The article explains why CSV becomes a bottleneck for large‑scale data, demonstrates how Parquet’s columnar, typed, and compressed format dramatically reduces storage, speeds up reads, and improves data safety, and provides step‑by‑step Python code for migrating and benchmarking the switch.
Bython is an open‑source Python preprocessor that lets you write Python code with curly braces instead of relying on indentation, offering easy installation via PyPI or source, full compatibility with the Python ecosystem, and straightforward command‑line usage.
This guide lists ten typical mistakes that new FastAPI developers make—such as using synchronous I/O in async endpoints, ignoring Pydantic models, mishandling database sessions, creating per‑request clients, misconfiguring CORS, monolithic code files, returning raw ORM objects, weak authentication, lacking timeouts/retries for external calls, and skipping testing—and provides concrete solutions with code examples to keep applications fast, reliable, and maintainable.
ktransformers is an open‑source AI model optimization framework that dramatically reduces memory usage and speeds up loading and inference, enabling ordinary laptops— even without a GPU— to run 7B‑13B large language models for coding, content creation, and academic assistance.
This guide explains why traditional dict.get() approaches fail on deep JSON, introduces the powerful jsonpath-ng library, demonstrates five common extraction patterns, shows how to integrate it into pytest automation with reusable utilities, and warns about typical pitfalls.
This article walks through the fundamentals of reinforcement learning, builds a custom drone‑landing simulation, defines state and action spaces, designs reward functions, implements a neural‑network policy with Bernoulli sampling, and trains it using REINFORCE with baseline techniques, while exposing common pitfalls such as reward‑cheating.
Ray Data is a scalable library built on Ray that offers high‑performance, streaming‑execution APIs for AI workloads, enabling efficient batch inference, data preprocessing, and training data ingestion across CPU and GPU resources, while supporting diverse data formats and seamless integration with popular frameworks.
This guide details a modular e‑commerce QA framework that organizes configuration, HTTP clients, test data, fixtures, and test cases, integrates multi‑process execution with CI/CD pipelines, and defines quality gates to ensure reliable, maintainable automated testing for large‑scale services.
Awex is a high‑performance Python framework that synchronizes training and inference weights for trillion‑parameter reinforcement‑learning models in seconds, using unified conversion, metadata management, and NCCL/RDMA transfer plans, dramatically reducing RL training latency and supporting diverse parallel strategies.
This article benchmarks memory consumption of concurrent programs across Rust, Go, Java, C#, Node.js, Python, Elixir and others, revealing surprising differences in how threads, async runtimes, and virtual threads scale from a single task up to one million concurrent tasks.
Amazon Bedrock introduces three service tiers—Priority, Standard, and Flex—enabling developers to match AI workload requirements with the appropriate performance level and cost, supported by concrete usage examples, a selection framework, and monitoring guidance.
The article shows why a naïve Pandas groupby on a 25‑million‑row DataFrame can take 40 minutes, identifies common performance killers, and demonstrates that converting the grouping column to the categorical dtype with observed=True and sort=False reduces runtime to about 4 seconds while also cutting memory usage dramatically.
This article explains the core principles of quick sort, why its divide‑and‑conquer strategy relies on a good pivot choice, how deterministic pivots can degrade performance on nearly sorted data, and provides a complete randomized Python implementation suitable for interview coding sessions.
The article explains how a test engineering team leveraged OpenAPI contracts and a Python‑driven AI generator to automatically produce comprehensive pytest cases for backend APIs, achieving 5–8× faster development while highlighting practical limits and best‑practice recommendations.
Python 3.14 introduces a collection of subtle yet useful enhancements—such as TypedDict's NotRequired, improved static analysis, lazy imports, clearer error messages, a new contextlib.chdir manager, refined async task cancellation, better subprocess isolation, richer pattern‑matching errors, and import‑time profiling—that together make scripts run smoother, easier to debug, and more maintainable.
This article explains experimental heterogeneity (HTE), clarifies key concepts such as CATE and ITE, discusses why analyzing treatment‑effect variation matters for business, compares statistical and machine‑learning methods, and introduces an open‑source Python tool that automates CATE discovery and reporting.
