This tutorial explains how to set up the Kivy development environment on macOS, Linux, and CentOS, create a simple hello‑world Python app, test it, install Buildozer, and use it to package the app into an Android APK (or iOS build), including common pitfalls and fixes.
This article explains why large‑language‑model agents become hard to manage, outlines the benefits of modular multi‑agent designs, compares several connection architectures, and provides concrete LangGraph code for supervisor‑based, tool‑calling, and custom workflow patterns.
This article explains how the Python interpreter, after runtime initialization, creates a stack frame, invokes a series of C functions such as PyEval_EvalCode, _PyEval_Vector, and _PyEval_EvalFrameDefault to traverse and execute bytecode, and details the underlying runtime stack structures and macro APIs that support instruction evaluation.
This guide demonstrates how HR recruiters can use Python and libraries such as pandas and openpyxl to create, read, modify, and manage Excel candidate data, perform filtering, grouping, charting, and automatically send interview notification emails, thereby streamlining recruitment workflows.
The author chronicles the challenges and solutions of running large language models locally using Ollama, experimenting with cloud GPUs on Google Colab, managing Python dependencies through Docker, and ultimately fine‑tuning a small Qwen model, providing a practical guide for AI enthusiasts.
This guide demonstrates six common ways to iterate over Python dictionaries—including traversing keys, values, key‑value pairs, using enumerate for indexed access, applying conditional filters within loops, and employing dict comprehensions for efficient transformation—while explaining the appropriate use cases and performance considerations.
This step‑by‑step guide shows how to generate a sample Excel file and then use pandas and openpyxl to read, write, update, filter, sort, group, format, chart, and finally save Excel workbooks, covering twenty essential operations for data automation.
This article shows how to turn a Python list with many elements into the first column of an Excel sheet by presenting two practical solutions—one using openpyxl and another leveraging pandas—complete with code examples and step‑by‑step explanations.
This article provides a comprehensive overview of essential Python concepts—including decorators, list comprehensions, generators, exception handling, modules, threading, shallow and deep copying, garbage collection, variable arguments, closures, method types, process‑thread differences, relational vs NoSQL databases, array vs linked‑list structures, and the distinction between HTTP and Web Service API testing—illustrated with clear explanations and runnable code examples.
The article explores Chris Lattner’s vision for Mojo—a Python‑compatible language designed for AI, GPU, and accelerator workloads—detailing its performance claims, SIMD support, complex‑number handling, and the growing developer community behind it.
This article demonstrates how to use Python's asyncio library together with the aiohttp package to perform asynchronous HTTP requests, covering installation, concurrent request execution, GET and POST examples, error handling, timeouts, SSL verification, and proxy usage, with complete code snippets.
This article introduces LangServe, explains its key features for LLM deployment, walks through environment setup, shows how to build a FastAPI‑based REST API with code examples, demonstrates testing via Postman and remote client calls, and summarizes its benefits for AI model serving.
This article introduces Python's async and await keywords, explains how they enable coroutine programming, and provides multiple practical examples including simple coroutine definitions, awaiting tasks, concurrent execution with asyncio.gather, HTTP requests using aiohttp, and resource management with custom coroutine-based managers.
This guide walks through setting up the open‑source Kotaemon framework, configuring Azure Document Intelligence and a visual large model, and implementing code to extract and caption images and tables from PDFs for end‑to‑end multimodal RAG applications.
This article provides a comprehensive guide to Python exception handling, covering the concept of exceptions, built‑in exception types, the try‑except‑else‑finally structure, custom exception creation, debugging techniques with assert and logging, and a practical file‑operation example to build robust programs.
This article demonstrates how to create various parameterized decorators in Python—including logging, performance measurement, authentication, caching, and file logging—by providing clear explanations and complete code examples for each use case in practical applications.
This tutorial introduces Python basics, covering installation, syntax, data types, control structures, functions, modules, file operations, exception handling, object-oriented programming, and a simple calculator project, providing clear explanations, practical code examples, and step-by-step guidance for beginners to start coding in Python.
The article shows how to replace the taxi‑hailing app’s deeply nested driver‑assignment if‑statements with early‑exit guard clauses, data‑driven decision tables, and composable functions, thereby flattening the logic, enhancing readability, simplifying testing, and making future extensions easier to implement.
This article introduces the core assertion methods in pytest, covering basic assertions, multiple assertions, exception handling, specific exception messages, list and dictionary checks, floating‑point approximations, string and tuple validation, subset verification, and log capture, each illustrated with clear Python code examples.
