[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-PavelGrigoryevDS--awesome-data-analysis":3,"tool-PavelGrigoryevDS--awesome-data-analysis":64},[4,17,27,35,43,56],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":16},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,3,"2026-04-05T11:01:52",[13,14,15],"开发框架","图像","Agent","ready",{"id":18,"name":19,"github_repo":20,"description_zh":21,"stars":22,"difficulty_score":23,"last_commit_at":24,"category_tags":25,"status":16},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",138956,2,"2026-04-05T11:33:21",[13,15,26],"语言模型",{"id":28,"name":29,"github_repo":30,"description_zh":31,"stars":32,"difficulty_score":23,"last_commit_at":33,"category_tags":34,"status":16},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",107662,"2026-04-03T11:11:01",[13,14,15],{"id":36,"name":37,"github_repo":38,"description_zh":39,"stars":40,"difficulty_score":23,"last_commit_at":41,"category_tags":42,"status":16},3704,"NextChat","ChatGPTNextWeb\u002FNextChat","NextChat 是一款轻量且极速的 AI 助手，旨在为用户提供流畅、跨平台的大模型交互体验。它完美解决了用户在多设备间切换时难以保持对话连续性，以及面对众多 AI 模型不知如何统一管理的痛点。无论是日常办公、学习辅助还是创意激发，NextChat 都能让用户随时随地通过网页、iOS、Android、Windows、MacOS 或 Linux 端无缝接入智能服务。\n\n这款工具非常适合普通用户、学生、职场人士以及需要私有化部署的企业团队使用。对于开发者而言，它也提供了便捷的自托管方案，支持一键部署到 Vercel 或 Zeabur 等平台。\n\nNextChat 的核心亮点在于其广泛的模型兼容性，原生支持 Claude、DeepSeek、GPT-4 及 Gemini Pro 等主流大模型，让用户在一个界面即可自由切换不同 AI 能力。此外，它还率先支持 MCP（Model Context Protocol）协议，增强了上下文处理能力。针对企业用户，NextChat 提供专业版解决方案，具备品牌定制、细粒度权限控制、内部知识库整合及安全审计等功能，满足公司对数据隐私和个性化管理的高标准要求。",87618,"2026-04-05T07:20:52",[13,26],{"id":44,"name":45,"github_repo":46,"description_zh":47,"stars":48,"difficulty_score":23,"last_commit_at":49,"category_tags":50,"status":16},2268,"ML-For-Beginners","microsoft\u002FML-For-Beginners","ML-For-Beginners 是由微软推出的一套系统化机器学习入门课程，旨在帮助零基础用户轻松掌握经典机器学习知识。这套课程将学习路径规划为 12 周，包含 26 节精炼课程和 52 道配套测验，内容涵盖从基础概念到实际应用的完整流程，有效解决了初学者面对庞大知识体系时无从下手、缺乏结构化指导的痛点。\n\n无论是希望转型的开发者、需要补充算法背景的研究人员，还是对人工智能充满好奇的普通爱好者，都能从中受益。课程不仅提供了清晰的理论讲解，还强调动手实践，让用户在循序渐进中建立扎实的技能基础。其独特的亮点在于强大的多语言支持，通过自动化机制提供了包括简体中文在内的 50 多种语言版本，极大地降低了全球不同背景用户的学习门槛。此外，项目采用开源协作模式，社区活跃且内容持续更新，确保学习者能获取前沿且准确的技术资讯。如果你正寻找一条清晰、友好且专业的机器学习入门之路，ML-For-Beginners 将是理想的起点。",84991,"2026-04-05T10:45:23",[14,51,52,53,15,54,26,13,55],"数据工具","视频","插件","其他","音频",{"id":57,"name":58,"github_repo":59,"description_zh":60,"stars":61,"difficulty_score":10,"last_commit_at":62,"category_tags":63,"status":16},3128,"ragflow","infiniflow\u002Fragflow","RAGFlow 是一款领先的开源检索增强生成（RAG）引擎，旨在为大语言模型构建更精准、可靠的上下文层。它巧妙地将前沿的 RAG 技术与智能体（Agent）能力相结合，不仅支持从各类文档中高效提取知识，还能让模型基于这些知识进行逻辑推理和任务执行。\n\n在大模型应用中，幻觉问题和知识滞后是常见痛点。RAGFlow 通过深度解析复杂文档结构（如表格、图表及混合排版），显著提升了信息检索的准确度，从而有效减少模型“胡编乱造”的现象，确保回答既有据可依又具备时效性。其内置的智能体机制更进一步，使系统不仅能回答问题，还能自主规划步骤解决复杂问题。\n\n这款工具特别适合开发者、企业技术团队以及 AI 研究人员使用。无论是希望快速搭建私有知识库问答系统，还是致力于探索大模型在垂直领域落地的创新者，都能从中受益。RAGFlow 提供了可视化的工作流编排界面和灵活的 API 接口，既降低了非算法背景用户的上手门槛，也满足了专业开发者对系统深度定制的需求。作为基于 Apache 2.0 协议开源的项目，它正成为连接通用大模型与行业专有知识之间的重要桥梁。",77062,"2026-04-04T04:44:48",[15,14,13,26,54],{"id":65,"github_repo":66,"name":67,"description_en":68,"description_zh":69,"ai_summary_zh":69,"readme_en":70,"readme_zh":71,"quickstart_zh":72,"use_case_zh":73,"hero_image_url":74,"owner_login":75,"owner_name":76,"owner_avatar_url":77,"owner_bio":78,"owner_company":79,"owner_location":79,"owner_email":80,"owner_twitter":79,"owner_website":79,"owner_url":81,"languages":79,"stars":82,"forks":83,"last_commit_at":84,"license":85,"difficulty_score":86,"env_os":87,"env_gpu":88,"env_ram":88,"env_deps":89,"category_tags":102,"github_topics":103,"view_count":23,"oss_zip_url":79,"oss_zip_packed_at":79,"status":16,"created_at":122,"updated_at":123,"faqs":124,"releases":125},3775,"PavelGrigoryevDS\u002Fawesome-data-analysis","awesome-data-analysis","🚀 500+ curated resources for Data Analysis & Data Science: Python, SQL, Statistics, ML, AI, Visualization, Cheatsheets, Roadmaps, Interview Prep. For beginners and experts. ","awesome-data-analysis 是一个专为数据分析和数据科学领域打造的精选资源库，汇集了超过 500 个高质量的学习资料、工具库、路线图、速查表及面试指南。面对数据科学领域技术栈繁杂、学习资源分散的痛点，它将原本零散的信息进行了系统化梳理与分类，帮助用户快速定位所需内容。\n\n该资源库覆盖面极广，不仅包含 Python、SQL、统计学基础等核心技能，还深入探讨了机器学习、MLOps、自然语言处理、时间序列分析以及云端基础设施等进阶主题。其独特的亮点在于结构清晰，从基础的数据清洗、可视化到复杂的工程化部署应有尽有，同时提供了专门的生产力提升建议（如 VS Code 插件）和职业发展指导。\n\n无论是刚入门希望建立知识体系的新手，还是寻求高效解决方案的资深工程师、研究人员，都能从中获益。对于准备求职面试的从业者，这里整理的实战笔记和面试题更是宝贵的备考资料。awesome-data-analysis 致力于让数据探索之路更加高效顺畅，是每一位数据工作者值得收藏的“导航地图”。","# Awesome Data Analysis [![Awesome](https:\u002F\u002Fcdn.rawgit.com\u002Fsindresorhus\u002Fawesome\u002Fd7305f38d29fed78fa85652e3a63e154dd8e8829\u002Fmedia\u002Fbadge.svg)](https:\u002F\u002Fgithub.com\u002Fsindresorhus\u002Fawesome)\n\n[![Web Page](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F🌐_Web_Page-696969)](https:\u002F\u002Fpavelgrigoryevds.github.io\u002Fawesome-data-analysis\u002F)\n[![PRs Welcome](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPRs-welcome-brightgreen.svg)](http:\u002F\u002Fmakeapullrequest.com)\n[![CC0](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLicense-CC0_1.0-lightgrey.svg)](https:\u002F\u002Fcreativecommons.org\u002Fpublicdomain\u002Fzero\u002F1.0\u002F)\n\n500+ curated resources for data analysis and data science: tools, libraries, roadmaps, cheatsheets, interview guides and more.\n\n**📖 For comfortable reading:** [Web version](https:\u002F\u002Fpavelgrigoryevds.github.io\u002Fawesome-data-analysis\u002F)\n\n**🌱 Want to improve?** [Suggest here](https:\u002F\u002Fgithub.com\u002FPavelGrigoryevDS\u002Fawesome-data-analysis\u002Fissues\u002F16) or [Welcome to Discussions](https:\u002F\u002Fgithub.com\u002FPavelGrigoryevDS\u002Fawesome-data-analysis\u002Fdiscussions)\n\n🌟 Join us in making data analysis more efficient! ![GitHub stars](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002FPavelGrigoryevDS\u002Fawesome-data-analysis?style=social)\n\nMaintained with ❤️\n\n---\n\n\u003Ca id=\"contents\">\u003C\u002Fa>\n\n## 📑 Contents\n\n- [🏆 Awesome Data Science Repositories](#awesome-data-science-repositories)\n- [🗺️ Roadmaps](#roadmaps)\n- [🐍 Python](#python)\n  - [Resources](#python-resources)\n  - [Data Manipulation with Pandas and Numpy](#python-data-manipulation-with-pandas-and-numpy)\n  - [Useful Python Tools for Data Analysis](#python-useful-python-tools-for-data-analysis)\n    - [Data Processing \\& Transformation](#python-data-processing-transformation)\n    - [Automated EDA and Visualization Tools](#python-automated-data-visualization-tools)\n    - [Data Quality \\& Validation](#python-data-quality-validation)\n    - [Feature Engineering & Selection](#python-feature-engineering-selection)\n    - [Specialized Data Tools](#python-specialized-data-tools)\n- [🗃️ SQL \\& Databases](#sql-databases)\n  - [Resources](#sql-databases-resources)\n  - [Tools](#sql-databases-tools)\n- [📊 Data Visualization](#data-visualization)\n  - [Resources](#data-visualization-resources)\n  - [Tools](#data-visualization-tools)\n- [📈 Dashboards & BI](#dashboards)\n  - [Resources](#dashboards-resources)\n  - [Tools](#dashboards-tools)\n  - [Software](#dashboards-software)\n- [🕸️ Web Scraping \\& Crawling](#web-scraping-crawling)\n  - [Resources](#web-scraping-crawling-resources)\n  - [Tools](#web-scraping-crawling-tools)\n- [🔢 Mathematics](#mathematics)\n- [🎲 Statistics \\& Probability](#statistics-probability)\n  - [Resources](#statistics-probability-resources)\n  - [Tools](#statistics-probability-tools)\n- [🧪 A\u002FB Testing](#ab-testing)\n- [⏳ Time Series Analysis](#time-series-analysis)\n  - [Resources](#time-series-analysis-resources)\n  - [Tools](#time-series-analysis-tools)\n- [⚙️ Data Engineering](#data-engineering)\n  - [Resources](#data-engineering-resources)\n  - [Tools](#data-engineering-tools)\n- [📖 Natural Language Processing (NLP)](#natural-language-processing-nlp)\n  - [Resources](#natural-language-processing-nlp-resources)\n  - [Tools](#natural-language-processing-nlp-tools)\n- [🤖 Machine Learning & AI](#machine-learning)\n  - [Resources](#machine-learning-resources)\n  - [Tools](#machine-learning-tools)\n- [🚀 MLOps](#mlops)\n  - [Resources](#mlops-resources)\n  - [Tools](#mlops-tools)\n- [🧠 AI Applications & Platforms](#ai-applications)\n  - [Resources](#ai-applications-resources)\n  - [Tools](#ai-applications-tools)\n- [☁️ Cloud Platforms & Infrastructure](#cloud-platforms)\n  - [Resources](#cloud-platform-resources)\n  - [Tools](#cloud-platform-tools)\n- [⚡ Productivity](#productivity)\n  - [Resources](#productivity-resources)\n  - [Useful Linux Tools](#productivity-useful-linux-tools)\n  - [Useful VS Code Extensions](#productivity-useful-vs-code-extensions)\n- [📚 Skill Development \\& Career](#skill-development-career-resources)\n  - [Practice Resources](#skill-development-career-resources-practice-resources)\n  - [Curated Jupyter Notebooks](#skill-development-career-resources-curated-jupyter-notebooks)\n  - [Data Sources \\& Datasets](#skill-development-career-resources-data-sources-datasets)\n  - [Resume and Interview Tips](#skill-development-career-resources-resume-and-interview-tips)\n- [📋 Cheatsheets](#cheatsheets)\n  - [GoalKicker Programming Notes](#cheatsheets-goalkicker)\n  - [Python](#cheatsheets-python)\n  - [Data Science \\& Machine Learning](#cheatsheets-data-science-machine-learning)\n  - [Linux \\& Git](#cheatsheets-linux-git)\n  - [Probability \\& Statistics](#cheatsheets-probability-statistics)\n  - [SQL \\& Databases](#cheatsheets-sql-databases)\n  - [Miscellaneous](#cheatsheets-miscellaneous)\n- [📦 Additional Python Libraries](#additional-python-libraries)\n- [📝 More Awesome Lists](#more-awesome-curations)\n- [🌐 Additional Resources and Tools](#additional-resources)\n- [🤝 Contributing](#contributing)\n- [📜 License](#license)\n\n---\n\n\u003Ca id=\"awesome-data-science-repositories\">\u003C\u002Fa>\n\n## 🏆 Awesome Data Science Repositories\n\nCurated collections of high-quality GitHub repos for inspiration and learning.\n\n- [Awesome Data Science](https:\u002F\u002Fgithub.com\u002Facademic\u002Fawesome-datascience) - A curated list of courses, books, tools, and resources for data science.\n- [Data Science for Beginners](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FData-Science-For-Beginners) - Microsoft's data science curriculum.  \n- [OSSU Data Science](https:\u002F\u002Fgithub.com\u002Fossu\u002Fdata-science) - Open Source Society University's self-study path.  \n- [Data Science Best Resources](https:\u002F\u002Fgithub.com\u002Ftirthajyoti\u002FData-science-best-resources) - Carefully curated links for data science resources in one place.\n- [Data Science Articles from CodeCut](https:\u002F\u002Fgithub.com\u002FCodeCutTech\u002FData-science) - A collection of articles, videos, and code related to data science.\n- [Data Science Using Python](https:\u002F\u002Fgithub.com\u002FWillKoehrsen\u002FData-Analysis) - Resources for data analysis using Python.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"roadmaps\">\u003C\u002Fa>\n\n## 🗺️ Roadmaps\n\nStep-by-step guides and skill trees to master data science and analytics.\n\n- [Data Analyst Roadmap](https:\u002F\u002Froadmap.sh\u002Fdata-analyst) - Structured learning path for analysts.\n- [Data Science Roadmap from A to Z](https:\u002F\u002Fgithub.com\u002FMoataz-Elmesmary\u002FData-Science-Roadmap) - Comprehensive roadmap for data science.\n- [Roadmap To Learn Data Science](https:\u002F\u002Fgithub.com\u002Fkrishnaik06\u002FPerfect-Roadmap-To-Learn-Data-Science-In-2025) - A comprehensive and updated roadmap for learning data science with modern tools and technologies.\n- [66DaysOfData](https:\u002F\u002Fgithub.com\u002Fmrankitgupta\u002FData-Analyst-Roadmap) - 66-day data analytics learning challenge.\n- [Data Analyst Roadmap for Professionals](https:\u002F\u002Fgithub.com\u002Fhemansnation\u002FData-Analyst-Roadmap) - 8-week program for analysts at all levels.\n- [Data Science Roadmap Tutorials](https:\u002F\u002Fgithub.com\u002FMrMimic\u002Fdata-scientist-roadmap) - Tutorials for the data science roadmap.\n- [Data Analyst Roadmap from Zero](https:\u002F\u002Fgithub.com\u002Fmtahiraslan\u002Fdata-analyst-roadmap) - Guide to becoming a data analyst from scratch.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"python\">\u003C\u002Fa>\n\n## 🐍 Python\n\n\u003Ca id=\"python-resources\">\u003C\u002Fa>\n\n### Resources\n\nA collection of resources for learning and mastering Python programming.\n\n- [Awesome Python](https:\u002F\u002Fgithub.com\u002Fvinta\u002Fawesome-python) - An opinionated list of awesome Python frameworks, libraries, software, and resources.\n- [30 Days Of Python](https:\u002F\u002Fgithub.com\u002FAsabeneh\u002F30-Days-Of-Python) - A 30-day programming challenge to learn the Python programming language.\n- [Real Python Tutorials](https:\u002F\u002Frealpython.com\u002F) - Tutorials on Python from Real Python.\n- [Awesome Python Data Science](https:\u002F\u002Fgithub.com\u002Fkrzjoa\u002Fawesome-python-data-science) - A curated list of Python resources for data science.\n- [Python Data Science Handbook](https:\u002F\u002Fgithub.com\u002Fjakevdp\u002FPythonDataScienceHandbook) - Full text of the \"Python Data Science Handbook\" in Jupyter Notebooks.\n- [Interactive Coding Challenges](https:\u002F\u002Fgithub.com\u002Fdonnemartin\u002Finteractive-coding-challenges) - 120+ interactive Python coding interview challenges.\n- [Clean Code Python](https:\u002F\u002Fgithub.com\u002Fzedr\u002Fclean-code-python) - Clean Code concepts adapted for Python.\n- [Best of Python](https:\u002F\u002Fgithub.com\u002Fml-tooling\u002Fbest-of-python) - A ranked list of awesome Python open-source libraries and tools.\n- [GeeksforGeeks Python](https:\u002F\u002Fwww.geeksforgeeks.org\u002Fpython-programming-language-tutorial\u002F) - Python tutorial from GeeksforGeeks.\n- [W3Schools Python](https:\u002F\u002Fwww.w3schools.com\u002Fpython\u002F) - A beginner-friendly tutorial and reference for the Python programming language.\n- [Tanu N Prabhu Python](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster) - This repository helps you understand Python from scratch.\n- [Think Python](https:\u002F\u002Fgithub.com\u002FAllenDowney\u002FThinkPython) - Jupyter notebooks and other resources for Think Python by Allen Downey.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"python-data-manipulation-with-pandas-and-numpy\">\u003C\u002Fa>\n\n### Data Manipulation with Pandas and Numpy\n\nTutorials and best practices for working with Pandas and Numpy.\n\n- [Awesome Pandas](https:\u002F\u002Fgithub.com\u002Ftommyod\u002Fawesome-pandas) - A curated list of resources for using the Pandas library.\n- [100 data puzzles for pandas](https:\u002F\u002Fgithub.com\u002Fajcr\u002F100-pandas-puzzles) - A collection of data puzzles to practice your Pandas skills.\n- [Pandas Tutor](https:\u002F\u002Fpandastutor.com\u002F) - Visualize Pandas operations step-by-step (perfect for beginners).\n- [Pandas Exercises](https:\u002F\u002Fgithub.com\u002Fguipsamora\u002Fpandas_exercises) - Exercises designed to help you improve your Pandas skills.\n- [Pandas Cookbook](https:\u002F\u002Fgithub.com\u002Fjvns\u002Fpandas-cookbook) - A cookbook with various recipes for using Pandas effectively.\n- [Hands-On Data Analysis with Pandas](https:\u002F\u002Fgithub.com\u002Fstefmolin\u002FHands-On-Data-Analysis-with-Pandas-2nd-edition) - Materials for following along with Hands-On Data Analysis with Pandas.\n- [Effective Pandas](https:\u002F\u002Fgithub.com\u002FTomAugspurger\u002Feffective-pandas) - A series focused on writing effective and idiomatic Pandas code.\n- [From Python to Numpy](https:\u002F\u002Fgithub.com\u002Frougier\u002Ffrom-python-to-numpy) - An open-access book on vectorization and efficient numerical computing with NumPy.\n- [NumPy 100 Exercises](https:\u002F\u002Fgithub.com\u002Frougier\u002Fnumpy-100) - A collection of 100 exercises to master the NumPy library for scientific computing.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"python-useful-python-tools-for-data-analysis\">\u003C\u002Fa>\n\n### Useful Python Tools for Data Analysis\n\nA collection of Python libraries for efficient data manipulation, cleaning, visualization, validation, and analysis.\n\n\u003Ca id=\"python-data-processing-transformation\">\u003C\u002Fa>\n\n#### Data Processing & Transformation\n\n- [Pandas](https:\u002F\u002Fgithub.com\u002Fpandas-dev\u002Fpandas) - Powerful Python library for data analysis and manipulation with flexible data structures.\n- [NumPy](https:\u002F\u002Fgithub.com\u002Fnumpy\u002Fnumpy) - Fundamental package for scientific computing in Python with multidimensional array support.\n- [Pandas DQ](https:\u002F\u002Fgithub.com\u002FAutoViML\u002Fpandas_dq) - Data type correction and automatic DataFrame cleaning.\n- [Vaex](https:\u002F\u002Fgithub.com\u002Fvaexio\u002Fvaex) - High-performance Python library for lazy Out-of-Core DataFrames.\n- [Polars](https:\u002F\u002Fgithub.com\u002Fpola-rs\u002Fpolars) - Multithreaded, vectorized query engine for DataFrames.\n- [Fugue](https:\u002F\u002Fgithub.com\u002Ffugue-project\u002Ffugue) - Unified interface for Pandas, Spark, and Dask.\n- [TheFuzz](https:\u002F\u002Fgithub.com\u002Fseatgeek\u002Fthefuzz) - Fuzzy string matching (Levenshtein distance).\n- [DateUtil](https:\u002F\u002Fgithub.com\u002Fdateutil\u002Fdateutil) - Extensions for standard Python datetime features.\n- [Arrow](https:\u002F\u002Fgithub.com\u002Farrow-py\u002Farrow) - Enhanced work with dates and times.\n- [Pendulum](https:\u002F\u002Fgithub.com\u002Fsdispater\u002Fpendulum) - Alternative to datetime with timezone support.\n- [Dask](https:\u002F\u002Fgithub.com\u002Fdask\u002Fdask) - Parallel computing for arrays and DataFrames.\n- [Modin](https:\u002F\u002Fgithub.com\u002Fmodin-project\u002Fmodin) - Speeds up Pandas by distributing computations.\n- [Pandarallel](https:\u002F\u002Fgithub.com\u002Fnalepae\u002Fpandarallel) - Parallel operations for pandas DataFrames.\n- [DataCleaner](https:\u002F\u002Fgithub.com\u002Frhiever\u002Fdatacleaner) - Python tool for automatically cleaning and preparing datasets.\n- [Pandas Flavor](https:\u002F\u002Fgithub.com\u002FZsailer\u002Fpandas_flavor) - Add custom methods to Pandas.\n- [Pandas DataReader](https:\u002F\u002Fgithub.com\u002Fpydata\u002Fpandas-datareader) - Reads data from various online sources into pandas DataFrames.\n- [Sklearn Pandas](https:\u002F\u002Fgithub.com\u002Fscikit-learn-contrib\u002Fsklearn-pandas) - Bridge between Pandas and Scikit-learn.\n- [CuPy](https:\u002F\u002Fgithub.com\u002Fcupy\u002Fcupy) - A NumPy-compatible array library accelerated by NVIDIA CUDA for high-performance computing.\n- [Numba](https:\u002F\u002Fgithub.com\u002Fnumba\u002Fnumba) - A JIT compiler that translates a subset of Python and NumPy code into fast machine code.\n- [Pandas Stubs](https:\u002F\u002Fgithub.com\u002Fpandas-dev\u002Fpandas-stubs) - Type stubs for pandas, improves IDE autocompletion.\n- [Petl](https:\u002F\u002Fgithub.com\u002Fpetl-developers\u002Fpetl) - ETL tool for data cleaning and transformation.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"python-automated-data-visualization-tools\">\u003C\u002Fa>\n\n#### Automated EDA and Visualization Tools\n\n- [AutoViz](https:\u002F\u002Fgithub.com\u002FAutoViML\u002FAutoViz) - Automatic data visualization in 1 line of code.\n- [Sweetviz](https:\u002F\u002Fgithub.com\u002Ffbdesignpro\u002Fsweetviz) - Automatic EDA with dataset comparison.\n- [Lux](https:\u002F\u002Fgithub.com\u002Flux-org\u002Flux) - Automatic DataFrame visualization in Jupyter.\n- [YData Profiling](https:\u002F\u002Fgithub.com\u002Fydataai\u002Fydata-profiling) - Data quality profiling & exploratory data analysis.\n- [Missingno](https:\u002F\u002Fgithub.com\u002FResidentMario\u002Fmissingno) - Visualize missing data patterns.\n- [Vizro](https:\u002F\u002Fgithub.com\u002Fmckinsey\u002Fvizro) - Low-code toolkit for building data visualization apps.\n- [Yellowbrick](https:\u002F\u002Fgithub.com\u002FDistrictDataLabs\u002Fyellowbrick) - Visual diagnostic tools for machine learning.\n- [Great Tables](https:\u002F\u002Fgithub.com\u002Fposit-dev\u002Fgreat-tables) - Create awesome display tables using Python.\n- [DataMapPlot](https:\u002F\u002Fgithub.com\u002FTutteInstitute\u002Fdatamapplot) - Create beautiful plots of data maps.\n- [Datashader](https:\u002F\u002Fgithub.com\u002Fholoviz\u002Fdatashader) - Quickly and accurately render even the largest data.\n- [PandasAI](https:\u002F\u002Fgithub.com\u002Fsinaptik-ai\u002Fpandas-ai) - Conversational data analysis using LLMs and RAG.\n- [Mito](https:\u002F\u002Fgithub.com\u002Fmito-ds\u002Fmito) - Jupyter extensions for faster code writing.\n- [D-Tale](https:\u002F\u002Fgithub.com\u002Fman-group\u002Fdtale) - Interactive GUI for data analysis in a browser.\n- [Pandasgui](https:\u002F\u002Fgithub.com\u002Fadamerose\u002Fpandasgui) - GUI for viewing and filtering DataFrames.\n- [PyGWalker](https:\u002F\u002Fgithub.com\u002FKanaries\u002Fpygwalker) - Interactive UIs for visual analysis of DataFrames.\n- [QGrid](https:\u002F\u002Fgithub.com\u002Fquantopian\u002Fqgrid) - Interactive grid for DataFrames in Jupyter.\n- [Pivottablejs](https:\u002F\u002Fgithub.com\u002Fnicolaskruchten\u002Fjupyter_pivottablejs) - Interactive PivotTable.js tables in Jupyter.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"python-data-quality-validation\">\u003C\u002Fa>\n\n#### Data Quality & Validation\n\n- [PyOD](https:\u002F\u002Fgithub.com\u002Fyzhao062\u002Fpyod) - Outlier and anomaly detection.\n- [Alibi Detect](https:\u002F\u002Fgithub.com\u002FSeldonIO\u002Falibi-detect) - Outlier, adversarial and drift detection.\n- [Pandera](https:\u002F\u002Fgithub.com\u002Funionai-oss\u002Fpandera) - Data validation through declarative schemas.\n- [Cerberus](https:\u002F\u002Fgithub.com\u002Fpyeve\u002Fcerberus) - Data validation through schemas.\n- [Pydantic](https:\u002F\u002Fgithub.com\u002Fpydantic\u002Fpydantic) - Data validation using Python type annotations.\n- [Dora](https:\u002F\u002Fgithub.com\u002FNathanEpstein\u002FDora) - Automate EDA: preprocessing, feature engineering, visualization.\n- [Great Expectations](https:\u002F\u002Fgithub.com\u002Fgreat-expectations\u002Fgreat_expectations) - Data validation and testing.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"python-feature-engineering-selection\">\u003C\u002Fa>\n\n#### Feature Engineering & Selection\n\n- [FeatureTools](https:\u002F\u002Fgithub.com\u002Falteryx\u002Ffeaturetools) - Automated feature engineering.\n- [Feature Engine](https:\u002F\u002Fgithub.com\u002Ffeature-engine\u002Ffeature_engine) - Feature engineering with Scikit-Learn compatibility.\n- [Prince](https:\u002F\u002Fgithub.com\u002FMaxHalford\u002Fprince) - Multivariate exploratory data analysis (PCA, CA, MCA).\n- [Fitter](https:\u002F\u002Fgithub.com\u002Fcokelaer\u002Ffitter) - Figures out the distribution your data comes from.\n- [Feature Selector](https:\u002F\u002Fgithub.com\u002FWillKoehrsen\u002Ffeature-selector) - Tool for dimensionality reduction of machine learning datasets.\n- [Category Encoders](https:\u002F\u002Fgithub.com\u002Fscikit-learn-contrib\u002Fcategory_encoders) - Extensive collection of categorical variable encoders.\n- [Imbalanced Learn](https:\u002F\u002Fgithub.com\u002Fscikit-learn-contrib\u002Fimbalanced-learn) - Handling imbalanced datasets.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"python-specialized-data-tools\">\u003C\u002Fa>\n\n#### Specialized Data Tools\n\n- [cuDF](https:\u002F\u002Fgithub.com\u002Frapidsai\u002Fcudf) - A GPU DataFrame library for loading, joining, and aggregating data.\n- [Faker](https:\u002F\u002Fgithub.com\u002Fjoke2k\u002Ffaker) - Generates fake data for testing.\n- [Mimesis](https:\u002F\u002Fgithub.com\u002Flk-geimfari\u002Fmimesis) - Generates realistic test data.\n- [Geopy](https:\u002F\u002Fgithub.com\u002Fgeopy\u002Fgeopy) - Geocoding addresses and calculating distances.\n- [PySAL](https:\u002F\u002Fgithub.com\u002Fpysal\u002Fpysal) - Spatial analysis functions.\n- [Scattertext](https:\u002F\u002Fgithub.com\u002FJasonKessler\u002Fscattertext) - Beautiful visualizations of language differences among document types.\n- [IGraph](https:\u002F\u002Fgithub.com\u002Figraph\u002Figraph) - A library for creating and manipulating graphs and networks, with bindings for multiple languages.\n- [Joblib](https:\u002F\u002Fgithub.com\u002Fjoblib\u002Fjoblib) - A lightweight pipelining library for Python, particularly useful for saving and loading large NumPy arrays.\n- [ImageIO](https:\u002F\u002Fgithub.com\u002Fimageio\u002Fimageio) - A library that provides an easy interface to read and write a wide range of image data.\n- [Texthero](https:\u002F\u002Fgithub.com\u002Fjbesomi\u002Ftexthero) - Text preprocessing, representation and visualization.\n- [Geopandas](https:\u002F\u002Fgithub.com\u002Fgeopandas\u002Fgeopandas) - Geographic data operations with pandas.\n- [NetworkX](https:\u002F\u002Fgithub.com\u002Fnetworkx\u002Fnetworkx) - Network analysis and graph theory.\n- [Chardet](https:\u002F\u002Fgithub.com\u002Fchardet\u002Fchardet) - Python library to detect the character encoding of text and files.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"sql-databases\">\u003C\u002Fa>\n\n## 🗃️ SQL & Databases\n\n\u003Ca id=\"sql-databases-resources\">\u003C\u002Fa>\n\n### Resources\n\nSQL tutorials and database design principles.\n\n- [SQLZoo - SQL Tutorial](https:\u002F\u002Fsqlzoo.net\u002Fwiki\u002FSQL_Tutorial) - Interactive SQL tutorial.\n- [SQL Bolt - Learn SQL](https:\u002F\u002Fsqlbolt.com\u002F) - Learn SQL through interactive lessons.\n- [SQL Tutorial](https:\u002F\u002Fwww.sqltutorial.org\u002F) - Comprehensive SQL tutorial resource.\n- [SQL Tutorial by W3Schools.](https:\u002F\u002Fwww.w3schools.com\u002Fsql\u002Fdefault.asp) - Comprehensive SQL tutorial.\n- [PostgreSQL Tutorial by W3Resource](https:\u002F\u002Fw3resource.com\u002FPostgreSQL\u002Ftutorial.php) - Tutorial for PostgreSQL.\n- [MySQL Tutorial by W3Resource](https:\u002F\u002Fwww.w3resource.com\u002Fmysql\u002Fmysql-tutorials.php) - Tutorial for MySQL.\n- [MongoDB Tutorial by W3Resource](https:\u002F\u002Fwww.w3resource.com\u002Fmongodb\u002Fnosql.php) - Tutorial for MongoDB.\n- [EverSQL](https:\u002F\u002Fwww.eversql.com\u002F) - AI-powered SQL query optimization and database observability tool.\n- [Awesome Database Learning](https:\u002F\u002Fgithub.com\u002Fpingcap\u002Fawesome-database-learning) - Educational resources on database internals, distributed systems, and storage.\n- [Awesome Postgres](https:\u002F\u002Fgithub.com\u002Fdhamaniasad\u002Fawesome-postgres) - A curated list of awesome PostgreSQL software, libraries, tools and resources.\n- [Awesome MySql](https:\u002F\u002Fgithub.com\u002Fshlomi-noach\u002Fawesome-mysql) - A curated list of awesome MySQL software, libraries, tools and resources.\n- [Awesome Clickhouse](https:\u002F\u002Fgithub.com\u002Fkorchasa\u002Fawesome-clickhouse) - A curated list of awesome ClickHouse software.\n- [Awesome MongoDB](https:\u002F\u002Fgithub.com\u002Framnes\u002Fawesome-mongodb) - A curated list of awesome MongoDB resources, libraries, tools, and applications.\n- [Awesome Duckdb](https:\u002F\u002Fgithub.com\u002Fdavidgasquez\u002Fawesome-duckdb) - Curated tools, resources, and extensions for DuckDB analytical database.\n- [Awesome SQLAlchemy](https:\u002F\u002Fgithub.com\u002Fdahlia\u002Fawesome-sqlalchemy) - A curated list of awesome tools for SQLAlchemy.\n- [Awesome Sql](https:\u002F\u002Fgithub.com\u002Fdanhuss\u002Fawesome-sql) - List of tools and techniques for working with relational databases.\n- [AnimateSQL](https:\u002F\u002Fanimatesql.com\u002F) - Interactive tool that visualizes the step-by-step execution of SQL queries.\n- [SQL Tips and Tricks](https:\u002F\u002Fgithub.com\u002Fben-nour\u002FSQL-tips-and-tricks) - Useful SQL techniques and optimizations for data analysis.\n- [Practice Window Functions](https:\u002F\u002Fwww.practicewindowfunctions.com) - Free interactive SQL tutorial site focused on mastering window functions through 80+ hands-on problems with hints and solutions.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"sql-databases-tools\">\u003C\u002Fa>\n\n### Tools\n\nA collection of libraries and drivers for seamless database access and interaction.\n\n- [PyODBC](https:\u002F\u002Fgithub.com\u002Fmkleehammer\u002Fpyodbc) - Python library for ODBC database access.\n- [SQLAlchemy](https:\u002F\u002Fgithub.com\u002Fsqlalchemy\u002Fsqlalchemy) - SQL toolkit and ORM for Python.\n- [Psycopg2](https:\u002F\u002Fgithub.com\u002Fpsycopg\u002Fpsycopg2) - PostgreSQL database adapter.\n- [MySQL Connector\u002FPython](https:\u002F\u002Fgithub.com\u002Fmysql\u002Fmysql-connector-python) - MySQL driver for Python.\n- [PonyORM](https:\u002F\u002Fgithub.com\u002Fponyorm\u002Fpony) - ORM for Python with dynamic query generation.\n- [PyMongo](https:\u002F\u002Fgithub.com\u002Fmongodb\u002Fmongo-python-driver) - Official MongoDB driver for Python.