Learn how to automate API test data creation by leveraging a large language model to parse OpenAPI specifications and generate comprehensive pytest functions covering valid, missing, type‑error, boundary, and format scenarios, with a step‑by‑step Python script that produces ready‑to‑run test files.
Learn how to create a modular Python script that batch processes Excel files—merging, cleaning, renaming, and more—using pandas and openpyxl, with a clear project structure and extensible design for real-world office automation.
The article explains how the Token‑Oriented Object Notation (TOON) format reduces token usage by 30‑60% and improves accuracy compared to JSON when feeding structured data to large language models, offering concrete syntax, benchmark results, code examples, and guidance on when to adopt it.
The article explains why the traditional setup.py build script is outdated, outlines the problems it causes, and shows how adopting a declarative pyproject.toml file modernizes Python packaging, improves maintainability, and unifies tool configuration.
This guide shows how to use the Qwen large language model to automatically generate pytest‑style API test scripts from an OpenAPI specification, covering setup, prompt design, model invocation, code extraction, and execution steps.
This guide explains how to create a non‑intrusive Python bot that automatically compensates weapon recoil in PUBG by capturing the screen, recognizing equipped gear with OpenCV and SSIM, and moving the mouse via pydirectinput based on weapon‑specific recoil tables.
An in‑depth, five‑minute guide compares six popular Python causal inference libraries—Bnlearn, Pgmpy, CausalNex, DoWhy, PyAgrum, and CausalImpact—using the Census Income dataset to illustrate structure learning, parameter estimation, inference, and causal effect validation, highlighting each tool’s strengths, limitations, and ideal use cases.
This article compares PHP and Python for web development, detailing their features, strengths, weaknesses, performance differences, typical use cases, and key factors to consider when choosing the most suitable language for a project.
This article introduces the Scrapeasy Python library, shows how to install it, initialize website and page objects, retrieve links, images, videos, and other file types, and download them with simple one‑line commands, providing a fast way to extract data from any site.
This article compares Amazon Bedrock AgentCore's container‑based and direct‑code deployment options, walks through a step‑by‑step Python example of the zip‑deployment workflow, and shows how the latter can cut iteration time from 30 seconds to about 10 seconds while simplifying setup.
This article walks through the design and implementation of a multimodal Retrieval‑Augmented Generation (RAG) system using LangChain 1.0, detailing a front‑end/back‑end separated architecture, FastAPI service setup, multimodal data handling, conversation history management, streaming responses, and Postman testing to verify the intelligent Q&A module.
Effective test automation requires clean environments; this guide shows how pytest's fixture yield pattern can automatically set up and tear down resources—such as users, orders, and products—ensuring zero‑pollution, reliable CI runs, and maintainable code through scoped fixtures and best‑practice tips.
This article explains how reinforcement learning (RL) underpins intelligent AI agents, covering the Markov Decision Process fundamentals, key RL components, multi‑hop reasoning on knowledge graphs, and a step‑by‑step LangGraph example that integrates an RL‑driven tutoring policy with Python code.
This article introduces Microsoft Research's open‑source AutoGen framework, explains its five core concepts—including human‑in‑the‑loop, code execution, tool integration, multi‑agent collaboration, and termination mechanisms—provides practical Python examples, and compares it with competing solutions to show why it matters for building complex AI systems.
This guide walks you through creating an AI chat assistant powered by the DeepSeek‑V3 large language model on Huawei Developer Space, covering cloud container setup, free token acquisition, MaaS model activation, environment configuration, code deployment with Gradio, and end‑to‑end testing.
This article reviews nine Python GUI libraries—ranging from the built‑in Tkinter to GPU‑accelerated Dear PyGui—detailing their core features, typical use cases, and code examples, and provides guidance on selecting the right toolkit for desktop, web, mobile, or terminal applications.
This article explains how using pandas loc and iloc inside Python loops can be extremely slow, demonstrates the performance difference with concrete timing examples, and shows that replacing them with the scalar accessors at and iat can accelerate the operation by up to sixty times.