This article provides a collection of Python scripts that cover essential property management system features such as resident data handling, automated payment reminders, monthly report generation, emergency notifications, maintenance request processing, visitor logging, facility booking, vehicle entry/exit tracking, announcement publishing, and database backup.
This article walks through the difference between greedy and non‑greedy regular expression patterns in Python, shows practical code examples, visual output, and even demonstrates how to use named groups for more precise matching.
While Python dominates data analysis and machine learning with its ease of use, Rust offers memory safety and near‑C performance; this article examines their respective strengths, the challenges of rewriting the Python interpreter in Rust, and how combining both can boost library speed and reliability.
This guide provides a comprehensive tutorial on using Python libraries such as pandas, openpyxl, and matplotlib for financial data processing, including reading/writing Excel files, data manipulation, and visualization techniques.
This article presents five hands‑on AI project ideas—from resume optimization to multimodal search—complete with step‑by‑step instructions, required Python libraries, and code snippets, helping beginners and intermediate developers quickly build valuable AI applications.
Python 3.13.0, the latest stable release, adds a new PyPy‑based interactive interpreter, experimental free‑threaded and JIT builds, enhanced type‑hinting, library updates, and extensive deprecations, while also promoting WASI, iOS, and Android as supported platforms.
This article outlines essential Python web‑scraping techniques, covering basic GET/POST requests, proxy usage, cookie handling, header manipulation to mimic browsers, simple captcha solutions, gzip compression handling, and multithreaded crawling with a thread‑pool template, providing practical code examples for each step.
This article provides a comprehensive collection of Python scripts that use Faker and standard libraries to generate a wide variety of random data types—including strings, numbers, dates, emails, addresses, files, media, and HTTP components—along with installation instructions and usage examples for testing APIs.
The article compares traditional Python web frameworks with modern asynchronous alternatives, presents benchmark results showing Sanic’s superior speed, discusses the importance of avoiding blocking I/O, and highlights Sanic’s ecosystem, production readiness, documentation, and community support for developers seeking high‑performance backend solutions.
The article first shares a frustrated JD interview experience regarding a 'personality' assessment, then introduces LeetCode problem 814 – Binary Tree Pruning – detailing the problem statement, examples, and providing concise post‑order traversal solutions in Java, C++, and Python.
This article walks through building a classic number‑guessing game in Python, presenting two community‑sourced solutions, detailed code snippets, explanations of the while‑loop and random modules, and enhancements for input validation and interactive range feedback, helping readers deepen their programming fundamentals.
This article explains how the if __name__ == '__main__' construct distinguishes between running a Python file as a script versus importing it as a module, prevents unintended execution, and provides practical examples for testing, main logic, and avoiding side effects.
This article walks through the theory behind ReAct agents and demonstrates how to design, implement, and test a ReAct‑style AI agent using LlamaIndex Workflows, complete with Python code, custom events, tool integration, and a full reasoning‑action‑observation loop.
This article compiles 30 essential interview questions covering fundamentals, API pagination, HTTP status codes, JSON schema validation, CORS, test automation frameworks, pytest parametrization and fixtures, mocking, coverage testing, load and stress testing, Locust and JMeter usage, security testing such as SQL injection, XSS, OAuth2, JWT, CI/CD concepts with Jenkins, GitHub Actions, Travis CI, Docker, and Python code quality tools like static analysis, black formatting, unit testing, refactoring, and technical debt management, each accompanied by concise explanations and runnable code snippets.
This guide explains why data analysis is essential, breaks it into three core stages—data collection, data mining, and data visualization—offers practical tool recommendations, and presents principles for efficient learning and skill development.
This article walks through a Python fan's question about implementing algebraic functions, presenting two clear solutions—one using basic operators and another leveraging the Sympy library—complete with code examples, output results, and step‑by‑step explanations for learners of all levels.
This article explains how to build a Python GUI application that scans nearby Wi‑Fi networks, reads a password dictionary, and attempts brute‑force connections using the pywifi library, providing step‑by‑step code, environment setup, and sample results for Wi‑Fi security testing.
This article walks through implementing the Chinese poker game "ZhaJinHua" in Python, covering the game rules, generating a full deck, dealing cards to five players, evaluating hand types, scoring, and determining the winner, and also introduces a comprehensive book on financial large‑model development.
This comprehensive guide walks you through core Python techniques for handling files and directories, making HTTP requests, processing data, managing system resources, performing text manipulation, executing mathematical calculations, building simple web applications, and interacting with various databases, all illustrated with ready‑to‑run code snippets.