\n- [SQLiteviz](https:\u002F\u002Fgithub.com\u002Flana-k\u002Fsqliteviz) - A tool for exploring SQLite databases and visualizing the results of your queries.\n- [SQLite](https:\u002F\u002Fgithub.com\u002Fsqlite\u002Fsqlite) - A C-language library that implements a small, fast, self-contained, high-reliability, full-featured SQL database engine.\n- [DB Browser for SQLite](https:\u002F\u002Fgithub.com\u002Fsqlitebrowser\u002Fsqlitebrowser) - A high quality, visual, open source tool to create, design, and edit database files compatible with SQLite.\n- [DBeaver](https:\u002F\u002Fgithub.com\u002Fdbeaver\u002Fdbeaver) - A free universal database tool and SQL client for developers, SQL programmers, and administrators.\n- [Beekeeper Studio](https:\u002F\u002Fgithub.com\u002Fbeekeeper-studio\u002Fbeekeeper-studio) - A modern, easy-to-use SQL client and database manager with a clean, cross-platform interface.\n- [SQLFluff](https:\u002F\u002Fgithub.com\u002Fsqlfluff\u002Fsqlfluff) - A modular SQL linter and auto-formatter designed to enforce consistent style and catch errors in SQL code.\n- [PyMySQL](https:\u002F\u002Fgithub.com\u002FPyMySQL\u002FPyMySQL) - A pure-Python MySQL client library for interacting with MySQL databases from Python applications.\n- [Vanna.AI](https:\u002F\u002Fgithub.com\u002Fvanna-ai\u002Fvanna) - An AI-powered tool for generating SQL queries from natural language questions.\n- [SQLChat](https:\u002F\u002Fgithub.com\u002Fsqlchat\u002Fsqlchat) - A chat-based SQL client that allows you to query databases using natural language conversations.\n- [Records](https:\u002F\u002Fgithub.com\u002Fkennethreitz-archive\u002Frecords) - SQL queries to databases via Python syntax.\n- [Dataset](https:\u002F\u002Fgithub.com\u002Fpudo\u002Fdataset) - JSON-like interface for working with SQL databases.\n- [SQLGlot](https:\u002F\u002Fgithub.com\u002Ftobymao\u002Fsqlglot) - A no-dependency SQL parser, transpiler, and optimizer for Python.\n- [TDengine](https:\u002F\u002Fgithub.com\u002Ftaosdata\u002FTDengine) - An open-source big data platform designed for time-series data, IoT, and industrial monitoring.\n- [TimescaleDB](https:\u002F\u002Fgithub.com\u002Ftimescale\u002Ftimescaledb) - An open-source time-series SQL database optimized for fast ingest and complex queries.\n- [DuckDB](https:\u002F\u002Fgithub.com\u002Fduckdb\u002Fduckdb) - In-memory analytical database for fast SQL queries.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"data-visualization\">\u003C\u002Fa>\n\n## 📊 Data Visualization\n\n\u003Ca id=\"data-visualization-resources\">\u003C\u002Fa>\n\n### Resources\n\nColor theory, chart selection guides, and storytelling tips.\n\n- [From Data to Viz](https:\u002F\u002Fgithub.com\u002Fholtzy\u002Fdata_to_viz) - A guide to choosing the right visualization based on your data.\n- [Awesome DataViz](https:\u002F\u002Fgithub.com\u002Fhal9ai\u002Fawesome-dataviz) - A curated list of awesome data visualization libraries, tools, and resources.\n- [Visualization Curriculum](https:\u002F\u002Fgithub.com\u002Fuwdata\u002Fvisualization-curriculum) - Interactive notebooks designed to teach data visualization concepts.\n- [Scientific Visualization Book](https:\u002F\u002Fgithub.com\u002Frougier\u002Fscientific-visualization-book) - Guide to creating effective scientific visualizations and plots.\n- [The Python Graph Gallery](https:\u002F\u002Fpython-graph-gallery.com\u002F) - A collection of Python graph examples for data visualization.\n- [FlowingData](https:\u002F\u002Fflowingdata.com\u002F) - Insights on data analysis and visualization.\n- [Data Visualization Catalogue](https:\u002F\u002Fdatavizcatalogue.com\u002Findex.html) - A comprehensive catalog of data visualization types.\n- [Data Viz Project](https:\u002F\u002Fdatavizproject.com\u002F) - A resource for selecting suitable visualizations.\n- [Chartopedia](https:\u002F\u002Fwww.anychart.com\u002Fchartopedia\u002Fusage-type\u002F) - A guide to help you select the appropriate chart types.\n- [DataForVisualization](https:\u002F\u002Fdataforvisualization.com\u002F) - Tutorials and insights on data visualization techniques.\n- [Truth & Beauty](https:\u002F\u002Ftruth-and-beauty.net\u002F) - Exploration of the aesthetics of data visualization.\n- [Cedric Scherer's DataViz Resources](https:\u002F\u002Fwww.cedricscherer.com\u002Ftop\u002Fdataviz\u002F) - A collection of top data visualization resources and inspiration.\n- [Information is Beautiful](https:\u002F\u002Finformationisbeautiful.net\u002F) - A site dedicated to visualizations that make complex ideas clear and engaging.\n- [Plottie](https:\u002F\u002Fplottie.art\u002F) - A vast library of scientific plots for visualization inspiration and ideas.\n- [Friends Don't Let Friends](https:\u002F\u002Fgithub.com\u002Fcxli233\u002FFriendsDontLetFriends) - A collection of bad data visualization practices and better alternatives.\n- [Natural Colours](https:\u002F\u002Fwww.c82.net\u002Fnatural-colors\u002F) - A digital archive of historical color systems and pigments.\n- [Colorgorical](http:\u002F\u002Fvrl.cs.brown.edu\u002Fcolor) - Resource for generating categorical color palettes using perceptual principles.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"data-visualization-tools\">\u003C\u002Fa>\n\n### Tools\n\nLibraries for static, interactive, and 3D visualizations.\n\n- [Matplotlib](https:\u002F\u002Fmatplotlib.org\u002Fstable\u002Fcontents.html) - A comprehensive library for creating static, animated, and interactive visualizations in Python.\n- [Seaborn](https:\u002F\u002Fseaborn.pydata.org\u002F) - A statistical data visualization library based on Matplotlib.\n- [Plotly](https:\u002F\u002Fplotly.com\u002Fpython\u002F) - A library for creating interactive plots and dashboards.\n- [Altair](https:\u002F\u002Fgithub.com\u002Fvega\u002Faltair) - A declarative statistical visualization library for Python.\n- [Bokeh](https:\u002F\u002Fdocs.bokeh.org\u002Fen\u002Flatest\u002F) - A library for creating interactive visualizations for modern web browsers.\n- [HoloViews](https:\u002F\u002Fholoviews.org\u002F) - A tool for building complex visualizations easily.\n- [Geopandas](https:\u002F\u002Fgeopandas.org\u002Fen\u002Fstable\u002F) - An extension of Pandas for geospatial data.\n- [Folium](https:\u002F\u002Fpython-visualization.github.io\u002Ffolium\u002F) - A library for visualizing data on interactive maps.\n- [Pygal](https:\u002F\u002Fpygal.org\u002Fen\u002Fstable\u002F) - A Python SVG charting library.\n- [Plotnine](https:\u002F\u002Fplotnine.readthedocs.io\u002Fen\u002Fstable\u002F) - A grammar of graphics for Python.\n- [Bqplot](https:\u002F\u002Fgithub.com\u002Fbqplot\u002Fbqplot) - A plotting library for IPython\u002FJupyter notebooks.\n- [PyPalettes](https:\u002F\u002Fgithub.com\u002FJosephBARBIERDARNAL\u002Fpypalettes) - A large (+2500) collection of color maps for Python.\n- [Deck.gl](https:\u002F\u002Fgithub.com\u002Fvisgl\u002Fdeck.gl) - A WebGL-powered framework for visual exploratory data analysis of large datasets.\n- [Python for Geo](https:\u002F\u002Fgithub.com\u002Fgeopandas\u002Fcontextily) - Contextily: add background basemaps to your plots in GeoPandas.\n- [OSMnx](https:\u002F\u002Fgithub.com\u002Fgboeing\u002Fosmnx) - A package to easily download, model, analyze, and visualize street networks from OpenStreetMap.\n- [Apache ECharts](https:\u002F\u002Fgithub.com\u002Fapache\u002Fecharts) - A powerful, interactive charting and visualization library for browser-based applications.\n- [VisPy](https:\u002F\u002Fgithub.com\u002Fvispy\u002Fvispy) - A high-performance interactive 2D\u002F3D data visualization library leveraging the power of OpenGL.\n- [Glumpy](https:\u002F\u002Fgithub.com\u002Fglumpy\u002Fglumpy) - A Python library for scientific visualization that is fast, scalable and beautiful, based on OpenGL.\n- [Pandas-bokeh](https:\u002F\u002Fgithub.com\u002FPatrikHlobil\u002FPandas-Bokeh) - Bokeh plotting backend for Pandas.\n- [QGIS](https:\u002F\u002Fgithub.com\u002Fqgis\u002FQGIS) - Free, open source, cross-platform geographic information system (GIS).\n- [Flourish](https:\u002F\u002Fflourish.studio\u002F) - Platform for creating interactive data visualizations and stories without coding.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"dashboards\">\u003C\u002Fa>\n\n## 📈 Dashboards & BI\n\n\u003Ca id=\"dashboards-resources\">\u003C\u002Fa>\n\n### Resources\n\nTtutorials for building and enhancing dashboards and visualizations using various tools and frameworks.\n\n- [Awesome Dashboards](https:\u002F\u002Fgithub.com\u002Fobazoud\u002Fawesome-dashboard) - A collection of outstanding dashboard and visualization resources.\n- [Best of Streamlit](https:\u002F\u002Fgithub.com\u002Fjrieke\u002Fbest-of-streamlit) - Showcase of community-built Streamlit applications.\n- [Awesome Dash](https:\u002F\u002Fgithub.com\u002Fucg8j\u002Fawesome-dash) - Comprehensive resources for Dash users.\n- [Awesome Panel](https:\u002F\u002Fgithub.com\u002Fawesome-panel\u002Fawesome-panel) - Resources and support for Panel users.\n- [Awesome Streamlit](https:\u002F\u002Fgithub.com\u002FMarcSkovMadsen\u002Fawesome-streamlit) - Curated list of Streamlit resources and components.\n- [Dash Enterprise Samples](https:\u002F\u002Fgithub.com\u002Fplotly\u002Fdash-sample-apps) - Production-ready Dash apps.\n- [geeksforgeeks - Tableau Tutorial](https:\u002F\u002Fwww.geeksforgeeks.org\u002Ftableau-tutorial\u002F) - Comprehensive tutorial on Tableau.\n- [geeksforgeeks - Power BI Tutorial](https:\u002F\u002Fwww.geeksforgeeks.org\u002Fpower-bi-tutorial\u002F) - Detailed tutorial on Power BI.\n- [Tableau Public Gallery](https:\u002F\u002Fpublic.tableau.com\u002Fapp\u002Fdiscover) - A curated collection of real-world interactive dashboards to inspire and learn from.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"dashboards-tools\">\u003C\u002Fa>\n\n### Tools\n\nFrameworks for building custom dashboard solutions.\n\n- [Dash](https:\u002F\u002Fgithub.com\u002Fplotly\u002Fdash) - Framework for creating interactive web applications.\n- [Streamlit](https:\u002F\u002Fgithub.com\u002Fstreamlit\u002Fstreamlit) - Simplified framework for building data applications.\n- [Panel](https:\u002F\u002Fgithub.com\u002Fholoviz\u002Fpanel) - Python library for creating custom interactive web apps and dashboards.\n- [Gradio](https:\u002F\u002Fgithub.com\u002Fgradio-app\u002Fgradio) - Tool for creating and sharing machine learning applications.\n- [OpenSearch Dashboards](https:\u002F\u002Fgithub.com\u002Fopensearch-project\u002FOpenSearch-Dashboards) - A powerful data visualization and dashboarding tool for OpenSearch data, forked from Kibana.\n- [GridStack.js](https:\u002F\u002Fgithub.com\u002Fgridstack\u002Fgridstack.js) - A library for building draggable, resizable responsive dashboard layouts.\n- [Tremor](https:\u002F\u002Fgithub.com\u002Ftremorlabs\u002Ftremor-npm) - A React library to build dashboards fast with pre-built components for charts, KPIs, and more.\n- [Appsmith](https:\u002F\u002Fgithub.com\u002Fappsmithorg\u002Fappsmith) - An open-source platform to build and deploy internal tools, admin panels, and CRUD apps quickly.\n- [Grafanalib](https:\u002F\u002Fgithub.com\u002Fweaveworks\u002Fgrafanalib) - A Python library for generating Grafana dashboards configuration as code.\n- [H2O Wave](https:\u002F\u002Fgithub.com\u002Fh2oai\u002Fwave) - A Python framework for rapidly building and deploying realtime web apps and dashboards for AI and analytics.\n- [Shiny for Python](https:\u002F\u002Fgithub.com\u002Fposit-dev\u002Fpy-shiny) - Python version of the popular R Shiny framework.\n- [Voilà](https:\u002F\u002Fgithub.com\u002Fvoila-dashboards\u002Fvoila) - Turn Jupyter notebooks into standalone web applications.\n- [Reflex](https:\u002F\u002Fgithub.com\u002Freflex-dev\u002Freflex) - Full-stack Python framework for building web apps.\n- [Taipy](https:\u002F\u002Fgithub.com\u002FAvaiga\u002Ftaipy) - Python library for building web applications and interactive dashboards.\n- [Evidence](https:\u002F\u002Fgithub.com\u002Fevidence-dev\u002Fevidence) - Business intelligence platform that uses SQL and Markdown for reports.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"dashboards-software\">\u003C\u002Fa>\n\n### Software\n\nA list of leading tools and platforms for data visualization and dashboard creation.\n\n- [Tableau](https:\u002F\u002Fwww.tableau.com) - Leading data visualization software.\n- [Microsoft Power BI](https:\u002F\u002Fpowerbi.microsoft.com) - Business analytics tool for visualizing data.\n- [QlikView](https:\u002F\u002Fwww.qlik.com\u002Fus\u002Fproducts\u002Fqlikview) - Tool for data visualization and business intelligence.\n- [Metabase](https:\u002F\u002Fwww.metabase.com) - User-friendly open-source BI tool.\n- [Apache Superset](https:\u002F\u002Fsuperset.apache.org) - Open-source data exploration and visualization platform.\n- [Preset](https:\u002F\u002Fpreset.io\u002F) - A platform for modern business intelligence, providing a hosted version of Apache Superset.\n- [Metabase](https:\u002F\u002Fgithub.com\u002Fmetabase\u002Fmetabase) - The simplest way to get analytics and business intelligence for everyone in your company.\n- [Redash](https:\u002F\u002Fgithub.com\u002Fgetredash\u002Fredash) - Tool for visualizing and sharing data insights.\n- [Grafana](https:\u002F\u002Fgrafana.com) - Dashboarding and monitoring tool.\n- [Datawrapper](https:\u002F\u002Fgithub.com\u002Fdatawrapper\u002Fdatawrapper) - User-friendly chart and map creation tool.\n- [ChartBlocks](https:\u002F\u002Fwww.chartblocks.com) - Online chart creation platform.\n- [Infogram](https:\u002F\u002Finfogram.com) - Tool for creating infographics and visual content.\n- [Google Data Studio](https:\u002F\u002Fdatastudio.google.com) - Free tool for creating interactive dashboards and reports.\n- [Rath](https:\u002F\u002Fgithub.com\u002FKanaries\u002FRath) - Next-generation automated data exploratory analysis and visualization platform.\n- [Kibana](https:\u002F\u002Fgithub.com\u002Felastic\u002Fkibana) - The official visualization and dashboarding tool for the Elastic Stack (Elasticsearch, Logstash, Beats).\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"web-scraping-crawling\">\u003C\u002Fa>\n\n## 🕸️ Web Scraping & Crawling\n\n\u003Ca id=\"web-scraping-crawling-resources\">\u003C\u002Fa>\n\n### Resources\n\nA collection of valuable resources, tutorials, and libraries for web scraping with Python.\n\n- [Awesome Web Scraping](https:\u002F\u002Fgithub.com\u002Florien\u002Fawesome-web-scraping) - List of libraries, tools, and APIs for web scraping and data processing.\n- [Python Scraping](https:\u002F\u002Fgithub.com\u002FREMitchell\u002Fpython-scraping) - Code samples from the book \"Web Scraping with Python\".\n- [Scraping Tutorial](https:\u002F\u002Fgithub.com\u002FBlatzar\u002Fscraping-tutorial) - Tutorial for scraping streaming sites.\n- [Webscraping from 0 to Hero](https:\u002F\u002Fgithub.com\u002FTheWebScrapingClub\u002Fwebscraping-from-0-to-hero) - An open project repository sharing knowledge and experiences about web scraping with Python.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"web-scraping-crawling-tools\">\u003C\u002Fa>\n\n### Tools\n\nA list of libraries and tools for web scraping.\n\n- [Requests](https:\u002F\u002Fgithub.com\u002Fpsf\u002Frequests) - A simple, yet elegant, HTTP library for Python.\n- [BeautifulSoup](https:\u002F\u002Fwww.crummy.com\u002Fsoftware\u002FBeautifulSoup\u002Fbs4\u002Fdoc\u002F) - A library for parsing HTML and XML documents.\n- [Selenium](https:\u002F\u002Fgithub.com\u002FSeleniumHQ\u002Fselenium) - A tool for automating web applications for testing purposes.\n- [Scrapy](https:\u002F\u002Fscrapy.org\u002F) - An open-source and collaborative web crawling framework for Python.\n- [Browser Use](https:\u002F\u002Fgithub.com\u002Fbrowser-use\u002Fbrowser-use) - A library for browser automation and web scraping.\n- [Gerapy](https:\u002F\u002Fgithub.com\u002FGerapy\u002FGerapy) - Distributed Crawler Management Framework based on Scrapy, Scrapyd, Django, and Vue.js.\n- [AutoScraper](https:\u002F\u002Fgithub.com\u002Falirezamika\u002Fautoscraper) - A smart, automatic, fast, and lightweight web scraper for Python.\n- [Feedparser](https:\u002F\u002Fgithub.com\u002Fkurtmckee\u002Ffeedparser) - A library to parse feeds in Python.\n- [Trafilatura](https:\u002F\u002Fgithub.com\u002Fadbar\u002Ftrafilatura) - A Python & command-line tool to gather text and metadata on the web.\n- [You-Get](https:\u002F\u002Fgithub.com\u002Fsoimort\u002Fyou-get) - A tiny command-line utility to download media contents (videos, audios, images) from the web.\n- [MechanicalSoup](https:\u002F\u002Fgithub.com\u002FMechanicalSoup\u002FMechanicalSoup) - A Python library for automating interaction with websites.\n- [ScrapeGraph AI](https:\u002F\u002Fgithub.com\u002FScrapeGraphAI\u002FScrapegraph-ai) - A Python scraper based on AI.\n- [Snscrape](https:\u002F\u002Fgithub.com\u002FJustAnotherArchivist\u002Fsnscrape) - A social networking service scraper in Python.\n- [Ferret](https:\u002F\u002Fgithub.com\u002FMontFerret\u002Fferret) - A web scraping system that lets you declaratively describe what data to extract using a simple query language.\n- [Grab](https:\u002F\u002Fgithub.com\u002Florien\u002Fgrab) - A Python framework for building web scraping apps, providing a high-level API for asynchronous requests.\n- [Playwright](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fplaywright-python) - Python version of the Playwright browser automation library.\n- [PyQuery](https:\u002F\u002Fgithub.com\u002Fgawel\u002Fpyquery) - A jQuery-like library for parsing HTML documents in Python.\n- [Helium](https:\u002F\u002Fgithub.com\u002Fmherrmann\u002Fhelium) - High-level Selenium wrapper for easier web automation.\n- [Scrapling](https:\u002F\u002Fgithub.com\u002FD4Vinci\u002FScrapling) - A framework for building web scrapers and crawlers.\n- [Crawl4AI](https:\u002F\u002Fgithub.com\u002Funclecode\u002Fcrawl4ai) - Advanced web crawling framework designed for AI and data extraction tasks.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"mathematics\">\u003C\u002Fa>\n\n## 🔢 Mathematics\n\nA collection of resources for learning mathematics, particularly in the context of data science and machine learning.\n\n- [Awesome Math](https:\u002F\u002Fgithub.com\u002Frossant\u002Fawesome-math) - A curated list of mathematics resources, books, and online courses.\n- [MML Bool](https:\u002F\u002Fgithub.com\u002Fmml-book\u002Fmml-book.github.io) - Comprehensive resource for mathematics in machine learning.\n- [3Blue1Brown](https:\u002F\u002Fwww.3blue1brown.com\u002F) - Visual explanations of mathematical concepts through animated videos.\n- [Immersive Linear Algebra](http:\u002F\u002Fimmersivemath.com\u002Fila\u002F) - Interactive resource for understanding linear algebra.\n- [Hackermath](https:\u002F\u002Fgithub.com\u002Famitkaps\u002Fhackermath) - Resource for learning statistics and mathematics for data science.\n- [Stats Maths with Python](https:\u002F\u002Fgithub.com\u002Ftirthajyoti\u002FStats-Maths-with-Python) - Collection of Python scripts and notebooks for statistics and mathematics.\n- [Fast.ai - Computational Linear Algebra](https:\u002F\u002Fgithub.com\u002Ffastai\u002Fnumerical-linear-algebra) - Resource for learning linear algebra computationally.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"statistics-probability\">\u003C\u002Fa>\n\n## 🎲 Statistics & Probability\n\n\u003Ca id=\"statistics-probability-resources\">\u003C\u002Fa>\n\n### Resources\n\nA selection of resources focused on statistics and probability, including tutorials and comprehensive guides.\n\n- [Awesome Statistics](https:\u002F\u002Fgithub.com\u002Ferikgahner\u002Fawesome-statistics) - A curated list of statistics resources, software, and learning materials.\n- [The Elements of Statistical Learning](https:\u002F\u002Fgithub.com\u002Fempathy87\u002FThe-Elements-of-Statistical-Learning-Python-Notebooks) - Notebooks for understanding statistical learning concepts.\n- [Seeing Theory](https:\u002F\u002Fgithub.com\u002Fseeingtheory\u002FSeeing-Theory) - Interactive visual resource for learning probability and statistics.\n- [Code repository for O'Reilly book](https:\u002F\u002Fgithub.com\u002Fgedeck\u002Fpractical-statistics-for-data-scientists) - Companion code for a practical statistics book.\n- [Statistical Learning Theory - Stanford University](https:\u002F\u002Fweb.stanford.edu\u002Fclass\u002Fcs229t\u002Fnotes.pdf) - Lecture notes on statistical learning theory.\n- [StatLect](https:\u002F\u002Fwww.statlect.com\u002F) - Comprehensive online textbook covering probability and statistics concepts.\n- [stanford.edu - Probabilities and Statistics](https:\u002F\u002Fstanford.edu\u002F~shervine\u002Fteaching\u002Fcs-229\u002Frefresher-probabilities-statistics) - Refresher course on probabilities and statistics from Stanford University.\n- [Bayesian Methods for Hackers](https:\u002F\u002Fgithub.com\u002FCamDavidsonPilon\u002FProbabilistic-Programming-and-Bayesian-Methods-for-Hackers) - Resource for learning Bayesian methods in Python.\n- [Bayesian Modeling and Computation in Python](https:\u002F\u002Fgithub.com\u002FBayesianModelingandComputationInPython\u002FBookCode_Edition1) - Code for the book \"Bayesian Modeling and Computation in Python\".\n- [Stat Trek](https:\u002F\u002Fstattrek.com\u002F) - A resource for learning statistics and probability, with tutorials and tools.\n- [Online Statistics Book](https:\u002F\u002Fonlinestatbook.com\u002F2\u002Findex.html) - An interactive online statistics book with simulations and demonstrations.\n- [All of Statistics](https:\u002F\u002Fgithub.com\u002Ftelmo-correa\u002Fall-of-statistics) - Resource for studying statistics based on Wasserman's book.\n- [Think Stats](https:\u002F\u002Fgithub.com\u002FAllenDowney\u002FThinkStats\u002Ftree\u002Fv3) - Book and code for an introduction to Probability and Statistics.\n- [Think Bayes 2](https:\u002F\u002Fgithub.com\u002FAllenDowney\u002FThinkBayes2) - Book and code for Bayesian statistical methods.\n- [Causal Inference: The Mixtape](https:\u002F\u002Fmixtape.scunning.com\u002F) - Practical guide to causal inference methods.\n- [The Effect](https:\u002F\u002Ftheeffectbook.net\u002F) - Modern introduction to causality and research design.\n- [The Statistics Handbook](https:\u002F\u002Fgithub.com\u002Fcarloocchiena\u002Fthe_statistics_handbook) - Open-source statistics hands-on handbook.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"statistics-probability-tools\">\u003C\u002Fa>\n\n### Tools\n\nA collection of tools focused on statistics and probability.\n\n- [SciPy](https:\u002F\u002Fgithub.com\u002Fscipy\u002Fscipy) - Fundamental library for scientific computing and statistics.\n- [Statsmodels](https:\u002F\u002Fgithub.com\u002Fstatsmodels\u002Fstatsmodels) - Statistical modeling, testing, and data exploration.\n- [PyMC](https:\u002F\u002Fgithub.com\u002Fpymc-devs\u002Fpymc) - A probabilistic programming library for Python that allows for flexible Bayesian modeling.\n- [Pingouin](https:\u002F\u002Fgithub.com\u002Fraphaelvallat\u002Fpingouin) - Statistical package with improved usability over SciPy.\n- [scikit-posthocs](https:\u002F\u002Fgithub.com\u002Fmaximtrp\u002Fscikit-posthocs) - Post-hoc tests for statistical analysis of data.\n- [Lifelines](https:\u002F\u002Fgithub.com\u002FCamDavidsonPilon\u002Flifelines) - Survival analysis and event history analysis in Python.\n- [scikit-survival](https:\u002F\u002Fgithub.com\u002Fsebp\u002Fscikit-survival) - Survival analysis built on scikit-learn for time-to-event prediction.\n- [Bootstrap](https:\u002F\u002Fgithub.com\u002Fcgevans\u002Fscikits-bootstrap) - Bootstrap confidence interval estimation methods.\n- [PyStan](https:\u002F\u002Fgithub.com\u002Fstan-dev\u002Fpystan) - Python interface to Stan for Bayesian statistical modeling.\n- [ArviZ](https:\u002F\u002Fgithub.com\u002Farviz-devs\u002Farviz) - Exploratory analysis of Bayesian models with visual diagnostics.\n- [PyGAM](https:\u002F\u002Fgithub.com\u002Fdswah\u002FpyGAM) - A Python library for generalized additive models with built-in smoothing and regularization.\n- [NumPyro](https:\u002F\u002Fgithub.com\u002Fpyro-ppl\u002Fnumpyro) - A probabilistic programming library built on JAX for high-performance Bayesian modeling.\n- [Causal Impact](https:\u002F\u002Fgithub.com\u002FWillianFuks\u002Ftfcausalimpact) - A Python implementation of the R package for causal inference using Bayesian structural time-series models.\n- [DoWhy](https:\u002F\u002Fgithub.com\u002Fpy-why\u002Fdowhy) - A Python library for causal inference that supports explicit modeling and testing of causal assumptions.\n- [Patsy](https:\u002F\u002Fgithub.com\u002Fpydata\u002Fpatsy) - A Python library for describing statistical models and building design matrices.\n- [Pomegranate](https:\u002F\u002Fgithub.com\u002Fjmschrei\u002Fpomegranate) - Fast and flexible probabilistic modeling library for Python with GPU support.\n- [Pgmpy](https:\u002F\u002Fgithub.com\u002Fpgmpy\u002Fpgmpy) - Python library for probabilistic and causal inference using graphical models.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"ab-testing\">\u003C\u002Fa>\n\n## 🧪 A\u002FB Testing\n\nA collection of resources focused on A\u002FB testing.\n\n- [DynamicYield A\u002FB Testing](https:\u002F\u002Fwww.dynamicyield.com\u002Fcourse\u002Ftesting-and-optimization\u002F) - An online course covering advanced testing and optimization techniques.\n- [Evan's Awesome A\u002FB Tools](https:\u002F\u002Fwww.evanmiller.org\u002Fab-testing\u002F) - A\u002FB test calculators.\n- [Experimentguide](https:\u002F\u002Fexperimentguide.com\u002F) - A practical guide to A\u002FB testing and experimentation from industry leaders.\n- [Google's A\u002FB Testing Course](https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fab-testing--ud257) - A free Udacity course covering the fundamentals of A\u002FB testing.\n- [So You Think You Can Test?](https:\u002F\u002Fwww.lukasvermeer.nl\u002Fconfidence\u002F) - Experience the challenges of A\u002FB testing through this educational simulation.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"time-series-analysis\">\u003C\u002Fa>\n\n## ⏳ Time Series Analysis\n\n\u003Ca id=\"time-series-analysis-resources\">\u003C\u002Fa>\n\n### Resources\n\nA collection of resources for understanding time series fundamentals and analytical techniques.\n\n- [Awesome Time Series](https:\u002F\u002Fgithub.com\u002Flmmentel\u002Fawesome-time-series) - A curated list of resources dedicated to time series analysis and forecasting.\n- [Forecasting: Principles and Practice](https:\u002F\u002Fotexts.com\u002Ffpp3\u002F) - Comprehensive textbook on forecasting methods with practical examples.\n- [NIST\u002FSEMATECH e-Handbook](https:\u002F\u002Fwww.itl.nist.gov\u002Fdiv898\u002Fhandbook\u002Fpmc\u002Fsection4\u002Fpmc4.htm) - Official time series analysis guide from NIST.\n- [Awesome Time Series Anomaly Detection](https:\u002F\u002Fgithub.com\u002Frob-med\u002Fawesome-TS-anomaly-detection) - A curated list of tools, datasets, and papers dedicated to time series anomaly detection.\n- [Awesome Time Series in Python](https:\u002F\u002Fgithub.com\u002FMaxBenChrist\u002Fawesome_time_series_in_python) - A comprehensive list of Python tools and libraries for time series analysis.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"time-series-analysis-tools\">\u003C\u002Fa>\n\n### Tools\n\nA collection of tools for working with temporal data.\n\n- [Facebook Prophet](https:\u002F\u002Fgithub.com\u002Ffacebook\u002Fprophet) - A procedure for forecasting time series data based on an additive model.\n- [Uber Orbit](https:\u002F\u002Fgithub.com\u002Fuber\u002Forbit) - A Python package for Bayesian time series forecasting and inference.\n- [sktime](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime) - A unified Python framework for machine learning with time series, compatible with scikit-learn.\n- [GluonTS](https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fgluonts) - A Python toolkit for probabilistic time series modeling, built on MXNet.\n- [Time-Series-Library](https:\u002F\u002Fgithub.com\u002Fthuml\u002FTime-Series-Library) - A library for deep learning-based time series analysis and forecasting.\n- [TimesFM](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Ftimesfm) - A pretrained time series foundation model from Google Research for zero-shot forecasting.\n- [PyTorch Forecasting](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fpytorch-forecasting) - A PyTorch-based library for time series forecasting with neural networks.\n- [Time-series-prediction](https:\u002F\u002Fgithub.com\u002FLongxingTan\u002FTime-series-prediction) - A collection of time series prediction methods and implementations.