This article presents seven practical DuckDB SQL patterns—querying files directly, treating partition folders as tables, deduplicating with QUALIFY, computing rolling metrics with window functions, pivot/unpivot, handling JSON arrays, and exporting results to Parquet—plus tips and a mini case study that show how to turn a notebook into a lightweight OLAP engine without leaving the Python environment.
This article walks through setting up MCP servers in both stdio and SSE modes, shows how to load their tools into LangChain agents using the new create_agent API, compares the two communication methods, and provides complete runnable Python examples.
This article systematically compares six popular Python causal inference libraries—Bnlearn, Pgmpy, CausalNex, DoWhy, PyAgrum, and CausalImpact—using the U.S. Census income dataset to demonstrate how each tool discovers causal effects of education on salary, highlighting their core features, strengths, weaknesses, and suitable scenarios.
LangExtract is an open‑source Python library that uses LLMs to turn messy documents—such as medical records, contracts, novels, or news articles—into structured data with just a few lines of code and optional visualisation.
This article introduces AIO Sandbox, an all‑in‑one Docker container that unifies browser, shell, VSCode, and Jupyter environments, provides agent‑oriented APIs and multi‑language SDKs, and demonstrates a quick‑start workflow and a Python example converting web pages to Markdown.
This article examines the 22 most‑used Python packages on PyPI, explaining their core functionality, download statistics, and typical use cases—from HTTP clients like urllib3 to compatibility tools like six, security libraries such as certifi and rsa, and utility modules for dates, YAML, and concurrency—providing developers with a concise guide to essential Python tooling.
The article introduces the Jupyter MCP server, an extension that implements the Model Context Protocol to enable seamless, secure integration of large language models with Jupyter’s kernels, file system, and terminals, and details its architecture, core features, deployment steps, API mechanisms, and security considerations.
This guide shows how to use Python, Selenium, and requests to log into QQ Space, retrieve authentication cookies, compute the required g_tk token, fetch the full friend list via QQ's API, identify users who have blocked you, and optionally blacklist them automatically.
This guide shows how to build a WeChat bot using the PyWeChatSpy library and Tuling AI API, covering registration, API key acquisition, image folder setup, and a complete Python script that parses messages, replies to 'pats' with random images, and forwards other texts to the Tuling service for automatic responses.
This article breaks down Python’s Global Interpreter Lock, explains when to use multiprocessing, multithreading or asyncio, provides concrete performance benchmarks and a hybrid process‑coroutine pattern, and guides you on choosing the right concurrency model for interview questions.
Mozilla.ai's any-llm v1.0 is an open‑source Python library that unifies access to multiple large language model providers, enabling developers to move between cloud‑based and on‑premise LLMs without rewriting code, while offering async‑first APIs, reusable connections, and extensive compatibility features.
This article compares Flask and FastAPI, explaining their core differences in speed, developer experience, and standards, and provides detailed code examples for installation, routing, async handling, dependency injection, validation, middleware, testing, deployment, and documentation to help developers decide which framework best fits their Python API projects.
This guide shows how to capture a page's HAR file with Chrome DevTools, analyze it using a Python script, and generate an Excel report that lists each menu's access time, highlighting requests that exceed a configurable timeout threshold.
This guide explains how to use a dedicated Oh My Zsh plugin to quickly switch and test PyPI mirror sources, improving package download speed for Python developers, especially in China, with step‑by‑step installation, usage commands, and a comprehensive list of supported mirrors.
This article reviews eleven high‑performance Python libraries—Polars, Numba, orjson, PyO3, Blosc, Awkward Array, Dask, Vaex, Modin, scikit‑learn‑intelex, uvloop and PyPy—showing how they achieve multi‑fold speedups through Rust, JIT, SIMD, lazy evaluation and parallel execution, and offers guidance on when to choose each tool.
This article explains why Python import errors occur, describes the role of sys.path, and presents five practical techniques—including modifying sys.path, setting PYTHONPATH, using relative imports, building a proper package structure, and leveraging importlib—for reliable cross‑directory module imports.
