agents”, compares it with routing and parallel modes, distinguishes it from Supervisor agents, and provides a step‑by‑step Python implementation using Pydantic AI, including model definitions, prompts, orchestration logic, worker execution, and a test example.
This article explains the purpose, syntax, parameters, return values, and practical examples of Python’s isinstance() and type() functions, compares their behavior regarding inheritance and polymorphism, and provides advanced usage scenarios for accurate type checking in code.
This tutorial teaches how to download files in Python using various modules such as requests, wget, urllib, urllib3, boto3, and asyncio, covering basic downloads, handling redirects, chunked large-file downloads, parallel batch downloads, progress bars, proxy usage, and asynchronous techniques.
This article explains how to cancel asyncio tasks, invoke sub‑coroutines, cancel sub‑coroutines, handle multiple task cancellations, and manage errors in Python's asynchronous programming, providing clear code examples for each scenario and demonstrating best practices for robust async code.
This article explains how to use Django REST Framework mixins to extend view functionality, providing step‑by‑step code examples for list, retrieve, create, update, delete, custom authentication, permissions, versioning, conditional rendering, and custom exception handling.
This article compiles ninety practical Python recommendations covering fundamentals, idiomatic coding styles, standard library usage, design patterns, performance profiling, testing tools, and deployment practices, aiming to help both beginners and experienced developers write cleaner, more efficient, and more maintainable Python code.
This tutorial walks through building semantic routing and parallel execution patterns for LLM agents using the lightweight PydanticAI framework, providing step‑by‑step code, example configurations, and practical observations to help developers create flexible AI‑driven workflows.
Computer scientist Zara Dar left her PhD to run a lucrative OnlyFans business and created a Python‑based Raspberry Pi notification robot that alerts her to new subscribers with a moving 3D‑printed penguin and sound playback.
The 2024 Python release brings groundbreaking speed gains, experimental GIL‑free execution, a JIT compiler, removal of legacy modules, and record‑breaking popularity, positioning Python as the most versatile and widely adopted language for AI, data science, and general development.
Learn how to automate MySQL database backups on both Linux and Windows by using a Python script that leverages mysqldump on Linux and a batch file for Windows, including directory management, retention policies, and execution commands to keep your data safely archived.
This article explains how to use Python's asyncio library to synchronize and communicate between coroutines, covering Event, Lock, Semaphore, Condition, and Queue primitives with clear code examples demonstrating each mechanism in practice.
This tutorial introduces Python's core data structures—lists, dictionaries, sets, and tuples—and demonstrates essential file operations for text, CSV, and JSON formats, providing practical code examples for each concept and explains their characteristics, common operations, and best practices for efficient programming.
This tutorial walks you through installing the Python requests library, using GET, POST, and PUT methods, handling query parameters, setting custom headers to bypass anti‑scraping measures, and saving both HTML content and images to local files, complete with runnable code examples.
This article walks through a Python community member's Excel automation issue, explains how to build a pandas mask to target specific rows, and provides a reusable function that writes a custom value into the desired cell based on flexible keyword arguments.
JetBrains' 2024 Developer Ecosystem Report, based on responses from over 23,000 developers, shows JavaScript remains the most used language, while TypeScript, Rust, and Python surge in popularity, with detailed usage percentages, growth trends, and the new Language Outlook Index highlighting future prospects.
Discover 13 practical Python techniques—from simultaneous variable assignment and tuple swapping to list and dictionary comprehensions, defaultdict, enumerate, slicing, zip, join, lambda functions, itertools, Counter, and f‑strings—that help you write cleaner, more efficient, and more maintainable code.
The article explains LeetCode 491, requiring all non‑decreasing subsequences of length ≥2 from an integer array, and details a depth‑first search approach that uses a per‑level set to skip duplicates, with complete example implementations in Java, C++, C, and Python.
This article explains why assigning a function to a variable without parentheses doesn’t invoke it, how to pass functions as arguments in Python, and demonstrates the concept with a Prisoner’s Dilemma code example, clarifying common misconceptions about function calls and references.
This guide explains how to build a modular, multi‑project API automation framework in Python, covering directory layout, configuration handling, HTTP client encapsulation, pytest‑based test cases, a shell script for execution, and best‑practice recommendations for logging and reporting.
This guide shows how to install the ZhipuAI SDK, obtain a free GLM‑4V‑Flash API key, craft prompts for image‑based calorie estimation, and build a Python demo that calculates food calories, BMI, and personalized diet advice using a multimodal large model.