\n- [PlotJuggler](https:\u002F\u002Fgithub.com\u002Ffacontidavide\u002FPlotJuggler) - A tool to visualize and analyze time series data logs in real-time.\n- [TSFresh](https:\u002F\u002Fgithub.com\u002Fblue-yonder\u002Ftsfresh) - Automatically extracting features from time series data.\n- [pmdarima](https:\u002F\u002Fgithub.com\u002Falkaline-ml\u002Fpmdarima) - Python library for ARIMA modeling and time series analysis.\n- [Kats](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FKats) - Toolkit for analyzing time series data from Facebook Research.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"data-engineering\">\u003C\u002Fa>\n\n## ⚙️ Data Engineering\n\n\u003Ca id=\"data-engineering-resources\">\u003C\u002Fa>\n\n### Resources\n\nA collection of resources to help you build and manage robust data pipelines and infrastructure.\n\n- [Data Engineer Handbook](https:\u002F\u002Fgithub.com\u002FDataExpert-io\u002Fdata-engineer-handbook) - A comprehensive guide covering fundamental and advanced data engineering concepts.\n- [Data Engineering Zoomcamp](https:\u002F\u002Fgithub.com\u002FDataTalksClub\u002Fdata-engineering-zoomcamp) - Free course on data engineering fundamentals.\n- [Awesome Data Engineering](https:\u002F\u002Fgithub.com\u002Figorbarinov\u002Fawesome-data-engineering) - A curated list of data engineering tools, software, and resources.\n- [Data Engineering Cookbook](https:\u002F\u002Fgithub.com\u002Fandkret\u002FCookbook) - Techniques and strategies for building reliable data platforms.\n- [Awesome Pipeline](https:\u002F\u002Fgithub.com\u002Fpditommaso\u002Fawesome-pipeline) - A curated list of pipeline toolkits for data processing and workflow management.\n- [Awesome DB Tools](https:\u002F\u002Fgithub.com\u002Fmgramin\u002Fawesome-db-tools) - A curated list of awesome database tools.\n- [Awesome Kafka](https:\u002F\u002Fgithub.com\u002Finfoslack\u002Fawesome-kafka) - Curated resources for learning and working with Apache Kafka: books, trainings, tools.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"data-engineering-tools\">\u003C\u002Fa>\n\n### Tools\n\nA collection of tools for building, deploying, and managing data pipelines and infrastructure.\n\n- [dbt-core](https:\u002F\u002Fgithub.com\u002Fdbt-labs\u002Fdbt-core) - A framework for transforming data in your warehouse using SQL and Jinja.\n- [Apache Spark](https:\u002F\u002Fgithub.com\u002Fapache\u002Fspark) - A unified engine for large-scale data processing and analytics.\n- [Apache Kafka](https:\u002F\u002Fgithub.com\u002Fapache\u002Fkafka) - A distributed event streaming platform for building real-time data pipelines.\n- [Dagster](https:\u002F\u002Fgithub.com\u002Fdagster-io\u002Fdagster) - A data orchestrator for machine learning, analytics, and ETL.\n- [Apache Airflow](https:\u002F\u002Fgithub.com\u002Fapache\u002Fairflow) - A platform to programmatically author, schedule, and monitor workflows.\n- [Apache Hive](https:\u002F\u002Fgithub.com\u002Fapache\u002Fhive) - A data warehouse software for reading, writing, and managing large datasets in distributed storage using SQL.\n- [Apache Hadoop](https:\u002F\u002Fgithub.com\u002Fapache\u002Fhadoop) - A framework that allows for the distributed processing of large data sets across clusters of computers.\n- [Luigi](https:\u002F\u002Fgithub.com\u002Fspotify\u002Fluigi) - A Python module for building complex and batch-oriented data pipelines.\n- [Apache Iceberg](https:\u002F\u002Fgithub.com\u002Fapache\u002Ficeberg) - A high-performance table format for huge analytic datasets.\n- [Apache Cassandra](https:\u002F\u002Fgithub.com\u002Fapache\u002Fcassandra) - A highly scalable distributed NoSQL database designed for handling large amounts of data across many commodity servers.\n- [Apache Flink](https:\u002F\u002Fgithub.com\u002Fapache\u002Fflink) - A framework for stateful computations over unbounded and bounded data streams (real-time stream processing).\n- [Apache Beam](https:\u002F\u002Fgithub.com\u002Fapache\u002Fbeam) - A unified model for defining both batch and streaming data-parallel processing pipelines.\n- [Apache Pulsar](https:\u002F\u002Fgithub.com\u002Fapache\u002Fpulsar) - A cloud-native, distributed messaging and streaming platform.\n- [Delta Lake](https:\u002F\u002Fgithub.com\u002Fdelta-io\u002Fdelta) - A storage layer that brings ACID transactions to Apache Spark and big data workloads.\n- [Apache Hudi](https:\u002F\u002Fgithub.com\u002Fapache\u002Fhudi) - An open data lakehouse platform, built on a high-performance open table format.\n- [Trino](https:\u002F\u002Fgithub.com\u002Ftrinodb\u002Ftrino) - A distributed SQL query engine designed for fast analytic queries against large datasets.\n- [DataHub](https:\u002F\u002Fgithub.com\u002Fdatahub-project\u002Fdatahub) - A metadata platform for the modern data stack.\n- [OpenLineage](https:\u002F\u002Fgithub.com\u002FOpenLineage\u002FOpenLineage) - An open framework for collection and analysis of data lineage.\n- [Kedro](https:\u002F\u002Fgithub.com\u002Fkedro-org\u002Fkedro) - A framework for creating reproducible, maintainable and modular data science code.\n- [Apache Calcite](https:\u002F\u002Fgithub.com\u002Fapache\u002Fcalcite) - A dynamic data management framework that allows for SQL parsing, optimization, and federation.\n- [Prefect](https:\u002F\u002Fgithub.com\u002FPrefectHQ\u002Fprefect) - Workflow orchestration for building resilient data pipelines.\n- [Apache Arrow](https:\u002F\u002Fgithub.com\u002Fapache\u002Farrow) - Universal columnar format and multi-language toolbox for fast data interchange.\n- [Kestra](https:\u002F\u002Fgithub.com\u002Fkestra-io\u002Fkestra) - An open-source, event-driven orchestrator that simplifies data workflow management.\n- [Conductor](https:\u002F\u002Fgithub.com\u002Fconductor-oss\u002Fconductor) - Orchestration engine for running complex, multi-step workflows and business processes.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"natural-language-processing-nlp\">\u003C\u002Fa>\n\n## 📖 Natural Language Processing (NLP)\n\n\u003Ca id=\"natural-language-processing-nlp-resources\">\u003C\u002Fa>\n\n### Resources\n\nA selection of resources for learning and applying natural language processing in Python.\n\n- [Awesome Nlp](https:\u002F\u002Fgithub.com\u002Fkeon\u002Fawesome-nlp) - A ranked list of awesome Python libraries for natural language processing (NLP).\n- [Hugging Face NLP Course](https:\u002F\u002Fhuggingface.co\u002Flearn\u002Fllm-course\u002Fchapter1\u002F1) - Official course on transformers and NLP from Hugging Face.\n- [Practical NLP Code](https:\u002F\u002Fgithub.com\u002Fpractical-nlp\u002Fpractical-nlp-code) - Code examples and notebooks for practical natural language processing.\n- [Oxford Deep NLP Lectures](https:\u002F\u002Fgithub.com\u002Foxford-cs-deepnlp-2017\u002Flectures) - Lecture materials from Oxford's Deep Natural Language Processing course.\n- [NLTK Book](https:\u002F\u002Fwww.nltk.org\u002Fbook\u002F) - Natural Language Processing with Python.\n- [NLP with Python by Susan Li](https:\u002F\u002Fgithub.com\u002Fsusanli2016\u002FNLP-with-Python) - Jupyter notebooks demonstrating various NLP techniques and applications.\n- [Hands on NLTK Tutorial](https:\u002F\u002Fgithub.com\u002Fhb20007\u002Fhands-on-nltk-tutorial) - The hands-on NLTK tutorial for NLP in Python.\n- [YSDA NLP Course](https:\u002F\u002Fgithub.com\u002Fyandexdataschool\u002Fnlp_course) - Yandex School of Data Analysis course on Natural Language Processing.\n- [The NLP Pandect](https:\u002F\u002Fgithub.com\u002Fivan-bilan\u002FThe-NLP-Pandect) - Comprehensive NLP guide covering theory, models, and practical implementations.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"natural-language-processing-nlp-tools\">\u003C\u002Fa>\n\n### Tools\n\nA collection of powerful libraries and frameworks for natural language processing.\n\n- [Natural Language Toolkit (NLTK)](https:\u002F\u002Fwww.nltk.org\u002F) - A leading platform for building Python programs to work with human language data.\n- [TextBlob](https:\u002F\u002Ftextblob.readthedocs.io\u002Fen\u002Fdev\u002F) - A simple library for processing textual data.\n- [SpaCy](https:\u002F\u002Fspacy.io\u002F) - An open-source software library for advanced NLP in Python.\n- [BERT](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Fbert) - A transformer-based model for NLP tasks.\n- [Flair](https:\u002F\u002Fgithub.com\u002FflairNLP\u002Fflair) - A simple framework for state-of-the-art NLP.\n- [OpenHands](https:\u002F\u002Fgithub.com\u002FAll-Hands-AI\u002FOpenHands) - A library and framework for building applications with large language models.\n- [Stanford CoreNLP](https:\u002F\u002Fgithub.com\u002Fstanfordnlp\u002FCoreNLP) - A Java suite of core NLP tools providing fundamental linguistic analysis capabilities.\n- [John Snow Labs Spark-NLP](https:\u002F\u002Fgithub.com\u002FJohnSnowLabs\u002Fspark-nlp) - A state-of-the-art Natural Language Processing library built on Apache Spark.\n- [TextAttack](https:\u002F\u002Fgithub.com\u002FQData\u002FTextAttack) - A Python framework for adversarial attacks, data augmentation, and model training in NLP.\n- [Gensim](https:\u002F\u002Fgithub.com\u002Fpiskvorky\u002Fgensim) - Topic modeling and natural language processing library for Python.\n- [Stanza](https:\u002F\u002Fgithub.com\u002Fstanfordnlp\u002Fstanza) - Python NLP library for many human languages, from the Stanford NLP Group.\n- [SentenceTransformers](https:\u002F\u002Fgithub.com\u002FUKPLab\u002Fsentence-transformers) - Framework for state-of-the-art sentence and text embeddings.\n- [LangExtract](https:\u002F\u002Fgithub.com\u002Fgoogle\u002Flangextract) - Google's library for structured information extraction from text using language models.\n- [Rasa](https:\u002F\u002Fgithub.com\u002FRasaHQ\u002Frasa) - Open-source framework for building contextual AI assistants and chatbots.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"machine-learning\">\u003C\u002Fa>\n\n## 🤖 Machine Learning & AI\n\n\u003Ca id=\"machine-learning-resources\">\u003C\u002Fa>\n\n### Resources\n\nA collection of resources to help you learn and apply machine learning concepts and techniques.\n\n- [Awesome Machine Learning](https:\u002F\u002Fgithub.com\u002Fjosephmisiti\u002Fawesome-machine-learning) - A curated list of awesome Machine Learning frameworks, libraries and software.\n- [Machine Learning Tutorials](https:\u002F\u002Fgithub.com\u002Fujjwalkarn\u002FMachine-Learning-Tutorials) - Machine learning and deep learning tutorials, articles and other resources.\n- [Awesome Deep Learning](https:\u002F\u002Fgithub.com\u002FChristosChristofidis\u002Fawesome-deep-learning) - A curated list of awesome Deep Learning tutorials, projects and communities.\n- [Best of ML Python](https:\u002F\u002Fgithub.com\u002Flukasmasuch\u002Fbest-of-ml-python) - A ranked list of awesome machine learning Python libraries and tools.\n- [Microsoft ML for Beginners](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FML-For-Beginners) - A beginner-friendly introduction to machine learning concepts and practices.\n- [mlcourse.ai](https:\u002F\u002Fgithub.com\u002FYorko\u002Fmlcourse.ai) - Open Machine Learning Course with practical assignments and real-world applications.\n- [Machine Learning Zoomcamp](https:\u002F\u002Fgithub.com\u002FDataTalksClub\u002Fmachine-learning-zoomcamp) - A free practical machine learning course focused on building and deploying models.\n- [Awesome Artificial Intelligence](https:\u002F\u002Fgithub.com\u002Fowainlewis\u002Fawesome-artificial-intelligence) - A curated list of artificial intelligence resources.\n- [Google Research](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Fgoogle-research) - Official repository for Google Research projects and publications.\n- [100 Days of ML Coding](https:\u002F\u002Fgithub.com\u002FAvik-Jain\u002F100-Days-Of-ML-Code) - A comprehensive coding challenge to learn machine learning over 100 days.\n- [Made With ML](https:\u002F\u002Fgithub.com\u002FGokuMohandas\u002FMade-With-ML) - Resource for building and deploying machine learning applications.\n- [Handson-ml3](https:\u002F\u002Fgithub.com\u002Fageron\u002Fhandson-ml3) - Hands-on guide to machine learning and deep learning using Python.\n- [AI For Beginners](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FAI-For-Beginners) - Microsoft's curriculum on artificial intelligence.\n- [LLMs-from-scratch](https:\u002F\u002Fgithub.com\u002Frasbt\u002FLLMs-from-scratch) - Educational repository for building LLMs from scratch.\n- [Awesome Generative AI Guide](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide) - A comprehensive guide to generative AI models, tools, and applications.\n- [Awesome LLM](https:\u002F\u002Fgithub.com\u002FHannibal046\u002FAwesome-LLM) - A curated list of papers, projects, and resources related to Large Language Models.\n- [Machine Learning with Python by Susan Li](https:\u002F\u002Fgithub.com\u002Fsusanli2016\u002FMachine-Learning-with-Python) - Jupyter notebooks covering various machine learning algorithms and applications.\n- [Understanding Deep Learning](https:\u002F\u002Fudlbook.github.io\u002Fudlbook\u002F) - Comprehensive and accessible textbook on deep learning fundamentals.\n- [Deep Learning Papers Reading Roadmap](https:\u002F\u002Fgithub.com\u002Ffloodsung\u002FDeep-Learning-Papers-Reading-Roadmap) - Curated roadmap of seminal deep learning papers for newcomers.\n- [Applied ML](https:\u002F\u002Fgithub.com\u002Feugeneyan\u002Fapplied-ml) - Curated resources and tools for applied machine learning in industry.\n- [Annotated deep learning paper implementations](https:\u002F\u002Fgithub.com\u002Flabmlai\u002Fannotated_deep_learning_paper_implementations) - Implementations of deep learning papers with annotated code.\n- [Ml From Scratch](https:\u002F\u002Fgithub.com\u002Feriklindernoren\u002FML-From-Scratch) - Core machine learning algorithms implemented in Python from scratch.\n- [Awesome Ai Ml Resources](https:\u002F\u002Fgithub.com\u002Farmankhondker\u002Fawesome-ai-ml-resources) - Carefully curated list of AI\u002FML books, courses, and practical tools.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"machine-learning-tools\">\u003C\u002Fa>\n\n### Tools\n\nA collection of tools for developing and deploying machine learning models.\n\n#### Machine Learning\n\n- [Scikit-learn](https:\u002F\u002Fgithub.com\u002Fscikit-learn\u002Fscikit-learn) - Machine learning library for classical algorithms and model building.\n- [XGBoost](https:\u002F\u002Fgithub.com\u002Fdmlc\u002Fxgboost) - Optimized distributed gradient boosting library for tree-based models.\n- [LightGBM](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FLightGBM) - Fast, distributed, high-performance gradient boosting framework.\n- [CatBoost](https:\u002F\u002Fgithub.com\u002Fcatboost\u002Fcatboost) - High-performance gradient boosting on decision trees with categorical features support.\n- [H2O-3](https:\u002F\u002Fgithub.com\u002Fh2oai\u002Fh2o-3) - Open-source distributed machine learning platform.\n- [cuML](https:\u002F\u002Fgithub.com\u002Frapidsai\u002Fcuml) - GPU-accelerated machine learning algorithms from RAPIDS.\n- [dlib](https:\u002F\u002Fgithub.com\u002Fdavisking\u002Fdlib) - Modern C++ toolkit containing machine learning algorithms and tools.\n- [SHAP](https:\u002F\u002Fgithub.com\u002Fshap\u002Fshap) - Game theoretic approach to explain the output of any machine learning model.\n- [InterpretML](https:\u002F\u002Fgithub.com\u002Finterpretml\u002Finterpret) - Fit interpretable models and explain blackbox machine learning.\n- [Optuna](https:\u002F\u002Fgithub.com\u002Foptuna\u002Foptuna) - Hyperparameter optimization framework.\n\n#### Deep Learning\n\n- [TensorFlow](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Ftensorflow) - End-to-end open source platform for machine learning and deep learning.\n- [PyTorch](https:\u002F\u002Fgithub.com\u002Fpytorch\u002Fpytorch) - Deep learning framework with strong support for research and production.\n- [PyTorch Lightning](https:\u002F\u002Fgithub.com\u002FLightning-AI\u002Fpytorch-lightning) - PyTorch wrapper for high-performance AI research.\n- [PyTorch Ignite](https:\u002F\u002Fgithub.com\u002Fpytorch\u002Fignite) - High-level library to help with training and evaluating neural networks.\n- [Keras](https:\u002F\u002Fgithub.com\u002Fkeras-team\u002Fkeras) - High-level neural networks API, running on top of TensorFlow.\n- [Fast.ai](https:\u002F\u002Fgithub.com\u002Ffastai\u002Ffastai) - Deep learning library simplifying training fast and accurate neural nets.\n- [HuggingFace Transformers](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Ftransformers) - Model-definition framework for state-of-the-art machine learning models.\n- [HuggingFace Diffusers](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fdiffusers) - Library for state-of-the-art pretrained diffusion models.\n- [PEFT](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fpeft) - Library for efficiently adapting large pretrained models.\n- [YOLOv5](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fyolov5) - Real-time object detection system.\n- [Ultralytics](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fultralytics) - YOLOv8 and other computer vision models.\n- [ONNX](https:\u002F\u002Fgithub.com\u002Fonnx\u002Fonnx) - Open standard for machine learning interoperability.\n- [PyTorch Geometric](https:\u002F\u002Fgithub.com\u002Fpyg-team\u002Fpytorch_geometric) - Geometric deep learning extension library for PyTorch.\n- [Pyro](https:\u002F\u002Fgithub.com\u002Fpyro-ppl\u002Fpyro) - Deep universal probabilistic programming with Python and PyTorch.\n- [Skorch](https:\u002F\u002Fgithub.com\u002Fskorch-dev\u002Fskorch) - Scikit-learn compatible neural network library.\n- [Sonnet](https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Fsonnet) - DeepMind's library for building complex neural networks.\n- [JAX](https:\u002F\u002Fgithub.com\u002Fjax-ml\u002Fjax) - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU\u002FTPU, and more.\n- [TensorFlow Models](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Fmodels) - Official TensorFlow repository with models and examples.\n- [Fenn](https:\u002F\u002Fgithub.com\u002Fpyfenn\u002Ffenn) - A simple framework that automates ML\u002FDL workflows by providing prebuilt trainers, templates, logging, configuration management, and much more.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"mlops\">\u003C\u002Fa>\n\n## 🚀 MLOps\n\n\u003Ca id=\"mlops-resources\">\u003C\u002Fa>\n\n### Resources\n\nMaterials and curated lists for machine learning operations.\n\n- [MLOps Zoomcamp](https:\u002F\u002Fgithub.com\u002FDataTalksClub\u002Fmlops-zoomcamp) - A free course focused on the practical aspects of deploying and maintaining ML systems.\n- [Awesome MLOps (visenger)](https:\u002F\u002Fgithub.com\u002Fvisenger\u002Fawesome-mlops) - A curated list of references for MLOps.\n- [Awesome MLOps (kelvins)](https:\u002F\u002Fgithub.com\u002Fkelvins\u002Fawesome-mlops) - A curated list of awesome MLOps tools.\n- [Awesome LLMOps](https:\u002F\u002Fgithub.com\u002Ftensorchord\u002FAwesome-LLMOps) - An awesome & curated list of best LLMOps tools for developers.\n- [LLM Zoomcamp](https:\u002F\u002Fgithub.com\u002FDataTalksClub\u002Fllm-zoomcamp) - A course dedicated to Large Language Models, their architecture and applications.\n- [ML Engineering Guide](https:\u002F\u002Fgithub.com\u002Fstas00\u002Fml-engineering) - A practical guide to machine learning engineering and MLOps best practices.\n- [Awesome Production Machine Learning](https:\u002F\u002Fgithub.com\u002FEthicalML\u002Fawesome-production-machine-learning) - A curated list of tools for deploying, monitoring, and maintaining ML systems in production.\n- [Llama Cookbook](https:\u002F\u002Fgithub.com\u002Fmeta-llama\u002Fllama-cookbook) - Official recipes and examples for working with Llama models.\n- [Awesome Kubeflow](https:\u002F\u002Fgithub.com\u002Fterrytangyuan\u002Fawesome-kubeflow) - Curated resources, tools, and projects for the Kubeflow machine learning platform.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"mlops-tools\">\u003C\u002Fa>\n\n### Tools\n\nPlatforms and utilities for deploying, monitoring, and maintaining ML systems.\n\n- [ColossalAI](https:\u002F\u002Fgithub.com\u002Fhpcaitech\u002FColossalAI) - High-performance distributed training framework.\n- [DVC](https:\u002F\u002Fgithub.com\u002Fiterative\u002Fdvc) - Version control system for machine learning projects.\n- [Evidently](https:\u002F\u002Fgithub.com\u002Fevidentlyai\u002Fevidently) - Tool for analyzing and monitoring data and model drift.\n- [Deepchecks](https:\u002F\u002Fgithub.com\u002Fdeepchecks\u002Fdeepchecks) - Validation for ML models and data.\n- [Sematic](https:\u002F\u002Fgithub.com\u002Fsematic-ai\u002Fsematic) - Tool to build, debug, and execute ML pipelines with native Python.\n- [netdata](https:\u002F\u002Fgithub.com\u002Fnetdata\u002Fnetdata) - Real-time performance monitoring.\n- [meilisearch](https:\u002F\u002Fgithub.com\u002Fmeilisearch\u002Fmeilisearch) - Fast, open-source search engine.\n- [vLLM](https:\u002F\u002Fgithub.com\u002Fvllm-project\u002Fvllm) - High-throughput and memory-efficient inference library for LLMs.\n- [haystack](https:\u002F\u002Fgithub.com\u002Fdeepset-ai\u002Fhaystack) - LLM framework for building search and question answering systems.\n- [Kubeflow](https:\u002F\u002Fgithub.com\u002Fkubeflow\u002Fkubeflow) - Machine learning toolkit for Kubernetes.\n- [Seldon Core](https:\u002F\u002Fgithub.com\u002FSeldonIO\u002Fseldon-core) - Open source platform for deploying and monitoring machine learning models in production.\n- [Feast](https:\u002F\u002Fgithub.com\u002Ffeast-dev\u002Ffeast) - A feature store for machine learning that manages and serves ML features to models.\n- [BentoML](https:\u002F\u002Fgithub.com\u002Fbentoml\u002FBentoML) - Framework for building, shipping, and scaling ML applications.\n- [MLflow](https:\u002F\u002Fgithub.com\u002Fmlflow\u002Fmlflow) - Open-source platform for the complete machine learning lifecycle.\n- [Wandb](https:\u002F\u002Fgithub.com\u002Fwandb\u002Fwandb) - Tool for experiment tracking, dataset versioning, and model management.\n- [Comet ML](https:\u002F\u002Fgithub.com\u002Fcomet-ml\u002Fopik) - ML platform for tracking, comparing and optimizing experiments.\n- [Netflix Metaflow](https:\u002F\u002Fgithub.com\u002FNetflix\u002Fmetaflow) - A human-friendly Python library for helping scientists and engineers build and manage real-life data science projects.\n- [mindsdb](https:\u002F\u002Fgithub.com\u002Fmindsdb\u002Fmindsdb) - Platform for integrating AI into databases and applications.\n- [KServe](https:\u002F\u002Fgithub.com\u002Fkserve\u002Fkserve) - Standardized serverless inference platform for deploying and serving machine learning models on Kubernetes.\n- [SQLFlow](https:\u002F\u002Fgithub.com\u002Fsql-machine-learning\u002Fsqlflow) - Brings machine learning capabilities to SQL, enabling model training and prediction using SQL syntax.\n- [Jina AI Serve](https:\u002F\u002Fgithub.com\u002Fjina-ai\u002Fserve) - Framework for building and deploying AI services that communicate via gRPC, HTTP and WebSockets.\n- [LiteLLM](https:\u002F\u002Fgithub.com\u002FBerriAI\u002Flitellm) - Unified interface to call all LLM APIs (OpenAI, Anthropic, Cohere, etc.) with consistent output formatting.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"ai-applications\">\u003C\u002Fa>\n\n## 🧠 AI Applications & Platforms\n\n\u003Ca id=\"ai-applications-resources\">\u003C\u002Fa>\n\n### Resources\n\nA collection of resources focused on AI applications and platforms.\n\n- [Awesome LLM Apps](https:\u002F\u002Fgithub.com\u002FShubhamsaboo\u002Fawesome-llm-apps) - Collection of awesome LLM apps with AI Agents and RAG using OpenAI, Anthropic, Gemini and opensource models.\n- [Awesome Generative AI](https:\u002F\u002Fgithub.com\u002Fsteven2358\u002Fawesome-generative-ai) - A curated list of modern Generative Artificial Intelligence projects and services.\n- [AI Agents for Beginners](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fai-agents-for-beginners) - Microsoft's course on designing and building AI agents.\n- [Generative AI for Beginners](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fgenerative-ai-for-beginners) - Course on generative AI for beginners from Microsoft.\n- [Ai Dev Tools Zoomcamp](https:\u002F\u002Fgithub.com\u002FDataTalksClub\u002Fai-dev-tools-zoomcamp) - Free hands-on course on modern tools for building and deploying AI applications.\n- [LLM Course](https:\u002F\u002Fgithub.com\u002Fmlabonne\u002Fllm-course) - Practical course to master large language models from start to finish.\n- [Awesome AI Agents](https:\u002F\u002Fgithub.com\u002Fe2b-dev\u002Fawesome-ai-agents) - A curated list of AI autonomous agents, environments, and frameworks.\n- [AI Collection](https:\u002F\u002Fgithub.com\u002Fai-collection\u002Fai-collection) - The Generative AI Landscape - A Collection of Awesome Generative AI Applications.\n- [Awesome AI Apps](https:\u002F\u002Fgithub.com\u002FArindam200\u002Fawesome-ai-apps) - A collection of projects showcasing RAG, agents, workflows, and other AI use cases.\n- [System Prompts and Models](https:\u002F\u002Fgithub.com\u002Fx1xhlol\u002Fsystem-prompts-and-models-of-ai-tools) - System Prompts, Internal Tools & AI Models from various AI applications and coding tools.\n- [RAG Techniques](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FRAG_Techniques) - Collection of advanced techniques for Retrieval-Augmented Generation.\n- [Awesome LangChain](https:\u002F\u002Fgithub.com\u002Fkyrolabs\u002Fawesome-langchain) - Awesome list of tools and projects with the awesome LangChain framework.\n- [Awesome AI Tools](https:\u002F\u002Fgithub.com\u002Fmahseema\u002Fawesome-ai-tools) - A curated list of Artificial Intelligence Top Tools.\n- [Awesome LLM Security](https:\u002F\u002Fgithub.com\u002Fcorca-ai\u002Fawesome-llm-security) - A curation of awesome tools, documents and projects about LLM Security.\n- [Claude Cookbooks](https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fclaude-cookbooks) - Official Anthropic examples and recipes for working with Claude AI.\n- [Hands On Large Language Models](https:\u002F\u002Fgithub.com\u002FHandsOnLLM\u002FHands-On-Large-Language-Models) - Covers LLM fundamentals, prompt engineering, and fine-tuning.  \n- [AI Engineering Hub](https:\u002F\u002Fgithub.com\u002Fpatchy631\u002Fai-engineering-hub) - Resources for building, deploying, and maintaining AI systems.\n- [Agents Towards Production](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002Fagents-towards-production) - Code-first tutorials for building production-grade GenAI agents.\n- [LLM Engineer Toolkit](https:\u002F\u002Fgithub.com\u002FKalyanKS-NLP\u002Fllm-engineer-toolkit) - Curated list of 120+ LLM libraries across various categories.\n- [GenAI Agents](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents) - Repository of AI agent implementations and tutorials.  \n- [AI Notes](https:\u002F\u002Fgithub.com\u002Fswyxio\u002Fai-notes) - Personal notes and essays on AI and software development.\n- [Open LLMs](https:\u002F\u002Fgithub.com\u002Feugeneyan\u002Fopen-llms) - Comprehensive list of open-source large language models and their capabilities.\n- [Prompt Engineering Guide](https:\u002F\u002Fgithub.com\u002Fdair-ai\u002FPrompt-Engineering-Guide) - Guides, papers, and resources for prompt engineering with LLMs.\n- [Prompt Engineering](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002Fprompt_engineering) - Collection of prompt engineering techniques and strategies.\n- [500 AI Agents Projects](https:\u002F\u002Fgithub.com\u002Fashishpatel26\u002F500-AI-Agents-Projects) - 500+ AI agent projects with code for learning and inspiration.\n- [Generative AI](https:\u002F\u002Fgithub.com\u002Fgenieincodebottle\u002Fgenerative-ai) - Roadmap and resources for mastering generative AI technologies.\n- [Awesome N8N](https:\u002F\u002Fgithub.com\u002Frestyler\u002Fawesome-n8n) - Collection of templates, integrations, and resources for the n8n automation platform.\n- [Free Llm Api Resources](https:\u002F\u002Fgithub.com\u002Fcheahjs\u002Ffree-llm-api-resources) - Up-to-date list of free APIs for accessing large language models (LLMs).\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"ai-applications-tools\">\u003C\u002Fa>\n\n### Tools\n\nA collection of frameworks, platforms, and end-user applications for building and deploying AI-powered solutions.\n\n#### AI Agents & Automation\n\n- [n8n](https:\u002F\u002Fgithub.com\u002Fn8n-io\u002Fn8n) - Workflow automation platform for connecting APIs and services.\n- [crewAI](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI) - Framework for orchestrating role-playing AI agents.