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.
This article introduces Python concurrency models—including threading, multiprocessing, and asyncio—provides step‑by‑step Flask web application examples, demonstrates SQLite database operations, and covers essential Git commands for version control, all with complete code snippets for practical learning.
This article introduces core Python concepts—including dictionaries and sets as key‑value and unique collections, file and directory manipulation with the os module, regular expression handling, logging setup, unit testing with unittest, performance profiling via cProfile, and using popular third‑party libraries such as requests and BeautifulSoup.
This article dissects Python’s built‑in set type, explaining how add, pop, remove, copy, update, union and other set operations map to low‑level C functions, how the hash table handles collisions, dummy entries, resizing, and the finger optimization that speeds up element removal.
This guide walks through essential Python concepts—including try/except error handling, file reading and writing, class definitions with inheritance, variable scopes, common built‑in functions, docstrings, comments, and command‑line argument processing—providing clear code examples for each topic.
This article walks through a Python‑based solution that extracts files sharing specific keywords, pulls numeric data from the second column, and concatenates the results horizontally using pandas, providing clear code snippets and practical tips for automating such file‑processing tasks.
This article explains how Python scripts can automate key operations tasks—connecting to remote servers, parsing logs, monitoring system health, batch deploying software, and handling backups—enhancing efficiency and reducing manual errors for engineers.
This article introduces Python fundamentals, covering variable declaration, built‑in data types, naming rules, control flow statements, function definitions with parameters, and module import techniques, providing code examples for each concept to aid beginners in programming.
This article classifies sorting algorithms into internal, external, comparison, and non‑comparison types, discusses stability and time‑complexity, and provides clear Python implementations with examples for Bubble Sort, Selection Sort, Insertion Sort, Shell Sort, Merge Sort, Quick Sort, Heap Sort, Counting Sort, Bucket Sort, and Radix Sort.
This guide presents a concise collection of Python fundamentals, covering list, dictionary, and set comprehensions, various unpacking techniques, conditional expressions, common built‑in functions such as zip, enumerate, sorted, reversed, join, map, filter, any/all, min/max, sum, reduce, as well as useful modules like itertools, os, sys, time, collections, functools, heapq, bisect, json, re, and numpy, each illustrated with clear code examples.
This tutorial explains ten crucial visualizations for linear regression—scatter plot, trend line, residual plot, normal probability plot, learning curve, bias‑variance tradeoff, residuals vs fitted, partial regression, leverage, and Cook's distance—each illustrated with clear Python code using scikit‑learn, matplotlib, seaborn, and statsmodels.
This article provides a comprehensive overview of Python's built-in functions, covering type conversion, mathematical operations, control flow utilities, sequence manipulation, input/output handling, common utilities, functional programming tools, memory management, and type checking with clear code examples.
PyPA, the Python Packaging Authority, oversees key tools such as pip, setuptools, packaging, wheel, virtualenv, twine, and build, each serving distinct roles in package installation, creation, distribution, and environment isolation, with pyproject.toml coordinating their interactions to streamline Python development workflows.
This article introduces Python decorators, explains their purpose, and provides practical examples of common decorators such as @staticmethod, @property, @lru_cache, @wraps, @retry, @contextmanager, @dataclass, @cached_property, @singledispatch, and @timeit, concluding with a summary of their benefits for clean, maintainable code.
This article introduces Python's built‑in math module, explains its most commonly used functions—including basic arithmetic, trigonometry, logarithms, constants, and utility helpers—and provides clear code examples that demonstrate how to perform each operation.
This article walks you through designing a lightweight, high‑performance MySQL helper class in Python, covering singleton connection management, robust exception handling, logging, context‑managed connections, and practical query/execute methods with example code and usage scenarios.
This article compares Python, Julia, and Rust—the three leading languages in data science—detailing their strengths, ecosystem support, performance, and deployment challenges to help developers choose the most suitable tool for their projects.
This article explains the fundamentals of face detection using the Haar‑cascade algorithm, how to train and apply detectors with OpenCV, and introduces the Local Binary Patterns Histograms (LBPH) method for face recognition, covering data preparation, parameter selection, and matching techniques.
This guide introduces the Python RQ library, shows how to install it with Redis, and provides multiple code examples covering queue creation, task execution, priority handling, timeouts, batch processing, scheduling, retries, and status tracking for building robust asynchronous back‑end systems.
This article explains the concepts of closures and decorators in Python, demonstrates how closures can capture and preserve state, and shows multiple decorator patterns—including simple, parameterized, and thread‑safe decorators—through clear code examples and explanations.
This tutorial explains how to create an automatic math‑grading tool in Python by generating synthetic digit images, training a small CNN on the data, segmenting handwritten equations with projection techniques, recognizing characters, evaluating the expressions, and overlaying the results back onto the original image.
This article introduces Python's decimal module, explains its importance for high‑precision calculations, and provides step‑by‑step examples covering basic creation, arithmetic operations, precision control, rounding, formatting, division, comparisons, advanced contexts, and custom rounding modes.