This article shares a festive greeting and demonstrates how to generate an ASCII Christmas tree using both a Bash shell script and a Python program, followed by details about the upcoming GOPS Global Operations Conference in Shenzhen, encouraging operations professionals to celebrate the holiday season.
This article introduces LlamaIndex Workflows, explains its event‑driven design, walks through a multi‑agent demo that combines weather search and email sending, provides complete Python code for defining events, steps, and the orchestrator, and compares its strengths and limitations against similar frameworks.
This article introduces core Python programming concepts, covering variable creation, common data types, conditional statements, loop structures, and function definitions with examples of default, keyword, and variable arguments, providing a concise foundation for writing simple Python scripts.
This article demonstrates how to store SQL queries in .ini files and use Python's mysql-connector-python library together with configparser and logging to perform MySQL CRUD operations, providing full code examples and explanations for configuration, connection handling, query execution, and log management.
This article provides a comprehensive guide to Python's path manipulation tools, covering the os.path module functions, the object‑oriented pathlib API, file‑matching with glob, and advanced file operations using shutil, complete with code examples and usage recommendations.
This article walks you through using Python's requests and lxml libraries to crawl a fund website, extract stock IDs, fetch detailed fund information, process the data, and save it to CSV for further analysis, complete with code snippets and screenshots.
This guide explains how to use Pandas to import data from various file formats such as CSV, Excel, JSON, SQL, HTML, HDF5, Pickle, TSV, fixed‑width files and the clipboard, and also demonstrates how to export DataFrames to formats like CSV, Excel, JSON, SQL, HTML, HDF5 and Pickle, providing clear code examples for each operation.
This article introduces the open‑source MarkItDown Python library, demonstrates converting Excel files to Markdown, shows how to expose its functionality via a Dockerized REST API, and explains advanced integration with visual AI models for richer document processing.
This article explains why the official Python documentation historically lacked a Chinese version, shows how to access the Chinese docs via https://docs.python.org/zh-cn, lists the available language versions and content coverage, and provides tips for searching within the translated documentation.
This guide shows how to automatically extract subtitles from English videos using OpenAI's Whisper, translate them into Chinese with a locally‑deployed Ollama large language model, and finally merge the bilingual subtitles back into the video using FFmpeg, all with GPU acceleration.
This comprehensive guide demonstrates how to read, write, modify, and format Excel, Word, and CSV files in Python using libraries such as xlrd/xlwt/xlutils for Excel, python-docx for Word, and the built‑in csv module, complete with code examples and feature comparison tables.
This article introduces five widely used Python libraries—Pandas, OpenPyXL, XlsxWriter, Tablib, and Dask—detailing their installation commands, core features for handling structured and large‑scale data, and practical code examples for reading, writing, and processing Excel and CSV files.
This guide walks you through creating a Retrieval‑Augmented Generation (RAG) question‑answering application by integrating Alibaba Cloud Milvus vector search, DashScope embedding models, and PAI EAS LLM services, covering prerequisites, service deployment, configuration, Python code setup, and execution steps.
This article presents a comprehensive guide to 45 essential NumPy operations, covering array creation, reshaping, arithmetic, statistical functions, linear algebra, and more, each illustrated with concise explanations and ready-to-run Python code examples to help readers efficiently leverage NumPy for scientific computing.
This guide explores essential Python concepts for advanced developers, covering object‑oriented programming, first‑class and higher‑order functions, closures, decorators, iterators, generators, context managers, memory management, concurrency models, the Global Interpreter Lock, and asynchronous programming with practical code examples.
This tutorial walks through thirty practical Python dictionary operations—from creating and accessing entries to merging, comprehensions, sorting, and deletion—providing clear code examples and expected outputs to help readers master this versatile data structure.
This article provides a detailed tutorial on using Python's matplotlib library for creating 2D and 3D visualizations, covering installation, library architecture, basic plotting, line charts, bar charts, scatter plots, contour maps, image handling, and customizations with complete code examples.
This article explains Python variable fundamentals—including naming rules, assignment, scope, and lifecycle—provides basic code examples, and demonstrates advanced usage of variables in API automation testing through dictionaries, class configurations, enums, result tracking, and context managers.
This article presents a concise collection of 40 Python one‑liners covering fundamental operations such as printing, comprehensions, file handling, string manipulation, mathematical calculations, data structure manipulations, and useful built‑in functions, each illustrated with clear code examples.
