This article explains how to use Python’s built‑in random module and NumPy’s random submodule to generate various types of pseudorandom numbers, covering functions such as random(), randint(), randrange(), uniform(), choice(), shuffle(), sample() and their NumPy equivalents with code examples and seed control.
This comprehensive guide covers essential programming fundamentals—including Python memory management, metaclasses, decorators, multithreading, data structures, algorithmic techniques, database concepts such as ACID, indexing, and transaction isolation, Redis operations, Linux memory handling, networking protocols, Docker/Kubernetes commands, and core testing methodologies—providing clear explanations and practical code examples.
November 2023’s TIOBE Index reveals Python still leads at 14.16%, while C# jumps to fifth place, narrowing its gap with Java to just 0.7%, as Java slips to fourth with its biggest monthly decline, and C and C++ also see modest drops.
The article explains how avoiding explicit for‑loops in Python encourages the use of more expressive constructs such as list comprehensions, generator expressions, map/reduce, and itertools, leading to shorter, more readable, and better‑structured code while showcasing practical examples and best‑practice recommendations.
This guide demonstrates how to use Python for end‑to‑end text data analysis, covering preprocessing, word‑frequency visualization, classification, sentiment detection, similarity measurement, entity recognition, keyword extraction, summarization, translation, and generation with clear code examples.
This tutorial explains how to parse, query, modify, and delete XML data in Python using the built‑in xml.etree.ElementTree and xml.dom.minidom modules, providing step‑by‑step code examples for reading files, handling strings, accessing elements, attributes, and writing updated XML back to disk.
This article explains Python's thread‑synchronization primitives—including locks, RLocks, conditions, events, semaphores, read‑write locks, queues, thread pools, the GIL, and high‑level concurrency libraries—while providing ten ready‑to‑run code examples for each mechanism.
This article introduces a modular Selenium UI automation framework built with the Page Object Model, layered architecture, Pytest execution, and YML‑driven test data, explaining its components, configuration files, coding practices, and steps for running tests efficiently in CI environments.
This tutorial explains how to use the open‑source RobustVideoMatting project to perform human portrait segmentation and replace video backgrounds, covering environment setup, model loading, custom image‑and‑video matting functions, and final video composition with OpenCV.
This tutorial explains how to use Python and OpenCV to read an answer‑sheet image, preprocess it, detect contours, apply perspective transformation, locate each option, and determine selected answers by analyzing pixel intensity within detected circles.
This article introduces Python's xlwt, xlrd, and openpyxl libraries for Excel data processing, explains how to install them, and provides step‑by‑step code examples for writing and reading .xls and .xlsx files, making it a practical guide for data‑handling tasks.
This article explains the memory‑management differences between mutable and immutable Python objects, demonstrates how each type behaves with code examples, and shows how immutability improves thread safety while providing practical synchronization techniques for mutable data in concurrent programs.
This guide introduces Python’s fundamental data types—including integers, floats, complex numbers, strings, booleans, lists, tuples, dictionaries, sets, NoneType, enums, functions, modules, and classes—explaining their characteristics and providing concise code examples that demonstrate creation, manipulation, and common operations for each type.
Python unpacking lets you extract multiple values from iterables in a single, readable statement, reducing code complexity and enabling concise operations such as simultaneous variable assignment, swapping, dictionary merging, and function argument handling, illustrated with numerous practical code examples.
This article explains how to compute the maximum number of books that can be stacked without rotation by converting the problem into a longest increasing subsequence (LIS) task, sorting books by length and width, applying a dynamic‑programming DP approach, and analyzing its time and space complexities.
This article explores whether Python is a compiled or interpreted language by examining its compilation stages, error‑message generation, differences across Python versions, and the broader distinction between static and dynamic language features, ultimately showing that Python combines both compilation and interpretation.
This article reviews recent Python standard‑library enhancements, showing how pathlib replaces os.path, secrets supersedes os.urandom, zoneinfo replaces pytz, dataclasses improve on namedtuple, proper logging outperforms print, f‑strings beat format, tomllib replaces tomli, and setuptools supersedes distutils, with code examples for each.
This article explains what LangChain is, outlines its core components such as Models, Indexes, Chains, Memory and Agents, and demonstrates how to build LLM‑driven applications with detailed Python code snippets, visual diagrams, and future development suggestions.
This article introduces Numba, explains its parallel, distributed, and GPU acceleration capabilities, and provides detailed Pandas benchmark examples that show how Numba’s JIT compilation dramatically speeds up data‑frame operations compared with default and Cython engines.