This guide compares the strengths and weaknesses of common data analysis tools—from basic spreadsheet software and multi‑dimensional tables to professional statistical packages, BI platforms, and programming languages like R and Python—helping readers select the most suitable solution for their needs.
This guide walks through pytest's core command-line syntax, showing how to select files, control verbosity, capture output, ignore tests, pick specific cases or markers, generate various reports, run tests in parallel, repeat failures, and view help, all with practical examples.
Discover seven essential open‑source Python repositories—from data analysis with Pandas and workflow automation with Apache Airflow to AI‑powered G4F, web crawling with Scrapy, Telegram bots via Ultroid, team chat with Zulip, and crypto trading with Freqtrade—each offering powerful capabilities and community support.
This tutorial explains Python’s magic (special) methods—including initialization, string representation, arithmetic, comparison, container protocols, iteration, attribute handling, context management, and callable behavior—showing how each method works with clear code examples to make classes more flexible and powerful.
This article walks through a fan’s question about Python class and instance variables, explains their differences with examples, and also introduces a ChatGPT‑powered guide for mastering Excel, highlighting key features and how to obtain the free e‑book collection.
This comprehensive guide walks through Python's object‑oriented programming essentials, covering class definitions, attributes, common magic methods, inheritance, polymorphism, exception handling, as well as module and package creation and usage, complete with clear code examples for each concept.
This guide explains how to install, configure, and use the pytest‑xdist plugin to run Python tests in parallel across multiple processes or machines, covering basic -n options, auto detection, distributed node setup, example test code, configuration files, and advanced features such as custom process names and SSH tunneling.
This guide explains how to serialize and deserialize Python objects using the pickle, json, and xml.etree.ElementTree modules, and demonstrates practical API testing scenarios—including POST, GET, PUT, DELETE, and nested JSON handling—so you can choose the right format for your needs.
This guide explains how to open and close files, perform various read and write operations, and manage files and directories in Python using built‑in functions and the os, os.path, and shutil modules, with clear code examples for each task.
This article walks through creating the Mofish Python library, using Click to build a command‑line tool that shows days remaining to holidays, packaging it with setuptools, and publishing the package to PyPI for easy installation and use.
This article explains how to convert between strings, lists, dictionaries, and JSON in Python using ast.literal_eval, json.loads, and json.dumps, and demonstrates practical applications such as API response handling in automation testing.
This article explains the concept of data encapsulation and the role of private attributes in object‑oriented programming, illustrating their benefits and usage through multiple Python code examples covering simple classes, private methods, inheritance, property decorators, and descriptors.
This tutorial introduces Python fundamentals, covering variable assignment, data types, control structures, functions, modules, exception handling, file operations, and object‑oriented programming with class definitions, methods, and object instantiation, illustrated by practical code examples.
This tutorial shows how to model nationwide train schedules as a graph, prepare CSV data, import it into Huawei Cloud GES, and write Cypher queries that discover faster, more flexible transfer routes than the official 12306 service, complete with Python code examples and visual results.
This article presents the SCNN model for multivariate time series forecasting, explains its decomposition into long‑term, seasonal, short‑term, and co‑evolving components, details the neural‑network‑based fusion and loss design, provides Python code snippets, and demonstrates its practical deployment for business volume prediction at Ctrip.
This guide explains how to identify, store, and share data dependencies, control test execution order, handle failure propagation, visualize relationships, and avoid excessive coupling in API automated test suites, using Python examples and tools like pytest and Graphviz.
This article introduces beginners to Python’s dynamic typing, strong typing, and the inner workings of its interpreter, contrasting them with static languages, and demonstrates how variable assignment and memory management differ through clear examples and explanations of CPython’s runtime behavior.
This article provides a concise Python cheat sheet covering common file and directory operations, CSV/JSON data handling, web request and crawling techniques, automation tasks such as scheduling and email sending, and various string manipulation utilities, each illustrated with ready‑to‑run code snippets.
This article explains the differences between class variables and instance variables in object‑oriented programming, illustrates their characteristics with clear Python examples, and demonstrates advanced patterns such as counting instances, default values, constants, static data, configuration options, and polymorphic behavior.
Learn step‑by‑step how to extract text from a CSV generated by a Python web crawler, clean it with stop‑words, create a word‑cloud visualization, compute word frequencies, and perform sentiment analysis using jieba and SnowNLP, with all code snippets provided.