\n- [autogen](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen) - Framework for building multi-agent conversational systems.\n- [AutoGPT](https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002FAutoGPT) - Autonomous AI agent that can complete complex tasks.\n- [LangGraph](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph) - Framework for building stateful, multi-actor applications with LLMs, with cycles and control flow.\n- [Agents.md](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fagents.md) - Open source framework for building agentic AI systems.\nogrammatically.\n- [OpenManus](https:\u002F\u002Fgithub.com\u002FFoundationAgents\u002FOpenManus) - Open-source platform for building and deploying AI agents.\n- [youtu-agent](https:\u002F\u002Fgithub.com\u002FTencentCloudADP\u002Fyoutu-agent) - Multi-modal intelligent agent framework by Tencent Cloud.\n- [trae-agent](https:\u002F\u002Fgithub.com\u002Fbytedance\u002Ftrae-agent) - Tool-using reasoning agent with execution-augmented reasoning.\n- [deepagents](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Fdeepagents) - LangChain framework for building sophisticated multi-agent systems.\n- [mem0](https:\u002F\u002Fgithub.com\u002Fmem0ai\u002Fmem0) - AI memory system for long-term context and personalized interactions.\n- [web-ui](https:\u002F\u002Fgithub.com\u002Fbrowser-use\u002Fweb-ui) - AI-powered browser automation framework for web interaction.\n- [Agent-S](https:\u002F\u002Fgithub.com\u002Fsimular-ai\u002FAgent-S) - Open agentic framework that autonomously interacts with computer GUIs like a human.\n- [Mastra](https:\u002F\u002Fgithub.com\u002Fmastra-ai\u002Fmastra) - Open-source AI agent platform for building and scaling production-grade autonomous agents.\n- [Langflow](https:\u002F\u002Fgithub.com\u002Flangflow-ai\u002Flangflow) - Powerful visual platform for building and deploying AI-powered agents and workflows.\n- [agenticSeek](https:\u002F\u002Fgithub.com\u002FFosowl\u002FagenticSeek) - Framework for building and deploying AI agents with advanced reasoning and tool use.\n- [Flowise](https:\u002F\u002Fgithub.com\u002FFlowiseAI\u002FFlowise) - Open-source UI visual tool for building custom LLM orchestration flows and AI agents.\n- [MetaGPT](https:\u002F\u002Fgithub.com\u002FFoundationAgents\u002FMetaGPT) - Multi-agent framework that simulates roles in a software company to build projects.\n- [Local Deep Research](https:\u002F\u002Fgithub.com\u002FLearningCircuit\u002Flocal-deep-research) - Local AI research assistant that searches web, papers, and documents.\n- [Gptme](https:\u002F\u002Fgithub.com\u002Fgptme\u002Fgptme) - AI agent CLI that writes code, uses terminal, browses web, and runs locally.\n- [Rowboat](https:\u002F\u002Fgithub.com\u002Frowboatlabs\u002Frowboat) - Open-source AI coworker that learns from your emails\u002Fmeetings to automate drafting, prep, and tasks.\n- [Everyrow](https:\u002F\u002Fgithub.com\u002Ffuturesearch\u002Feveryrow-sdk) - AI-powered data operations SDK. Semantic deduplication, fuzzy merging, and intelligent ranking for data analysis workflows.\n- [Personal Ai Infrastructure](https:\u002F\u002Fgithub.com\u002Fdanielmiessler\u002FPersonal_AI_Infrastructure) - Framework for building a personal AI assistant with memory, skills, and learning ability.\n- [N8N Workflows](https:\u002F\u002Fgithub.com\u002FZie619\u002Fn8n-workflows) - Collection of ready-to-use workflow templates for the n8n automation platform.\n- [Skyvern](https:\u002F\u002Fgithub.com\u002FSkyvern-AI\u002Fskyvern) - AI browser automation using LLMs & computer vision. Playwright-compatible SDK + no-code workflows.\n- [OpenWork](https:\u002F\u002Fgithub.com\u002Fdifferent-ai\u002Fopenwork) - Open-source desktop alternative to Claude Cowork for running agents, skills, and MCP locally with team collaboration features.\n- [DeepAnalyze](https:\u002F\u002Fgithub.com\u002Fruc-datalab\u002FDeepAnalyze) - Agentic LLM for autonomous data science, which can autonomously complete a wide range of data-centric tasks without human intervention.\n\n\n#### Development Frameworks & Tools\n\n- [LangChain](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchain) - Framework for developing applications powered by language models.\n- [LlamaIndex](https:\u002F\u002Fgithub.com\u002Frun-llama\u002Fllama_index) - Data framework for LLM-based applications with RAG capabilities.\n- [openai-python](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fopenai-python) - Official Python library for OpenAI API.\n- [openai-agents-python](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fopenai-agents-python) - Official OpenAI framework for building AI agents.\n- [ragflow](https:\u002F\u002Fgithub.com\u002Finfiniflow\u002Fragflow) - Open-source RAG (Retrieval-Augmented Generation) workflow platform.\n- [firecrawl](https:\u002F\u002Fgithub.com\u002Ffirecrawl\u002Ffirecrawl) - Web crawling and data extraction service for AI applications.\n- [Fabric](https:\u002F\u002Fgithub.com\u002Fdanielmiessler\u002FFabric) - Framework for augmenting humans using AI.\n- [Dyad](https:\u002F\u002Fgithub.com\u002Fdyad-sh\u002Fdyad) - Open-source platform for building AI applications with custom API keys.\n- [Langflow](https:\u002F\u002Fgithub.com\u002Flangflow-ai\u002Flangflow) - Powerful visual platform for building and deploying AI-powered agents and workflows.\n- [NeMo](https:\u002F\u002Fgithub.com\u002FNVIDIA-NeMo\u002FNeMo) - Scalable generative AI framework from NVIDIA for LLMs, Multimodal, and Speech AI.\n- [Deepcode](https:\u002F\u002Fgithub.com\u002FHKUDS\u002FDeepCode) - AI-powered agent framework for automatic code generation from research papers and text.\n\n#### Code Generation & Assistance\n\n- [gpt-engineer](https:\u002F\u002Fgithub.com\u002FAntonOsika\u002Fgpt-engineer) - AI-powered code generation tool.\n- [gpt-pilot](https:\u002F\u002Fgithub.com\u002FPythagora-io\u002Fgpt-pilot) - AI pair programmer that writes entire applications.\n- [tabby](https:\u002F\u002Fgithub.com\u002FTabbyML\u002Ftabby) - Self-hosted AI coding assistant.\n\n#### Model Deployment & Platforms\n\n- [Ollama](https:\u002F\u002Fgithub.com\u002Fjmorganca\u002Follama) - Tool for running large language models locally.\n- [OpenLLM](https:\u002F\u002Fgithub.com\u002Fbentoml\u002FOpenLLM) - Open platform for operating large language models in production.\n- [LocalAI](https:\u002F\u002Fgithub.com\u002Fmudler\u002FLocalAI) - Self-hosted, local-first AI model deployment platform.\n- [dify](https:\u002F\u002Fgithub.com\u002Flanggenius\u002Fdify) - Visual LLM application development platform.\n- [LLaMA-Factory](https:\u002F\u002Fgithub.com\u002Fhiyouga\u002FLLaMA-Factory) - Easy-to-use LLM fine-tuning framework.\n- [unsloth](https:\u002F\u002Fgithub.com\u002Funslothai\u002Funsloth) - Library for faster and more memory-efficient LLM fine-tuning.\n- [LocalGPT](https:\u002F\u002Fgithub.com\u002FPromtEngineer\u002FlocalGPT) - Fully private, on-premise document intelligence platform for chatting with your documents using local LLMs.\n\n#### AI Reliability & Debugging\n\n- [DeepEval](https:\u002F\u002Fgithub.com\u002Fconfident-ai\u002Fdeepeval) - Pytest-style unit testing framework for LLMs. Metrics for RAG, agents, hallucination, summarization, and custom criteria.\n- [RAGAS](https:\u002F\u002Fgithub.com\u002Fvibrantlabsai\u002Fragas) - Evaluation toolkit for LLM apps. Metrics, test generation, and insights for optimizing RAG pipelines and agents.\n- [Phoenix](https:\u002F\u002Fgithub.com\u002FArize-ai\u002Fphoenix) - AI observability platform. Tracing, datasets, experiments, and playground for troubleshooting and evaluating LLM apps.\n- [WFGY](https:\u002F\u002Fgithub.com\u002Fonestardao\u002FWFGY) - Open-source debugging infrastructure for RAG and AI agents. Includes 16-problem RAG failure map and TXT stress-test engine.\n\n#### End-User Applications\n\n- [open-webui](https:\u002F\u002Fgithub.com\u002Fopen-webui\u002Fopen-webui) - Web interface for interacting with various LLMs.\n- [ComfyUI](https:\u002F\u002Fgithub.com\u002Fcomfyanonymous\u002FComfyUI) - Visual node-based interface for Stable Diffusion.\n- [lobe-chat](https:\u002F\u002Fgithub.com\u002Flobehub\u002Flobe-chat) - Modern AI conversation interface.\n- [LibreChat](https:\u002F\u002Fgithub.com\u002Fdanny-avila\u002FLibreChat) - Open-source ChatGPT alternative.\n- [quivr](https:\u002F\u002Fgithub.com\u002FQuivrHQ\u002Fquivr) - Personal second brain and AI assistant.\n- [upscayl](https:\u002F\u002Fgithub.com\u002Fupscayl\u002Fupscayl) - AI-powered image upscaling tool.\n- [facefusion](https:\u002F\u002Fgithub.com\u002Ffacefusion\u002Ffacefusion) - AI face swapping and enhancement tool.\n- [DocsGPT](https:\u002F\u002Fgithub.com\u002Farc53\u002FDocsGPT) - Documentation-based question answering system.\n- [Deep Research](https:\u002F\u002Fgithub.com\u002Fdzhng\u002Fdeep-research) - AI-powered research assistant for iterative, deep research on any topic.\n- [Screenpipe](https:\u002F\u002Fgithub.com\u002Fmediar-ai\u002Fscreenpipe) - Local AI that records, searches, and automates tasks based on your screen and audio.\n- [Jaaz](https:\u002F\u002Fgithub.com\u002F11cafe\u002Fjaaz) - Open-source multimodal creative assistant and privacy-focused alternative to Canva\u002FManus for local image\u002Fvideo generation.\n- [DeepTutor](https:\u002F\u002Fgithub.com\u002FHKUDS\u002FDeepTutor) - AI-powered personalized learning assistant with document Q&A, exercise generation, and deep research capabilities.\n\n#### Additional Tools\n\n- [Bagel](https:\u002F\u002Fgithub.com\u002FByteDance-Seed\u002FBagel) - Open-source unified multimodal model for understanding and generating images.\n- [Whisper](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fwhisper) - Robust speech recognition model for transcription and translation.\n- [ChatTTS](https:\u002F\u002Fgithub.com\u002F2noise\u002FChatTTS) - Generative TTS model optimized for natural, expressive daily dialogue with fine-grained prosody control.\n- [NeuTTS](https:\u002F\u002Fgithub.com\u002Fneuphonic\u002Fneutts) - On-device TTS model with instant voice cloning from audio samples..\n- [Everything Claude Code](https:\u002F\u002Fgithub.com\u002Faffaan-m\u002Feverything-claude-code) - Collection of resources, guides, and tools for effective Claude Code AI assistant use.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"cloud-platforms\">\u003C\u002Fa>\n\n## ☁️ Cloud Platforms & Infrastructure\n\n\u003Ca id=\"cloud-platform-resources\">\u003C\u002Fa>\n\n### Resources\n\nA collection of resources for mastering cloud-native technologies, containerization, and infrastructure management.\n\n- [Awesome Cloud Native](https:\u002F\u002Fgithub.com\u002Frootsongjc\u002Fawesome-cloud-native) - A curated list of resources for cloud native technologies.\n- [Awesome Kubernetes](https:\u002F\u002Fgithub.com\u002Framitsurana\u002Fawesome-kubernetes) - A curated list for awesome Kubernetes resources.\n- [Awesome Docker](https:\u002F\u002Fgithub.com\u002Fveggiemonk\u002Fawesome-docker) - A curated list of Docker resources and projects.\n- [AWS Well-Architected Labs](https:\u002F\u002Fgithub.com\u002Fawslabs\u002Faws-well-architected-labs) - Hands-on labs to help you learn about the AWS Well-Architected Framework.\n- [Kubernetes The Hard Way](https:\u002F\u002Fgithub.com\u002Fkelseyhightower\u002Fkubernetes-the-hard-way) - Tutorial for bootstrapping a Kubernetes cluster the hard way on Google Cloud Platform.\n- [Awesome Compose](https:\u002F\u002Fgithub.com\u002Fdocker\u002Fawesome-compose) - A curated list of Docker Compose samples.\n- [AWS EKS Best Practices](https:\u002F\u002Fgithub.com\u002Faws\u002Faws-eks-best-practices) - A best practices guide for Amazon EKS.\n- [Awesome Selfhosted](https:\u002F\u002Fgithub.com\u002Fawesome-selfhosted\u002Fawesome-selfhosted) - A list of Free Software network services and web applications which can be hosted locally.\n- [Awesome Selfhosted Docker](https:\u002F\u002Fgithub.com\u002Fhotheadhacker\u002Fawesome-selfhost-docker) - A curated list of awesome selfhosted applications and solutions using Docker.\n- [Awesome Kubernetes Resources](https:\u002F\u002Fgithub.com\u002Ftomhuang12\u002Fawesome-k8s-resources) - A curated list of awesome Kubernetes tutorials, tools, and resources.\n- [Awesome Cloud Security](https:\u002F\u002Fgithub.com\u002F4ndersonLin\u002Fawesome-cloud-security) - A curated list of awesome cloud security resources, tools, and best practices.\n- [DevOps Exercises](https:\u002F\u002Fgithub.com\u002Fbregman-arie\u002Fdevops-exercises) - Linux, Jenkins, AWS, SRE, Prometheus, Docker, Python, Ansible, Git, Kubernetes, Terraform, OpenStack, SQL, and more.\n- [Awesome Cloudsec Labs](https:\u002F\u002Fgithub.com\u002Fiknowjason\u002FAwesome-CloudSec-Labs) - Curated hands-on labs and exercises for learning cloud security platforms.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"cloud-platform-tools\">\u003C\u002Fa>\n\n### Tools\n\nTools for containerization, orchestration, infrastructure as code, and cloud-native development.\n\n#### Containerization & Orchestration\n\n- [Docker](https:\u002F\u002Fgithub.com\u002Fdocker) - Open platform for developing, shipping, and running applications in containers.\n- [Docker Compose](https:\u002F\u002Fgithub.com\u002Fdocker\u002Fcompose) - A tool for defining and running multi-container Docker applications.\n- [Kubernetes](https:\u002F\u002Fgithub.com\u002Fkubernetes\u002Fkubernetes) - Production-grade container orchestration system.\n- [Kompose](https:\u002F\u002Fgithub.com\u002Fkubernetes\u002Fkompose) - Conversion tool from Docker Compose to Kubernetes.\n\n#### Infrastructure as Code\n\n- [Terraform](https:\u002F\u002Fgithub.com\u002Fhashicorp\u002Fterraform) - Infrastructure as Code tool.\n- [OpenTofu](https:\u002F\u002Fgithub.com\u002Fopentofu\u002Fopentofu) - Open source fork of Terraform.\n- [Pulumi](https:\u002F\u002Fgithub.com\u002Fpulumi\u002Fpulumi) - Modern IaC platform using familiar programming languages.\n- [CDK8s](https:\u002F\u002Fgithub.com\u002Fcdk8s-team\u002Fcdk8s) - Define Kubernetes apps using familiar languages.\n\n#### CI\u002FCD & GitOps\n\n- [Jenkins](https:\u002F\u002Fgithub.com\u002Fjenkinsci\u002Fjenkins) - Open source automation server.\n- [Argo CD](https:\u002F\u002Fgithub.com\u002Fargoproj\u002Fargo-cd) - Declarative GitOps continuous delivery.\n- [Argo Workflows](https:\u002F\u002Fgithub.com\u002Fargoproj\u002Fargo-workflows) - Container-native workflow engine.\n- [Tekton](https:\u002F\u002Fgithub.com\u002Ftektoncd\u002Fpipeline) - Kubernetes-native CI\u002FCD framework.\n- [Spinnaker](https:\u002F\u002Fgithub.com\u002Fspinnaker\u002Fspinnaker) - Multi-cloud continuous delivery.\n- [Dagger](https:\u002F\u002Fgithub.com\u002Fdagger\u002Fdagger) - Portable devkit for CI\u002FCD pipelines.\n\n#### Service Mesh & API Gateways\n\n- [Traefik](https:\u002F\u002Fgithub.com\u002Ftraefik\u002Ftraefik) - Modern HTTP reverse proxy and load balancer.\n- [Kong](https:\u002F\u002Fgithub.com\u002FKong\u002Fkong) - Cloud-native API Gateway.\n- [Apache APISIX](https:\u002F\u002Fgithub.com\u002Fapache\u002Fapisix) - Dynamic API gateway.\n- [Envoy Gateway](https:\u002F\u002Fgithub.com\u002Fenvoyproxy\u002Fgateway) - Manages Envoy Proxy as gateway.\n- [Higress](https:\u002F\u002Fgithub.com\u002Falibaba\u002Fhigress) - Cloud-native API gateway based on Istio.\n- [Meshery](https:\u002F\u002Fgithub.com\u002Fmeshery\u002Fmeshery) - Service mesh management.\n\n#### Kubernetes Ecosystem\n\n- [Helm](https:\u002F\u002Fgithub.com\u002Fhelm\u002Fhelm) - Package manager for Kubernetes.\n- [Kustomize](https:\u002F\u002Fgithub.com\u002Fkubernetes-sigs\u002Fkustomize) - Configuration customization for Kubernetes.\n- [Kubernetes Dashboard](https:\u002F\u002Fgithub.com\u002Fkubernetes\u002Fdashboard) - Web-based UI for Kubernetes.\n- [Skaffold](https:\u002F\u002Fgithub.com\u002FGoogleContainerTools\u002Fskaffold) - Continuous development for Kubernetes.\n- [Tilt](https:\u002F\u002Fgithub.com\u002Ftilt-dev\u002Ftilt) - Local development for Kubernetes.\n- [Flagger](https:\u002F\u002Fgithub.com\u002Ffluxcd\u002Fflagger) - Progressive delivery operator.\n- [KubeVela](https:\u002F\u002Fgithub.com\u002Fkubevela\u002Fkubevela) - Application delivery platform.\n- [KubeSphere](https:\u002F\u002Fgithub.com\u002Fkubesphere\u002Fkubesphere) - Kubernetes multi-cloud management.\n\n#### Developer Platforms & Control Planes\n\n- [Crossplane](https:\u002F\u002Fgithub.com\u002Fcrossplane\u002Fcrossplane) - Cloud native control plane.\n- [Artifact Hub](https:\u002F\u002Fgithub.com\u002Fartifacthub\u002Fhub) - Kubernetes packages and Helm charts.\n- [Devtron](https:\u002F\u002Fgithub.com\u002Fdevtron-labs\u002Fdevtron) - Kubernetes dashboard.\n- [Harness](https:\u002F\u002Fgithub.com\u002Fharness\u002Fharness) - End-to-end developer platform.\n\n#### Additional Tools\n\n- [Vagrant](https:\u002F\u002Fgithub.com\u002Fhashicorp\u002Fvagrant) - Tool for building and managing portable virtual development environments as code.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"productivity\">\u003C\u002Fa>\n\n## ⚡ Productivity\n\n\u003Ca id=\"productivity-resources\">\u003C\u002Fa>\n\n### Resources\n\nA collection of resources to enhance productivity.\n\n- [Positron](https:\u002F\u002Fgithub.com\u002Fposit-dev\u002Fpositron) - A next-generation data science IDE.\n- [Nanobrowser](https:\u002F\u002Fgithub.com\u002Fnanobrowser\u002Fnanobrowser) - An open-source AI web automation tool with multi-agent system that runs directly in your browser.\n- [Best of Jupyter](https:\u002F\u002Fgithub.com\u002Fml-tooling\u002Fbest-of-jupyter) - Ranked list of notable Jupyter Notebook, Hub, and Lab projects.\n- [Deepnote](https:\u002F\u002Fgithub.com\u002Fdeepnote\u002Fdeepnote) - AI native data science notebook platform compatible with Jupyter, featuring real-time collaboration, environment management, and integrations.\n- [AFFiNE](https:\u002F\u002Fgithub.com\u002Ftoeverything\u002FAFFiNE) - All-in-one workspace for notes, docs, and data visualization.\n- [Marimo](https:\u002F\u002Fgithub.com\u002Fmarimo-team\u002Fmarimo) - Reactive Python notebook for reproducible and interactive data science.\n- [ChatGPT Data Science Prompts](https:\u002F\u002Fgithub.com\u002Ftravistangvh\u002FChatGPT-Data-Science-Prompts) - A collection of useful prompts for data scientists using ChatGPT.\n- [Gamma.app](https:\u002F\u002Fgamma.app\u002F) - AI-powered platform for creating and sharing presentations and documents.\n- [Cookiecutter Data Science](https:\u002F\u002Fgithub.com\u002Fdrivendataorg\u002Fcookiecutter-data-science) - A standardized project structure for data science projects.\n- [Learn Regex](https:\u002F\u002Fgithub.com\u002Fziishaned\u002Flearn-regex) - Comprehensive guide to learning regular expressions with examples and exercises.\n- [Awesome Regex](https:\u002F\u002Fgithub.com\u002Faloisdg\u002Fawesome-regex) - Curated collection of regex tools, libraries, and learning resources.\n- [The Markdown Guide](https:\u002F\u002Fwww.markdownguide.org\u002F) - Comprehensive guide to learning Markdown.\n- [Readme-AI](https:\u002F\u002Fgithub.com\u002Feli64s\u002Freadme-ai) - A tool to automatically generate README.md files for your projects.\n- [Markdown Here](https:\u002F\u002Fgithub.com\u002Fadam-p\u002Fmarkdown-here) - Extension for writing emails in Markdown and rendering them before sending.\n- [MarkText](https:\u002F\u002Fgithub.com\u002Fmarktext\u002Fmarktext) - Simple and elegant markdown editor for documentation.\n- [QuarkDown](https:\u002F\u002Fgithub.com\u002Fiamgio\u002Fquarkdown) - Lightweight markdown processor for fast document rendering.\n- [screenshot-to-code](https:\u002F\u002Fgithub.com\u002Fabi\u002Fscreenshot-to-code) - AI tool that converts screenshots into code for various frontend stacks.\n- [Codebeautify](https:\u002F\u002Fcodebeautify.org\u002F) - All-in-one online code formatter and beautifier for Python, SQL, JSON, and more.\n- [Notion](https:\u002F\u002Fwww.notion.com\u002F) - An all-in-one workspace for note-taking and task management.\n- [Trello](https:\u002F\u002Ftrello.com\u002Fhome) - A visual project management tool.\n- [Habitica](https:\u002F\u002Fgithub.com\u002FHabitRPG\u002Fhabitica) - A habit-building and productivity app that treats your life like a role-playing game.\n- [Bujo](https:\u002F\u002Fbulletjournal.com\u002F) - Tools to help transform the way you work and live.\n- [Parabola](https:\u002F\u002Fparabola.io\u002F) - An AI-powered workflow builder for organizing data.\n- [Asana](https:\u002F\u002Fasana.com\u002F) - A project management platform for tracking work and projects.\n- [Puter](https:\u002F\u002Fgithub.com\u002FHeyPuter\u002Fputer) - An open-source, browser-based computing environment and cloud OS.\n- [Milkdown](https:\u002F\u002Fgithub.com\u002FMilkdown\u002Fmilkdown) - Plugin-driven, WYSIWYG markdown editor framework inspired by Typora.\n- [PDFMathTranslate](https:\u002F\u002Fgithub.com\u002FPDFMathTranslate\u002FPDFMathTranslate) - AI tool for bilingual scientific PDF translation preserving formulas, charts, and layout.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"productivity-useful-linux-tools\">\u003C\u002Fa>\n\n### Useful Linux Tools\n\nA selection of tools to enhance productivity and functionality in Linux environments.\n\n- [tldr-pages](https:\u002F\u002Fgithub.com\u002Ftldr-pages\u002Ftldr) - Simplified and community-driven man pages with practical examples.\n- [Bat](https:\u002F\u002Fgithub.com\u002Fsharkdp\u002Fbat) - Cat clone with syntax highlighting.\n- [Exa](https:\u002F\u002Fgithub.com\u002Fogham\u002Fexa) - Modern replacement for ls.\n- [Ripgrep](https:\u002F\u002Fgithub.com\u002FBurntSushi\u002Fripgrep) - Faster grep alternative.\n- [Zoxide](https:\u002F\u002Fgithub.com\u002Fajeetdsouza\u002Fzoxide) - Smarter cd command.\n- [Peek](https:\u002F\u002Fgithub.com\u002Fphw\u002Fpeek) - Simple animated GIF screen recorder with an easy to use interface.\n- [CopyQ](https:\u002F\u002Fgithub.com\u002Fhluk\u002FCopyQ) - Clipboard manager with advanced features.\n- [Translate Shell](https:\u002F\u002Fgithub.com\u002Fsoimort\u002Ftranslate-shell) - Command-line translator using Google Translate, Bing Translator, Yandex.Translate, etc.\n- [Espanso](https:\u002F\u002Fgithub.com\u002Fespanso\u002Fespanso) - Cross-platform Text Expander written in Rust.\n- [Flameshot](https:\u002F\u002Fgithub.com\u002Fflameshot-org\u002Fflameshot) - Powerful yet simple to use screenshot software.\n- [DrawIO Desktop](https:\u002F\u002Fgithub.com\u002Fjgraph\u002Fdrawio-desktop) - An open-source diagramming software for making flowcharts, process diagrams, and more.\n- [Inkscape](https:\u002F\u002Fgithub.com\u002Finkscape\u002Finkscape) - A powerful, free, and open-source vector graphics editor for creating and editing visualizations.\n- [Rclone](https:\u002F\u002Frclone.org\u002F) - A command-line program to manage files on cloud storage.\n- [Rsync](https:\u002F\u002Frsync.samba.org\u002F) - A fast and versatile file copying tool that can synchronize files and directories between two locations over a network or locally.\n- [Timeshift](https:\u002F\u002Fgithub.com\u002Flinuxmint\u002Ftimeshift) - System restore tool for Linux that creates filesystem snapshots using rsync+hardlinks or BTRFS snapshots.\n- [Backintime](https:\u002F\u002Fgithub.com\u002Fbit-team\u002Fbackintime) - A comfortable and well-configurable graphical frontend for incremental backups.\n- [Fzf](https:\u002F\u002Fgithub.com\u002Fjunegunn\u002Ffzf) - A command-line fuzzy finder.\n- [Osquery](https:\u002F\u002Fgithub.com\u002Fosquery\u002Fosquery) - SQL powered operating system instrumentation, monitoring, and analytics.\n- [GNU Parallel](https:\u002F\u002Fwww.gnu.org\u002Fsoftware\u002Fparallel\u002F) - A tool to run jobs in parallel.\n- [HTop](https:\u002F\u002Fhtop.dev\u002F) - An interactive process viewer.\n- [Ncdu](https:\u002F\u002Fdev.yorhel.nl\u002Fncdu) - A disk usage analyzer with an ncurses interface.\n- [Thefuck](https:\u002F\u002Fgithub.com\u002Fnvbn\u002Fthefuck) - A command line tool to correct your previous console command.\n- [Miller](https:\u002F\u002Fgithub.com\u002Fjohnkerl\u002Fmiller) - A tool for querying, processing, and formatting data in various file formats (CSV, JSON, etc.), like awk\u002Fsed\u002Fcut for data.\n- [jq](https:\u002F\u002Fgithub.com\u002Fjqlang\u002Fjq) - Command-line JSON processor for parsing and manipulating JSON data.\n- [yq](https:\u002F\u002Fgithub.com\u002Fmikefarah\u002Fyq) - Portable command-line YAML processor (like jq for YAML and XML).\n- [q](https:\u002F\u002Fgithub.com\u002Fharelba\u002Fq) - Run SQL directly on CSV or TSV files from the command line.\n- [VisiData](https:\u002F\u002Fgithub.com\u002Fsaulpw\u002Fvisidata) - Interactive multitool for tabular data exploration in the terminal.\n- [csvkit](https:\u002F\u002Fgithub.com\u002Fwireservice\u002Fcsvkit) - Suite of command-line tools for working with CSV data.\n- [httpie](https:\u002F\u002Fgithub.com\u002Fhttpie\u002Fcli) - Modern command-line HTTP client for API testing and debugging.\n- [glances](https:\u002F\u002Fgithub.com\u002Fnicolargo\u002Fglances) - Cross-platform system monitoring tool for resource usage analysis.\n- [hyperfine](https:\u002F\u002Fgithub.com\u002Fsharkdp\u002Fhyperfine) - Command-line benchmarking tool for performance testing.\n- [termgraph](https:\u002F\u002Fgithub.com\u002Fmkaz\u002Ftermgraph) - Draw basic graphs in the terminal for quick data visualization.  \n- [fd](https:\u002F\u002Fgithub.com\u002Fsharkdp\u002Ffd) - Simple, fast and user-friendly alternative to 'find'.\n- [dust](https:\u002F\u002Fgithub.com\u002Fbootandy\u002Fdust) - More intuitive version of du written in rust.\n- [bottom](https:\u002F\u002Fgithub.com\u002FClementTsang\u002Fbottom) - Cross-platform graphical process\u002Fsystem monitor.\n- [Keychain](https:\u002F\u002Fgithub.com\u002Fdanielrobbins\u002Fkeychain) - Tool for managing and securely storing passwords and secrets.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"productivity-useful-vs-code-extensions\">\u003C\u002Fa>\n\n### Useful VS Code Extensions\n\nA collection of extensions to enhance functionality and productivity in Visual Studio Code.\n\n- [JDBC Adapter](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems\u002F?itemName=cweijan.dbclient-jdbc) - Connect to various databases using JDBC.\n- [DBCode - Connect](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=DBCode.dbcode) - Database client for managing and querying databases.\n- [Markdown All in One](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=yzhang.markdown-all-in-one) - Essential tools for Markdown editing.\n- [Markdown Preview GitHub Styles](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=bierner.markdown-preview-github-styles) - Changes VS Code's markdown preview to match GitHub's styling.\n- [Snippington Python Pandas Basic](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=snippington.snp-pandas-basic) - Basic tools for working with Pandas in Python.\n- [PDF Viewer for Visual Studio Code](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=mathematic.vscode-pdf) - View PDF files directly in VS Code.\n- [Quick Python Print](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=WeidaWang.quick-python-print) - Quickly handle print operations in Python.\n- [Rainbow CSV](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=mechatroner.rainbow-csv) - Highlight CSV and TSV files and run SQL-like queries.\n- [Remove Blank Lines](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=thamaraiselvam.remove-blank-lines) - Extension to remove empty lines in documents.\n- [PDF Preview in VSCode](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems\u002Ftomoki1207.pdf) - Show PDF previews in VS Code.\n- [CSV to Table](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=phplasma.csv-to-table) - Convert CSV\u002FTSV\u002FPSV files to ASCII formatted tables.\n- [Data Preview](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=RandomFractalsInc.vscode-data-preview) - Import, view, slice, and export data.\n- [Data Wrangler](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=ms-toolsai.datawrangler) - Tool for cleaning and preparing tabular datasets.\n- [Error Lens](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=usernamehw.errorlens) - Enhances the display of errors and warnings in code.\n- [Indent Rainbow](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=oderwat.indent-rainbow) - Makes indentation easier to read.\n- [Markdown Table Editor](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=TakumiI.markdowntable) - Add features to edit Markdown tables.\n- [WYSIWYG Editor for Markdown](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=cweijan.vscode-office) - View Word and Excel files and edit Markdown.