This article introduces the Differential Evolution (DE) algorithm, detailing its core concepts of mutation, crossover, and selection, outlines the step-by-step procedure, provides a Python implementation, compares DE with Genetic Algorithms, and highlights practical applications across engineering, machine learning, and image processing.
This tutorial demonstrates how to use Python's built‑in modules together with the third‑party itchat library to monitor, capture, and resend WeChat messages that have been withdrawn, providing step‑by‑step code and explanations for installing dependencies, handling different message types, and running the script on various operating systems.
This article reviews and compares five popular Python automation testing frameworks—Robot Framework, Pytest, UnitTest, Behave, and Lettuce—covering their prerequisites, advantages, disadvantages, and how they stack up against each other to help newcomers choose the right tool for functional, unit, or behavior‑driven testing.
This article lists and briefly explains fifteen common Python design patterns, including Singleton, Factory, Abstract Factory, Builder, Prototype, Adapter, Decorator, Proxy, Observer, Strategy, Template Method, Iterator, Chain of Responsibility, Command, and State, helping developers understand their purposes and usage.
This article explains the maximum‑sum submatrix problem, presents a brute‑force enumeration, introduces a 2‑dimensional prefix‑sum technique to compute submatrix sums in O(1), and provides a complete Python implementation with complexity analysis.
Object Factory is a design pattern that centralizes object creation, enhancing decoupling, extensibility, and management of instances, and this article explains its principles, benefits, and provides five Python code examples illustrating various scenarios such as type-based creation, configuration-driven instantiation, logging factories, singleton factories, and object caching.
This article introduces the most frequent Python errors—including syntax, runtime, and logical mistakes—explains their typical causes, and provides practical solutions and debugging techniques such as using IDEs, print statements, and unit tests, complemented by a case study and useful tips.
This article explains the concepts and practical usage scenarios of global and local variables in Python, illustrating their roles in data sharing, configuration, temporary storage, scope isolation, and exception handling with clear code examples for interface automation and general programming.
This article explains the Command design pattern, describes its key participants and advantages, and provides five Python examples demonstrating command encapsulation, undo/redo functionality, file operations, shopping‑cart management, and menu handling to illustrate how the pattern decouples request senders from receivers.
This article walks through a Python regex challenge of extracting strings that start with a specific Chinese prefix, contain six digits, four digits, and the character "号", showing the original issue, code attempts, and the final refined solution using pandas.
The 2022 JetBrains Python Developer Survey of over 23,000 respondents shows 93% now use Python 3, highlights the decline of Python 2, rising popularity of FastAPI, dominant IDEs like PyCharm and VS Code, and shifting preferences in frameworks, databases, big‑data tools, and operating systems.
This article explains how to use Python scripts to automate the analysis and refactoring of iOS configuration data items in Baidu App, detailing the extraction of public properties, usage detection across modules, CSV reporting, and the overall refactoring workflow to improve maintainability and reduce risk.
This article walks through extracting six‑digit numeric codes from strings containing letters, numbers, and symbols using Python regular expressions and pandas, presenting multiple community‑sourced approaches, complete code examples, and visual explanations to help readers solve similar data extraction challenges.
This article presents ten practical Python multiprocessing examples demonstrating various inter‑process communication techniques—including pipes, queues, shared memory, managers, events, semaphores, condition variables, and shared counters—to help developers implement concurrent data exchange and synchronization across processes.
This article explains how to prepare the Spark API environment, define prompts for an AI model, and automatically generate valid and invalid equivalence classes and corresponding test cases for a subway ticket self‑service purchase system, complete with expected outcomes.
This article presents ten practical Python code examples for common data visualization scenarios—including line, scatter, bar, pie, box, heatmap, area, histogram, tree, and map visualizations—demonstrating how to use matplotlib, seaborn, and geopandas to effectively convey data insights.
This article explains what a Python interpreter is, outlines how Python code is executed, and compares the six most popular Python implementations—CPython, PyPy, Stackless, Jython, IronPython, and GraalPython—helping developers choose the right one for their projects.
This article introduces ten widely used machine learning algorithms for data prediction, explains their core concepts, and provides complete Python code snippets using scikit‑learn and related libraries to help readers implement regression, classification, and time‑series forecasting tasks.
This article walks through a practical Pandas solution for handling CSV files without headers, demonstrating how to drop duplicate rows and then promote the first data row to serve as the DataFrame index, complete with code snippets and helpful tips for Python automation.