Discover ten essential Python automation techniques—from closures and decorators to generators, context managers, currying, memoization, comprehensions, and callable classes—that transform repetitive code into clean, efficient, and scalable solutions, complete with practical examples and expert tips for modern developers.
This tutorial walks through setting up a Python environment, explains the three essential components of LangChain 1.0’s create_agent API, details the built‑in message types, and demonstrates four streaming output modes using a weather‑assistant example to help developers quickly adopt the new agent framework.
Robyn, a Python web framework powered by a Rust runtime, achieved a four‑fold reduction in data‑pipeline latency, slashed CPU usage from over 60 % to under 5 %, and dramatically improved memory consumption, demonstrating how Rust’s performance and safety can transform backend services.
This article introduces uv, a Rust‑based, all‑in‑one Python tool that dramatically speeds up package installation, environment handling, project initialization, and version management, offering compatibility with existing workflows while providing simple installation commands and powerful features like uvx for one‑off tool execution.
This article explains how to combine Python for training deep‑learning models with LabVIEW for rapid Windows‑based UI development and model deployment, showing step‑by‑step usage of LabVIEW's Python Node and array passing techniques.
This article compares Python's literal [] and the list() constructor, examining their speed differences, readability implications, default‑argument pitfalls, object identity, and appropriate use cases such as creating empty lists, converting iterables, and avoiding mutable default parameters.
This article explains what Python dataclasses are, how they simplify data‑oriented class definitions, and demonstrates advanced features like custom field behavior, the __post_init__ method, and immutable (frozen) classes with clear code examples.
This guide walks through LangChain 1.0’s three major upgrades— the new create_agent API that replaces legacy agent builders, standardized content_blocks for unified model output, and a streamlined package structure—while showing how middleware hooks, built‑in and custom middleware, and improved structured output simplify production‑grade AI agent development.
The open‑source Python library blind_watermark embeds invisible, tamper‑resistant watermarks into images, audio, and video, allowing creators and businesses to protect copyright, trace unauthorized use, and recover evidence even after cropping, compression, or AI editing, all with just a few lines of code.
The GitHub Octoverse 2025 report reveals TypeScript surpassing Python as the platform's top language, driven by AI‑assisted tools and the rapid adoption of modern front‑end frameworks, while highlighting continued growth for Python in AI and data‑science and the rise of hybrid tech stacks.
Learn how to connect to MongoDB using PyMongo, create databases and collections, and perform essential CRUD operations—including inserting single or multiple documents, querying with filters and regex, updating records with update_one and update_many, counting, sorting, pagination, and deleting—complete with Python code examples.
Discover ten lesser‑known Jupyter Notebook features—from magic commands that list variables and benchmark code to shortcuts, export utilities, and interactive help—that turn a simple notebook into a highly efficient, production‑ready data‑science workspace.
Using Python's functools.partial, you can pre‑fill common arguments to create concise, readable wrappers that eliminate repetitive parameter passing, improve safety, and streamline code in scenarios ranging from email utilities to Flask handlers and machine‑learning pipelines, while avoiding over‑use pitfalls.
This tutorial explains how to use Python, Selenium, and an image‑recognition service to bypass 12306's captcha and slider verification, providing a complete code example and detailing the challenges of image verification and anti‑automation measures.
This guide explains why the default PyPI source is slow for Chinese users and provides step‑by‑step instructions for temporarily or permanently switching pip to domestic mirror servers, covering command‑line configuration and manual file editing across Windows, macOS, and Linux.
This article reviews the most popular Python web frameworks of 2024—FastAPI, Django, Flask, Requests, Asyncio, and Django REST Framework—detailing their usage trends, strengths, weaknesses, and ideal use cases, while also highlighting emerging tools and how to choose the right one for your project.
This tutorial walks through creating a complete Alien Invasion game using Python and Pygame, covering the main program, game logic, settings, ship, alien, bullet, button, scoring, and game statistics modules, while showcasing screenshots of gameplay and emphasizing modular, object‑oriented design.