\n- [Prettier](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=esbenp.prettier-vscode) - Code formatting extension for VS Code.\n- [Project Manager](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=alefragnani.project-manager) - Easily switch between projects.\n- [Python Indent](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=KevinRose.vsc-python-indent) - Automatically indent Python code.\n- [SandDance](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=msrvida.vscode-sanddance) - Visually explore and present your data.\n- [SQL Notebooks](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=cmoog.sqlnotebook) - Open SQL files as VSCode Notebooks.\n- [SQL Tools](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems\u002F?itemName=mtxr.sqltools) - Database management tools for VSCode.\n- [Kanban Board](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems\u002F?itemName=mkloubert.vscode-kanban) - A Kanban board extension for organizing tasks within VS Code.\n- [Path Autocomplete](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=ionutvmi.path-autocomplete) - Provides path completion for files and directories in VS Code.\n- [Path Intellisense](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems\u002F?itemName=christian-kohler.path-intellisense) - Autocompletes filenames in your code.\n- [Python Imports Utils](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=mgesbert.python-path) - Utilities for managing Python imports.\n- [Workspace Dashboard](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=kruemelkatze.vscode-dashboard) - Organize your workspaces in a speed-dial manner.\n- [Remote Development](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=ms-vscode-remote.vscode-remote-extensionpack) - Open any folder in a container, on a remote machine, or in WSL.\n- [Text Power Tools](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems\u002F?itemName=qcz.text-power-tools) - An all-in-one solution with 240+ commands for text manipulation.\n- [Toggle Quotes](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems\u002F?itemName=BriteSnow.vscode-toggle-quotes) - Toggle between single, double, and backticks for strings.\n- [Comment Translate](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems\u002F?itemName=intellsmi.comment-translate) - Helps translate comments, strings, and variable names in your code.\n- [Text Marker](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems\u002F?itemName=ryu1kn.text-marker) - Select text in your code and mark all matches with configurable highlight color.\n- [Bookmarks](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems\u002F?itemName=alefragnani.Bookmarks) - Mark lines in your code and jump to them easily.\n- [Dendron](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems\u002F?itemName=dendron.dendron) - A hierarchical note-taking tool that grows as you do.\n- [Gitignore Generator](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems\u002F?itemName=rubbersheep.gi) - Simplifies the process of generating .gitignore files.\n- [Test Explorer UI](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems\u002F?itemName=hbenl.vscode-test-explorer) - Run your tests in the sidebar of Visual Studio Code.\n- [Python Test Explorer](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems\u002F?itemName=LittleFoxTeam.vscode-python-test-adapter) - Run your Python tests in the sidebar of Visual Studio Code.\n- [VSCode Markdownlint](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=DavidAnson.vscode-markdownlint) - A VS Code extension to lint and style check markdown files.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"skill-development-career-resources\">\u003C\u002Fa>\n\n## 📚 Skill Development & Career\n\n\u003Ca id=\"skill-development-career-resources-practice-resources\">\u003C\u002Fa>\n\n### Practice Resources\n\nA collection of resources to enhance skills and advance your career in data analysis and related fields.\n\n- [LeetCode](https:\u002F\u002Fleetcode.com\u002Fproblemset\u002F) - A platform for preparing technical coding interviews.\n- [Kaggle Competitions](https:\u002F\u002Fwww.kaggle.com\u002Fcompetitions) - Platform for participating in data analysis and machine learning competitions.\n- [Makeovermonday](https:\u002F\u002Fmakeovermonday.co.uk\u002F) - A platform focused on enhancing data visualization practices.\n- [Workout Wednesday](https:\u002F\u002Fworkout-wednesday.com\u002F) - Engage in weekly challenges to improve your visualization skills.\n- [Official TidyTuesday Repository](https:\u002F\u002Fgithub.com\u002Frfordatascience\u002Ftidytuesday) - Repository for the TidyTuesday project, promoting data analysis.\n- [DrivenData Competitions](https:\u002F\u002Fwww.drivendata.org\u002Fcompetitions\u002F) - Data analysis competitions with a social impact focus.\n- [Codecademy Data Science Path](https:\u002F\u002Fwww.codecademy.com\u002Flearn\u002Fpaths\u002Fdata-science) - Interactive courses for learning data analysis.\n- [SQL Masterclass](https:\u002F\u002Fgithub.com\u002Fdatawithdanny\u002Fsql-masterclass?tab=readme-ov-file#course-content) - A course to master SQL for data analysis, complete with real-world projects.\n- [Hugging Face Tasks](https:\u002F\u002Fhuggingface.co\u002Ftasks) - Hands-on practice with specific NLP and machine learning tasks using real models.\n- [Awesome LeetCode Resources](https:\u002F\u002Fgithub.com\u002Fashishps1\u002Fawesome-leetcode-resources) - Collection of curated resources and strategies for LeetCode practice.\n- [Leetcode Company Wise Problems](https:\u002F\u002Fgithub.com\u002Fliquidslr\u002Fleetcode-company-wise-problems) - Company-wise Leetcode problems for interview preparation.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"skill-development-career-resources-curated-jupyter-notebooks\">\u003C\u002Fa>\n\n### Curated Jupyter Notebooks\n\nA selection of curated Jupyter notebooks to support learning and exploration in data science and analysis.\n\n- [Awesome Notebooks](https:\u002F\u002Fgithub.com\u002Fjupyter-naas\u002Fawesome-notebooks) - Data & AI notebook templates catalog organized by tools.\n- [Data Science Ipython Notebooks](https:\u002F\u002Fgithub.com\u002Fdonnemartin\u002Fdata-science-ipython-notebooks) - Data science Python notebooks covering various topics.\n- [Pydata Book](https:\u002F\u002Fgithub.com\u002Fwesm\u002Fpydata-book) - Materials and IPython notebooks for \"Python for Data Analysis\" by Wes McKinney.\n- [Spark py Notebooks](https:\u002F\u002Fgithub.com\u002Fjadianes\u002Fspark-py-notebooks) - Apache Spark & Python tutorials for big data analysis and machine learning.\n- [DataMiningNotebooks](https:\u002F\u002Fgithub.com\u002Feclarson\u002FDataMiningNotebooks) - Example notebooks for data mining accompanying the course at Southern Methodist University.\n- [Pythondataanalysis](https:\u002F\u002Fgithub.com\u002Fhnawaz007\u002Fpythondataanalysis) - Python data repository with Jupyter notebooks and scripts.\n- [Python For Data Analysis](https:\u002F\u002Fgithub.com\u002Fcuttlefishh\u002Fpython-for-data-analysis) - An introduction to data science using Python and Pandas with Jupyter notebooks.\n- [Jdwittenauer Ipython Notebooks](https:\u002F\u002Fgithub.com\u002Fjdwittenauer\u002Fipython-notebooks) - A collection of IPython notebooks covering various topics.\n- [DataScienceInteractivePython](https:\u002F\u002Fgithub.com\u002FGeostatsGuy\u002FDataScienceInteractivePython) - A collection of interactive Python notebooks for learning data science concepts.\n- [Unsloth Notebooks](https:\u002F\u002Fgithub.com\u002Funslothai\u002Fnotebooks) - Optimized notebooks for faster AI model training and fine-tuning.\n- [Huggingface Notebooks](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fnotebooks) - Official Hugging Face notebooks for NLP, vision, audio, and diffusion models.\n- [Deep Learning with Python Notebooks](https:\u002F\u002Fgithub.com\u002Ffchollet\u002Fdeep-learning-with-python-notebooks) - Official Jupyter notebooks from François Chollet's Deep Learning with Python book.\n- [PythonNumericalDemos](https:\u002F\u002Fgithub.com\u002FGeostatsGuy\u002FPythonNumericalDemos) - Python notebooks for geostatistics and numerical demonstrations.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"skill-development-career-resources-data-sources-datasets\">\u003C\u002Fa>\n\n### Data Sources & Datasets\n\nA collection of resources for accessing datasets and data sources for analysis and projects.\n\n- [Kaggle Datasets](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets) - Extensive collection of datasets for practice in data analysis.\n- [Opendatasets](https:\u002F\u002Fgithub.com\u002FJovianHQ\u002Fopendatasets) - A Python library for downloading datasets from Kaggle, Google Drive, and other online sources.\n- [Datasette](https:\u002F\u002Fgithub.com\u002Fsimonw\u002Fdatasette) - An open source multi-tool for exploring and publishing data.\n- [Awesome Public Datasets](https:\u002F\u002Fgithub.com\u002Fawesomedata\u002Fawesome-public-datasets) - Curated list of high-quality open datasets.\n- [Open Data Sources](https:\u002F\u002Fgithub.com\u002Fdatasciencemasters\u002Fdata) - Collection of various open data sources.\n- [Free Datasets for Projects](https:\u002F\u002Fwww.dataquest.io\u002Fblog\u002Ffree-datasets-for-projects\u002F) - Dataquest's compilation of free datasets.\n- [Data World](https:\u002F\u002Fdata.world\u002F) - The enterprise data catalog that CIOs, governance professionals, data analysts, and engineers trust in the AI era.\n- [Awesome Public Real Time Datasets](https:\u002F\u002Fgithub.com\u002Fbytewax\u002Fawesome-public-real-time-datasets) - A list of publicly available datasets with real-time data.\n- [Google Dataset Search](https:\u002F\u002Fdatasetsearch.research.google.com\u002F) - A search engine for datasets from across the web.\n- [NASA Open Data Portal](https:\u002F\u002Fdata.nasa.gov\u002F) - A site for NASA's open data initiative, providing access to NASA's data resources.\n- [The World Bank Data](https:\u002F\u002Fdata.worldbank.org\u002F) - Free and open access to global development data by The World Bank.\n- [Voice Datasets](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fvoice_datasets) - A collection of audio and speech datasets for voice AI and machine learning.\n- [HuggingFace Datasets](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fdatasets) - A lightweight library to easily share and access datasets for audio, computer vision, and NLP.\n- [TensorFlow Datasets](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Fdatasets) - A collection of ready-to-use datasets for use with TensorFlow and other Python ML frameworks.\n- [NLP Datasets](https:\u002F\u002Fgithub.com\u002Fniderhoff\u002Fnlp-datasets) - A curated list of datasets for natural language processing (NLP) tasks.\n- [TorchVision Datasets](https:\u002F\u002Fgithub.com\u002Fpytorch\u002Fvision) - The torchvision.datasets module provides many built-in computer vision datasets.\n- [LLM Datasets](https:\u002F\u002Fgithub.com\u002Fmlabonne\u002Fllm-datasets) - A collection of datasets and resources for training and fine-tuning Large Language Models (LLMs).\n- [Unsplash Datasets](https:\u002F\u002Fgithub.com\u002Funsplash\u002Fdatasets) - A collection of datasets from Unsplash, useful for computer vision and research.\n- [Awesome JSON Datasets](https:\u002F\u002Fgithub.com\u002Fjdorfman\u002Fawesome-json-datasets?tab=readme-ov-file#bitcoin) - A curated list of awesome JSON datasets that are publicly available without authentication.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"skill-development-career-resources-resume-and-interview-tips\">\u003C\u002Fa>\n\n### Resume and Interview Tips\n\nA variety of resources to help you prepare for interviews and enhance your resume.\n\n- [Data Science Interview Questions Answers](https:\u002F\u002Fgithub.com\u002FyoussefHosni\u002FData-Science-Interview-Questions-Answers) - Curated list of data science interview questions and answers.\n- [Data Science Interview Preperation Resources](https:\u002F\u002Fgithub.com\u002FyoussefHosni\u002FData-Science-Interview-Preperation-Resources) - Resource to help you prepare for your upcoming data science interviews.\n- [Data Science Interviews](https:\u002F\u002Fgithub.com\u002Falexeygrigorev\u002Fdata-science-interviews) - A comprehensive collection of data science interview questions and resources.\n- [Interviews AI](https:\u002F\u002Fgithub.com\u002FBoltzmannEntropy\u002Finterviews.ai) - AI interview preparation guide with questions and solutions.\n- [The Data Science Interview Book](https:\u002F\u002Fbook.thedatascienceinterviewproject.com\u002F) - A comprehensive resource to prepare for data science and machine learning interviews.\n- [Machine Learning Interviews Book](https:\u002F\u002Fgithub.com\u002Fchiphuyen\u002Fml-interviews-book) - A comprehensive guide to preparing for machine learning engineering interviews.\n- [MLQuestions](https:\u002F\u002Fgithub.com\u002Fandrewekhalel\u002FMLQuestions) - Collection of machine learning interview questions and answers.\n- [Interview](https:\u002F\u002Fgithub.com\u002FOlshansk\u002Finterview) - Everything you need to prepare for your technical interview.\n- [Interviews](https:\u002F\u002Fgithub.com\u002Fkdn251\u002Finterviews) - Personal tech interview study guide covering algorithms and data structures.\n- [Devinterview](https:\u002F\u002Fdevinterview.io\u002F) - Ace your next tech interview with confidence.\n- [Interviewqs](https:\u002F\u002Fwww.interviewqs.com\u002F) - Ace your next data science interview.\n- [Cracking Data Science Interview](https:\u002F\u002Fgithub.com\u002Fkhanhnamle1994\u002Fcracking-the-data-science-interview) - A Collection of Cheatsheets, Books, Questions, and Portfolio For DS\u002FML Interview Prep.\n- [Interview Query](https:\u002F\u002Fwww.interviewquery.com\u002F) - Another platform to prepare for data science interviews.\n- [Awesome Behavioral Interviews](https:\u002F\u002Fgithub.com\u002Fashishps1\u002Fawesome-behavioral-interviews) - Curated resources for mastering behavioral and system design interviews.\n- [Enhancv Data Scientist Resumes](https:\u002F\u002Fenhancv.com\u002Fresume-examples\u002Fdata-scientist\u002F) - A collection of resume examples and tips tailored for data scientists.\n- [Data Science Portfolio](https:\u002F\u002Fwww.datascienceportfol.io\u002F) - A platform to create and showcase your data science portfolio.\n- [InterviewBit - SQL Interview Questions](https:\u002F\u002Fwww.interviewbit.com\u002Fsql-interview-questions\u002F) - Collection of SQL interview questions.\n- [StrataScratch](https:\u002F\u002Fwww.stratascratch.com\u002F) - Platform with real data science interview questions from top companies.\n- [LeetCode Patterns](https:\u002F\u002Fgithub.com\u002Fseanprashad\u002Fleetcode-patterns) - Curated collection of coding patterns and strategies for technical interviews.\n- [Bartosz Jarocki's CV](https:\u002F\u002Fgithub.com\u002FBartoszJarocki\u002Fcv) - Modern, open-source technical resume template and example.\n- [Awesome-CV](https:\u002F\u002Fgithub.com\u002Fposquit0\u002FAwesome-CV) - Professional CV and resume templates built with LaTeX.\n- [Reactive-Resume](https:\u002F\u002Fgithub.com\u002FAmruthPillai\u002FReactive-Resume) - Open-source resume builder with multiple templates and customization options.\n- [Best Resume Ever](https:\u002F\u002Fgithub.com\u002Fsalomonelli\u002Fbest-resume-ever) - Collection of modern resume templates and CV examples.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"cheatsheets\">\u003C\u002Fa>\n\n## 📋 Cheatsheets\n\nA collection of cheatsheets across various domains to aid in quick reference and learning.\n\n\u003Ca id=\"cheatsheets-goalkicker\">\u003C\u002Fa>\n\n### GoalKicker Programming Notes\n\n- [Python Notes for Professionals](https:\u002F\u002Fbooks.goalkicker.com\u002FPythonBook\u002FPythonNotesForProfessionals.pdf) - A massive collection of Python concepts, idioms, and best practices for all levels.\n- [SQL Notes for Professionals](https:\u002F\u002Fbooks.goalkicker.com\u002FSQLBook\u002FSQLNotesForProfessionals.pdf) - A definitive guide to SQL syntax, queries, and database interaction concepts.\n- [PostgreSQL Notes for Professionals](https:\u002F\u002Fbooks.goalkicker.com\u002FPostgreSQLBook\u002FPostgreSQLNotesForProfessionals.pdf) - A professional compendium of knowledge for PostgreSQL administration and development.\n- [MySQL Notes for Professionals](https:\u002F\u002Fbooks.goalkicker.com\u002FMySQLBook\u002FMySQLNotesForProfessionals.pdf) - Essential reference material for working with the MySQL database management system.\n- [Oracle Database Notes for Professionals](https:\u002F\u002Fbooks.goalkicker.com\u002FOracleDatabaseBook\u002FOracleDatabaseNotesForProfessionals.pdf) - A guide to Oracle Database concepts, PL\u002FSQL, and administration tasks.\n- [MongoDB Notes for Professionals](https:\u002F\u002Fbooks.goalkicker.com\u002FMongoDBBook\u002FMongoDBNotesForProfessionals.pdf) - A practical guide to working with NoSQL and MongoDB for modern application development.\n- [Bash Notes for Professionals](https:\u002F\u002Fbooks.goalkicker.com\u002FBashBook\u002FBashNotesForProfessionals.pdf) - A comprehensive guide to shell scripting and command-line mastery.\n- [Git Notes for Professionals](https:\u002F\u002Fbooks.goalkicker.com\u002FGitBook\u002FGitNotesForProfessionals.pdf) - Everything you need to know about version control with Git, from basics to advanced workflows.\n- [Linux Notes for Professionals](https:\u002F\u002Fbooks.goalkicker.com\u002FLinuxBook\u002FLinuxNotesForProfessionals.pdf) - A deep dive into Linux system administration, commands, and environment management.\n- [Microsoft SQL Server Notes for Professionals](https:\u002F\u002Fbooks.goalkicker.com\u002FMicrosoftSQLServerBook\u002FMicrosoftSQLServerNotesForProfessionals.pdf) - A detailed reference for developing and administering MS SQL Server databases.\n- [PowerShell Notes for Professionals](https:\u002F\u002Fbooks.goalkicker.com\u002FPowerShellBook\u002FPowerShellNotesForProfessionals.pdf) - A guide to task automation and configuration management using PowerShell.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"cheatsheets-python\">\u003C\u002Fa>\n\n### Python\n\n- [Python Cheat Sheet](https:\u002F\u002Fvivitoa.github.io\u002Fpython-cheat-sheet\u002F) - Comprehensive Python syntax and examples.\n- [Learn Python](https:\u002F\u002Fgithub.com\u002Ftrekhleb\u002Flearn-python) - Interactive Python learning.\n- [Pythoncheatsheet](https:\u002F\u002Fwww.pythoncheatsheet.org\u002F) - Quick reference for Python basics and advanced topics.\n- [Comprehensive Python Cheatsheet](https:\u002F\u002Fgithub.com\u002Fgto76\u002Fpython-cheatsheet) - Detailed Python functions and libraries.\n- [Python Cheatsheet](https:\u002F\u002Fgithub.com\u002Fwilfredinni\u002Fpython-cheatsheet) - A comprehensive cheatsheet for the Python programming language.\n- [Pysheeet](https:\u002F\u002Fgithub.com\u002Fcrazyguitar\u002Fpysheeet) - Concise Python cheat sheet for quick reference and interview prep.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"cheatsheets-data-science-machine-learning\">\u003C\u002Fa>\n\n### Data Science & Machine Learning\n\n- [DS Cheatsheets](https:\u002F\u002Fgithub.com\u002FFavioVazquez\u002Fds-cheatsheets) - List of Data Science Cheatsheets.\n- [DS Notes \\& Cheatsheets](https:\u002F\u002Fgithub.com\u002Fmerveenoyan\u002Fmy_notes) - Cheatsheets for data science, ML, computer science and more.\n- [Data Science Cheat Sheets (Math)](https:\u002F\u002Fgithub.com\u002Fml874\u002FData-Science-Cheatsheet) - Cheat sheets for quick reference in data science mathematics.\n- [Pandas Cheat Sheet](https:\u002F\u002Fpandas.pydata.org\u002FPandas_Cheat_Sheet.pdf) - Data manipulation with Pandas.\n- [PySpark Cheatsheet](https:\u002F\u002Fgithub.com\u002Fkevinschaich\u002Fpyspark-cheatsheet) - Common PySpark patterns.\n- [Machine Learning Cheat Sheet](https:\u002F\u002Fgithub.com\u002Fsoulmachine\u002Fmachine-learning-cheat-sheet) - Concise machine learning cheat sheets covering key concepts and equations.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"cheatsheets-linux-git\">\u003C\u002Fa>\n\n### Linux & Git\n\n- [Linux Cheatsheet](https:\u002F\u002Fgithub.com\u002Fgto76\u002Flinux-cheatsheet) - Linux commands and shortcuts.\n- [Linux Bash Commands](https:\u002F\u002Fgithub.com\u002Ftrinib\u002FLinux-Bash-Commands) - Comprehensive list of Linux\u002FBash commands for developers and sysadmins.\n- [Bash Awesome Cheatsheets](https:\u002F\u002Fgithub.com\u002FLeCoupa\u002Fawesome-cheatsheets\u002Fblob\u002Fmaster\u002Flanguages\u002Fbash.sh) - Bash scripting essentials.\n- [Unix Commands Reference](https:\u002F\u002Fgithub.com\u002FAdiBro\u002FData-Science-Resources\u002Fblob\u002Fmaster\u002FCheat-Sheets\u002FCL-Git\u002FUnix-Commands-Reference.pdf) - Unix terminal basics.\n- [GitHub Cheat Sheet](https:\u002F\u002Fgithub.com\u002Ftiimgreen\u002Fgithub-cheat-sheet) - Git\u002FGitHub workflows and tips.\n- [Git Awesome Cheatsheets](https:\u002F\u002Fgithub.com\u002FLeCoupa\u002Fawesome-cheatsheets\u002Fblob\u002Fmaster\u002Ftools\u002Fgit.sh) - Git commands and best practices.\n- [Git and Git Flow Cheat Sheet](https:\u002F\u002Fgithub.com\u002Farslanbilal\u002Fgit-cheat-sheet) - Branching strategies.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"cheatsheets-probability-statistics\">\u003C\u002Fa>\n\n### Probability & Statistics\n\n- [Stanford CME 106 Cheatsheets](https:\u002F\u002Fgithub.com\u002Fshervinea\u002Fstanford-cme-106-probability-and-statistics) - Probability and statistics for engineers.\n- [10-Page Probability Cheatsheet](https:\u002F\u002Fgithub.com\u002Fwzchen\u002Fprobability_cheatsheet) - In-depth probability concepts.\n- [Statistics Cheatsheet](https:\u002F\u002Fgithub.com\u002Fkhanhnamle1994\u002Fcracking-the-data-science-interview\u002Fblob\u002Fmaster\u002FCheatsheets\u002Fstats_cheatsheet.pdf) - Key statistical methods.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"cheatsheets-sql-databases\">\u003C\u002Fa>\n\n### SQL & Databases\n\n- [Quick SQL Cheatsheet](https:\u002F\u002Fgithub.com\u002Fenochtangg\u002Fquick-SQL-cheatsheet) - Handy SQL reference guide.\n- [PostgreSQL Cheatsheet](https:\u002F\u002Fwww.postgresonline.com\u002Fdownloads\u002Fspecial_feature\u002Fpostgresql83_psql_cheatsheet.pdf) - A handy reference for the most common PostgreSQL psql commands and queries.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"cheatsheets-miscellaneous\">\u003C\u002Fa>\n\n### Miscellaneous\n\n- [CheatSheet for CheatSheets](https:\u002F\u002Fgithub.com\u002Fplusminuschirag\u002FCheatSheet-for-CheatSheets) - Mega-repository of cheat sheets.\n- [Dataquest - Power BI Cheat Sheet](https:\u002F\u002Fwww.dataquest.io\u002Fcheat-sheet\u002Fpower-bi-cheat-sheet\u002F) - A helpful resource for Power BI users.\n- [Data Structures Cheat Sheet](https:\u002F\u002Fwww.clear.rice.edu\u002Fcomp160\u002Fdata_cheat.html) - A concise reference for common data structures and their properties.\n- [Matplotlib Cheatsheets](https:\u002F\u002Fgithub.com\u002Fmatplotlib\u002Fcheatsheets) - Official cheatsheets for the Matplotlib plotting library in Python.\n- [VSCode Awesome Cheatsheets](https:\u002F\u002Fgithub.com\u002FLeCoupa\u002Fawesome-cheatsheets\u002Fblob\u002Fmaster\u002Ftools\u002Fvscode.md) - VS Code shortcuts.\n- [Markdown Cheatsheet](https:\u002F\u002Fgithub.com\u002Ftchapi\u002Fmarkdown-cheatsheet) - Formatting for GitHub READMEs.\n- [Emoji Cheat Sheet](https:\u002F\u002Fgithub.com\u002Fikatyang\u002Femoji-cheat-sheet) - Emojis in Markdown.\n- [Docker Cheat Sheet](https:\u002F\u002Fgithub.com\u002Fwsargent\u002Fdocker-cheat-sheet) - Docker commands and workflows.\n- [Docker Awesome Cheatsheets](https:\u002F\u002Fgithub.com\u002FLeCoupa\u002Fawesome-cheatsheets\u002Fblob\u002Fmaster\u002Ftools\u002Fdocker.sh) - Containerization basics.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"additional-python-libraries\">\u003C\u002Fa>\n\n## 📦 Additional Python Libraries\n\nA collection of supplementary Python libraries that enhance development workflow, automate processes, and maintain project quality beyond core data analysis tools.\n\n### Code Quality & Development\n\n- [Black](https:\u002F\u002Fgithub.com\u002Fpsf\u002Fblack) - Uncompromising Python code formatter.\n- [Pre-commit](https:\u002F\u002Fgithub.com\u002Fpre-commit\u002Fpre-commit) - Framework for managing pre-commit hooks.\n- [Pylint](https:\u002F\u002Fgithub.com\u002Fpylint-dev\u002Fpylint) - Python code static analysis.\n- [Mypy](https:\u002F\u002Fgithub.com\u002Fpython\u002Fmypy) - Optional static typing for Python.\n- [Rich](https:\u002F\u002Fgithub.com\u002FTextualize\u002Frich) - Rich text and beautiful formatting in the terminal.\n- [Icecream](https:\u002F\u002Fgithub.com\u002Fgruns\u002Ficecream) - Debugging without using print.\n- [Pandas-log](https:\u002F\u002Fgithub.com\u002Feyaltrabelsi\u002Fpandas-log) - Logs pandas operations for data transformation tracking.\n- [PandasVet](https:\u002F\u002Fgithub.com\u002Fdeppen8\u002Fpandas-vet) - Code style validator for Pandas.\n- [Pydeps](https:\u002F\u002Fgithub.com\u002Fthebjorn\u002Fpydeps) - Python module dependency graphs.\n- [PyForest](https:\u002F\u002Fgithub.com\u002F8080labs\u002Fpyforest) - Automated Python imports for data science.\n- [Complexipy](https:\u002F\u002Fgithub.com\u002Frohaquinlop\u002Fcomplexipy) - Blazingly fast cognitive complexity analysis for Python, written in Rust.\n\n[⬆ back to contents](#contents)\n\n---\n\n### Documentation & File Processing\n\n- [Sphinx](https:\u002F\u002Fgithub.com\u002Fsphinx-doc\u002Fsphinx) - Documentation generator.\n- [Pdoc](https:\u002F\u002Fgithub.com\u002Fmitmproxy\u002Fpdoc) - API documentation for Python projects.\n- [Mkdocs](https:\u002F\u002Fgithub.com\u002Fmkdocs\u002Fmkdocs) - Project documentation with Markdown.\n- [OpenPyXL](https:\u002F\u002Fopenpyxl.readthedocs.io\u002Fen\u002Fstable\u002F) - Read\u002Fwrite Excel files.\n- [Tablib](https:\u002F\u002Fgithub.com\u002Fjazzband\u002Ftablib) - Exports data to XLSX, JSON, CSV.\n- [PyPDF2](https:\u002F\u002Fgithub.com\u002Fpy-pdf\u002FPyPDF2) - Reads and writes PDF files.\n- [Python-docx](https:\u002F\u002Fgithub.com\u002Fpython-openxml\u002Fpython-docx) - Reads and writes Word documents.\n- [CleverCSV](https:\u002F\u002Fgithub.com\u002Falan-turing-institute\u002FCleverCSV) - Smart CSV reader for messy data.\n- [Python-markdownify](https:\u002F\u002Fgithub.com\u002Fmatthewwithanm\u002Fpython-markdownify) - Convert HTML to Markdown.\n- [Xlwings](https:\u002F\u002Fgithub.com\u002Fxlwings\u002Fxlwings) - Integration of Python with Excel.\n- [Xmltodict](https:\u002F\u002Fgithub.com\u002Fmartinblech\u002Fxmltodict) - Converts XML to Python dictionaries.\n- [MarkItDown](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fmarkitdown) - Python tool for converting files and office documents to Markdown.\n- [Jupyter-book](https:\u002F\u002Fgithub.com\u002Fexecutablebooks\u002Fjupyter-book) - Build publication-quality books from Jupyter notebooks.\n- [WeasyPrint](https:\u002F\u002Fgithub.com\u002FKozea\u002FWeasyPrint) - Convert HTML to PDF.\n- [PyMuPDF](https:\u002F\u002Fgithub.com\u002Fpymupdf\u002FPyMuPDF) - Advanced PDF manipulation library.\n- [Camelot](https:\u002F\u002Fgithub.com\u002Fcamelot-dev\u002Fcamelot) - PDF table extraction library.\n- [Marker](https:\u002F\u002Fgithub.com\u002Fdatalab-to\u002Fmarker) - Fast, high-accuracy PDF and document conversion tool with layout preservation.\n\n[⬆ back to contents](#contents)\n\n---\n\n### Web & APIs\n\n- [HTTPX](https:\u002F\u002Fgithub.com\u002Fencode\u002Fhttpx) - Next-generation HTTP client for Python.\n- [FastAPI](https:\u002F\u002Fgithub.com\u002Ffastapi\u002Ffastapi) - Modern web framework for building APIs.\n- [Flask](https:\u002F\u002Fgithub.com\u002Fpallets\u002Fflask) - Lightweight Python web framework for building applications and APIs.\n- [Typer](https:\u002F\u002Fgithub.com\u002Ffastapi\u002Ftyper) - Library for building CLI applications.\n- [Requests-cache](https:\u002F\u002Fgithub.com\u002Freclosedev\u002Frequests-cache) - Persistent caching for requests library.\n- [Aiohttp](https:\u002F\u002Fgithub.com\u002Faio-libs\u002Faiohttp) - Asynchronous HTTP client\u002Fserver framework for asyncio and Python.\n\n[⬆ back to contents](#contents)\n\n---\n\n### Miscellaneous\n\n- [UV](https:\u002F\u002Fgithub.com\u002Fastral-sh\u002Fuv) - An extremely fast Python package installer and resolver.\n- [Funcy](https:\u002F\u002Fgithub.com\u002FSuor\u002Ffuncy) - Fancy functional tools for Python.\n- [Pillow](https:\u002F\u002Fgithub.com\u002Fpython-pillow\u002FPillow) - Image processing library.\n- [Ftfy](https:\u002F\u002Fgithub.com\u002Frspeer\u002Fpython-ftfy) - Fixes broken Unicode strings.