This article explains why YAML is ideal for managing API test framework configurations, outlines its syntax and structure, and provides concrete examples for global settings, environment-specific parameters, endpoint definitions, best‑practice guidelines, and Python parsing with PyYAML, helping teams build maintainable and scalable test suites.
This tutorial demonstrates how to use Python to automatically play piano pieces by simulating keyboard presses on an online piano, detailing a four‑step implementation that includes a play function, multithreaded melody threads, sheet‑music definition, and browser automation to launch the autopiano webpage.
Microsoft’s VS Code Python extension has ended official support for Python 3.7 and will progressively drop support for older 3.x releases, citing security, library compatibility, and the language’s new annual release cycle, while Python’s popularity continues to surge worldwide.
This article presents practical Python examples for enhancing network security, covering symmetric and asymmetric encryption, hash functions, password hashing, SSL/TLS communication, SQL injection prevention, XSS mitigation, CSRF protection, and secure password storage to safeguard data and privacy.
This article introduces several lesser‑known Python standard‑library modules—including difflib, sched, binascii, tty, and weakref—explaining their key functions, typical use‑cases, and providing concise code examples to help developers quickly incorporate these utilities into their projects.
This article explains Python's property access protocols and descriptor mechanisms, demonstrating how to customize attribute behavior using special methods like __getattribute__ and __getattr__.
This article explains the benefits of automating deployment for Django‑Vue applications and provides step‑by‑step instructions, including code repository integration, frontend Vue build, backend Django deployment, and static file handling, accompanied by practical Python subprocess scripts to streamline the entire process.
This article walks through a Python community question about combining three aggregated columns into a single table using Pandas, explains why direct multi‑table merging isn’t possible, and provides concise code examples and best‑practice tips for merging DataFrames efficiently.
The article presents a dynamic‑programming solution to compute the maximum sum of a contiguous subarray when you may replace at most one element with a given value, detailing problem constraints, input/output format, DP state definitions, transition formulas, reference Python code, and complexity analysis.
This article introduces ten popular Python libraries—Augmentor, imgaug, albumentations, nlpaug, textaugment, pytorch‑geometric, audiomentations, nlpaugment, keras‑augment, and OpenCV—that provide powerful image, text, audio, and graph data augmentation techniques to improve model generalization and robustness.
This tutorial explains how to use the open‑source RobustVideoMatting project with Python, PyTorch, and OpenCV to perform human portrait segmentation and replace video backgrounds, covering repository setup, model loading, custom segmentation functions, and full video compositing steps.
This article introduces Python coroutines as a lightweight concurrency model and provides ten practical code examples demonstrating how to use the asyncio, gevent, and trio libraries for asynchronous I/O, concurrent network requests, file operations, task scheduling, and more.
This guide provides a comprehensive overview of Fabric 2, covering installation, connection setup, command execution, interactive prompts, file transfer, multi‑host orchestration, fab CLI usage, and a custom FabConnection wrapper for advanced automation tasks.
The article explains how StackBlitz's WebContainers, a browser‑based container environment, now fully integrates the WebAssembly System Interface (WASI), allowing near‑native speed, secure sandboxed execution of multiple languages such as Rust, Python, C/C++, and introducing new CLI tools and future language support.
This article walks through a Python automation challenge where several DataFrames need to be written to the same workbook sheet, explains why simple appends fail, and provides a complete pandas concat solution with code examples to reliably combine and export the data.
This guide shows how to create daily etcd snapshots on a Kubernetes cluster, upload them to MinIO, and orchestrate the whole process with a Python script, CronJob, Docker, Drone CI/CD, and ArgoCD for seamless backup automation.
This tutorial explains how to generate animated line, pie, and bar charts in Python using Matplotlib's FuncAnimation, covering required imports, data preparation, chart configuration, animation loops, and saving the resulting GIFs for effective data visualization.
This article presents a curated list of Python web‑scraping utilities—including file download assistants, novel and video grabbers, proxy pool builders, and various automation scripts—along with installation commands, usage examples, source links, and brief operational explanations for each tool.
This article presents a detailed curriculum covering an opening ceremony, Python fundamentals and advanced topics, Django development (including ORM and authentication), frontend basics with HTML, CSS, JavaScript, jQuery, a Layui UI framework module, and a hands‑on K8s Dashboard web project, followed by recommended reading links.
Partial functions and currying are essential functional programming techniques that enable flexible function handling and parameter management through practical code examples.
This tutorial walks you through the complete process of setting up the Mirai ecosystem, configuring mirai-console-loader, handling slider verification with mirai-login-solver-selenium, installing mirai-api-http, switching to OpenJDK, and finally writing and running a Python bot using graia-application-mirai.