This tutorial walks you through creating a basic alien‑invader shooter using Python's Pygame library, covering essential game libraries, class design for Game, Alien, Hero, Generator, and Rocket, collision detection, win/lose logic, and provides the complete source code with explanations.
This article explains two practical techniques—histogram‑based per‑column synthesis and Gaussian‑Mixture‑Model generation—for creating large, privacy‑preserving synthetic datasets that retain the statistical distributions and inter‑column relationships of the original data, and shows how to evaluate their quality.
LangChain’s stateless LLMs require a memory component to retain conversation context, and this article explains the importance of memory, compares built‑in memory types, and walks through two practical examples—custom buffer memory and basic message history—showing how to implement and use them with code.
LangGraph extends LangChain by letting developers define stateful, multi‑agent LLM workflows as graphs with nodes, edges, and shared state, and the article walks through core concepts, typical use cases, and a detailed example that shows how to define state, nodes, edges, compile and run the graph.
This guide explains what LangChain toolkits are, why they simplify building domain‑specific AI agents, lists common built‑in toolkits, and walks through the step‑by‑step process of instantiating a toolkit, retrieving its tools, and creating an OpenAI‑powered agent, illustrated with a SQL database example.
This guide explains how LangChain’s Expression Language (LCEL) lets you declaratively connect prompts, models, and output parsers into chains, walks through environment setup, dependency installation, and detailed code examples ranging from a basic joke generator to retrieval‑augmented generation and memory‑enabled agents.
Learn how LangChain enables multimodal interactions by preparing inputs, constructing prompts, invoking models like GPT‑4o, and processing responses, with a complete example that demonstrates image‑question answering, code walkthrough, environment setup, and key considerations for API keys and image URLs.
This guide explains what LangChain tools are, why clear descriptions matter, and walks through three ways to create them—using the @tool decorator, StructuredTool with Pydantic models, and custom BaseTool subclasses—plus examples of built‑in tools and reference links.
This article explains the core concept of LangChain agents—combining an LLM, a set of tools, and a reasoning‑action loop—to enable dynamic decision‑making, tool invocation, and iterative observation for solving complex, multi‑step tasks.
This article explains how PaddleOCR, an open‑source OCR engine from Baidu, achieves high‑accuracy text extraction from handwritten notes, scanned PDFs, invoices, IDs and multilingual documents, offering offline cross‑platform support, free commercial use, and step‑by‑step guidance for rapid deployment.
Discover how to transform Claude AI into a powerful research assistant that automatically fetches Bilibili video subtitles, converts them into clean markdown, analyzes the content, and produces structured product selection reports, all without manually watching videos, using Python scripts, cookies, and Claude Skills.
This guide explains how to use industry sector classification to support value investing by scraping sector and stock data, parsing JSON responses, filtering by keywords, and exposing the results via FastAPI services, then displaying them in a Uniapp front‑end table for easy stock selection.
This article explains the different notification channels, walks through creating an Enterprise WeChat application, obtaining the required CorpID and secret, and provides a complete Python script to send alert messages via the WeChat API, illustrated with screenshots of the setup and results.
Nvidia announced native Python support for its CUDA toolkit, detailing new Python‑centric APIs, projects like CuTile and Cutlass, and a layered strategy that democratizes GPU programming for AI developers while preserving performance and expanding the ecosystem.
FastMCP lets Python developers replace hundreds of lines of LLM‑to‑database and API glue code with a 30‑line, low‑code server that offers quick setup, built‑in security, and extensible tool chaining for AI applications.
This article traces Python's 16‑year evolution from the 2008 release of Python 3.0 to the 2024 launch of Python 3.13, highlighting major syntax reforms, the rise of async/await, and the maturation of its type‑hinting system with performance benchmarks and code examples.
This article explains how LangChain's Indexing API adds state management and synchronization to the classic load‑split‑embed‑store RAG pipeline, detailing the RecordManager component, the index function workflow, key parameters, implementation considerations, and best‑practice code examples for production‑grade vector stores.