Learn how to create a Python logging wrapper that reduces repetitive code, ensures log entries show the correct filename and line number using the inspect module, and loads configuration once for global use, improving readability and maintainability in large applications.
This article explains the concept of deep copy in Python, demonstrating how it recursively copies all nested objects using the copy module's deepcopy function, with practical examples including basic data types, lists, dictionaries, and custom objects.
This article outlines six practical habits—following PEP 8, using the latest Python version, commenting code, employing linters, leveraging built‑in functions and libraries, and promptly fixing issues—to help new Python developers write clean, maintainable, and error‑free code throughout their careers.
This article explains how to create a panoramic image by detecting SIFT keypoints, matching features with KNN, estimating a homography using RANSAC, and warping the images with OpenCV, providing full Python code and step‑by‑step instructions.
This article explains why third‑party payment is needed, outlines Alipay’s workflow, guides through obtaining APPID and generating public/private keys, and provides a complete Python implementation—including key handling, request signing, and API usage—to integrate Alipay payments into backend applications.
This guide provides ready‑to‑run Python scripts for creating 3D scatter, line, surface, bar, and contour charts with Matplotlib, plus interactive 3D visualizations using Plotly, and includes pip installation commands for all required libraries.
This article explains Python's built‑in map() function, covering its syntax, basic usage with lambda and regular functions, and advanced scenarios such as handling multiple iterables, dictionaries, strings, conditional filtering, zip, nested lists, and compares it with list comprehensions, providing clear code examples throughout.
This article demonstrates ten practical techniques for iterating over rows, columns, and values of a pandas DataFrame and applying common transformations such as apply, vectorized operations, map, mask, groupby, cumulative sum, and rolling calculations, each illustrated with concise Python code examples.
This article explains Python's set type, covering its definition, creation (including empty sets), key properties such as unordered uniqueness, conversion using set(), the eight basic and four augmented set operators, common set methods, and practical applications like data deduplication.
This article walks through a Python web‑scraping project that fetches skin images, names, and prices from Honor of Kings, covering project background, required tools, site analysis, code implementation, data processing, and legal considerations.
The article explains Python's dict resizing mechanism, detailing when the hash table grows (when used entries reach two‑thirds of capacity), how the new size is chosen as the smallest power‑of‑two ≥ ma_used × 3, and the exact steps the interpreter takes to reallocate and rebuild the underlying key and entry arrays.
This article demonstrates how to use Python to fetch Amap traffic iso‑chrones via API calls, process city and district IDs, and visualize the 30‑minute travel range on an interactive map with Folium, providing complete code snippets for data acquisition, parsing, and map rendering.
This article explains how to build a Python script that searches a comic website, extracts chapter links and image URLs using requests and BeautifulSoup, and downloads the images into organized folders with multithreaded support, while outlining required modules and potential improvements.
This guide demonstrates how to install pandas and openpyxl, create DataFrames, write them to Excel worksheets, merge cells, combine multiple DataFrames into separate sheets, merge multiple Excel files, apply styles and conditional formatting, and remove duplicate rows using Python.
This guide explains how Python’s built‑in zip() and dict() functions can be combined to pair elements from two separate lists and create a dictionary, covering function basics, step‑by‑step examples, practical use cases such as user credentials and student scores, and a concise summary of the technique.
This article explains how the open‑source PySnooper library replaces manual print statements by automatically logging line execution, variable values, and timing through a simple decorator, offering a lightweight yet powerful way to debug Python code across various projects.
This article explains how pandas loc and iloc work, demonstrates their slow performance when used inside loops, and shows that using the at and iat accessors provides a dramatically faster alternative for scalar assignments in DataFrames.
This article introduces Python's functools module, explaining key utilities such as partial functions, lru_cache, update_wrapper, wraps, cmp_to_key, singledispatch, FunctionTool, cache, and partialmethod, and provides practical code examples demonstrating how to create preset request functions, cache results, update signatures, and implement polymorphic behavior.
This article explores the free GLM-4-Flash API from Zhipu AI, detailing its lightweight architecture, performance specs, a logic‑puzzle demonstration, and provides a comprehensive step‑by‑step tutorial—including data upload, model fine‑tuning, deployment commands and example code for building a LangChain‑based knowledge‑base retrieval system.
This article introduces Python's higher-order functions—including map, filter, reduce, partial, lambda, and related modules such as itertools, functools, contextlib, concurrent.futures, and decorator—explaining their concepts and demonstrating practical code examples for simplifying API testing and automation.
This article outlines six practical Python coding habits—including following PEP 8, using the latest version, commenting effectively, employing linters, leveraging built‑in functions and libraries, and fixing issues promptly—to help developers write clean, maintainable, and error‑free code.
This article introduces Python's Abstract Syntax Tree (AST), explains its purpose for code analysis and transformation, and provides ten detailed examples that demonstrate building, inspecting, modifying, and generating code using the ast module in a standard Python environment.