\n- [JmesPath](https:\u002F\u002Fgithub.com\u002Fjmespath\u002Fjmespath.py) - Queries JSON data (SQL-like for JSON).\n- [Glom](https:\u002F\u002Fgithub.com\u002Fmahmoud\u002Fglom) - Transforms nested data structures.\n- [Diagrams](https:\u002F\u002Fgithub.com\u002Fmingrammer\u002Fdiagrams) - Diagrams as code for cloud architecture.\n- [Pytest](https:\u002F\u002Fgithub.com\u002Fpytest-dev\u002Fpytest) - Framework for writing small tests.\n- [Pampy](https:\u002F\u002Fgithub.com\u002Fsantinic\u002Fpampy) - Pattern matching for Python dictionaries.\n- [Pygorithm](https:\u002F\u002Fgithub.com\u002FOmkarPathak\u002Fpygorithm) - A Python module for learning all major algorithms.\n- [GitPython](https:\u002F\u002Fgithub.com\u002Fgitpython-developers\u002FGitPython) - A Python library used to interact with Git repositories.\n- [TQDM](https:\u002F\u002Fgithub.com\u002Ftqdm\u002Ftqdm) - Progress bars for loops and operations.\n- [Loguru](https:\u002F\u002Fgithub.com\u002FDelgan\u002Floguru) - Python logging made simple.\n- [Click](https:\u002F\u002Fgithub.com\u002Fpallets\u002Fclick) - Beautiful command line interfaces.\n- [Poetry](https:\u002F\u002Fgithub.com\u002Fpython-poetry\u002Fpoetry) - Python dependency management and packaging.\n- [Hydra](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fhydra) - Elegant configuration management.\n- [papermill](https:\u002F\u002Fgithub.com\u002Fnteract\u002Fpapermill) - Tool for parameterizing and executing Jupyter notebooks programmatically.\n- [Python Telegram Bot](https:\u002F\u002Fgithub.com\u002Fpython-telegram-bot\u002Fpython-telegram-bot) - Pure Python framework for the Telegram Bot API with async support.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"more-awesome-curations\">\u003C\u002Fa>\n\n## 📝 More Awesome Lists\n\nA curated list of other awesome lists on various topics and technologies.\n\n- [Awesome](https:\u002F\u002Fgithub.com\u002Fsindresorhus\u002Fawesome) - A curated list of awesome lists.\n- [Freecodecamp](https:\u002F\u002Fgithub.com\u002FfreeCodeCamp\u002FfreeCodeCamp) - Open source platform with thousands of interactive lessons for learning web development.\n- [Awesome Big Data](https:\u002F\u002Fgithub.com\u002Foxnr\u002Fawesome-bigdata) - A curated list of awesome big data frameworks, resources, and tools.\n- [Awesome Geospatial](https:\u002F\u002Fgithub.com\u002Fsacridini\u002FAwesome-Geospatial) - A curated list of awesome geospatial libraries, tools, and resources.\n- [Awesome Chatgpt Prompts](https:\u002F\u002Fgithub.com\u002Ff\u002Fawesome-chatgpt-prompts) - A repository for ChatGPT prompt curation.\n- [Awesome Jupyter](https:\u002F\u002Fgithub.com\u002Fmarkusschanta\u002Fawesome-jupyter) - Curated list of Jupyter projects, libraries, and resources.\n- [Awesome Business Intelligence](https:\u002F\u002Fgithub.com\u002Fthenaturalist\u002Fawesome-business-intelligence) - Actively curated list of awesome BI tools.\n- [Awesome Prompt Engineering](https:\u002F\u002Fgithub.com\u002Fpromptslab\u002FAwesome-Prompt-Engineering) - A curated list of resources for prompt engineering with LLMs like ChatGPT.\n- [Awesome Product Design](https:\u002F\u002Fgithub.com\u002Fttt30ga\u002Fawesome-product-design) - A collection of bookmarks, resources, articles about product design.\n- [Awesome Shell](https:\u002F\u002Fgithub.com\u002Falebcay\u002Fawesome-shell) - A curated list of awesome command-line frameworks, toolkits, and guides.\n- [Awesome FastAPI](https:\u002F\u002Fgithub.com\u002Fmjhea0\u002Fawesome-fastapi) - A curated list of awesome FastAPI frameworks, libraries, and resources.\n- [Awesome Linux Software](https:\u002F\u002Fgithub.com\u002Fluong-komorebi\u002FAwesome-Linux-Software) - A list of awesome applications and tools for Linux.\n- [Awesome Product Management](https:\u002F\u002Fgithub.com\u002Fdend\u002Fawesome-product-management) - A curated list of resources for product managers and aspiring PMs.\n- [Awesome Python Applications](https:\u002F\u002Fgithub.com\u002Fmahmoud\u002Fawesome-python-applications) - A list of free software and applications written in Python.\n- [Awesome AutoHotkey](https:\u002F\u002Fgithub.com\u002Fahkscript\u002Fawesome-AutoHotkey) - A curated list of awesome AutoHotkey libraries, scripts, and resources.\n- [Awesome Productivity](https:\u002F\u002Fgithub.com\u002Fjyguyomarch\u002Fawesome-productivity) - A curated list of delightful productivity resources.\n- [Awesome Scientific Writing](https:\u002F\u002Fgithub.com\u002Fwriting-resources\u002Fawesome-scientific-writing) - A curated list of resources for scientific writing, publishing, and research.\n- [Awesome LaTeX](https:\u002F\u002Fgithub.com\u002Fegeerardyn\u002Fawesome-LaTeX) - A curated list of LaTeX resources, libraries, and tools.\n- [Awesome Actions](https:\u002F\u002Fgithub.com\u002Fsdras\u002Fawesome-actions) - A curated list of awesome GitHub Actions for automation.\n- [Awesome Quarto](https:\u002F\u002Fgithub.com\u002Fmcanouil\u002Fawesome-quarto) - A curated list of Quarto resources, including talks, tools, examples, and articles. Contributions are welcome!\n- [Awesome Vscode](https:\u002F\u002Fgithub.com\u002Fviatsko\u002Fawesome-vscode) - A comprehensive list of useful VS Code extensions and resources.\n- [Awesome Readme](https:\u002F\u002Fgithub.com\u002Fmatiassingers\u002Fawesome-readme) - Collection of well-crafted README files for inspiration.\n- [Awesome GitHub Profile Readme](https:\u002F\u002Fgithub.com\u002Fabhisheknaiidu\u002Fawesome-github-profile-readme) - A collection of awesome GitHub profile READMEs and resources.\n- [Awesome Code Review](https:\u002F\u002Fgithub.com\u002Fjoho\u002Fawesome-code-review?tab=readme-ov-file#awesome-code-review-) - A collection of resources for code review practices.\n- [Awesome Certificates](https:\u002F\u002Fgithub.com\u002FPanXProject\u002Fawesome-certificates) - A curated list of IT and developer certifications and learning resources.\n- [Awesome Tunneling](https:\u002F\u002Fgithub.com\u002Fanderspitman\u002Fawesome-tunneling) - A list of ngrok alternatives and tunneling software.\n- [Anomaly Detection Resources](https:\u002F\u002Fgithub.com\u002Fyzhao062\u002Fanomaly-detection-resources) - Books, papers, videos, and toolboxes related to anomaly detection.\n- [Awesome Claude Prompts](https:\u002F\u002Fgithub.com\u002Flanggptai\u002Fawesome-claude-prompts) - Collection of powerful prompts for Anthropic's Claude AI.\n- [Awesome Linux](https:\u002F\u002Fgithub.com\u002Finputsh\u002Fawesome-linux) - Curated list of Linux applications, tools, and resources for users and developers.\n- [Awesome for Beginners](https:\u002F\u002Fgithub.com\u002FMunGell\u002Fawesome-for-beginners) - List of beginner-friendly projects for contributing to open-source software.\n- [Best websites a programmer should visit](https:\u002F\u002Fgithub.com\u002Fsdmg15\u002FBest-websites-a-programmer-should-visit) - Curated list of helpful websites for programmers and engineers.\n- [Awesome Creative Coding](https:\u002F\u002Fgithub.com\u002Fterkelg\u002Fawesome-creative-coding) - Curated list of creative coding resources and libraries.\n- [Awesome AI in Finance](https:\u002F\u002Fgithub.com\u002Fgeorgezouq\u002Fawesome-ai-in-finance) - Curated list of AI applications, tools, and research in finance.\n- [Awesome Algorithms](https:\u002F\u002Fgithub.com\u002Ftayllan\u002Fawesome-algorithms) - Collection of resources for learning and practicing algorithms and data structures.\n- [Awesome Serverless](https:\u002F\u002Fgithub.com\u002Fanaibol\u002Fawesome-serverless) - Curated resources for serverless architectures and cloud computing.\n- [Awesome R](https:\u002F\u002Fgithub.com\u002Fqinwf\u002Fawesome-R) - Curated list of R packages, frameworks, and learning resources.\n- [Awesome AI System Prompts](https:\u002F\u002Fgithub.com\u002Fdontriskit\u002Fawesome-ai-system-prompts) - Collection of effective system prompts for various AI models.\n- [Awesome Osint](https:\u002F\u002Fgithub.com\u002Fjivoi\u002Fawesome-osint) - Curated list of Open Source Intelligence (OSINT) tools and resources.\n- [Awesome Telegram](https:\u002F\u002Fgithub.com\u002Febertti\u002Fawesome-telegram) - Collection of Telegram bots, channels, and tools for developers.\n- [Free for Dev](https:\u002F\u002Fgithub.com\u002Fripienaar\u002Ffree-for-dev) - List of SaaS, PaaS, and IaaS offerings with free developer tiers.\n- [Font-Awesome](https:\u002F\u002Fgithub.com\u002FFortAwesome\u002FFont-Awesome) - Icon library and toolkit for scalable vector graphics on the web.\n- [Awesome Docs](https:\u002F\u002Fgithub.com\u002Ftestthedocs\u002Fawesome-docs) - Curated list of essential tools and resources for creating great documentation.\n- [Awesome Testing](https:\u002F\u002Fgithub.com\u002FTheJambo\u002Fawesome-testing) - Curated list of software testing resources: tools, frameworks, books, and best practices.\n- [Awesome Graphql](https:\u002F\u002Fgithub.com\u002Fchentsulin\u002Fawesome-graphql) - Comprehensive collection of resources, libraries, and tools for working with GraphQL.\n- [Awesome Remote Job](https:\u002F\u002Fgithub.com\u002Flukasz-madon\u002Fawesome-remote-job) - Resources, tips, and tools for finding and thriving in remote work.\n- [Awesome Asyncio](https:\u002F\u002Fgithub.com\u002Ftimofurrer\u002Fawesome-asyncio) - Curated list of frameworks, libraries, and utilities for asyncio-based Python programming.\n- [Awesome Zsh Plugins](https:\u002F\u002Fgithub.com\u002Funixorn\u002Fawesome-zsh-plugins) - Massive collection of plugins, themes, and resources for customizing Zsh.\n- [Awesome Scalability](https:\u002F\u002Fgithub.com\u002Fbinhnguyennus\u002Fawesome-scalability) - Structured guide to design patterns for building scalable and reliable systems.\n- [Books](https:\u002F\u002Fgithub.com\u002Flinsa-io\u002Fbooks) - Collection of links to free technical books on programming, databases, DevOps, and analytics.\n- [Free Programming Books](https:\u002F\u002Fgithub.com\u002FEbookFoundation\u002Ffree-programming-books) - Largest multilingual collection of free programming books and learning materials.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"additional-resources\">\u003C\u002Fa>\n\n## 🌐 Additional Resources and Tools\n\nA wide range of resources and tools designed to facilitate learning, development, and exploration across different domains.\n\n- [OSSU Computer Science](https:\u002F\u002Fgithub.com\u002Fossu\u002Fcomputer-science) - Path to a free, self-taught education in computer science.\n- [UC Berkeley - Data 8](https:\u002F\u002Fgithub.com\u002Fdata-8\u002Ftextbook) - Course materials for the Data Science Foundations course.\n- [PaddleOCR](https:\u002F\u002Fgithub.com\u002FPaddlePaddle\u002FPaddleOCR) - Production-ready OCR toolkit with multilingual and document AI support.\n- [A collective list of free APIs](https:\u002F\u002Fgithub.com\u002Fpublic-apis\u002Fpublic-apis) - A comprehensive list of free APIs for various purposes.\n- [arXiv.org](https:\u002F\u002Farxiv.org\u002F) - A free distribution service and open-access archive for scholarly articles.\n- [Elicit](https:\u002F\u002Felicit.com\u002F) - An AI research assistant that helps automate parts of literature review.\n- [500+ AI\u002FML\u002FDL\u002FNLP Projects](https:\u002F\u002Fgithub.com\u002Fashishpatel26\u002F500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code) - A massive collection of AI and machine learning projects with code for learning and portfolios.\n- [Full Stack Fastapi Template](https:\u002F\u002Fgithub.com\u002Ffastapi\u002Ffull-stack-fastapi-template) - Full-stack template with FastAPI, React, and PostgreSQL.\n- [Kittl](https:\u002F\u002Fwww.kittl.com\u002F) - Platform for creating and editing charts and data visualizations.\n- [Zasper](https:\u002F\u002Fgithub.com\u002Fzasper-io\u002Fzasper) - High Performace IDE for Jupyter Notebooks.\n- [Sketch](https:\u002F\u002Fwww.sketch.com\u002F) - Toolkit designed for designers, focusing on their workflow.\n- [Growth.Design](https:\u002F\u002Fgrowth.design\u002F) - A collection of product case studies and behavioral psychology insights for data-driven decision-making.\n- [Markdown Badges](https:\u002F\u002Fgithub.com\u002FIleriayo\u002Fmarkdown-badges) - Collection of badges for GitHub profiles and Markdown files.\n- [CS Video Courses](https:\u002F\u002Fgithub.com\u002FDeveloper-Y\u002Fcs-video-courses) - Curated list of free university computer science video courses.\n- [Build Your Own X](https:\u002F\u002Fgithub.com\u002Fcodecrafters-io\u002Fbuild-your-own-x) - Tutorials on how to build your own technology from scratch.\n- [What Happens When](https:\u002F\u002Fgithub.com\u002Falex\u002Fwhat-happens-when) - Technical explanation of what happens when you type a URL and press Enter.\n- [Devops Exercises](https:\u002F\u002Fgithub.com\u002Fbregman-arie\u002Fdevops-exercises) - Extensive collection of exercises and questions for DevOps and Linux interview prep.\n- [Free Certifications](https:\u002F\u002Fgithub.com\u002Fcloudcommunity\u002FFree-Certifications) - Regularly updated list of free certification courses from top cloud and tech companies.\n- [A To Z Resources For Students](https:\u002F\u002Fgithub.com\u002Fdipakkr\u002FA-to-Z-Resources-for-Students) - Comprehensive list of free resources for students learning programming and tech.\n- [Summer Internships](https:\u002F\u002Fgithub.com\u002FSimplifyJobs\u002FSummer2026-Internships) - Up-to-date list of summer internships in tech with deadline tracking.\n- [Football Analytics](https:\u002F\u002Fgithub.com\u002Feddwebster\u002Ffootball_analytics) - Open learning course and toolkit for football data analysis with Python and R.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"contributing\">\u003C\u002Fa>\n\n## 🤝 Contributing\n\n**We welcome your contributions!**\n\nSee [CONTRIBUTING.md](https:\u002F\u002Fgithub.com\u002FPavelGrigoryevDS\u002Fawesome-data-analysis\u002Fblob\u002Fmain\u002FCONTRIBUTING.md) for how to add resources.\n\n[⬆ back to contents](#contents)\n\n---\n\n\u003Ca id=\"license\">\u003C\u002Fa>\n\n## 📜 License\n\n[![CC0](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FPavelGrigoryevDS_awesome-data-analysis_readme_b7657951a0bb.png)](http:\u002F\u002Fcreativecommons.org\u002Fpublicdomain\u002Fzero\u002F1.0\u002F)\n\nThis work is dedicated to the public domain under the [CC0 1.0 Universal](https:\u002F\u002Fcreativecommons.org\u002Fpublicdomain\u002Fzero\u002F1.0\u002F) license.\n\n[⬆ back to contents](#contents)\n","# 令人惊叹的数据分析 [![Awesome](https:\u002F\u002Fcdn.rawgit.com\u002Fsindresorhus\u002Fawesome\u002Fd7305f38d29fed78fa85652e3a63e154dd8e8829\u002Fmedia\u002Fbadge.svg)](https:\u002F\u002Fgithub.com\u002Fsindresorhus\u002Fawesome)\n\n[![网页](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F🌐_Web_Page-696969)](https:\u002F\u002Fpavelgrigoryevds.github.io\u002Fawesome-data-analysis\u002F)\n[![欢迎提交PR](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPRs-welcome-brightgreen.svg)](http:\u002F\u002Fmakeapullrequest.com)\n[![CC0](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLicense-CC0_1.0-lightgrey.svg)](https:\u002F\u002Fcreativecommons.org\u002Fpublicdomain\u002Fzero\u002F1.0\u002F)\n\n500+ 精选数据分析与数据科学资源：工具、库、学习路线图、速查表、面试指南等。\n\n**📖 为了更舒适的阅读体验：** [网页版](https:\u002F\u002Fpavelgrigoryevds.github.io\u002Fawesome-data-analysis\u002F)\n\n**🌱 想要改进吗？** [在此提出建议](https:\u002F\u002Fgithub.com\u002FPavelGrigoryevDS\u002Fawesome-data-analysis\u002Fissues\u002F16) 或 [欢迎参与讨论](https:\u002F\u002Fgithub.com\u002FPavelGrigoryevDS\u002Fawesome-data-analysis\u002Fdiscussions)\n\n🌟 让我们一起让数据分析更加高效！![GitHub 星标](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002FPavelGrigoryevDS\u002Fawesome-data-analysis?style=social)\n\n用心维护\n\n---\n\n\u003Ca id=\"contents\">\u003C\u002Fa>\n\n## 📑 目录\n\n- [🏆 优秀数据科学仓库](#awesome-data-science-repositories)\n- [🗺️ 学习路线图](#roadmaps)\n- [🐍 Python](#python)\n  - [资源](#python-resources)\n  - [使用 Pandas 和 NumPy 进行数据处理](#python-data-manipulation-with-pandas-and-numpy)\n  - [用于数据分析的实用 Python 工具](#python-useful-python-tools-for-data-analysis)\n    - [数据处理与转换](#python-data-processing-transformation)\n    - [自动化 EDA 和可视化工具](#python-automated-data-visualization-tools)\n    - [数据质量与验证](#python-data-quality-validation)\n    - [特征工程与选择](#python-feature-engineering-selection)\n    - [专用数据工具](#python-specialized-data-tools)\n- [🗃️ SQL 与数据库](#sql-databases)\n  - [资源](#sql-databases-resources)\n  - [工具](#sql-databases-tools)\n- [📊 数据可视化](#data-visualization)\n  - [资源](#data-visualization-resources)\n  - [工具](#data-visualization-tools)\n- [📈 仪表板与商业智能](#dashboards)\n  - [资源](#dashboards-resources)\n  - [工具](#dashboards-tools)\n  - [软件](#dashboards-software)\n- [🕸️ 网页抓取与爬虫](#web-scraping-crawling)\n  - [资源](#web-scraping-crawling-resources)\n  - [工具](#web-scraping-crawling-tools)\n- [🔢 数学](#mathematics)\n- [🎲 统计学与概率论](#statistics-probability)\n  - [资源](#statistics-probability-resources)\n  - [工具](#statistics-probability-tools)\n- [🧪 A\u002FB 测试](#ab-testing)\n- [⏳ 时间序列分析](#time-series-analysis)\n  - [资源](#time-series-analysis-resources)\n  - [工具](#time-series-analysis-tools)\n- [⚙️ 数据工程](#data-engineering)\n  - [资源](#data-engineering-resources)\n  - [工具](#data-engineering-tools)\n- [📖 自然语言处理 (NLP)](#natural-language-processing-nlp)\n  - [资源](#natural-language-processing-nlp-resources)\n  - [工具](#natural-language-processing-nlp-tools)\n- [🤖 机器学习与人工智能](#machine-learning)\n  - [资源](#machine-learning-resources)\n  - [工具](#machine-learning-tools)\n- [🚀 MLOps](#mlops)\n  - [资源](#mlops-resources)\n  - [工具](#mlops-tools)\n- [🧠 AI 应用与平台](#ai-applications)\n  - [资源](#ai-applications-resources)\n  - [工具](#ai-applications-tools)\n- [☁️ 云平台与基础设施](#cloud-platforms)\n  - [资源](#cloud-platform-resources)\n  - [工具](#cloud-platform-tools)\n- [⚡ 生产效率](#productivity)\n  - [资源](#productivity-resources)\n  - [实用的 Linux 工具](#productivity-useful-linux-tools)\n  - [有用的 VS Code 扩展](#productivity-useful-vs-code-extensions)\n- [📚 技能提升与职业发展](#skill-development-career-resources)\n  - [练习资源](#skill-development-career-resources-practice-resources)\n  - [精选 Jupyter 笔记本](#skill-development-career-resources-curated-jupyter-notebooks)\n  - [数据源与数据集](#skill-development-career-resources-data-sources-datasets)\n  - [简历和面试技巧](#skill-development-career-resources-resume-and-interview-tips)\n- [📋 速查表](#cheatsheets)\n  - [GoalKicker 编程笔记](#cheatsheets-goalkicker)\n  - [Python](#cheatsheets-python)\n  - [数据科学与机器学习](#cheatsheets-data-science-machine-learning)\n  - [Linux 与 Git](#cheatsheets-linux-git)\n  - [概率与统计](#cheatsheets-probability-statistics)\n  - [SQL 与数据库](#cheatsheets-sql-databases)\n  - [其他](#cheatsheets-miscellaneous)\n- [📦 其他 Python 库](#additional-python-libraries)\n- [📝 更多 Awesome 列表](#more-awesome-curations)\n- [🌐 其他资源和工具](#additional-resources)\n- [🤝 贡献](#contributing)\n- [📜 许可证](#license)\n\n---\n\n\u003Ca id=\"awesome-data-science-repositories\">\u003C\u002Fa>\n\n## 🏆 优秀数据科学仓库\n\n精心挑选的高质量 GitHub 仓库集合，供您获取灵感和学习参考。\n\n- [Awesome Data Science](https:\u002F\u002Fgithub.com\u002Facademic\u002Fawesome-datascience) - 一个精选的数据科学课程、书籍、工具和资源列表。\n- [面向初学者的数据科学](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FData-Science-For-Beginners) - 微软提供的数据科学课程体系。\n- [OSSU 数据科学](https:\u002F\u002Fgithub.com\u002Fossu\u002Fdata-science) - 开放源代码社会大学的自学路径。\n- [数据科学最佳资源](https:\u002F\u002Fgithub.com\u002Ftirthajyoti\u002FData-science-best-resources) - 精心整理的数据科学资源链接合集。\n- [来自 CodeCut 的数据科学文章](https:\u002F\u002Fgithub.com\u002FCodeCutTech\u002FData-science) - 一系列关于数据科学的文章、视频和代码。\n- [使用 Python 进行数据分析](https:\u002F\u002Fgithub.com\u002FWillKoehrsen\u002FData-Analysis) - 提供使用 Python 进行数据分析的相关资源。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"roadmaps\">\u003C\u002Fa>\n\n## 🗺️ 路线图\n\n逐步指南和技能树，助你掌握数据科学和分析。\n\n- [数据分析师路线图](https:\u002F\u002Froadmap.sh\u002Fdata-analyst) - 面向分析师的结构化学习路径。\n- [从A到Z的数据科学路线图](https:\u002F\u002Fgithub.com\u002FMoataz-Elmesmary\u002FData-Science-Roadmap) - 数据科学的全面路线图。\n- [学习数据科学的路线图](https:\u002F\u002Fgithub.com\u002Fkrishnaik06\u002FPerfect-Roadmap-To-Learn-Data-Science-In-2025) - 一份全面且更新的学习数据科学路线图，涵盖现代工具与技术。\n- [66天数据之旅](https:\u002F\u002Fgithub.com\u002Fmrankitgupta\u002FData-Analyst-Roadmap) - 为期66天的数据分析学习挑战。\n- [面向专业人士的数据分析师路线图](https:\u002F\u002Fgithub.com\u002Fhemansnation\u002FData-Analyst-Roadmap) - 针对各层次分析师的8周课程。\n- [数据科学路线图教程](https:\u002F\u002Fgithub.com\u002FMrMimic\u002Fdata-scientist-roadmap) - 数据科学路线图的相关教程。\n- [从零开始的数据分析师路线图](https:\u002F\u002Fgithub.com\u002Fmtahiraslan\u002Fdata-analyst-roadmap) - 从零起步成为数据分析师的指南。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"python\">\u003C\u002Fa>\n\n## 🐍 Python\n\n\u003Ca id=\"python-resources\">\u003C\u002Fa>\n\n### 资源\n\n用于学习和精通Python编程的资源合集。\n\n- [Awesome Python](https:\u002F\u002Fgithub.com\u002Fvinta\u002Fawesome-python) - 一份精选的Python框架、库、软件及资源列表。\n- [30天学Python](https:\u002F\u002Fgithub.com\u002FAsabeneh\u002F30-Days-Of-Python) - 一个为期30天的Python编程学习挑战。\n- [Real Python教程](https:\u002F\u002Frealpython.com\u002F) - Real Python提供的Python教程。\n- [Awesome Python数据科学](https:\u002F\u002Fgithub.com\u002Fkrzjoa\u002Fawesome-python-data-science) - 精选的Python数据科学资源列表。\n- [Python数据科学手册](https:\u002F\u002Fgithub.com\u002Fjakevdp\u002FPythonDataScienceHandbook) - 《Python数据科学手册》的完整文本，以Jupyter Notebook形式呈现。\n- [交互式编码挑战](https:\u002F\u002Fgithub.com\u002Fdonnemartin\u002Finteractive-coding-challenges) - 超过120个交互式的Python面试编码挑战。\n- [Clean Code Python](https:\u002F\u002Fgithub.com\u002Fzedr\u002Fclean-code-python) - 适用于Python的整洁代码理念。\n- [Python最佳资源](https:\u002F\u002Fgithub.com\u002Fml-tooling\u002Fbest-of-python) - 一份排名靠前的Python开源库和工具列表。\n- [GeeksforGeeks Python](https:\u002F\u002Fwww.geeksforgeeks.org\u002Fpython-programming-language-tutorial\u002F) - GeeksforGeeks提供的Python教程。\n- [W3Schools Python](https:\u002F\u002Fwww.w3schools.com\u002Fpython\u002F) - 适合初学者的Python编程语言教程与参考。\n- [Tanu N Prabhu Python](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster) - 此仓库帮助你从零开始理解Python。\n- [Think Python](https:\u002F\u002Fgithub.com\u002FAllenDowney\u002FThinkPython) - Allen Downey的《Think Python》配套Jupyter笔记本及其他资源。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"python-data-manipulation-with-pandas-and-numpy\">\u003C\u002Fa>\n\n### 使用Pandas和NumPy进行数据处理\n\n关于如何使用Pandas和NumPy的教程及最佳实践。\n\n- [Awesome Pandas](https:\u002F\u002Fgithub.com\u002Ftommyod\u002Fawesome-pandas) - 一份精选的Pandas库使用资源列表。\n- [100个Pandas数据谜题](https:\u002F\u002Fgithub.com\u002Fajcr\u002F100-pandas-puzzles) - 一系列用于练习Pandas技能的数据谜题。\n- [Pandas Tutor](https:\u002F\u002Fpandastutor.com\u002F) - 逐步可视化Pandas操作（非常适合初学者）。\n- [Pandas练习](https:\u002F\u002Fgithub.com\u002Fguipsamora\u002Fpandas_exercises) - 旨在提升Pandas技能的练习。\n- [Pandas食谱](https:\u002F\u002Fgithub.com\u002Fjvns\u002Fpandas-cookbook) - 包含多种高效使用Pandas技巧的食谱。\n- [动手实践：使用Pandas进行数据分析](https:\u002F\u002Fgithub.com\u002Fstefmolin\u002FHands-On-Data-Analysis-with-Pandas-2nd-edition) - 用于配合《动手实践：使用Pandas进行数据分析》一书学习的材料。\n- [高效Pandas](https:\u002F\u002Fgithub.com\u002FTomAugspurger\u002Feffective-pandas) - 专注于编写高效且符合Python习惯用法的Pandas代码系列。\n- [从Python到NumPy](https:\u002F\u002Fgithub.com\u002Frougier\u002Ffrom-python-to-numpy) - 一本关于向量化及使用NumPy进行高效数值计算的开放获取书籍。\n- [NumPy 100个练习](https:\u002F\u002Fgithub.com\u002Frougier\u002Fnumpy-100) - 一套包含100个练习的资源，帮助掌握用于科学计算的NumPy库。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"python-useful-python-tools-for-data-analysis\">\u003C\u002Fa>\n\n### 用于数据分析的实用Python工具\n\n一系列用于高效数据处理、清洗、可视化、验证和分析的Python库。\n\n\u003Ca id=\"python-data-processing-transformation\">\u003C\u002Fa>\n\n#### 数据处理与转换\n\n- [Pandas](https:\u002F\u002Fgithub.com\u002Fpandas-dev\u002Fpandas) - 功能强大的 Python 数据分析与操作库，提供灵活的数据结构。\n- [NumPy](https:\u002F\u002Fgithub.com\u002Fnumpy\u002Fnumpy) - Python 中用于科学计算的基础包，支持多维数组。\n- [Pandas DQ](https:\u002F\u002Fgithub.com\u002FAutoViML\u002Fpandas_dq) - 用于数据类型校正和 DataFrame 自动清洗的工具。\n- [Vaex](https:\u002F\u002Fgithub.com\u002Fvaexio\u002Fvaex) - 高性能的 Python 库，支持惰性加载的大规模 DataFrame。\n- [Polars](https:\u002F\u002Fgithub.com\u002Fpola-rs\u002Fpolars) - 面向 DataFrame 的多线程、向量化查询引擎。\n- [Fugue](https:\u002F\u002Fgithub.com\u002Ffugue-project\u002Ffugue) - 提供 Pandas、Spark 和 Dask 的统一接口。\n- [TheFuzz](https:\u002F\u002Fgithub.com\u002Fseatgeek\u002Fthefuzz) - 模糊字符串匹配（Levenshtein 距离）。\n- [DateUtil](https:\u002F\u002Fgithub.com\u002Fdateutil\u002Fdateutil) - 扩展标准 Python datetime 功能的工具。\n- [Arrow](https:\u002F\u002Fgithub.com\u002Farrow-py\u002Farrow) - 增强日期和时间处理功能。\n- [Pendulum](https:\u002F\u002Fgithub.com\u002Fsdispater\u002Fpendulum) - 支持时区的 datetime 替代品。\n- [Dask](https:\u002F\u002Fgithub.com\u002Fdask\u002Fdask) - 用于数组和 DataFrame 的并行计算框架。\n- [Modin](https:\u002F\u002Fgithub.com\u002Fmodin-project\u002Fmodin) - 通过分布式计算加速 Pandas。\n- [Pandarallel](https:\u002F\u002Fgithub.com\u002Fnalepae\u002Fpandarallel) - 为 Pandas DataFrame 提供并行操作。\n- [DataCleaner](https:\u002F\u002Fgithub.com\u002Frhiever\u002Fdatacleaner) - 自动清理和准备数据集的 Python 工具。\n- [Pandas Flavor](https:\u002F\u002Fgithub.com\u002FZsailer\u002Fpandas_flavor) - 为 Pandas 添加自定义方法。\n- [Pandas DataReader](https:\u002F\u002Fgithub.com\u002Fpydata\u002Fpandas-datareader) - 从各种在线源读取数据到 Pandas DataFrame。\n- [Sklearn Pandas](https:\u002F\u002Fgithub.com\u002Fscikit-learn-contrib\u002Fsklearn-pandas) - 连接 Pandas 和 Scikit-learn 的桥梁。\n- [CuPy](https:\u002F\u002Fgithub.com\u002Fcupy\u002Fcupy) - 一个兼容 NumPy 的数组库，利用 NVIDIA CUDA 加速高性能计算。\n- [Numba](https:\u002F\u002Fgithub.com\u002Fnumba\u002Fnumba) - 一种 JIT 编译器，可将 Python 和 NumPy 的子集代码转换为高效的机器码。\n- [Pandas Stubs](https:\u002F\u002Fgithub.com\u002Fpandas-dev\u002Fpandas-stubs) - Pandas 的类型存根文件，改善 IDE 自动补全功能。\n- [Petl](https:\u002F\u002Fgithub.com\u002Fpetl-developers\u002Fpetl) - 用于数据清洗和转换的 ETL 工具。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"python-automated-data-visualization-tools\">\u003C\u002Fa>\n\n#### 自动化 EDA 和可视化工具\n\n- [AutoViz](https:\u002F\u002Fgithub.com\u002FAutoViML\u002FAutoViz) - 一行代码即可实现自动数据可视化。\n- [Sweetviz](https:\u002F\u002Fgithub.com\u002Ffbdesignpro\u002Fsweetviz) - 自动 EDA 并支持数据集比较。\n- [Lux](https:\u002F\u002Fgithub.com\u002Flux-org\u002Flux) - 在 Jupyter 中自动可视化 DataFrame。\n- [YData Profiling](https:\u002F\u002Fgithub.com\u002Fydataai\u002Fydata-profiling) - 数据质量剖析与探索性数据分析。\n- [Missingno](https:\u002F\u002Fgithub.com\u002FResidentMario\u002Fmissingno) - 可视化缺失数据模式。\n- [Vizro](https:\u002F\u002Fgithub.com\u002Fmckinsey\u002Fvizro) - 低代码工具箱，用于构建数据可视化应用。\n- [Yellowbrick](https:\u002F\u002Fgithub.com\u002FDistrictDataLabs\u002Fyellowbrick) - 机器学习的可视化诊断工具。\n- [Great Tables](https:\u002F\u002Fgithub.com\u002Fposit-dev\u002Fgreat-tables) - 使用 Python 创建精美的展示表格。\n- [DataMapPlot](https:\u002F\u002Fgithub.com\u002FTutteInstitute\u002Fdatamapplot) - 制作精美的数据地图图表。\n- [Datashader](https:\u002F\u002Fgithub.com\u002Fholoviz\u002Fdatashader) - 快速且准确地渲染超大规模数据。\n- [PandasAI](https:\u002F\u002Fgithub.com\u002Fsinaptik-ai\u002Fpandas-ai) - 使用 LLM 和 RAG 进行对话式数据分析。\n- [Mito](https:\u002F\u002Fgithub.com\u002Fmito-ds\u002Fmito) - Jupyter 扩展，提升代码编写效率。\n- [D-Tale](https:\u002F\u002Fgithub.com\u002Fman-group\u002Fdtale) - 浏览器中的交互式数据可视化界面。\n- [Pandasgui](https:\u002F\u002Fgithub.com\u002Fadamerose\u002Fpandasgui) - 用于查看和筛选 DataFrame 的 GUI。\n- [PyGWalker](https:\u002F\u002Fgithub.com\u002FKanaries\u002Fpygwalker) - 用于 DataFrame 可视化分析的交互式 UI。\n- [QGrid](https:\u002F\u002Fgithub.com\u002Fquantopian\u002Fqgrid) - Jupyter 中的交互式 DataFrame 网格。\n- [Pivottablejs](https:\u002F\u002Fgithub.com\u002Fnicolaskruchten\u002Fjupyter_pivottablejs) - 在 Jupyter 中使用交互式 PivotTable.js 表格。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"python-data-quality-validation\">\u003C\u002Fa>\n\n#### 数据质量与验证\n\n- [PyOD](https:\u002F\u002Fgithub.com\u002Fyzhao062\u002Fpyod) - 异常值与异常检测。\n- [Alibi Detect](https:\u002F\u002Fgithub.com\u002FSeldonIO\u002Falibi-detect) - 异常值、对抗样本及数据漂移检测。\n- [Pandera](https:\u002F\u002Fgithub.com\u002Funionai-oss\u002Fpandera) - 通过声明式模式进行数据验证。\n- [Cerberus](https:\u002F\u002Fgithub.com\u002Fpyeve\u002Fcerberus) - 基于模式的数据验证。\n- [Pydantic](https:\u002F\u002Fgithub.com\u002Fpydantic\u002Fpydantic) - 使用 Python 类型注解进行数据验证。\n- [Dora](https:\u002F\u002Fgithub.com\u002FNathanEpstein\u002FDora) - 自动化 EDA：预处理、特征工程、可视化。\n- [Great Expectations](https:\u002F\u002Fgithub.com\u002Fgreat-expectations\u002Fgreat_expectations) - 数据验证与测试。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"python-feature-engineering-selection\">\u003C\u002Fa>\n\n#### 特征工程与选择\n\n- [FeatureTools](https:\u002F\u002Fgithub.com\u002Falteryx\u002Ffeaturetools) - 自动化特征工程工具。\n- [Feature Engine](https:\u002F\u002Fgithub.com\u002Ffeature-engine\u002Ffeature_engine) - 兼容 Scikit-Learn 的特征工程库。\n- [Prince](https:\u002F\u002Fgithub.com\u002FMaxHalford\u002Fprince) - 多变量探索性数据分析（PCA、CA、MCA）。\n- [Fitter](https:\u002F\u002Fgithub.com\u002Fcokelaer\u002Ffitter) - 识别数据分布类型。\n- [Feature Selector](https:\u002F\u002Fgithub.com\u002FWillKoehrsen\u002Ffeature-selector) - 用于机器学习数据集降维的工具。\n- [Category Encoders](https:\u002F\u002Fgithub.com\u002Fscikit-learn-contrib\u002Fcategory_encoders) - 丰富的分类变量编码工具集。