AutoGen, Microsoft’s advanced multi‑agent framework, lets developers quickly assemble collaborative LLM agents—supporting chat, tool use, and hierarchical group chats—through concise Python code, with examples ranging from simple two‑agent dialogues to complex three‑agent reporting pipelines, while outlining its strengths, limitations, and upcoming v0.4 enhancements.
The article explains LeetCode 814, where a binary tree of 0s and 1s is pruned by recursively removing subtrees lacking a 1, using a post‑order traversal that returns null for nodes with value 0 and no retained children, achieving O(n) time and O(h) space.
This article explains how the IceCream library's ic() function provides a more systematic and powerful alternative to print‑based debugging in Python, offering detailed execution traces, integrated assignment, and clear visualization of simple and complex data structures.
This guide shows how to create a Flask web service that translates text using ZhipuAI's free, high‑speed GLM‑4‑Flash API, covering environment setup, API key configuration, asynchronous task handling, endpoint implementation, and retrieving results via a unique task ID.
This article walks through a Python list‑processing problem that requires summing numbers while skipping any segment that starts with a 6 and ends with the next 9, presenting multiple solution approaches—including index tricks, flag control, while loops, and recursion—to help readers understand and implement the algorithm effectively.
This article introduces the Python itertools module, demonstrates common functions such as count, cycle, repeat, combinations, permutations, chain, filterfalse, dropwhile, accumulate, and groupby with code examples, and discusses important limitations like infinite loops, memory consumption, and iterator irreversibility.
This article compiles twenty‑three Python web‑scraping projects, each described with its purpose, key features, and a direct GitHub repository link, offering developers a ready‑made toolbox for data collection across platforms such as WeChat, DouBan, Zhihu, Bilibili, and more.
This article provides a comprehensive guide to Python lists, covering creation, element access, modification, slicing, adding/removing elements, sorting, length, list comprehensions, and comparisons with strings and sets.
This article presents a collection of 25 frequently encountered Python programming tasks, ranging from printing "Hello, World!" to checking file existence, each accompanied by concise explanations and ready-to-use code examples that illustrate fundamental concepts and techniques.
This article explains the fundamentals of socket programming in Python by demonstrating how to build a lightweight UDP-based chatroom, covering socket creation, common methods, and complete server‑client examples with code snippets for multi‑user communication.
This article explains the LeetCode 739 'Daily Temperatures' problem, describing how to compute the next warmer day for each temperature using a monotonic stack, and provides complete implementations in Java, C++, and Python, along with step‑by‑step analysis and example walkthroughs.
This article explains why the and operator has higher precedence than or in Python, demonstrates the evaluation order with clear code examples, and provides additional tips on how these logical operators return one of their operands.
This guide explains how to install Tesseract OCR and the Python libraries pytesseract and Pillow, configure the engine path, perform image-to-text extraction with example code, and apply various preprocessing, detection, and post‑processing methods to significantly improve OCR accuracy.
This article demonstrates how to use Python, pandas, and Plotly to download, clean, and visualize World Happiness Report data from 2015‑2019, revealing the strongest correlations between happiness scores and factors such as GDP per capita, healthy life expectancy, and social support.
This tutorial walks through a complete house price prediction project, covering data collection from Kaggle, preprocessing with pandas and scikit‑learn, model training using RandomForestRegressor, evaluation, and deployment of a Flask API for real‑time predictions, providing full code examples.
This article introduces Python task automation, demonstrating how to batch rename files, send emails, process Excel spreadsheets, and schedule recurring jobs using standard libraries and third‑party modules, while providing best‑practice tips and hands‑on exercises for efficient scripting.
This tutorial guides readers through creating an AWS account, configuring the AWS CLI, uploading and downloading CSV files to S3, and using Python with pandas to clean, deduplicate, and aggregate the data before re‑uploading the results, illustrating end‑to‑end cloud data processing.
This tutorial covers Python's core programming concepts including variables, data types, control structures, functions, object-oriented programming, and file operations, providing a comprehensive foundation for beginners.
This article examines why Python is slower than compiled languages and presents five practical solutions—PyPy, Pyston, Nuitka, Cython, and Numba—detailing their mechanisms, compatibility, and trade‑offs for developers seeking to boost Python performance.
This tutorial explains the fundamentals of data encryption, introduces the concept of differential privacy, and guides you through implementing and testing a simple Caesar Cipher encryption and decryption algorithm in Python, complete with code examples and execution instructions.