This article demonstrates ten practical Python list techniques—including sorting by ID, filtering by condition, locating elements, removing items, merging lists, finding intersections and differences, converting to dictionaries, using list comprehensions, and deduplicating—each illustrated with clear code examples and expected output.
This article introduces Python's built‑in shutil module, explains its key functions for copying, moving, deleting, archiving, and retrieving file information, and provides comprehensive code examples demonstrating how to prepare test files and perform common file‑system tasks such as copying files, moving directories, and creating archives.
This article explains how to create a Python script that automatically compensates weapon recoil in PUBG by capturing the screen, recognizing equipment with OpenCV and SSIM, and moving the mouse via pynput and pydirectinput without modifying the game memory.
This guide walks through ten practical Python examples for parsing, constructing, validating, updating, deleting, and iterating JSON data via HTTP requests, including custom date serialization and schema validation, each demonstrated with complete code and expected outcomes.
This article walks through a real‑world Python JSON parsing issue where extra quotes break the data, presents multiple regex‑based and parsing approaches from community members, and provides ready‑to‑run code snippets to clean and convert the malformed strings into valid JSON.
This article presents six hands‑on Python generative‑AI projects—ranging from a Llama 2 chatbot built with Streamlit and Replicate to natural‑language‑to‑SQL conversion using LlamaIndex and SQLAlchemy—complete with environment setup, required code snippets, deployment tips and resource links for further exploration.
This article provides a detailed overview of over a hundred essential Python libraries spanning environment management, packaging, file handling, date‑time utilities, text processing, office document formats, databases, networking, web frameworks, and concurrency, illustrating Python's vast ecosystem for diverse development needs.
This article demonstrates ten essential Python string manipulation techniques—including concatenation, splitting, searching, replacement, case conversion, slicing, formatting, whitespace trimming, list conversion, and URL encoding/decoding—each illustrated with clear code examples and expected output for practical API development.
This article presents a series of Python requests code snippets demonstrating how to upload and download files, compare contents, process CSV and JSON payloads, verify file size, name, MD5 checksum, MIME type, and handle multiple file uploads in typical backend API scenarios.
This article surveys the most suitable programming languages for artificial intelligence, detailing why Python, R, Java, LISP, Prolog, C++, Haskell, JavaScript, and Julia each excel in AI development, and provides practical FAQs for developers choosing the right language.
This article explains how to construct a multi‑agent AI research assistant using LangGraph and the open‑source GPT‑Researcher project, detailing the system architecture, agent roles, state design, workflow creation, parallel sub‑processes, and code examples for autonomous online research and report generation.
This article dissects CPython's dictionary implementation, explaining how a PyDictObject and its PyDictKeysObject are allocated, how method groups map to Python magic methods, and the detailed C logic for inserting, updating, and retrieving key‑value pairs, including __missing__ handling.
This tutorial demonstrates how to perform fundamental Redis operations—including setting, retrieving, and deleting keys, working with hashes, lists, and sorted sets—using Python's redis client and includes complete test code for each scenario.
This article explains why pandas loc/iloc operations inside Python loops are extremely slow, demonstrates the performance difference with concrete examples, and shows that using the scalar‑access methods at and iat can speed up the same task by dozens of times.
This article walks through using PaddlePaddle's linear algebra API to vectorize face images, load the ORL dataset, implement PCA for dimensionality reduction, and evaluate a simple face‑recognition classifier, providing full code, installation steps, and experimental results.
This article introduces the pytest-httpserver plugin for launching a simple HTTP server during tests, provides installation instructions, and walks through ten practical examples covering GET, POST, headers, query parameters, request counting, error codes, redirects, file uploads, timeouts, and client certificate verification.
This guide walks you through installing the PHPy extension on Ubuntu 22.04, configuring it, and running a simple PHP script that calls Python code, complete with required packages, configuration steps, and example code.
This tutorial walks you through building a fully functional Pong game in Python using the turtle library, featuring an AI paddle that automatically tracks the ball, complete with step‑by‑step code snippets and a complete source listing for easy copy‑paste implementation.
In this post a Python enthusiast shares a real‑world troubleshooting session where a pyecharts chart fails to display in Jupyter, discusses common causes such as missing git or offline resources, and provides a concrete fix by setting the online host URL via CurrentConfig, along with practical tips for future queries.
This article explains the WSGI/ASGI ecosystem, compares uWSGI, Nginx, and uvicorn, and then introduces Granian—a Rust‑written ASGI server that supports WSGI, ASGI, and RSGI, offering superior performance, HTTP/2‑3 support, and easy integration for Python web applications.
LangGraph Studio is a new visual IDE that simplifies the development, debugging, and interactive iteration of complex LLM‑based agent applications, offering features such as graph visualization, real‑time state inspection, code‑aware debugging, and seamless integration with LangSmith, with step‑by‑step guidance for desktop users.
This article introduces functional programming techniques in Python, covering first‑class functions, treating functions as objects, higher‑order functions, lambda expressions, and built‑in utilities like map, filter, and reduce, with clear code examples and explanations of their practical uses.