This article demonstrates how to encapsulate MySQL operations in a Python MySQLWrapper class, providing modular methods for connecting, querying, inserting, updating, and deleting records, along with a complete example and usage instructions.
This tutorial explains how to define and call Python functions, covering various parameter types—including positional, default, *args, and **kwargs—demonstrates return values, the pass statement, and provides a comprehensive example that combines all these concepts.
This tutorial demonstrates how to build a reusable Python HTTP client by defining a base HttpRequest class with standard methods, extending it with a RestApiRequest subclass that adds authentication handling, and provides a complete code example showing GET and POST requests.
This article explains why and when Python creates .pyc files, shows how to compile them manually with py_compile, demonstrates importing compiled bytecode via importlib, dissects the .pyc file header and payload, and explores the CPython marshal implementation and simple bytecode obfuscation techniques.
This article explains duck typing and polymorphism in Python, two fundamental object-oriented programming concepts, with practical code examples demonstrating how objects can be treated based on their behavior rather than their type.
This article explains how Python's super() works by invoking the next class in the Method Resolution Order (MRO), demonstrates MRO behavior with multiple inheritance examples, and presents advanced techniques such as metaclasses, descriptors, class decorators, __slots__, and singleton patterns.
This tutorial explains Python dictionaries in depth, covering their definition, how to create, access, modify, add, delete, copy, traverse, and nest dictionary entries, with clear code examples for each operation to help beginners master this essential data structure.
This article introduces pytest, a popular Python testing framework, outlining its key features, installation steps, how to write and run basic, parameterized, and parallel tests, generate HTML reports, and integrate with CI/CD pipelines, providing code examples throughout.
This article walks through a Python community Q&A where a user’s xlwings code fails to insert a chart image into Excel, explains that using an absolute file path or passing the figure object directly resolves the error, and shares the final working solution.
This article explains the key concepts of concurrency, parallelism, synchronization, asynchronous execution, blocking, and non‑blocking in Python, providing clear explanations and practical code samples for each concept, including API automation examples for HTTP requests.
This article introduces Python coroutines, explains their basic concepts and advantages, demonstrates how to implement them using async and await, and provides practical examples for concurrent HTTP requests and resource management, illustrating the underlying mechanics of async def, await, and the asyncio library.
This tutorial demonstrates how to use Python's Random module to generate customizable, secure passwords by selecting character types, specifying length, and shuffling the result, complete with full source code and step-by-step explanations.
This article provides a comprehensive guide to Python fundamentals, covering CSV file reading and writing, powerful iterator utilities from itertools, advanced collection types like Counter, defaultdict and OrderedDict, techniques for error and warning output, code debugging, style checking, profiling, and practical performance‑optimisation principles.
This guide explains Python's dictionary unpacking using the ** operator, showing how it streamlines passing keyword arguments to functions and formatting strings, while covering basic examples, practical advantages, and important precautions to avoid common errors.
This tutorial demonstrates essential Python list operations, covering case‑sensitive and case‑insensitive sorting, reverse ordering, iteration with for and while loops, element removal by value or index, clearing, appending, inserting, extending, item modification, slicing, indexing, length checking, and mixed‑type lists, all illustrated with clear code examples.
This article explains Python's yield from feature, which simplifies nested generator calls and data flow by delegating sub-generator iteration results to the current generator.
This guide provides a thorough overview of Python programming, covering basic data types, control flow statements, function definitions, modules and packages, exception handling, file I/O, object‑oriented concepts, iterators, generators, context managers, decorators, functional programming, asynchronous code, metaclasses, standard and third‑party libraries, type annotations, design patterns, and practical tips such as enums and dataclasses.
RedMonk’s Q3 2024 language ranking shows a stable top‑20 list with JavaScript, Python and Java leading, highlights TypeScript’s surge, notes the decline of C and Objective‑C, and introduces emerging languages such as Ballerina, Bicep, Zig, Grain and Moonbit.
This article presents a curated set of Python automation scripts covering clipboard management, code quality analysis, file integrity checks, stock price forecasting, bulk image downloading, network port scanning, secure password storage, mass email dispatch, README file creation, and intelligent file organization, each accompanied by concise explanations and ready‑to‑run code examples.
This article introduces various methods to customize sorting in Python, including using sorted() function, list's .sort() method, and custom comparison functions.
This guide explains how to use the Oh My Zsh pypi plugin to list, switch, and test various PyPI mirror sources, improving package download speeds for developers, especially those in mainland China, by providing simple CLI commands and connectivity checks.