\n- [Imbalanced Learn](https:\u002F\u002Fgithub.com\u002Fscikit-learn-contrib\u002Fimbalanced-learn) - 处理不平衡数据集的工具。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"python-specialized-data-tools\">\u003C\u002Fa>\n\n#### 专用数据工具\n\n- [cuDF](https:\u002F\u002Fgithub.com\u002Frapidsai\u002Fcudf) - 一个用于加载、连接和聚合数据的 GPU DataFrame 库。\n- [Faker](https:\u002F\u002Fgithub.com\u002Fjoke2k\u002Ffaker) - 生成用于测试的虚假数据。\n- [Mimesis](https:\u002F\u002Fgithub.com\u002Flk-geimfari\u002Fmimesis) - 生成逼真的测试数据。\n- [Geopy](https:\u002F\u002Fgithub.com\u002Fgeopy\u002Fgeopy) - 地理编码地址并计算距离。\n- [PySAL](https:\u002F\u002Fgithub.com\u002Fpysal\u002Fpysal) - 空间分析函数。\n- [Scattertext](https:\u002F\u002Fgithub.com\u002FJasonKessler\u002Fscattertext) - 文档类型之间语言差异的精美可视化。\n- [IGraph](https:\u002F\u002Fgithub.com\u002Figraph\u002Figraph) - 用于创建和操作图与网络的库，提供多种语言的绑定。\n- [Joblib](https:\u002F\u002Fgithub.com\u002Fjoblib\u002Fjoblib) - Python 的轻量级流水线库，特别适用于保存和加载大型 NumPy 数组。\n- [ImageIO](https:\u002F\u002Fgithub.com\u002Fimageio\u002Fimageio) - 提供简单接口来读取和写入各种图像数据的库。\n- [Texthero](https:\u002F\u002Fgithub.com\u002Fjbesomi\u002Ftexthero) - 文本预处理、表示和可视化。\n- [Geopandas](https:\u002F\u002Fgithub.com\u002Fgeopandas\u002Fgeopandas) - 使用 pandas 进行地理数据操作。\n- [NetworkX](https:\u002F\u002Fgithub.com\u002Fnetworkx\u002Fnetworkx) - 网络分析与图论。\n- [Chardet](https:\u002F\u002Fgithub.com\u002Fchardet\u002Fchardet) - 用于检测文本和文件字符编码的 Python 库。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"sql-databases\">\u003C\u002Fa>\n\n\n\n## 🗃️ SQL 与数据库\n\n\u003Ca id=\"sql-databases-resources\">\u003C\u002Fa>\n\n### 资源\n\nSQL 教程和数据库设计原则。\n\n- [SQLZoo - SQL 教程](https:\u002F\u002Fsqlzoo.net\u002Fwiki\u002FSQL_Tutorial) - 交互式 SQL 教程。\n- [SQL Bolt - 学习 SQL](https:\u002F\u002Fsqlbolt.com\u002F) - 通过互动课程学习 SQL。\n- [SQL 教程](https:\u002F\u002Fwww.sqltutorial.org\u002F) - 全面的 SQL 教学资源。\n- [W3Schools 的 SQL 教程](https:\u002F\u002Fwww.w3schools.com\u002Fsql\u002Fdefault.asp) - 全面的 SQL 教程。\n- [W3Resource 的 PostgreSQL 教程](https:\u002F\u002Fw3resource.com\u002FPostgreSQL\u002Ftutorial.php) - PostgreSQL 教程。\n- [W3Resource 的 MySQL 教程](https:\u002F\u002Fwww.w3resource.com\u002Fmysql\u002Fmysql-tutorials.php) - MySQL 教程。\n- [W3Resource 的 MongoDB 教程](https:\u002F\u002Fwww.w3resource.com\u002Fmongodb\u002Fnosql.php) - MongoDB 教程。\n- [EverSQL](https:\u002F\u002Fwww.eversql.com\u002F) - 基于 AI 的 SQL 查询优化和数据库可观ility 工具。\n- [Awesome Database Learning](https:\u002F\u002Fgithub.com\u002Fpingcap\u002Fawesome-database-learning) - 关于数据库内部机制、分布式系统和存储的教育资源。\n- [Awesome Postgres](https:\u002F\u002Fgithub.com\u002Fdhamaniasad\u002Fawesome-postgres) - 精选的 PostgreSQL 软件、库、工具和资源列表。\n- [Awesome MySql](https:\u002F\u002Fgithub.com\u002Fshlomi-noach\u002Fawesome-mysql) - 精选的 MySQL 软件、库、工具和资源列表。\n- [Awesome Clickhouse](https:\u002F\u002Fgithub.com\u002Fkorchasa\u002Fawesome-clickhouse) - 精选的 ClickHouse 软件列表。\n- [Awesome MongoDB](https:\u002F\u002Fgithub.com\u002Framnes\u002Fawesome-mongodb) - 精选的 MongoDB 资源、库、工具和应用列表。\n- [Awesome Duckdb](https:\u002F\u002Fgithub.com\u002Fdavidgasquez\u002Fawesome-duckdb) - 为 DuckDB 分析型数据库精选的工具、资源和扩展。\n- [Awesome SQLAlchemy](https:\u002F\u002Fgithub.com\u002Fdahlia\u002Fawesome-sqlalchemy) - 为 SQLAlchemy 精选的优秀工具列表。\n- [Awesome Sql](https:\u002F\u002Fgithub.com\u002Fdanhuss\u002Fawesome-sql) - 用于操作关系型数据库的工具和技术列表。\n- [AnimateSQL](https:\u002F\u002Fanimatesql.com\u002F) - 交互式工具，可可视化 SQL 查询的逐步执行过程。\n- [SQL 技巧与窍门](https:\u002F\u002Fgithub.com\u002Fben-nour\u002FSQL-tips-and-tricks) - 用于数据分析的实用 SQL 技术和优化方法。\n- [练习窗口函数](https:\u002F\u002Fwww.practicewindowfunctions.com) - 免费的交互式 SQL 教学网站，专注于通过 80 多个带提示和解答的实际问题来掌握窗口函数。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"sql-databases-tools\">\u003C\u002Fa>\n\n### 工具\n\n一系列用于无缝访问和操作数据库的库和驱动程序。\n\n- [PyODBC](https:\u002F\u002Fgithub.com\u002Fmkleehammer\u002Fpyodbc) - 用于 ODBC 数据库访问的 Python 库。\n- [SQLAlchemy](https:\u002F\u002Fgithub.com\u002Fsqlalchemy\u002Fsqlalchemy) - Python 的 SQL 工具包和 ORM。\n- [Psycopg2](https:\u002F\u002Fgithub.com\u002Fpsycopg\u002Fpsycopg2) - PostgreSQL 数据库适配器。\n- [MySQL Connector\u002FPython](https:\u002F\u002Fgithub.com\u002Fmysql\u002Fmysql-connector-python) - Python 的 MySQL 驱动程序。\n- [PonyORM](https:\u002F\u002Fgithub.com\u002Fponyorm\u002Fpony) - 支持动态查询生成的 Python ORM。\n- [PyMongo](https:\u002F\u002Fgithub.com\u002Fmongodb\u002Fmongo-python-driver) - 官方的 MongoDB Python 驱动程序。\n- [SQLiteviz](https:\u002F\u002Fgithub.com\u002Flana-k\u002Fsqliteviz) - 用于探索 SQLite 数据库并可视化查询结果的工具。\n- [SQLite](https:\u002F\u002Fgithub.com\u002Fsqlite\u002Fsqlite) - 用 C 语言实现的小型、快速、自包含、高可靠性且功能齐全的 SQL 数据库引擎。\n- [DB Browser for SQLite](https:\u002F\u002Fgithub.com\u002Fsqlitebrowser\u002Fsqlitebrowser) - 高质量、可视化、开源的工具，可用于创建、设计和编辑与 SQLite 兼容的数据库文件。\n- [DBeaver](https:\u002F\u002Fgithub.com\u002Fdbeaver\u002Fdbeaver) - 开发人员、SQL 程序员和管理员使用的免费通用数据库工具和 SQL 客户端。\n- [Beekeeper Studio](https:\u002F\u002Fgithub.com\u002Fbeekeeper-studio\u002Fbeekeeper-studio) - 现代、易用的 SQL 客户端和数据库管理器，具有简洁的跨平台界面。\n- [SQLFluff](https:\u002F\u002Fgithub.com\u002Fsqlfluff\u002Fsqlfluff) - 模块化的 SQL 静态分析工具和自动格式化工具，旨在强制执行一致的代码风格并捕获 SQL 代码中的错误。\n- [PyMySQL](https:\u002F\u002Fgithub.com\u002FPyMySQL\u002FPyMySQL) - 纯 Python 实现的 MySQL 客户端库，用于从 Python 应用程序中与 MySQL 数据库交互。\n- [Vanna.AI](https:\u002F\u002Fgithub.com\u002Fvanna-ai\u002Fvanna) - 基于 AI 的工具，可根据自然语言问题生成 SQL 查询。\n- [SQLChat](https:\u002F\u002Fgithub.com\u002Fsqlchat\u002Fsqlchat) - 基于聊天的 SQL 客户端，允许用户使用自然语言对话查询数据库。\n- [Records](https:\u002F\u002Fgithub.com\u002Fkennethreitz-archive\u002Frecords) - 通过 Python 语法向数据库执行 SQL 查询。\n- [Dataset](https:\u002F\u002Fgithub.com\u002Fpudo\u002Fdataset) - 类似 JSON 的接口，用于操作 SQL 数据库。\n- [SQLGlot](https:\u002F\u002Fgithub.com\u002Ftobymao\u002Fsqlglot) - 无依赖的 SQL 解析器、转译器和优化器，专为 Python 设计。\n- [TDengine](https:\u002F\u002Fgithub.com\u002Ftaosdata\u002FTDengine) - 开源大数据平台，专为时序数据、物联网和工业监控设计。\n- [TimescaleDB](https:\u002F\u002Fgithub.com\u002Ftimescale\u002Ftimescaledb) - 开源的时序 SQL 数据库，针对快速数据插入和复杂查询进行了优化。\n- [DuckDB](https:\u002F\u002Fgithub.com\u002Fduckdb\u002Fduckdb) - 内存中的分析型数据库，用于快速执行 SQL 查询。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"data-visualization\">\u003C\u002Fa>\n\n## 📊 数据可视化\n\n\u003Ca id=\"data-visualization-resources\">\u003C\u002Fa>\n\n### 资源\n\n色彩理论、图表选择指南和故事讲述技巧。\n\n- [From Data to Viz](https:\u002F\u002Fgithub.com\u002Fholtzy\u002Fdata_to_viz) - 一本根据你的数据选择合适可视化方式的指南。\n- [Awesome DataViz](https:\u002F\u002Fgithub.com\u002Fhal9ai\u002Fawesome-dataviz) - 一个精选的数据可视化库、工具和资源列表。\n- [Visualization Curriculum](https:\u002F\u002Fgithub.com\u002Fuwdata\u002Fvisualization-curriculum) - 用于教授数据可视化概念的交互式笔记本。\n- [Scientific Visualization Book](https:\u002F\u002Fgithub.com\u002Frougier\u002Fscientific-visualization-book) - 创造有效科学可视化和图表的指南。\n- [The Python Graph Gallery](https:\u002F\u002Fpython-graph-gallery.com\u002F) - 一个用于数据可视化的Python图表示例集合。\n- [FlowingData](https:\u002F\u002Fflowingdata.com\u002F) - 数据分析和可视化的见解。\n- [Data Visualization Catalogue](https:\u002F\u002Fdatavizcatalogue.com\u002Findex.html) - 一份全面的数据可视化类型目录。\n- [Data Viz Project](https:\u002F\u002Fdatavizproject.com\u002F) - 一个帮助选择合适可视化方式的资源。\n- [Chartopedia](https:\u002F\u002Fwww.anychart.com\u002Fchartopedia\u002Fusage-type\u002F) - 一份帮助你选择适当图表类型的指南。\n- [DataForVisualization](https:\u002F\u002Fdataforvisualization.com\u002F) - 数据可视化技术的教程和见解。\n- [Truth & Beauty](https:\u002F\u002Ftruth-and-beauty.net\u002F) - 探索数据可视化的美学。\n- [Cedric Scherer's DataViz Resources](https:\u002F\u002Fwww.cedricscherer.com\u002Ftop\u002Fdataviz\u002F) - 一组顶级的数据可视化资源和灵感。\n- [Information is Beautiful](https:\u002F\u002Finformationisbeautiful.net\u002F) - 一个致力于将复杂概念清晰且引人入胜地可视化的网站。\n- [Plottie](https:\u002F\u002Fplottie.art\u002F) - 一个庞大的科学图表库，提供可视化灵感和创意。\n- [Friends Don't Let Friends](https:\u002F\u002Fgithub.com\u002Fcxli233\u002FFriendsDontLetFriends) - 一系列不良数据可视化实践及其更好的替代方案。\n- [Natural Colours](https:\u002F\u002Fwww.c82.net\u002Fnatural-colors\u002F) - 一个数字档案，收录了历史上的色彩系统和颜料。\n- [Colorgorical](http:\u002F\u002Fvrl.cs.brown.edu\u002Fcolor) - 一个基于感知原则生成分类色彩调色板的资源。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"data-visualization-tools\">\u003C\u002Fa>\n\n### 工具\n\n用于静态、交互式和3D可视化的库。\n\n- [Matplotlib](https:\u002F\u002Fmatplotlib.org\u002Fstable\u002Fcontents.html) - 一个功能全面的Python库，可用于创建静态、动画和交互式可视化。\n- [Seaborn](https:\u002F\u002Fseaborn.pydata.org\u002F) - 一个基于Matplotlib的统计数据可视化库。\n- [Plotly](https:\u002F\u002Fplotly.com\u002Fpython\u002F) - 一个用于创建交互式图表和仪表板的库。\n- [Altair](https:\u002F\u002Fgithub.com\u002Fvega\u002Faltair) - 一个声明式的Python统计可视化库。\n- [Bokeh](https:\u002F\u002Fdocs.bokeh.org\u002Fen\u002Flatest\u002F) - 一个用于在现代浏览器中创建交互式可视化的库。\n- [HoloViews](https:\u002F\u002Fholoviews.org\u002F) - 一个可以轻松构建复杂可视化工具。\n- [Geopandas](https:\u002F\u002Fgeopandas.org\u002Fen\u002Fstable\u002F) - Pandas的一个扩展，专门处理地理空间数据。\n- [Folium](https:\u002F\u002Fpython-visualization.github.io\u002Ffolium\u002F) - 一个用于在交互式地图上可视化数据的库。\n- [Pygal](https:\u002F\u002Fpygal.org\u002Fen\u002Fstable\u002F) - 一个Python SVG图表库。\n- [Plotnine](https:\u002F\u002Fplotnine.readthedocs.io\u002Fen\u002Fstable\u002F) - 一个适用于Python的图形语法库。\n- [Bqplot](https:\u002F\u002Fgithub.com\u002Fbqplot\u002Fbqplot) - 一个适用于IPython\u002FJupyter笔记本的绘图库。\n- [PyPalettes](https:\u002F\u002Fgithub.com\u002FJosephBARBIERDARNAL\u002Fpypalettes) - 一个包含超过2500种颜色映射的Python库。\n- [Deck.gl](https:\u002F\u002Fgithub.com\u002Fvisgl\u002Fdeck.gl) - 一个基于WebGL的框架，用于对大型数据集进行视觉化探索性数据分析。\n- [Python for Geo](https:\u002F\u002Fgithub.com\u002Fgeopandas\u002Fcontextily) - Contextily：为GeoPandas中的图表添加背景底图。\n- [OSMnx](https:\u002F\u002Fgithub.com\u002Fgboeing\u002Fosmnx) - 一个方便从OpenStreetMap下载、建模、分析和可视化街道网络的软件包。\n- [Apache ECharts](https:\u002F\u002Fgithub.com\u002Fapache\u002Fecharts) - 一个功能强大、交互式的图表和可视化库，适用于基于浏览器的应用程序。\n- [VisPy](https:\u002F\u002Fgithub.com\u002Fvispy\u002Fvispy) - 一个高性能的交互式2D\u002F3D数据可视化库，利用OpenGL的强大功能。\n- [Glumpy](https:\u002F\u002Fgithub.com\u002Fglumpy\u002Fglumpy) - 一个基于OpenGL的Python科学可视化库，速度快、可扩展且美观。\n- [Pandas-bokeh](https:\u002F\u002Fgithub.com\u002FPatrikHlobil\u002FPandas-Bokeh) - Bokeh的Pandas绘图后端。\n- [QGIS](https:\u002F\u002Fgithub.com\u002Fqgis\u002FQGIS) - 一个免费、开源、跨平台的地理信息系统（GIS）。\n- [Flourish](https:\u002F\u002Fflourish.studio\u002F) - 一个无需编码即可创建交互式数据可视化和故事的平台。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"dashboards\">\u003C\u002Fa>\n\n## 📈 仪表板与商业智能\n\n\u003Ca id=\"dashboards-resources\">\u003C\u002Fa>\n\n### 资源\n\n使用各种工具和框架构建及优化仪表板和可视化效果的教程。\n\n- [Awesome Dashboards](https:\u002F\u002Fgithub.com\u002Fobazoud\u002Fawesome-dashboard) - 一组出色的仪表板和可视化资源。\n- [Best of Streamlit](https:\u002F\u002Fgithub.com\u002Fjrieke\u002Fbest-of-streamlit) - 社区构建的Streamlit应用展示。\n- [Awesome Dash](https:\u002F\u002Fgithub.com\u002Fucg8j\u002Fawesome-dash) - 面向Dash用户的综合资源。\n- [Awesome Panel](https:\u002F\u002Fgithub.com\u002Fawesome-panel\u002Fawesome-panel) - 针对Panel用户提供的资源和支持。\n- [Awesome Streamlit](https:\u002F\u002Fgithub.com\u002FMarcSkovMadsen\u002Fawesome-streamlit) - 一个精心挑选的Streamlit资源和组件列表。\n- [Dash Enterprise Samples](https:\u002F\u002Fgithub.com\u002Fplotly\u002Fdash-sample-apps) - 可直接投入生产的Dash应用程序。\n- [geeksforgeeks - Tableau Tutorial](https:\u002F\u002Fwww.geeksforgeeks.org\u002Ftableau-tutorial\u002F) - 一篇关于Tableau的全面教程。\n- [geeksforgeeks - Power BI Tutorial](https:\u002F\u002Fwww.geeksforgeeks.org\u002Fpower-bi-tutorial\u002F) - 一篇关于Power BI的详细教程。\n- [Tableau Public Gallery](https:\u002F\u002Fpublic.tableau.com\u002Fapp\u002Fdiscover) - 一个精选的真实世界交互式仪表板集合，可供启发和学习。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"dashboards-tools\">\u003C\u002Fa>\n\n### 工具\n\n用于构建自定义仪表板解决方案的框架。\n\n- [Dash](https:\u002F\u002Fgithub.com\u002Fplotly\u002Fdash) - 用于创建交互式Web应用的框架。\n- [Streamlit](https:\u002F\u002Fgithub.com\u002Fstreamlit\u002Fstreamlit) - 简化的数据应用开发框架。\n- [Panel](https:\u002F\u002Fgithub.com\u002Fholoviz\u002Fpanel) - 用于创建自定义交互式Web应用和仪表板的Python库。\n- [Gradio](https:\u002F\u002Fgithub.com\u002Fgradio-app\u002Fgradio) - 用于创建和分享机器学习应用的工具。\n- [OpenSearch Dashboards](https:\u002F\u002Fgithub.com\u002Fopensearch-project\u002FOpenSearch-Dashboards) - 面向OpenSearch数据的强大数据可视化与仪表板工具，由Kibana分叉而来。\n- [GridStack.js](https:\u002F\u002Fgithub.com\u002Fgridstack\u002Fgridstack.js) - 用于构建可拖拽、可调整大小且响应式的仪表板布局的库。\n- [Tremor](https:\u002F\u002Fgithub.com\u002Ftremorlabs\u002Ftremor-npm) - 基于React的库，通过预构建的图表、KPI等组件快速搭建仪表板。\n- [Appsmith](https:\u002F\u002Fgithub.com\u002Fappsmithorg\u002Fappsmith) - 开源平台，可快速构建和部署内部工具、管理面板及CRUD应用。\n- [Grafanalib](https:\u002F\u002Fgithub.com\u002Fweaveworks\u002Fgrafanalib) - 用于以代码形式生成Grafana仪表板配置的Python库。\n- [H2O Wave](https:\u002F\u002Fgithub.com\u002Fh2oai\u002Fwave) - Python框架，用于快速构建和部署面向AI与分析的实时Web应用和仪表板。\n- [Shiny for Python](https:\u002F\u002Fgithub.com\u002Fposit-dev\u002Fpy-shiny) - 流行R Shiny框架的Python版本。\n- [Voilà](https:\u002F\u002Fgithub.com\u002Fvoila-dashboards\u002Fvoila) - 将Jupyter笔记本转换为独立的Web应用。\n- [Reflex](https:\u002F\u002Fgithub.com\u002Freflex-dev\u002Freflex) - 用于构建Web应用的全栈Python框架。\n- [Taipy](https:\u002F\u002Fgithub.com\u002FAvaiga\u002Ftaipy) - 用于构建Web应用和交互式仪表板的Python库。\n- [Evidence](https:\u002F\u002Fgithub.com\u002Fevidence-dev\u002Fevidence) - 使用SQL和Markdown生成报表的商业智能平台。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"dashboards-software\">\u003C\u002Fa>\n\n### 软件\n\n用于数据可视化和仪表板创建的领先工具与平台列表。\n\n- [Tableau](https:\u002F\u002Fwww.tableau.com) - 领先的数据可视化软件。\n- [Microsoft Power BI](https:\u002F\u002Fpowerbi.microsoft.com) - 用于数据可视化的商业分析工具。\n- [QlikView](https:\u002F\u002Fwww.qlik.com\u002Fus\u002Fproducts\u002Fqlikview) - 数据可视化与商业智能工具。\n- [Metabase](https:\u002F\u002Fwww.metabase.com) - 用户友好的开源BI工具。\n- [Apache Superset](https:\u002F\u002Fsuperset.apache.org) - 开源的数据探索与可视化平台。\n- [Preset](https:\u002F\u002Fpreset.io\u002F) - 提供Apache Superset托管版本的现代商业智能平台。\n- [Metabase](https:\u002F\u002Fgithub.com\u002Fmetabase\u002Fmetabase) - 为公司内所有人提供分析与商业智能的最简单方式。\n- [Redash](https:\u002F\u002Fgithub.com\u002Fgetredash\u002Fredash) - 用于可视化和共享数据洞察的工具。\n- [Grafana](https:\u002F\u002Fgrafana.com) - 仪表板与监控工具。\n- [Datawrapper](https:\u002F\u002Fgithub.com\u002Fdatawrapper\u002Fdatawrapper) - 用户友好的图表和地图制作工具。\n- [ChartBlocks](https:\u002F\u002Fwww.chartblocks.com) - 在线图表制作平台。\n- [Infogram](https:\u002F\u002Finfogram.com) - 用于创建信息图和可视化内容的工具。\n- [Google Data Studio](https:\u002F\u002Fdatastudio.google.com) - 免费的交互式仪表板和报告制作工具。\n- [Rath](https:\u002F\u002Fgithub.com\u002FKanaries\u002FRath) - 新一代自动化数据探索与可视化平台。\n- [Kibana](https:\u002F\u002Fgithub.com\u002Felastic\u002Fkibana) - Elastic Stack（Elasticsearch、Logstash、Beats）的官方可视化与仪表板工具。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"web-scraping-crawling\">\u003C\u002Fa>\n\n## 🕸️ 网页抓取与爬虫\n\n\u003Ca id=\"web-scraping-crawling-resources\">\u003C\u002Fa>\n\n### 资源\n\n使用Python进行网页抓取的宝贵资源、教程和库集合。\n\n- [Awesome Web Scraping](https:\u002F\u002Fgithub.com\u002Florien\u002Fawesome-web-scraping) - 网页抓取和数据处理相关的库、工具和API列表。\n- [Python Scraping](https:\u002F\u002Fgithub.com\u002FREMitchell\u002Fpython-scraping) - 来自《用Python进行网页抓取》一书的代码示例。\n- [Scraping Tutorial](https:\u002F\u002Fgithub.com\u002FBlatzar\u002Fscraping-tutorial) - 流媒体网站抓取教程。\n- [Webscraping from 0 to Hero](https:\u002F\u002Fgithub.com\u002FTheWebScrapingClub\u002Fwebscraping-from-0-to-hero) - 一个开放项目仓库，分享关于使用Python进行网页抓取的知识和经验。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"web-scraping-crawling-tools\">\u003C\u002Fa>\n\n### 工具\n\n用于网络爬虫的库和工具列表。\n\n- [Requests](https:\u002F\u002Fgithub.com\u002Fpsf\u002Frequests) - 一个简单而优雅的 Python HTTP 库。\n- [BeautifulSoup](https:\u002F\u002Fwww.crummy.com\u002Fsoftware\u002FBeautifulSoup\u002Fbs4\u002Fdoc\u002F) - 用于解析 HTML 和 XML 文档的库。\n- [Selenium](https:\u002F\u002Fgithub.com\u002FSeleniumHQ\u002Fselenium) - 一个用于测试目的的 Web 应用程序自动化工具。\n- [Scrapy](https:\u002F\u002Fscrapy.org\u002F) - 一个开源且协作式的 Python 网络爬虫框架。\n- [Browser Use](https:\u002F\u002Fgithub.com\u002Fbrowser-use\u002Fbrowser-use) - 一个用于浏览器自动化和网络爬取的库。\n- [Gerapy](https:\u002F\u002Fgithub.com\u002FGerapy\u002FGerapy) - 基于 Scrapy、Scrapyd、Django 和 Vue.js 的分布式爬虫管理框架。\n- [AutoScraper](https:\u002F\u002Fgithub.com\u002Falirezamika\u002Fautoscraper) - 一个智能、自动、快速且轻量级的 Python 网络爬虫。\n- [Feedparser](https:\u002F\u002Fgithub.com\u002Fkurtmckee\u002Ffeedparser) - 一个用于在 Python 中解析信息源的库。\n- [Trafilatura](https:\u002F\u002Fgithub.com\u002Fadbar\u002Ftrafilatura) - 一个用于在网络上收集文本和元数据的 Python 及命令行工具。\n- [You-Get](https:\u002F\u002Fgithub.com\u002Fsoimort\u002Fyou-get) - 一个小型命令行实用程序，用于从网上下载媒体内容（视频、音频、图片）。\n- [MechanicalSoup](https:\u002F\u002Fgithub.com\u002FMechanicalSoup\u002FMechanicalSoup) - 一个用于自动化与网站交互的 Python 库。\n- [ScrapeGraph AI](https:\u002F\u002Fgithub.com\u002FScrapeGraphAI\u002FScrapegraph-ai) - 一个基于 AI 的 Python 爬虫。\n- [Snscrape](https:\u002F\u002Fgithub.com\u002FJustAnotherArchivist\u002Fsnscrape) - 一个用 Python 编写的社交网络服务爬虫。\n- [Ferret](https:\u002F\u002Fgithub.com\u002FMontFerret\u002Fferret) - 一个网络爬取系统，允许你使用简单的查询语言声明式地描述要提取的数据。\n- [Grab](https:\u002F\u002Fgithub.com\u002Florien\u002Fgrab) - 一个用于构建网络爬虫应用的 Python 框架，提供用于异步请求的高级 API。\n- [Playwright](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fplaywright-python) - Playwright 浏览器自动化库的 Python 版本。\n- [PyQuery](https:\u002F\u002Fgithub.com\u002Fgawel\u002Fpyquery) - 一个类似于 jQuery 的库，用于在 Python 中解析 HTML 文档。\n- [Helium](https:\u002F\u002Fgithub.com\u002Fmherrmann\u002Fhelium) - 一个高层次的 Selenium 封装，便于进行 Web 自动化。\n- [Scrapling](https:\u002F\u002Fgithub.com\u002FD4Vinci\u002FScrapling) - 一个用于构建网络爬虫和抓取器的框架。\n- [Crawl4AI](https:\u002F\u002Fgithub.com\u002Funclecode\u002Fcrawl4ai) - 一个专为 AI 和数据提取任务设计的高级网络爬虫框架。\n\n[回到目录](#contents)\n\n---\n\n\u003Ca id=\"mathematics\">\u003C\u002Fa>\n\n## 🔢 数学\n\n一系列学习数学的资源，特别是在数据科学和机器学习背景下的资源。\n\n- [Awesome Math](https:\u002F\u002Fgithub.com\u002Frossant\u002Fawesome-math) - 一份精选的数学资源、书籍和在线课程列表。\n- [MML Bool](https:\u002F\u002Fgithub.com\u002Fmml-book\u002Fmml-book.github.io) - 一份关于机器学习中数学的全面资源。\n- [3Blue1Brown](https:\u002F\u002Fwww.3blue1brown.com\u002F) - 通过动画视频对数学概念进行可视化解释。\n- [Immersive Linear Algebra](http:\u002F\u002Fimmersivemath.com\u002Fila\u002F) - 一个交互式资源，用于理解线性代数。\n- [Hackermath](https:\u002F\u002Fgithub.com\u002Famitkaps\u002Fhackermath) - 一份用于学习数据科学中统计学和数学的资源。\n- [Stats Maths with Python](https:\u002F\u002Fgithub.com\u002Ftirthajyoti\u002FStats-Maths-with-Python) - 一组用于统计学和数学的 Python 脚本和笔记本。\n- [Fast.ai - Computational Linear Algebra](https:\u002F\u002Fgithub.com\u002Ffastai\u002Fnumerical-linear-algebra) - 一份用于以计算方式学习线性代数的资源。\n\n[回到目录](#contents)\n\n---\n\n\u003Ca id=\"statistics-probability\">\u003C\u002Fa>\n\n## 🎲 统计学与概率论\n\n\u003Ca id=\"statistics-probability-resources\">\u003C\u002Fa>\n\n### 资源\n\n一系列专注于统计学和概率论的资源，包括教程和综合指南。\n\n- [Awesome Statistics](https:\u002F\u002Fgithub.com\u002Ferikgahner\u002Fawesome-statistics) - 一份精选的统计学资源、软件和学习材料列表。\n- [The Elements of Statistical Learning](https:\u002F\u002Fgithub.com\u002Fempathy87\u002FThe-Elements-of-Statistical-Learning-Python-Notebooks) - 用于理解统计学习概念的笔记本。\n- [Seeing Theory](https:\u002F\u002Fgithub.com\u002Fseeingtheory\u002FSeeing-Theory) - 一个交互式视觉资源，用于学习概率和统计。\n- [O'Reilly 书籍代码仓库](https:\u002F\u002Fgithub.com\u002Fgedeck\u002Fpractical-statistics-for-data-scientists) - 一本实用统计学书籍的配套代码。\n- [斯坦福大学统计学习理论](https:\u002F\u002Fweb.stanford.edu\u002Fclass\u002Fcs229t\u002Fnotes.pdf) - 关于统计学习理论的讲义。\n- [StatLect](https:\u002F\u002Fwww.statlect.com\u002F) - 一本涵盖概率和统计概念的综合性在线教科书。\n- [斯坦福大学的概率与统计复习课程](https:\u002F\u002Fstanford.edu\u002F~shervine\u002Fteaching\u002Fcs-229\u002Frefresher-probabilities-statistics) - 斯坦福大学提供的概率与统计复习课程。\n- [Bayesian Methods for Hackers](https:\u002F\u002Fgithub.com\u002FCamDavidsonPilon\u002FProbabilistic-Programming-and-Bayesian-Methods-for-Hackers) - 一份用于学习 Python 中贝叶斯方法的资源。\n- [Python 中的贝叶斯建模与计算](https:\u002F\u002Fgithub.com\u002FBayesianModelingandComputationInPython\u002FBookCode_Edition1) - 一本书《Python 中的贝叶斯建模与计算》的代码。\n- [Stat Trek](https:\u002F\u002Fstattrek.com\u002F) - 一个包含教程和工具的学习统计学和概率论的资源。\n- [在线统计学书籍](https:\u002F\u002Fonlinestatbook.com\u002F2\u002Findex.html) - 一本带有模拟和演示的交互式在线统计学书籍。\n- [All of Statistics](https:\u002F\u002Fgithub.com\u002Ftelmo-correa\u002Fall-of-statistics) - 一份基于 Wasserman 书籍的统计学学习资源。\n- [Think Stats](https:\u002F\u002Fgithub.com\u002FAllenDowney\u002FThinkStats\u002Ftree\u002Fv3) - 一本介绍概率与统计的书籍及其代码。\n- [Think Bayes 2](https:\u002F\u002Fgithub.com\u002FAllenDowney\u002FThinkBayes2) - 一本关于贝叶斯统计方法的书籍及其代码。\n- [Causal Inference: The Mixtape](https:\u002F\u002Fmixtape.scunning.com\u002F) - 一份关于因果推断方法的实用指南。\n- [The Effect](https:\u002F\u002Ftheeffectbook.net\u002F) - 一本现代的关于因果关系和研究设计的入门书籍。\n- [The Statistics Handbook](https:\u002F\u002Fgithub.com\u002Fcarloocchiena\u002Fthe_statistics_handbook) - 一本开源的统计学实践手册。\n\n[回到目录](#contents)\n\n---\n\n\u003Ca id=\"statistics-probability-tools\">\u003C\u002Fa>\n\n### 工具\n\n专注于统计与概率的工具集合。\n\n- [SciPy](https:\u002F\u002Fgithub.com\u002Fscipy\u002Fscipy) - 科学计算和统计的基础库。\n- [Statsmodels](https:\u002F\u002Fgithub.com\u002Fstatsmodels\u002Fstatsmodels) - 用于统计建模、检验及数据探索。\n- [PyMC](https:\u002F\u002Fgithub.com\u002Fpymc-devs\u002Fpymc) - Python中的概率编程库，支持灵活的贝叶斯建模。\n- [Pingouin](https:\u002F\u002Fgithub.com\u002Fraphaelvallat\u002Fpingouin) - 相较于SciPy，具有更好易用性的统计包。\n- [scikit-posthocs](https:\u002F\u002Fgithub.com\u002Fmaximtrp\u002Fscikit-posthocs) - 用于数据分析的事后检验工具。\n- [Lifelines](https:\u002F\u002Fgithub.com\u002FCamDavidsonPilon\u002Flifelines) - Python中的生存分析与事件历史分析工具。\n- [scikit-survival](https:\u002F\u002Fgithub.com\u002Fsebp\u002Fscikit-survival) - 基于scikit-learn的生存分析库，用于时间至事件预测。\n- [Bootstrap](https:\u002F\u002Fgithub.com\u002Fcgevans\u002Fscikits-bootstrap) - 用于自助法置信区间估计的方法。\n- [PyStan](https:\u002F\u002Fgithub.com\u002Fstan-dev\u002Fpystan) - Stan的Python接口，用于贝叶斯统计建模。\n- [ArviZ](https:\u002F\u002Fgithub.com\u002Farviz-devs\u002Farviz) - 提供可视化诊断的贝叶斯模型探索性分析工具。\n- [PyGAM](https:\u002F\u002Fgithub.com\u002Fdswah\u002FpyGAM) - Python库，用于广义加性模型，内置平滑与正则化功能。\n- [NumPyro](https:\u002F\u002Fgithub.com\u002Fpyro-ppl\u002Fnumpyro) - 基于JAX的概率编程库，适用于高性能贝叶斯建模。\n- [Causal Impact](https:\u002F\u002Fgithub.com\u002FWillianFuks\u002Ftfcausalimpact) - R包的Python实现，利用贝叶斯结构化时间序列模型进行因果推断。\n- [DoWhy](https:\u002F\u002Fgithub.com\u002Fpy-why\u002Fdowhy) - Python库，支持显式建模与检验因果假设。\n- [Patsy](https:\u002F\u002Fgithub.com\u002Fpydata\u002Fpatsy) - 用于描述统计模型并构建设计矩阵的Python库。\n- [Pomegranate](https:\u002F\u002Fgithub.com\u002Fjmschrei\u002Fpomegranate) - 快速且灵活的Python概率建模库，支持GPU加速。\n- [Pgmpy](https:\u002F\u002Fgithub.com\u002Fpgmpy\u002Fpgmpy) - 使用图模型进行概率与因果推理的Python库。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"ab-testing\">\u003C\u002Fa>\n\n## 🧪 A\u002FB测试\n\n专注于A\u002FB测试的相关资源集合。\n\n- [DynamicYield A\u002FB测试课程](https:\u002F\u002Fwww.dynamicyield.com\u002Fcourse\u002Ftesting-and-optimization\u002F) - 涵盖高级测试与优化技术的在线课程。\n- [Evan's Awesome A\u002FB Tools](https:\u002F\u002Fwww.evanmiller.org\u002Fab-testing\u002F) - A\u002FB测试计算器。\n- [Experimentguide](https:\u002F\u002Fexperimentguide.com\u002F) - 行业领先者提供的A\u002FB测试与实验实践指南。\n- [Google A\u002FB测试课程](https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fab-testing--ud257) - Udacity免费课程，讲解A\u002FB测试基础。\n- [So You Think You Can Test?](https:\u002F\u002Fwww.lukasvermeer.nl\u002Fconfidence\u002F) - 通过教育模拟体验A\u002FB测试的挑战。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"time-series-analysis\">\u003C\u002Fa>\n\n## ⏳ 时间序列分析\n\n\u003Ca id=\"time-series-analysis-resources\">\u003C\u002Fa>\n\n### 资源\n\n用于理解时间序列基础及分析技术的资源集合。\n\n- [Awesome Time Series](https:\u002F\u002Fgithub.com\u002Flmmentel\u002Fawesome-time-series) - 精选的时间序列分析与预测资源列表。\n- [Forecasting: Principles and Practice](https:\u002F\u002Fotexts.com\u002Ffpp3\u002F) - 包含实用案例的全面预测方法教材。\n- [NIST\u002FSEMATECH e-手册](https:\u002F\u002Fwww.itl.nist.gov\u002Fdiv898\u002Fhandbook\u002Fpmc\u002Fsection4\u002Fpmc4.htm) - NIST官方发布的时间序列分析指南。\n- [Awesome Time Series Anomaly Detection](https:\u002F\u002Fgithub.com\u002Frob-med\u002Fawesome-TS-anomaly-detection) - 专门针对时间序列异常检测的工具、数据集和论文精选列表。\n- [Awesome Time Series in Python](https:\u002F\u002Fgithub.com\u002FMaxBenChrist\u002Fawesome_time_series_in_python) - Python中用于时间序列分析的全面工具与库列表。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"time-series-analysis-tools\">\u003C\u002Fa>\n\n### 工具\n\n用于处理时间数据的工具集合。\n\n- [Facebook Prophet](https:\u002F\u002Fgithub.com\u002Ffacebook\u002Fprophet) - 基于加法模型的时间序列预测程序。\n- [Uber Orbit](https:\u002F\u002Fgithub.com\u002Fuber\u002Forbit) - 用于贝叶斯时间序列预测与推断的Python包。\n- [sktime](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime) - 与scikit-learn兼容的统一时间序列机器学习框架。\n- [GluonTS](https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fgluonts) - 基于MXNet的概率时间序列建模工具包。\n- [Time-Series-Library](https:\u002F\u002Fgithub.com\u002Fthuml\u002FTime-Series-Library) - 用于基于深度学习的时间序列分析与预测的库。\n- [TimesFM](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Ftimesfm) - Google Research推出的预训练时间序列基础模型，可用于零样本预测。\n- [PyTorch Forecasting](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fpytorch-forecasting) - 基于PyTorch的神经网络时间序列预测库。\n- [Time-series-prediction](https:\u002F\u002Fgithub.com\u002FLongxingTan\u002FTime-series-prediction) - 时间序列预测方法与实现的集合。\n- [PlotJuggler](https:\u002F\u002Fgithub.com\u002Ffacontidavide\u002FPlotJuggler) - 实时可视化与分析时间序列数据日志的工具。\n- [TSFresh](https:\u002F\u002Fgithub.com\u002Fblue-yonder\u002Ftsfresh) - 自动从时间序列数据中提取特征。\n- [pmdarima](https:\u002F\u002Fgithub.com\u002Falkaline-ml\u002Fpmdarima) - 用于ARIMA建模及时间序列分析的Python库。\n- [Kats](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FKats) - Facebook Research推出的时间序列数据分析工具包。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"data-engineering\">\u003C\u002Fa>\n\n## ⚙️ 数据工程\n\n\u003Ca id=\"data-engineering-resources\">\u003C\u002Fa>\n\n### 资源\n\n一系列资源，帮助您构建和管理健壮的数据管道与基础设施。\n\n- [数据工程师手册](https:\u002F\u002Fgithub.com\u002FDataExpert-io\u002Fdata-engineer-handbook) - 一本涵盖基础及高级数据工程概念的全面指南。\n- [数据工程Zoomcamp](https:\u002F\u002Fgithub.com\u002FDataTalksClub\u002Fdata-engineering-zoomcamp) - 免费的数据工程基础课程。\n- [Awesome Data Engineering](https:\u002F\u002Fgithub.com\u002Figorbarinov\u002Fawesome-data-engineering) - 精选的数据工程工具、软件和资源列表。\n- [数据工程 Cookbook](https:\u002F\u002Fgithub.com\u002Fandkret\u002FCookbook) - 构建可靠数据平台的技术与策略。\n- [Awesome Pipeline](https:\u002F\u002Fgithub.com\u002Fpditommaso\u002Fawesome-pipeline) - 用于数据处理和工作流管理的精选管道工具集。\n- [Awesome DB Tools](https:\u002F\u002Fgithub.com\u002Fmgramin\u002Fawesome-db-tools) - 精选的数据库工具列表。\n- [Awesome Kafka](https:\u002F\u002Fgithub.com\u002Finfoslack\u002Fawesome-kafka) - 学习和使用Apache Kafka的相关资源：书籍、培训、工具等。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"data-engineering-tools\">\u003C\u002Fa>\n\n### 工具\n\n一系列用于构建、部署和管理数据管道与基础设施的工具。\n\n- [dbt-core](https:\u002F\u002Fgithub.com\u002Fdbt-labs\u002Fdbt-core) - 一个使用SQL和Jinja在数据仓库中进行数据转换的框架。\n- [Apache Spark](https:\u002F\u002Fgithub.com\u002Fapache\u002Fspark) - 一个用于大规模数据处理和分析的统一引擎。\n- [Apache Kafka](https:\u002F\u002Fgithub.com\u002Fapache\u002Fkafka) - 一个分布式事件流平台，用于构建实时数据管道。\n- [Dagster](https:\u002F\u002Fgithub.com\u002Fdagster-io\u002Fdagster) - 一个用于机器学习、数据分析和ETL的数据编排工具。\n- [Apache Airflow](https:\u002F\u002Fgithub.com\u002Fapache\u002Fairflow) - 一个用于以编程方式编写、调度和监控工作流的平台。\n- [Apache Hive](https:\u002F\u002Fgithub.com\u002Fapache\u002Fhive) - 一个数据仓库软件，允许使用SQL读取、写入和管理分布在分布式存储中的大型数据集。\n- [Apache Hadoop](https:\u002F\u002Fgithub.com\u002Fapache\u002Fhadoop) - 一个框架，可在计算机集群上对大型数据集进行分布式处理。\n- [Luigi](https:\u002F\u002Fgithub.com\u002Fspotify\u002Fluigi) - 一个用于构建复杂且批处理型数据管道的Python模块。\n- [Apache Iceberg](https:\u002F\u002Fgithub.com\u002Fapache\u002Ficeberg) - 一种用于超大规模分析数据集的高性能表格式。\n- [Apache Cassandra](https:\u002F\u002Fgithub.com\u002Fapache\u002Fcassandra) - 一个高度可扩展的分布式NoSQL数据库，专为在大量商品化服务器上处理海量数据而设计。\n- [Apache Flink](https:\u002F\u002Fgithub.com\u002Fapache\u002Fflink) - 一个用于无界和有界数据流上的状态化计算框架（实时流处理）。\n- [Apache Beam](https:\u002F\u002Fgithub.com\u002Fapache\u002Fbeam) - 一个用于定义批处理和流式数据并行处理管道的统一模型。\n- [Apache Pulsar](https:\u002F\u002Fgithub.com\u002Fapache\u002Fpulsar) - 一个云原生的分布式消息传递和流媒体平台。\n- [Delta Lake](https:\u002F\u002Fgithub.com\u002Fdelta-io\u002Fdelta) - 一个存储层，为Apache Spark和大数据工作负载带来ACID事务特性。\n- [Apache Hudi](https:\u002F\u002Fgithub.com\u002Fapache\u002Fhudi) - 一个开放的数据湖仓平台，基于高性能的开放表格式构建。\n- [Trino](https:\u002F\u002Fgithub.com\u002Ftrinodb\u002Ftrino) - 一个分布式SQL查询引擎，专为快速查询大型数据集而设计。\n- [DataHub](https:\u002F\u002Fgithub.com\u002Fdatahub-project\u002Fdatahub) - 一个面向现代数据栈的元数据平台。\n- [OpenLineage](https:\u002F\u002Fgithub.com\u002FOpenLineage\u002FOpenLineage) - 一个用于收集和分析数据血缘关系的开放框架。\n- [Kedro](https:\u002F\u002Fgithub.com\u002Fkedro-org\u002Fkedro) - 一个用于创建可重复、可维护且模块化的数据科学代码的框架。\n- [Apache Calcite](https:\u002F\u002Fgithub.com\u002Fapache\u002Fcalcite) - 一个动态数据管理框架，支持SQL解析、优化和联邦查询。\n- [Prefect](https:\u002F\u002Fgithub.com\u002FPrefectHQ\u002Fprefect) - 一个用于构建弹性数据管道的工作流编排工具。\n- [Apache Arrow](https:\u002F\u002Fgithub.com\u002Fapache\u002Farrow) - 一种通用的列式数据格式及多语言工具箱，用于高效的数据交换。\n- [Kestra](https:\u002F\u002Fgithub.com\u002Fkestra-io\u002Fkestra) - 一个开源的事件驱动型编排工具，简化数据工作流管理。\n- [Conductor](https:\u002F\u002Fgithub.com\u002Fconductor-oss\u002Fconductor) - 一个用于运行复杂多步骤工作流和业务流程的编排引擎。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"natural-language-processing-nlp\">\u003C\u002Fa>\n\n## 📖 自然语言处理（NLP）\n\n\u003Ca id=\"natural-language-processing-nlp-resources\">\u003C\u002Fa>\n\n### 资源\n\n一些用于学习和应用Python中自然语言处理技术的资源。