This tutorial explains the fundamentals of data encryption, introduces differential privacy, and guides you through implementing a simple Caesar Cipher encrypt‑and‑decrypt program in Python, complete with code examples, execution instructions, and sample output.
This guide walks you through installing an IDE with an AI coding plugin, prompting the AI to generate a full-featured Tkinter lottery GUI, running it locally, and then repackaging it with Flask for deployment on Alibaba Cloud SAE, complete with step‑by‑step instructions and screenshots.
This guide walks through preparing the Kivy development environment on macOS and CentOS, creating a simple hello‑world Python app, installing and using Buildozer to package the app for Android (and iOS), and troubleshooting common build issues with code examples and virtual‑machine recommendations.
This article walks through Python generators in depth—covering basic yield behavior, the __next__, send, throw, and close methods, pre‑activation states, delegation with yield from, generator expressions pitfalls, and their role in coroutine implementation—while briefly recounting a personal conflict that sparked the write‑up.
This article examines a puzzling Python list loop that prints nothing, explains why the loop behaves unexpectedly, and provides step‑by‑step annotated code examples to help readers understand list mutation during iteration and avoid common pitfalls.
This article introduces CrewAI, a high‑level Python framework for constructing multi‑agent systems, explains its core concepts such as Crew, Agent, Tool, Task and Process, walks through a complete demo with code, evaluates its strengths and limitations, and showcases the new Flows feature for more flexible workflow orchestration.
This tutorial guides you through installing Apache Airflow, initializing its database, starting the web server and scheduler, creating a Python DAG that reads, cleans, groups, and saves CSV data, configuring the DAG directory, and monitoring the pipeline via the Airflow web UI.
LeetCode 34 asks for the first and last indices of a target in a non‑decreasing integer array, returning [-1,-1] when absent, and can be solved in O(log n) time by applying two binary‑search passes—one locating the leftmost occurrence and the other the rightmost—illustrated with Java, C++, and Python implementations.
This tutorial introduces basic image processing techniques using OpenCV in Python, covering image reading, displaying, grayscale conversion, cropping, resizing, rotation, flipping, and saving, with step‑by‑step code examples and explanations to help beginners apply these operations in real projects.
This article introduces ten widely used Python image‑processing libraries—including Pillow, OpenCV, scikit‑image, imageio, mahotas, SimpleITK, imgaug, face_recognition, Pyradiomics, and tqdm—provides brief descriptions, pip installation commands, and runnable code examples to help developers choose the right tool for their computer‑vision tasks.
The Numpy‑ML project by David Bourgin provides a 30,000‑line pure‑NumPy library that implements a wide range of classic machine‑learning algorithms, data‑preprocessing tools, and neural‑network components, offering an educational resource for deepening algorithmic understanding rather than replacing mature frameworks.
This tutorial demonstrates how to apply an LSTM deep‑learning model in Python to forecast stock closing prices, covering data acquisition, preprocessing, model construction, training, evaluation, and visualization of results for time‑series prediction.
This article presents a comprehensive collection of Python file handling examples, covering basic reading and writing, appending, line extraction, file copying, moving, deletion, directory management, and advanced text processing techniques such as searching, replacing, and statistical analysis, all illustrated with clear code snippets.
This article explores five hybrid recursion‑iteration techniques, outlines their inherent limitations, and provides a step‑by‑step guide for selecting the most suitable approach based on problem nature, performance needs, readability, and resource constraints.
This tutorial introduces the fundamentals of deep learning, covering neural network basics, Keras fundamentals, and provides a step‑by‑step Python example that loads the Iris dataset, preprocesses data, builds, compiles, trains, evaluates, visualizes, and predicts with a simple neural network model.
This article walks through a community question about saving several mixed-type Python lists to a txt file, presenting three code solutions—including direct writes, global variable iteration, and a concise eval loop—while comparing their flexibility and suitability.
This tutorial provides detailed instructions for registering the Volcengine API, locating and editing the .cloudiderc file, setting environment variables, installing the Volcengine Python SDK, and troubleshooting common issues when running the LangChain quick‑start examples on a cloud IDE.
This tutorial introduces fundamental natural language processing techniques, covering text preprocessing steps such as tokenization and stop‑word removal, followed by TF‑IDF feature extraction, and provides complete Python code examples to practice these concepts on a sample dataset.