This article explains Python's multiprocessing module, compares it with multithreading, outlines its benefits for CPU utilization and resource isolation, and provides five practical code examples demonstrating concurrent task execution, parallel calculations, data processing, API requests, and test case automation.
This article explains how to install the Python Colorama module, use its Fore, Back, and Style classes to add colors and styles to terminal output, and provides complete example code demonstrating colored and formatted CLI messages.
This guide walks you through Docker fundamentals, installation on Linux and Windows, basic container commands, image management, custom Dockerfile creation, multi‑stage builds for smaller images, and how to export and import images for production use.
This article introduces a Python‑based face‑recognition door access management system built with Django, REST framework, JWT, Redis and Dlib, detailing its features, required environment setup, step‑by‑step installation commands, configuration files, and how to run the project on both PC and mobile interfaces.
This tutorial explains how to install Pillow, create GIF animations, and perform common image processing tasks such as resizing, cropping, adding watermarks, rotating, and applying filter effects using Python code examples.
The article outlines the major performance and architectural goals for Python 3.12, describing the Faster CPython project’s specialized opcodes, multithreading proposals, object size reductions, memory‑management improvements, and API stability efforts that aim to boost speed and maintainability.
This article presents eighteen practical Python tricks, covering input handling, conditional checks, parity testing, variable swapping, palindrome detection, list comprehensions, argument packing, dictionary merging, sorting, pretty printing, and list reversal, each illustrated with clear bad‑practice and improved code examples.
This article explains the limitations of UUID, introduces ULID as a time‑and‑randomness based identifier with 1.21×10⁻²⁴ unique values per millisecond, details its specification, binary layout, and shows how to generate and manipulate ULIDs in Python for distributed systems.
This article explains what constitutes a Redis big key, the performance problems they cause, and provides practical methods—including redis-cli, SCAN, and RdbTools—to locate them and safely delete them using batch or asynchronous techniques with example Python code.
This article explains how to use Setuptools to manage dependencies, package source code, and provide command-line tools, and demonstrates a step‑by‑step example of integrating Sphinx to automatically generate documentation and include it in a Python package build.
This article examines the governance of WeChat's NLP algorithm microservices, outlining the management, performance, and scheduling challenges they face and presenting solutions such as automated CI/CD pipelines, dynamic scaling, DAG‑based service composition, a custom tracing system, the PyInter interpreter, and an improved load‑balancing algorithm.
This article explains the concepts, differences, and practical code examples of deep copy and shallow copy in Python 3, illustrating how each method works, their impact on nested objects, and how to apply them in API automation parameterization scenarios.
This tutorial shows how to use Python and the OpenCV library to programmatically overlay a national flag onto a profile picture, providing step‑by‑step code, scaling calculations, and instructions for saving the combined image.
This article reviews four Python IDEs—Eric7, Wing IDE, Pyzo, and Thonny—highlighting their features, installation requirements, target audiences, and strengths to help developers choose the most suitable environment for their projects.
This guide walks through ten core data preprocessing methods—including handling missing values, type conversion, standardization, encoding, smoothing, outlier treatment, text cleaning, word‑frequency counting, sentiment analysis, and topic modeling—each illustrated with concise Python code examples.
This article explains what coroutines are, how they differ from ordinary functions by supporting multiple pause points, demonstrates their behavior with Python code and visual diagrams, covers their historical origins, and details low‑level implementation using stack frames and heap allocation for efficient user‑level concurrency.
Discover how the Haversine formula, rooted in centuries of navigation history, computes the shortest great‑circle distance between any two points on Earth, illustrated with Python code that measures distances and flight times among Beijing, Shanghai, Guangzhou, and Shenzhen.
This article outlines key principles and best practices for designing a scalable Python automated testing architecture, covering test strategy definition, framework selection, modular and keyword‑driven design, test data management, error handling, reporting, CI integration, and provides a complete example implementation.
Python, created by Guido van Rossum in the late 1980s, has grown from a readable, object‑oriented scripting language into one of the world’s most popular languages, driven by its simplicity, extensive libraries, and widespread use in web development, data analysis, AI, and more.
This guide explains how to install MkDocs, set up a docs folder, write Markdown files, configure the mkdocs.yml file, and use mkdocs build and serve commands to generate and preview static documentation sites for deployment.
This article explains several methods for converting Python scripts into standalone executables—including pyinstaller, cx_Freeze, py2exe, py2app, Nuitka, and a combination of Nuitka with pyinstaller—detailing their commands, platform support, advantages, and common pitfalls such as missing imports and GIL-related issues.