This article explains the concept of LangChain retrievers, compares them with vector stores, and provides step‑by‑step code examples for MultiQueryRetriever and EnsembleRetriever, showing how to boost recall and combine semantic and keyword search for robust RAG pipelines.
This article presents a structured, step‑by‑step learning path that guides beginners from Python fundamentals through AI API mastery, Retrieval‑Augmented Generation, deep MCP protocol knowledge, and advanced multi‑agent development, complete with practical code examples and performance‑monitoring techniques.
This guide outlines how to establish clear code conventions, design a standardized Python project layout, and automate enforcement with tools like Black, Flake8, pre‑commit hooks, and CI pipelines, while integrating documentation, code reviews, and regular refactoring into the team workflow.
The article explains how to convert an API automation framework into a command‑line tool so tests can be run without an IDE, detailing benefits, step‑by‑step transformation, example code, and report standardization for CI/CD integration.
Polars is a high‑performance DataFrame library written in Rust with a Python API, offering lightning‑fast operations, zero‑copy I/O, parallel execution, lazy evaluation, and seamless integration with Arrow, making it a compelling alternative to pandas for large‑scale data processing.
Python 3.14 introduces optional no‑GIL support, free‑threading, a concurrent interpreter and performance gains, while Guido van Rossum cautions about over‑hyped expectations, discussing the trade‑offs, impact on AI workloads, and the language’s future in an in‑depth interview.
This article explains the fundamental differences between workflows and agents, introduces seven core design patterns—including three workflow patterns and four agent patterns—provides Python examples using Ollama, and shows how to combine these patterns to create robust, autonomous AI applications.
This guide introduces nine Python libraries—including smart_open, filelock, watchdog, zstandard, dataclasses-json, polars, fsspec, pandas, and tracemalloc—that together enable fast, memory‑efficient, and reliable handling of large files, remote storage, and concurrent workflows.
Python remains a top language in 2025 thanks to its simple syntax, massive library ecosystem and broad applicability, and this article curates ten GitHub repositories—ranging from AI and data‑science tutorials to automation scripts and beginner‑friendly projects—each explained with concrete reasons why they’re valuable for learning and building real‑world applications.
This article introduces pyproc, a library that lets Go applications invoke Python functions via Unix Domain Sockets with sub‑45 µs latency, explaining the problem of mixing Go and Python ecosystems, the architecture, performance benefits, suitable use cases, and a step‑by‑step quick‑start guide with full code examples.
This article explains why transient failures require retry logic in API automation, outlines core goals for a retry system, and provides two Python solutions—using the tenacity library and a custom decorator—along with best‑practice tips and Pytest integration.
This guide explains the concept of blind watermarking for image copyright protection, shows how to install the blind-watermark Python package, and provides step‑by‑step code examples for embedding and extracting both text and image watermarks while preserving the original visual appearance.
The article demonstrates how Tencent's Hunyuan large model can generate and iteratively refine Python scripts that automatically compare Excel‑based reports, highlight differences, and handle multiple files, thereby streamlining regression testing and reducing manual effort.
This article explains what plugins are, shows how Python’s dynamic features enable flexible plugin systems, and provides step‑by‑step examples using pluggy and stevedore—including defining specifications, implementing plugins, packaging with entry points, and invoking them via driver or extension managers—while recommending stevedore for practical use.
The article presents a step‑by‑step 5S documentation standard and 6A workflow that turn AI from a careless code generator into a reliable assistant, cutting bug‑fix time dramatically and delivering up to a 90% efficiency gain in small‑to‑medium projects.
This article walks through ten hidden Jupyter Notebook features—including magic commands, terminal integration, shortcut keys, markdown styling, collapsible headings, nbconvert exporting, and tab completion—showing how each can streamline debugging, documentation, and reporting to save roughly an hour of work per day.
This guide shows how to use Python's Faker library together with direct MySQL operations to generate realistic test data in bulk, automate data creation, integrate with testing frameworks, and follow safety best practices, eliminating manual data preparation bottlenecks in API automation.