The article shares a candid interview anecdote about reasons for leaving a job, then presents LeetCode problem 542 “01 Matrix”, explains its DP and BFS approaches, and provides full Java, C++, and Python implementations.
This article explains Python generators, covering generator functions and expressions, their usage with yield, the send method, and the underlying CPython state‑machine implementation, illustrated with clear code examples; it also discusses how generators manage execution flow and improve memory efficiency.
This article explains Python's iterator protocol by showing how objects become iterable through __iter__, how the built‑in iter function creates iterator objects, the CPython internals of PyObject_GetIter, and how the __next__ method advances elements, with concrete code examples and source‑level snippets.
This article explains Python's higher‑order functions, showing how functions can be passed as arguments through concepts, simple examples, decorators, sorting, data processing, logging, and asynchronous callbacks, each illustrated with clear code snippets.
This guide explains vector embeddings, OpenAI's text‑embedding models, and how to use PyMilvus with Zilliz Cloud to create a Retrieval‑Augmented Generation (RAG) system for fast, accurate semantic search.
This article introduces Python's built‑in data types—including numeric, sequence, mapping, set, boolean, None and other auxiliary types—explaining their characteristics, typical use cases, and providing concrete code examples to help readers write clear and efficient Python programs.
This article explains Python's dict, set, and frozenset containers, lists their most frequently used methods, demonstrates how to subclass dict and create a container class, and describes the hash‑table implementation details that give these data structures their high performance.
This article explains Python's instance, class, and static methods, detailing their definitions, differences, and typical use cases, and provides clear code examples—including a simple MyClass demonstration and a comprehensive Date class illustration—to help developers understand and apply each method type effectively.
The 2024 JetBrains and Python Software Foundation survey of 25,000 developers reveals Python's continued dominance, detailed demographics, tool preferences, language trends, age distribution, web and data‑science usage, cloud adoption, and security insights, highlighting why newcomers still favor Python as their first language.
This article explains Python's core philosophy that every entity—whether a basic data type, function, class, module, or collection—is an object, illustrating each concept with clear code snippets and showing how to inspect types and attributes using type() and dir().
This article presents a step‑by‑step guide to creating a Python‑based Wi‑Fi password cracking GUI using the pywifi library and Tkinter, explains environment setup, code implementation, and usage, while also containing promotional material for related AI services and community groups.
This guide explains how to prepare the environment, install required Python libraries, and use a complete script that connects to a remote server via SSH to check SSL certificate expiration dates and send email alerts when certificates are near expiry.
By integrating PAI‑Designer with PyODPS, users can build visual offline workflows that overcome ODPS’s lack of network access, dynamic configuration, and image‑processing limits, using reusable Python components, OSS role‑ARNs, remote configuration fetching, and custom Docker images to read/write MaxCompute and OSS data.
This article presents a collection of Python code examples that demonstrate how to schedule recurring tasks such as printing greetings, backing up files, scraping data, cleaning caches, sending emails, monitoring servers, updating databases, uploading to cloud storage, executing scripts, and analyzing logs.
This tutorial explains how to download a B‑site dance video, extract GIF frames, transform them into ASCII art, rename and sort the frames, convert them to images, assemble a video with OpenCV, and finally add background music using MoviePy.
This article compiles a comprehensive, subjectively curated collection of Python libraries for data visualization, machine learning, deep learning, AutoML, model interpretability, resource monitoring, and debugging, providing brief descriptions and links to each tool for developers and researchers.
This tutorial introduces 18 core Python built‑in functions, explains their typical use cases, and provides clear code examples so beginners can write more efficient, concise, and readable programs while laying a solid foundation in Python fundamentals.
This article introduces ddddocr, an open‑source deep‑learning OCR library for recognizing double‑digit numbers, explains its background, key features, installation steps, and provides detailed PHP examples for basic OCR, target detection, and slider detection functionalities.
This tutorial introduces Python, explains how to install it, and covers fundamental syntax including comments, variables, data types, string manipulation, collections, control flow, functions, modules, exception handling, file operations, classes, comprehensions, and common utilities, providing clear code examples for each concept.
An in‑depth comparison of Python, Julia, and Rust reveals each language’s strengths and weaknesses for data‑science tasks, covering ease of development, library ecosystems, performance, deployment challenges, and suitability for rapid prototyping versus high‑performance, memory‑safe applications.
The article walks through a first‑time user’s experience with PyODPS, showing how its Python‑based DataFrame API offers more flexible JSON field statistics, multi‑condition filtering, and custom aggregations than traditional ODPS SQL, while noting a steep learning curve and syntax quirks.