This article explores tools like Pyan, Pycallgraph, and NetworkX with Matplotlib for visualizing function call relationships in Python programs, including code examples and implementation methods.
This article explains Python's DB‑API standard, shows how to install and use PyMySQL for MySQL operations, demonstrates basic CRUD commands, safeguards against SQL injection, and provides detailed examples of using DBUtils connection pools with both locking and lock‑free multithreaded approaches.
This guide shows how to download, convert, 4‑bit quantize, and run Meta’s 7‑billion‑parameter LLaMA model on a single 16‑inch Apple laptop using Python, torch, and the llama.cpp repository, demonstrating that the quantized model fits in memory and generates responses quickly, with optional scaling to larger models.
This article introduces five widely used Python libraries—Pandas, NumPy, NLTK, Scikit-learn, and Matplotlib—explaining their core functionalities for data cleaning, statistical analysis, natural language processing, machine‑learning modeling, and visualization, and provides practical code snippets for each.
This article introduces Mojo, a new AI‑focused programming language that combines Python’s ease of use with C‑grade performance, explains its claimed 35,000× speed boost, and walks through its seven key features and code examples for developers.
Python string prefixes such as u (Unicode), r (raw), b (bytes), and f (formatted) indicate special string types, with each prefix altering how the string is interpreted, and the article explains their meanings, usage, and provides code examples for each.
This article provides a detailed cheat‑sheet of frequently used regular expressions for validating numbers, characters, dates, URLs, IP addresses, and other special formats, complete with explanations and ready‑to‑copy code snippets for developers needing quick pattern references.
This article explains the INI, YAML, and JSON configuration file formats, provides example files for each, and demonstrates how to parse them in Python using configparser, PyYAML, and the built‑in json module, especially for API automation testing.
This article presents practical Python performance optimization techniques, including using local variables, minimizing function calls, employing mappings over conditionals, iterating directly over sequences, preferring generator expressions, pre‑compiling code, and structuring modules with functions to reduce overhead and improve efficiency.
The article examines how Python Requests, Scrapy, and Go’s charset package detect page encodings, reveals why they often mis‑decode Chinese GB‑series pages, and proposes a unified strategy—prefer header charset, then HTML meta, finally a reliable heuristic—to eliminate garbled text in web scraping.
This guide shows how to split an avatar image into nine equal parts with Pillow, overlay each part onto a resized flag background, and combine the frames into an animated GIF, including full Python code and installation instructions.
Data Envelopment Analysis (DEA) is a powerful operations‑research technique that evaluates the relative efficiency of decision‑making units with multiple inputs and outputs by automatically assigning optimal weights, offering a comprehensive alternative to traditional ratio methods, and can be applied through linear programming models and Python implementations.
This article explains the concepts and usage of *args and **kwargs in Python functions, provides code examples, and demonstrates how they can be leveraged for flexible parameter handling, request automation, dynamic test data generation, and robust assertions in API testing scenarios.
This article explains how Python's * and ** operators unpack sequences and dictionaries, handle variable numbers of positional and keyword arguments, and are applied in API automation, iterators, and generators, providing flexible and dynamic coding techniques.
Former Meta engineer Aydyn Tairov quickly ported the Python implementation of Llama2 to the newly released Mojo language, demonstrating that Mojo’s SIMD primitives can accelerate Python code by up to 250 times and even make the Python version run 20% faster than the original C implementation.
This article introduces Python's http.server module for creating HTTP servers and http.cookies module for managing cookies, providing practical examples for web development tasks.
This article explains what web honeypots are, outlines their distinctive JSONP‑hijacking behavior, and provides a step‑by‑step guide—including asset collection with CrawlerGo, data cleaning, dictionary matching, multithreaded scanning, and proxy‑pool integration—to automatically identify and filter honeypots from large domain lists.
This article explains how Flask's Config class works as a dictionary‑based configuration manager, demonstrates multiple ways to set and load settings—including direct assignment, update, from_object, from_pyfile, environment variables, JSON, and constructor arguments—and provides code examples for each method.
This article explains the difference between machine learning and deep learning, compares ML algorithms with traditional logic code, introduces the scikit‑learn library, demonstrates data preprocessing, model training with RandomForestRegressor, and shows how to build a voting regressor for disease progression prediction using Python.
This article explains Python's set data type, covering its definition, unordered and unique characteristics, creation methods, basic and augmented operators, practical usage examples, handling techniques, and common application scenarios such as data deduplication.