\n\n- [Awesome Nlp](https:\u002F\u002Fgithub.com\u002Fkeon\u002Fawesome-nlp) - 一份关于自然语言处理（NLP）的优秀Python库排名列表。\n- [Hugging Face NLP课程](https:\u002F\u002Fhuggingface.co\u002Flearn\u002Fllm-course\u002Fchapter1\u002F1) - Hugging Face官方提供的关于Transformer和NLP的课程。\n- [Practical NLP Code](https:\u002F\u002Fgithub.com\u002Fpractical-nlp\u002Fpractical-nlp-code) - 实用自然语言处理的代码示例和笔记本。\n- [牛津深度NLP讲座](https:\u002F\u002Fgithub.com\u002Foxford-cs-deepnlp-2017\u002Flectures) - 来自牛津大学深度自然语言处理课程的讲义资料。\n- [NLTK书](https:\u002F\u002Fwww.nltk.org\u002Fbook\u002F) - 使用Python进行自然语言处理。\n- [Susan Li的Python NLP教程](https:\u002F\u002Fgithub.com\u002Fsusanli2016\u002FNLP-with-Python) - 展示各种NLP技术和应用的Jupyter笔记本。\n- [Hands on NLTK教程](https:\u002F\u002Fgithub.com\u002Fhb20007\u002Fhands-on-nltk-tutorial) - Python中NLP的实践教程。\n- [YSDA NLP课程](https:\u002F\u002Fgithub.com\u002Fyandexdataschool\u002Fnlp_course) - Yandex数据科学学校关于自然语言处理的课程。\n- [The NLP Pandect](https:\u002F\u002Fgithub.com\u002Fivan-bilan\u002FThe-NLP-Pandect) - 一本全面的NLP指南，涵盖理论、模型和实际应用。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"natural-language-processing-nlp-tools\">\u003C\u002Fa>\n\n### 工具\n\n一系列用于自然语言处理的强大库和框架。\n\n- [Natural Language Toolkit (NLTK)](https:\u002F\u002Fwww.nltk.org\u002F) - 一个用于构建处理人类语言数据的 Python 程序的领先平台。\n- [TextBlob](https:\u002F\u002Ftextblob.readthedocs.io\u002Fen\u002Fdev\u002F) - 一个用于处理文本数据的简单库。\n- [SpaCy](https:\u002F\u002Fspacy.io\u002F) - 一个用于 Python 中高级 NLP 的开源软件库。\n- [BERT](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Fbert) - 一个基于 Transformer 的 NLP 任务模型。\n- [Flair](https:\u002F\u002Fgithub.com\u002FflairNLP\u002Fflair) - 一个用于最先进 NLP 的简单框架。\n- [OpenHands](https:\u002F\u002Fgithub.com\u002FAll-Hands-AI\u002FOpenHands) - 一个用于构建大型语言模型应用的库和框架。\n- [Stanford CoreNLP](https:\u002F\u002Fgithub.com\u002Fstanfordnlp\u002FCoreNLP) - 一套 Java 核心 NLP 工具，提供基础的语言分析能力。\n- [John Snow Labs Spark-NLP](https:\u002F\u002Fgithub.com\u002FJohnSnowLabs\u002Fspark-nlp) - 基于 Apache Spark 构建的最先进的自然语言处理库。\n- [TextAttack](https:\u002F\u002Fgithub.com\u002FQData\u002FTextAttack) - 一个用于 NLP 中对抗攻击、数据增强和模型训练的 Python 框架。\n- [Gensim](https:\u002F\u002Fgithub.com\u002Fpiskvorky\u002Fgensim) - 一个用于 Python 的主题建模和自然语言处理库。\n- [Stanza](https:\u002F\u002Fgithub.com\u002Fstanfordnlp\u002Fstanza) - 来自斯坦福 NLP 小组的多语言 Python NLP 库。\n- [SentenceTransformers](https:\u002F\u002Fgithub.com\u002FUKPLab\u002Fsentence-transformers) - 一个用于最先进句子和文本嵌入的框架。\n- [LangExtract](https:\u002F\u002Fgithub.com\u002Fgoogle\u002Flangextract) - 谷歌使用语言模型从文本中提取结构化信息的库。\n- [Rasa](https:\u002F\u002Fgithub.com\u002FRasaHQ\u002Frasa) - 一个用于构建上下文感知 AI 助手和聊天机器人的开源框架。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"machine-learning\">\u003C\u002Fa>\n\n## 🤖 机器学习与人工智能\n\n\u003Ca id=\"machine-learning-resources\">\u003C\u002Fa>\n\n### 资源\n\n一系列帮助您学习和应用机器学习概念与技术的资源。\n\n- [Awesome Machine Learning](https:\u002F\u002Fgithub.com\u002Fjosephmisiti\u002Fawesome-machine-learning) - 一个精选的机器学习框架、库和软件列表。\n- [Machine Learning Tutorials](https:\u002F\u002Fgithub.com\u002Fujjwalkarn\u002FMachine-Learning-Tutorials) - 机器学习和深度学习教程、文章及其他资源。\n- [Awesome Deep Learning](https:\u002F\u002Fgithub.com\u002FChristosChristofidis\u002Fawesome-deep-learning) - 一个精选的深度学习教程、项目和社区列表。\n- [Best of ML Python](https:\u002F\u002Fgithub.com\u002Flukasmasuch\u002Fbest-of-ml-python) - 一个排名靠前的机器学习 Python 库和工具列表。\n- [Microsoft ML for Beginners](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FML-For-Beginners) - 一本面向初学者的机器学习概念与实践入门书。\n- [mlcourse.ai](https:\u002F\u002Fgithub.com\u002FYorko\u002Fmlcourse.ai) - 一门开放的机器学习课程，包含实践作业和真实世界的应用。\n- [Machine Learning Zoomcamp](https:\u002F\u002Fgithub.com\u002FDataTalksClub\u002Fmachine-learning-zoomcamp) - 一门免费的实践机器学习课程，专注于模型的构建和部署。\n- [Awesome Artificial Intelligence](https:\u002F\u002Fgithub.com\u002Fowainlewis\u002Fawesome-artificial-intelligence) - 一个精选的人工智能资源列表。\n- [Google Research](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Fgoogle-research) - 谷歌研究项目和出版物的官方仓库。\n- [100 Days of ML Coding](https:\u002F\u002Fgithub.com\u002FAvik-Jain\u002F100-Days-Of-ML-Code) - 一个为期 100 天的综合编码挑战，旨在学习机器学习。\n- [Made With ML](https:\u002F\u002Fgithub.com\u002FGokuMohandas\u002FMade-With-ML) - 一个用于构建和部署机器学习应用的资源。\n- [Handson-ml3](https:\u002F\u002Fgithub.com\u002Fageron\u002Fhandson-ml3) - 一本使用 Python 进行机器学习和深度学习的实践指南。\n- [AI For Beginners](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FAI-For-Beginners) - 微软关于人工智能的课程。\n- [LLMs-from-scratch](https:\u002F\u002Fgithub.com\u002Frasbt\u002FLLMs-from-scratch) - 一个用于从头开始构建 LLM 的教育性仓库。\n- [Awesome Generative AI Guide](https:\u002F\u002Fgithub.com\u002Faishwaryanr\u002Fawesome-generative-ai-guide) - 一份关于生成式 AI 模型、工具和应用的全面指南。\n- [Awesome LLM](https:\u002F\u002Fgithub.com\u002FHannibal046\u002FAwesome-LLM) - 一个精选的大语言模型相关论文、项目和资源列表。\n- [Machine Learning with Python by Susan Li](https:\u002F\u002Fgithub.com\u002Fsusanli2016\u002FMachine-Learning-with-Python) - 包含各种机器学习算法和应用的 Jupyter 笔记本。\n- [Understanding Deep Learning](https:\u002F\u002Fudlbook.github.io\u002Fudlbook\u002F) - 一本全面且易于理解的深度学习基础教材。\n- [Deep Learning Papers Reading Roadmap](https:\u002F\u002Fgithub.com\u002Ffloodsung\u002FDeep-Learning-Papers-Reading-Roadmap) - 一份为新手精心挑选的深度学习经典论文路线图。\n- [Applied ML](https:\u002F\u002Fgithub.com\u002Feugeneyan\u002Fapplied-ml) - 一组为工业界应用机器学习而精选的资源和工具。\n- [Annotated deep learning paper implementations](https:\u002F\u002Fgithub.com\u002Flabmlai\u002Fannotated_deep_learning_paper_implementations) - 对深度学习论文进行实现，并附有注释代码。\n- [Ml From Scratch](https:\u002F\u002Fgithub.com\u002Feriklindernoren\u002FML-From-Scratch) - 用 Python 从零开始实现的核心机器学习算法。\n- [Awesome Ai Ml Resources](https:\u002F\u002Fgithub.com\u002Farmankhondker\u002Fawesome-ai-ml-resources) - 一个精心挑选的 AI\u002FML 书籍、课程和实用工具列表。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"machine-learning-tools\">\u003C\u002Fa>\n\n### 工具\n\n用于开发和部署机器学习模型的一系列工具。\n\n#### 机器学习\n\n- [Scikit-learn](https:\u002F\u002Fgithub.com\u002Fscikit-learn\u002Fscikit-learn) - 经典算法和模型构建的机器学习库。\n- [XGBoost](https:\u002F\u002Fgithub.com\u002Fdmlc\u002Fxgboost) - 针对基于树的模型优化的分布式梯度提升库。\n- [LightGBM](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FLightGBM) - 快速、分布式、高性能的梯度提升框架。\n- [CatBoost](https:\u002F\u002Fgithub.com\u002Fcatboost\u002Fcatboost) - 支持分类特征的高性能梯度提升决策树。\n- [H2O-3](https:\u002F\u002Fgithub.com\u002Fh2oai\u002Fh2o-3) - 开源分布式机器学习平台。\n- [cuML](https:\u002F\u002Fgithub.com\u002Frapidsai\u002Fcuml) - RAPIDS 提供的 GPU 加速机器学习算法。\n- [dlib](https:\u002F\u002Fgithub.com\u002Fdavisking\u002Fdlib) - 包含机器学习算法和工具的现代 C++ 工具包。\n- [SHAP](https:\u002F\u002Fgithub.com\u002Fshap\u002Fshap) - 基于博弈论的方法，用于解释任何机器学习模型的输出。\n- [InterpretML](https:\u002F\u002Fgithub.com\u002Finterpretml\u002Finterpret) - 拟合可解释模型并解释黑盒机器学习。\n- [Optuna](https:\u002F\u002Fgithub.com\u002Foptuna\u002Foptuna) - 超参数优化框架。\n\n#### 深度学习\n\n- [TensorFlow](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Ftensorflow) - 用于机器学习和深度学习的端到端开源平台。\n- [PyTorch](https:\u002F\u002Fgithub.com\u002Fpytorch\u002Fpytorch) - 强有力支持研究与生产的深度学习框架。\n- [PyTorch Lightning](https:\u002F\u002Fgithub.com\u002FLightning-AI\u002Fpytorch-lightning) - 用于高性能 AI 研究的 PyTorch 封装。\n- [PyTorch Ignite](https:\u002F\u002Fgithub.com\u002Fpytorch\u002Fignite) - 帮助训练和评估神经网络的高级库。\n- [Keras](https:\u002F\u002Fgithub.com\u002Fkeras-team\u002Fkeras) - 运行在 TensorFlow 之上的高级神经网络 API。\n- [Fast.ai](https:\u002F\u002Fgithub.com\u002Ffastai\u002Ffastai) - 简化快速且准确训练神经网络的深度学习库。\n- [HuggingFace Transformers](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Ftransformers) - 用于最先进机器学习模型的模型定义框架。\n- [HuggingFace Diffusers](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fdiffusers) - 用于最先进的预训练扩散模型的库。\n- [PEFT](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fpeft) - 用于高效微调大型预训练模型的库。\n- [YOLOv5](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fyolov5) - 实时目标检测系统。\n- [Ultralytics](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fultralytics) - YOLOv8 及其他计算机视觉模型。\n- [ONNX](https:\u002F\u002Fgithub.com\u002Fonnx\u002Fonnx) - 用于机器学习互操作性的开放标准。\n- [PyTorch Geometric](https:\u002F\u002Fgithub.com\u002Fpyg-team\u002Fpytorch_geometric) - PyTorch 的几何深度学习扩展库。\n- [Pyro](https:\u002F\u002Fgithub.com\u002Fpyro-ppl\u002Fpyro) - 结合 Python 和 PyTorch 的深度通用概率编程。\n- [Skorch](https:\u002F\u002Fgithub.com\u002Fskorch-dev\u002Fskorch) - 与 Scikit-learn 兼容的神经网络库。\n- [Sonnet](https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Fsonnet) - DeepMind 用于构建复杂神经网络的库。\n- [JAX](https:\u002F\u002Fgithub.com\u002Fjax-ml\u002Fjax) - 对 Python + NumPy 程序进行可组合变换：求导、向量化、编译为 GPU\u002FTPU 等。\n- [TensorFlow Models](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Fmodels) - TensorFlow 官方仓库，包含模型和示例。\n- [Fenn](https:\u002F\u002Fgithub.com\u002Fpyfenn\u002Ffenn) - 一个简单的框架，通过提供预制的训练器、模板、日志记录、配置管理等功能，自动化 ML\u002FDL 工作流。\n\n[回到目录](#contents)\n\n---\n\n\u003Ca id=\"mlops\">\u003C\u002Fa>\n\n## 🚀 MLOps\n\n\u003Ca id=\"mlops-resources\">\u003C\u002Fa>\n\n### 资源\n\n用于机器学习运维的材料和精选列表。\n\n- [MLOps Zoomcamp](https:\u002F\u002Fgithub.com\u002FDataTalksClub\u002Fmlops-zoomcamp) - 一门专注于 ML 系统部署和维护实践方面的免费课程。\n- [Awesome MLOps (visenger)](https:\u002F\u002Fgithub.com\u002Fvisenger\u002Fawesome-mlops) - MLOps 相关参考资料的精选列表。\n- [Awesome MLOps (kelvins)](https:\u002F\u002Fgithub.com\u002Fkelvins\u002Fawesome-mlops) - 精选的 MLOps 工具列表。\n- [Awesome LLMOps](https:\u002F\u002Fgithub.com\u002Ftensorchord\u002FAwesome-LLMOps) - 专为开发者准备的优秀 LLMOps 工具精选列表。\n- [LLM Zoomcamp](https:\u002F\u002Fgithub.com\u002FDataTalksClub\u002Fllm-zoomcamp) - 一门专门介绍大型语言模型、其架构和应用的课程。\n- [ML Engineering Guide](https:\u002F\u002Fgithub.com\u002Fstas00\u002Fml-engineering) - 机器学习工程及 MLOps 最佳实践的实用指南。\n- [Awesome Production Machine Learning](https:\u002F\u002Fgithub.com\u002FEthicalML\u002Fawesome-production-machine-learning) - 用于在生产环境中部署、监控和维护 ML 系统的工具精选列表。\n- [Llama Cookbook](https:\u002F\u002Fgithub.com\u002Fmeta-llama\u002Fllama-cookbook) - 使用 Llama 模型的官方配方和示例。\n- [Awesome Kubeflow](https:\u002F\u002Fgithub.com\u002Fterrytangyuan\u002Fawesome-kubeflow) - Kubeflow 机器学习平台的精选资源、工具和项目。\n\n[回到目录](#contents)\n\n---\n\n\u003Ca id=\"mlops-tools\">\u003C\u002Fa>\n\n### 工具\n\n用于部署、监控和维护机器学习系统的平台和实用工具。\n\n- [ColossalAI](https:\u002F\u002Fgithub.com\u002Fhpcaitech\u002FColossalAI) - 高性能分布式训练框架。\n- [DVC](https:\u002F\u002Fgithub.com\u002Fiterative\u002Fdvc) - 适用于机器学习项目的版本控制系统。\n- [Evidently](https:\u002F\u002Fgithub.com\u002Fevidentlyai\u002Fevidently) - 用于分析和监控数据及模型漂移的工具。\n- [Deepchecks](https:\u002F\u002Fgithub.com\u002Fdeepchecks\u002Fdeepchecks) - 用于机器学习模型和数据验证的工具。\n- [Sematic](https:\u002F\u002Fgithub.com\u002Fsematic-ai\u002Fsematic) - 使用原生 Python 构建、调试和执行机器学习流水线的工具。\n- [netdata](https:\u002F\u002Fgithub.com\u002Fnetdata\u002Fnetdata) - 实时性能监控工具。\n- [meilisearch](https:\u002F\u002Fgithub.com\u002Fmeilisearch\u002Fmeilisearch) - 快速的开源搜索引擎。\n- [vLLM](https:\u002F\u002Fgithub.com\u002Fvllm-project\u002Fvllm) - 面向大语言模型的高吞吐量、内存高效的推理库。\n- [haystack](https:\u002F\u002Fgithub.com\u002Fdeepset-ai\u002Fhaystack) - 用于构建搜索和问答系统的 LLM 框架。\n- [Kubeflow](https:\u002F\u002Fgithub.com\u002Fkubeflow\u002Fkubeflow) - 面向 Kubernetes 的机器学习工具包。\n- [Seldon Core](https:\u002F\u002Fgithub.com\u002FSeldonIO\u002Fseldon-core) - 用于在生产环境中部署和监控机器学习模型的开源平台。\n- [Feast](https:\u002F\u002Fgithub.com\u002Ffeast-dev\u002Ffeast) - 机器学习特征存储，负责管理和为模型提供特征数据。\n- [BentoML](https:\u002F\u002Fgithub.com\u002Fbentoml\u002FBentoML) - 用于构建、交付和扩展机器学习应用的框架。\n- [MLflow](https:\u002F\u002Fgithub.com\u002Fmlflow\u002Fmlflow) - 用于管理机器学习完整生命周期的开源平台。\n- [Wandb](https:\u002F\u002Fgithub.com\u002Fwandb\u002Fwandb) - 用于实验跟踪、数据集版本控制和模型管理的工具。\n- [Comet ML](https:\u002F\u002Fgithub.com\u002Fcomet-ml\u002Fopik) - 用于跟踪、比较和优化机器学习实验的平台。\n- [Netflix Metaflow](https:\u002F\u002Fgithub.com\u002FNetflix\u002Fmetaflow) - 一种人性化的 Python 库，帮助科学家和工程师构建并管理实际的数据科学项目。\n- [mindsdb](https:\u002F\u002Fgithub.com\u002Fmindsdb\u002Fmindsdb) - 将 AI 集成到数据库和应用程序中的平台。\n- [KServe](https:\u002F\u002Fgithub.com\u002Fkserve\u002Fkserve) - 标准化的无服务器推理平台，用于在 Kubernetes 上部署和提供机器学习模型服务。\n- [SQLFlow](https:\u002F\u002Fgithub.com\u002Fsql-machine-learning\u002Fsqlflow) - 为 SQL 引入机器学习功能，允许使用 SQL 语法进行模型训练和预测。\n- [Jina AI Serve](https:\u002F\u002Fgithub.com\u002Fjina-ai\u002Fserve) - 用于构建和部署通过 gRPC、HTTP 和 WebSockets 进行通信的 AI 服务的框架。\n- [LiteLLM](https:\u002F\u002Fgithub.com\u002FBerriAI\u002Flitellm) - 统一接口，可调用所有 LLM API（OpenAI、Anthropic、Cohere 等），并保持一致的输出格式。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"ai-applications\">\u003C\u002Fa>\n\n## 🧠 人工智能应用与平台\n\n\u003Ca id=\"ai-applications-resources\">\u003C\u002Fa>\n\n### 资源\n\n专注于AI应用和平台的资源合集。\n\n- [Awesome LLM Apps](https:\u002F\u002Fgithub.com\u002FShubhamsaboo\u002Fawesome-llm-apps) - 收录了使用OpenAI、Anthropic、Gemini及开源模型构建的LLM应用、AI智能体和RAG技术的优秀项目。\n- [Awesome Generative AI](https:\u002F\u002Fgithub.com\u002Fsteven2358\u002Fawesome-generative-ai) - 精选的现代生成式人工智能项目与服务列表。\n- [AI Agents for Beginners](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fai-agents-for-beginners) - 微软提供的关于设计和构建AI智能体的课程。\n- [Generative AI for Beginners](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fgenerative-ai-for-beginners) - 微软为初学者准备的生成式AI课程。\n- [Ai Dev Tools Zoomcamp](https:\u002F\u002Fgithub.com\u002FDataTalksClub\u002Fai-dev-tools-zoomcamp) - 免费的实践课程，教授如何使用现代工具构建和部署AI应用。\n- [LLM Course](https:\u002F\u002Fgithub.com\u002Fmlabonne\u002Fllm-course) - 从头到尾掌握大型语言模型的实用课程。\n- [Awesome AI Agents](https:\u002F\u002Fgithub.com\u002Fe2b-dev\u002Fawesome-ai-agents) - 精选的AI自主智能体、环境和框架列表。\n- [AI Collection](https:\u002F\u002Fgithub.com\u002Fai-collection\u002Fai-collection) - 生成式AI全景图——精选的优秀生成式AI应用集合。\n- [Awesome AI Apps](https:\u002F\u002Fgithub.com\u002FArindam200\u002Fawesome-ai-apps) - 展示RAG、智能体、工作流及其他AI应用场景的项目合集。\n- [系统提示词与模型](https:\u002F\u002Fgithub.com\u002Fx1xhlol\u002Fsystem-prompts-and-models-of-ai-tools) - 来自各类AI应用和编程工具的系统提示词、内部工具及AI模型。\n- [RAG技术](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FRAG_Techniques) - 检索增强生成领域的高级技术合集。\n- [Awesome LangChain](https:\u002F\u002Fgithub.com\u002Fkyrolabs\u002Fawesome-langchain) - 使用LangChain框架的优秀工具和项目的清单。\n- [Awesome AI Tools](https:\u002F\u002Fgithub.com\u002Fmahseema\u002Fawesome-ai-tools) - 精选的人工智能顶级工具列表。\n- [Awesome LLM Security](https:\u002F\u002Fgithub.com\u002Fcorca-ai\u002Fawesome-llm-security) - 关于LLM安全的优秀工具、文档和项目的精选。\n- [Claude Cookbooks](https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fclaude-cookbooks) - Anthropic官方提供的Claude AI使用示例与教程。\n- [Hands On Large Language Models](https:\u002F\u002Fgithub.com\u002FHandsOnLLM\u002FHands-On-Large-Language-Models) - 涵盖LLM基础、提示工程和微调等内容。\n- [AI Engineering Hub](https:\u002F\u002Fgithub.com\u002Fpatchy631\u002Fai-engineering-hub) - 用于构建、部署和维护AI系统的资源。\n- [Agents Towards Production](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002Fagents-towards-production) - 面向生产级GenAI智能体开发的代码驱动教程。\n- [LLM Engineer Toolkit](https:\u002F\u002Fgithub.com\u002FKalyanKS-NLP\u002Fllm-engineer-toolkit) - 涵盖多个领域的120余种LLM相关库的精选列表。\n- [GenAI Agents](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents) - AI智能体实现与教程的仓库。\n- [AI Notes](https:\u002F\u002Fgithub.com\u002Fswyxio\u002Fai-notes) - 关于AI和软件开发的个人笔记与文章。\n- [Open LLMs](https:\u002F\u002Fgithub.com\u002Feugeneyan\u002Fopen-llms) - 全面的开源大型语言模型及其能力列表。\n- [Prompt Engineering Guide](https:\u002F\u002Fgithub.com\u002Fdair-ai\u002FPrompt-Engineering-Guide) - 关于LLM提示工程的指南、论文和资源。\n- [Prompt Engineering](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002Fprompt_engineering) - 提示工程技巧与策略的合集。\n- [500 AI Agents Projects](https:\u002F\u002Fgithub.com\u002Fashishpatel26\u002F500-AI-Agents-Projects) - 500多个带有代码的AI智能体项目，供学习和启发。\n- [Generative AI](https:\u002F\u002Fgithub.com\u002Fgenieincodebottle\u002Fgenerative-ai) - 掌握生成式AI技术的路线图与资源。\n- [Awesome N8N](https:\u002F\u002Fgithub.com\u002Frestyler\u002Fawesome-n8n) - n8n自动化平台的模板、集成及资源合集。\n- [Free Llm Api Resources](https:\u002F\u002Fgithub.com\u002Fcheahjs\u002Ffree-llm-api-resources) - 最新更新的免费大型语言模型（LLM）API列表。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"ai-applications-tools\">\u003C\u002Fa>\n\n### 工具\n\n用于构建和部署AI驱动解决方案的框架、平台和终端用户应用合集。\n\n#### AI智能体与自动化\n\n- [n8n](https:\u002F\u002Fgithub.com\u002Fn8n-io\u002Fn8n) - 用于连接 API 和服务的工作流自动化平台。\n- [crewAI](https:\u002F\u002Fgithub.com\u002FcrewAIInc\u002FcrewAI) - 用于编排角色扮演型 AI 代理的框架。\n- [autogen](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen) - 用于构建多智能体对话系统的框架。\n- [AutoGPT](https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002FAutoGPT) - 能够完成复杂任务的自主 AI 代理。\n- [LangGraph](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph) - 使用 LLM 构建具有状态和多主体应用的框架，支持循环和控制流。\n- [Agents.md](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fagents.md) - 用于构建智能体式 AI 系统的开源框架。\n- [OpenManus](https:\u002F\u002Fgithub.com\u002FFoundationAgents\u002FOpenManus) - 用于构建和部署 AI 代理的开源平台。\n- [youtu-agent](https:\u002F\u002Fgithub.com\u002FTencentCloudADP\u002Fyoutu-agent) - 腾讯云推出的多模态智能代理框架。\n- [trae-agent](https:\u002F\u002Fgithub.com\u002Fbytedance\u002Ftrae-agent) - 具有执行增强推理能力的工具使用型推理代理。\n- [deepagents](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Fdeepagents) - LangChain 框架，用于构建复杂的多智能体系统。\n- [mem0](https:\u002F\u002Fgithub.com\u002Fmem0ai\u002Fmem0) - 用于长期上下文和个性化交互的 AI 记忆系统。\n- [web-ui](https:\u002F\u002Fgithub.com\u002Fbrowser-use\u002Fweb-ui) - 基于 AI 的浏览器自动化框架，用于网页交互。\n- [Agent-S](https:\u002F\u002Fgithub.com\u002Fsimular-ai\u002FAgent-S) - 开源智能体框架，能够像人类一样自主与计算机 GUI 交互。\n- [Mastra](https:\u002F\u002Fgithub.com\u002Fmastra-ai\u002Fmastra) - 开源 AI 代理平台，用于构建和扩展生产级自主代理。\n- [Langflow](https:\u002F\u002Fgithub.com\u002Flangflow-ai\u002Flangflow) - 强大的可视化平台，用于构建和部署 AI 驱动的智能体及工作流。\n- [agenticSeek](https:\u002F\u002Fgithub.com\u002FFosowl\u002FagenticSeek) - 用于构建和部署具备高级推理与工具使用能力的 AI 代理的框架。\n- [Flowise](https:\u002F\u002Fgithub.com\u002FFlowiseAI\u002FFlowise) - 开源 UI 可视化工具，用于构建自定义 LLM 编排流程和 AI 代理。\n- [MetaGPT](https:\u002F\u002Fgithub.com\u002FFoundationAgents\u002FMetaGPT) - 多智能体框架，模拟软件公司中的不同角色以完成项目开发。\n- [Local Deep Research](https:\u002F\u002Fgithub.com\u002FLearningCircuit\u002Flocal-deep-research) - 本地 AI 研究助手，可搜索网络、论文和文档。\n- [Gptme](https:\u002F\u002Fgithub.com\u002Fgptme\u002Fgptme) - AI 代理命令行界面，能够编写代码、使用终端、浏览网页并在本地运行。\n- [Rowboat](https:\u002F\u002Fgithub.com\u002Frowboatlabs\u002Frowboat) - 开源 AI 同事，通过学习用户的邮件和会议内容来自动化起草、准备和任务处理。\n- [Everyrow](https:\u002F\u002Fgithub.com\u002Ffuturesearch\u002Feveryrow-sdk) - 基于 AI 的数据操作 SDK。提供语义去重、模糊合并和智能排序功能，适用于数据分析工作流。\n- [Personal Ai Infrastructure](https:\u002F\u002Fgithub.com\u002Fdanielmiessler\u002FPersonal_AI_Infrastructure) - 用于构建具备记忆、技能和学习能力的个人 AI 助手的框架。\n- [N8N Workflows](https:\u002F\u002Fgithub.com\u002FZie619\u002Fn8n-workflows) - n8n 自动化平台的即用型工作流模板集合。\n- [Skyvern](https:\u002F\u002Fgithub.com\u002FSkyvern-AI\u002Fskyvern) - 基于 LLM 和计算机视觉的 AI 浏览器自动化工具。兼容 Playwright 的 SDK + 无代码工作流。\n- [OpenWork](https:\u002F\u002Fgithub.com\u002Fdifferent-ai\u002Fopenwork) - 开源桌面替代方案，类似于 Claude Cowork，可在本地运行智能体、技能和 MCP，并支持团队协作功能。\n- [DeepAnalyze](https:\u002F\u002Fgithub.com\u002Fruc-datalab\u002FDeepAnalyze) - 自主数据科学智能体 LLM，无需人工干预即可独立完成各类以数据为中心的任务。\n\n\n#### 开发框架与工具\n\n- [LangChain](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchain) - 用于开发语言模型驱动应用的框架。\n- [LlamaIndex](https:\u002F\u002Fgithub.com\u002Frun-llama\u002Fllama_index) - 具备 RAG 功能的 LLM 应用数据框架。\n- [openai-python](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fopenai-python) - OpenAI API 的官方 Python 库。\n- [openai-agents-python](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fopenai-agents-python) - OpenAI 官方的 AI 代理构建框架。\n- [ragflow](https:\u002F\u002Fgithub.com\u002Finfiniflow\u002Fragflow) - 开源 RAG（检索增强生成）工作流平台。\n- [firecrawl](https:\u002F\u002Fgithub.com\u002Ffirecrawl\u002Ffirecrawl) - 用于 AI 应用的网页爬取和数据提取服务。\n- [Fabric](https:\u002F\u002Fgithub.com\u002Fdanielmiessler\u002FFabric) - 利用 AI 增强人类能力的框架。\n- [Dyad](https:\u002F\u002Fgithub.com\u002Fdyad-sh\u002Fdyad) - 开源平台，用于构建使用自定义 API 密钥的 AI 应用。\n- [Langflow](https:\u002F\u002Fgithub.com\u002Flangflow-ai\u002Flangflow) - 强大的可视化平台，用于构建和部署 AI 驱动的智能体及工作流。\n- [NeMo](https:\u002F\u002Fgithub.com\u002FNVIDIA-NeMo\u002FNeMo) - NVIDIA 提供的可扩展生成式 AI 框架，适用于 LLM、多模态和语音 AI。\n- [Deepcode](https:\u002F\u002Fgithub.com\u002FHKUDS\u002FDeepCode) - 基于 AI 的代理框架，可从科研论文和文本中自动生成代码。\n\n#### 代码生成与辅助\n\n- [gpt-engineer](https:\u002F\u002Fgithub.com\u002FAntonOsika\u002Fgpt-engineer) - 基于 AI 的代码生成工具。\n- [gpt-pilot](https:\u002F\u002Fgithub.com\u002FPythagora-io\u002Fgpt-pilot) - AI 配对程序员，可编写整个应用程序。\n- [tabby](https:\u002F\u002Fgithub.com\u002FTabbyML\u002Ftabby) - 自托管的 AI 编码助手。\n\n#### 模型部署与平台\n\n- [Ollama](https:\u002F\u002Fgithub.com\u002Fjmorganca\u002Follama) - 用于在本地运行大型语言模型的工具。\n- [OpenLLM](https:\u002F\u002Fgithub.com\u002Fbentoml\u002FOpenLLM) - 用于在生产环境中运行大型语言模型的开放平台。\n- [LocalAI](https:\u002F\u002Fgithub.com\u002Fmudler\u002FLocalAI) - 自托管、以本地优先的 AI 模型部署平台。\n- [dify](https:\u002F\u002Fgithub.com\u002Flanggenius\u002Fdify) - 可视化的 LLM 应用开发平台。\n- [LLaMA-Factory](https:\u002F\u002Fgithub.com\u002Fhiyouga\u002FLLaMA-Factory) - 易用的 LLM 微调框架。\n- [unsloth](https:\u002F\u002Fgithub.com\u002Funslothai\u002Funsloth) - 用于更快速、更节省内存的 LLM 微调库。\n- [LocalGPT](https:\u002F\u002Fgithub.com\u002FPromtEngineer\u002FlocalGPT) - 完全私密的本地文档智能平台，可通过本地 LLM 与您的文档进行对话。\n\n#### AI 可靠性与调试\n\n- [DeepEval](https:\u002F\u002Fgithub.com\u002Fconfident-ai\u002Fdeepeval) - 类似 Pytest 的 LLM 单元测试框架。提供 RAG、智能体、幻觉、摘要等指标，以及自定义评估标准。\n- [RAGAS](https:\u002F\u002Fgithub.com\u002Fvibrantlabsai\u002Fragas) - LLM 应用评估工具包。包含指标、测试生成和优化建议，可用于改进 RAG 流程和智能体。\n- [Phoenix](https:\u002F\u002Fgithub.com\u002FArize-ai\u002Fphoenix) - AI 可观测性平台。提供追踪、数据集、实验和试用环境等功能，用于排查和评估 LLM 应用。\n- [WFGY](https:\u002F\u002Fgithub.com\u002Fonestardao\u002FWFGY) - RAG 和 AI 代理的开源调试基础设施。包括 16 种 RAG 故障问题地图和 TXT 应力测试引擎。\n\n#### 终端用户应用\n\n- [open-webui](https:\u002F\u002Fgithub.com\u002Fopen-webui\u002Fopen-webui) - 用于与各类大模型交互的Web界面。\n- [ComfyUI](https:\u002F\u002Fgithub.com\u002Fcomfyanonymous\u002FComfyUI) - 面向Stable Diffusion的可视化节点式界面。\n- [lobe-chat](https:\u002F\u002Fgithub.com\u002Flobehub\u002Flobe-chat) - 现代化的AI对话界面。\n- [LibreChat](https:\u002F\u002Fgithub.com\u002Fdanny-avila\u002FLibreChat) - 开源的ChatGPT替代品。\n- [quivr](https:\u002F\u002Fgithub.com\u002FQuivrHQ\u002Fquivr) - 个人第二大脑及AI助手。\n- [upscayl](https:\u002F\u002Fgithub.com\u002Fupscayl\u002Fupscayl) - 基于AI的图像超分辨率工具。\n- [facefusion](https:\u002F\u002Fgithub.com\u002Ffacefusion\u002Ffacefusion) - AI人脸换脸与增强工具。\n- [DocsGPT](https:\u002F\u002Fgithub.com\u002Farc53\u002FDocsGPT) - 基于文档的问答系统。\n- [Deep Research](https:\u002F\u002Fgithub.com\u002Fdzhng\u002Fdeep-research) - 面向任何主题的迭代式深度研究的AI研究助手。\n- [Screenpipe](https:\u002F\u002Fgithub.com\u002Fmediar-ai\u002Fscreenpipe) - 本地AI，可根据屏幕和音频记录、搜索并自动化任务。\n- [Jaaz](https:\u002F\u002Fgithub.com\u002F11cafe\u002Fjaaz) - 开源多模态创意助手，也是面向本地图像\u002F视频生成的、注重隐私的Canva\u002FManus替代方案。\n- [DeepTutor](https:\u002F\u002Fgithub.com\u002FHKUDS\u002FDeepTutor) - 具备文档问答、习题生成及深度研究能力的AI个性化学习助手。\n\n#### 其他工具\n\n- [Bagel](https:\u002F\u002Fgithub.com\u002FByteDance-Seed\u002FBagel) - 开源统一多模态模型，用于理解和生成图像。\n- [Whisper](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fwhisper) - 强大的语音识别模型，适用于转录和翻译。\n- [ChatTTS](https:\u002F\u002Fgithub.com\u002F2noise\u002FChatTTS) - 针对自然、富有表现力的日常对话优化的生成式TTS模型，支持细粒度韵律控制。\n- [NeuTTS](https:\u002F\u002Fgithub.com\u002Fneuphonic\u002Fneutts) - 设备端TTS模型，可通过音频样本实现即时语音克隆。\n- [Everything Claude Code](https:\u002F\u002Fgithub.com\u002Faffaan-m\u002Feverything-claude-code) - 一套资源、指南和工具，用于高效使用Claude Code AI助手。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"cloud-platforms\">\u003C\u002Fa>\n\n\n\n## ☁️ 云平台与基础设施\n\n\u003Ca id=\"cloud-platform-resources\">\u003C\u002Fa>\n\n### 资源\n\n一系列用于掌握云原生技术、容器化及基础设施管理的资源集合。\n\n- [Awesome Cloud Native](https:\u002F\u002Fgithub.com\u002Frootsongjc\u002Fawesome-cloud-native) - 云原生技术精选资源列表。\n- [Awesome Kubernetes](https:\u002F\u002Fgithub.com\u002Framitsurana\u002Fawesome-kubernetes) - Kubernetes相关优秀资源精选列表。\n- [Awesome Docker](https:\u002F\u002Fgithub.com\u002Fveggiemonk\u002Fawesome-docker) - Docker资源与项目精选列表。\n- [AWS Well-Architected Labs](https:\u002F\u002Fgithub.com\u002Fawslabs\u002Faws-well-architected-labs) - 实践实验室，帮助学习AWS Well-Architected框架。\n- [Kubernetes The Hard Way](https:\u002F\u002Fgithub.com\u002Fkelseyhightower\u002Fkubernetes-the-hard-way) - 在Google Cloud Platform上手动搭建Kubernetes集群的教程。\n- [Awesome Compose](https:\u002F\u002Fgithub.com\u002Fdocker\u002Fawesome-compose) - Docker Compose示例精选列表。\n- [AWS EKS最佳实践](https:\u002F\u002Fgithub.com\u002Faws\u002Faws-eks-best-practices) - Amazon EKS的最佳实践指南。\n- [Awesome Selfhosted](https:\u002F\u002Fgithub.com\u002Fawesome-selfhosted\u002Fawesome-selfhosted) - 可在本地托管的自由软件网络服务和Web应用列表。\n- [Awesome Selfhosted Docker](https:\u002F\u002Fgithub.com\u002Fhotheadhacker\u002Fawesome-selfhost-docker) - 使用Docker的优秀自托管应用与解决方案精选列表。\n- [Awesome Kubernetes Resources](https:\u002F\u002Fgithub.com\u002Ftomhuang12\u002Fawesome-k8s-resources) - Kubernetes教程、工具及资源精选列表。\n- [Awesome Cloud Security](https:\u002F\u002Fgithub.com\u002F4ndersonLin\u002Fawesome-cloud-security) - 云安全资源、工具及最佳实践精选列表。\n- [DevOps练习](https:\u002F\u002Fgithub.com\u002Fbregman-arie\u002Fdevops-exercises) - 涉及Linux、Jenkins、AWS、SRE、Prometheus、Docker、Python、Ansible、Git、Kubernetes、Terraform、OpenStack、SQL等。\n- [Awesome Cloudsec Labs](https:\u002F\u002Fgithub.com\u002Fiknowjason\u002FAwesome-CloudSec-Labs) - 云安全平台的学习型动手实验室与练习精选。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"cloud-platform-tools\">\u003C\u002Fa>\n\n### 工具\n\n用于容器化、编排、基础设施即代码以及云原生开发的工具。\n\n#### 容器化与编排\n\n- [Docker](https:\u002F\u002Fgithub.com\u002Fdocker) - 一个用于在容器中开发、交付和运行应用程序的开放平台。\n- [Docker Compose](https:\u002F\u002Fgithub.com\u002Fdocker\u002Fcompose) - 一个用于定义和运行多容器 Docker 应用程序的工具。\n- [Kubernetes](https:\u002F\u002Fgithub.com\u002Fkubernetes\u002Fkubernetes) - 一个生产级的容器编排系统。\n- [Kompose](https:\u002F\u002Fgithub.com\u002Fkubernetes\u002Fkompose) - 一个将 Docker Compose 转换为 Kubernetes 的工具。\n\n#### 基础设施即代码\n\n- [Terraform](https:\u002F\u002Fgithub.com\u002Fhashicorp\u002Fterraform) - 一个基础设施即代码工具。\n- [OpenTofu](https:\u002F\u002Fgithub.com\u002Fopentofu\u002Fopentofu) - Terraform 的开源分支。\n- [Pulumi](https:\u002F\u002Fgithub.com\u002Fpulumi\u002Fpulumi) - 一个使用熟悉编程语言的现代 IaC 平台。\n- [CDK8s](https:\u002F\u002Fgithub.com\u002Fcdk8s-team\u002Fcdk8s) - 使用熟悉语言定义 Kubernetes 应用程序。\n\n#### CI\u002FCD 和 GitOps\n\n- [Jenkins](https:\u002F\u002Fgithub.com\u002Fjenkinsci\u002Fjenkins) - 一个开源自动化服务器。\n- [Argo CD](https:\u002F\u002Fgithub.com\u002Fargoproj\u002Fargo-cd) - 一种声明式的 GitOps 持续交付工具。\n- [Argo Workflows](https:\u002F\u002Fgithub.com\u002Fargoproj\u002Fargo-workflows) - 一个基于容器的工作流引擎。\n- [Tekton](https:\u002F\u002Fgithub.com\u002Ftektoncd\u002Fpipeline) - 一个 Kubernetes 原生的 CI\u002FCD 框架。\n- [Spinnaker](https:\u002F\u002Fgithub.com\u002Fspinnaker\u002Fspinnaker) - 一个多云持续交付平台。\n- [Dagger](https:\u002F\u002Fgithub.com\u002Fdagger\u002Fdagger) - 一个用于 CI\u002FCD 流程的可移植开发工具包。\n\n#### 服务网格与 API 网关\n\n- [Traefik](https:\u002F\u002Fgithub.com\u002Ftraefik\u002Ftraefik) - 一个现代化的 HTTP 反向代理和负载均衡器。\n- [Kong](https:\u002F\u002Fgithub.com\u002FKong\u002Fkong) - 一个云原生 API 网关。\n- [Apache APISIX](https:\u002F\u002Fgithub.com\u002Fapache\u002Fapisix) - 一个动态 API 网关。\n- [Envoy Gateway](https:\u002F\u002Fgithub.com\u002Fenvoyproxy\u002Fgateway) - 一个管理 Envoy Proxy 作为网关的项目。\n- [Higress](https:\u002F\u002Fgithub.com\u002Falibaba\u002Fhigress) - 一个基于 Istio 的云原生 API 网关。\n- [Meshery](https:\u002F\u002Fgithub.com\u002Fmeshery\u002Fmeshery) - 一个服务网格管理工具。\n\n#### Kubernetes 生态系统\n\n- [Helm](https:\u002F\u002Fgithub.com\u002Fhelm\u002Fhelm) - 一个用于 Kubernetes 的软件包管理器。\n- [Kustomize](https:\u002F\u002Fgithub.com\u002Fkubernetes-sigs\u002Fkustomize) - 一个用于 Kubernetes 配置定制的工具。\n- [Kubernetes Dashboard](https:\u002F\u002Fgithub.com\u002Fkubernetes\u002Fdashboard) - 一个基于 Web 的 Kubernetes 用户界面。\n- [Skaffold](https:\u002F\u002Fgithub.com\u002FGoogleContainerTools\u002Fskaffold) - 一个用于 Kubernetes 的持续开发工具。\n- [Tilt](https:\u002F\u002Fgithub.com\u002Ftilt-dev\u002Ftilt) - 一个用于 Kubernetes 的本地开发工具。\n- [Flagger](https:\u002F\u002Fgithub.com\u002Ffluxcd\u002Fflagger) - 一个渐进式交付操作符。\n- [KubeVela](https:\u002F\u002Fgithub.com\u002Fkubevela\u002Fkubevela) - 一个应用交付平台。\n- [KubeSphere](https:\u002F\u002Fgithub.com\u002Fkubesphere\u002Fkubesphere) - 一个 Kubernetes 多云管理平台。\n\n#### 开发者平台与控制平面\n\n- [Crossplane](https:\u002F\u002Fgithub.com\u002Fcrossplane\u002Fcrossplane) - 一个云原生控制平面。\n- [Artifact Hub](https:\u002F\u002Fgithub.com\u002Fartifacthub\u002Fhub) - 提供 Kubernetes 包和 Helm 图表。\n- [Devtron](https:\u002F\u002Fgithub.com\u002Fdevtron-labs\u002Fdevtron) - 一个 Kubernetes 控制面板。\n- [Harness](https:\u002F\u002Fgithub.com\u002Fharness\u002Fharness) - 一个端到端的开发者平台。\n\n#### 其他工具\n\n- [Vagrant](https:\u002F\u002Fgithub.com\u002Fhashicorp\u002Fvagrant) - 一个用于构建和管理可移植虚拟开发环境的工具，支持基础设施即代码。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"productivity\">\u003C\u002Fa>\n\n## ⚡ 生产力\n\n\u003Ca id=\"productivity-resources\">\u003C\u002Fa>\n\n### 资源\n\n一系列旨在提升生产力的资源。\n\n- [Positron](https:\u002F\u002Fgithub.com\u002Fposit-dev\u002Fpositron) - 一款新一代数据科学 IDE。\n- [Nanobrowser](https:\u002F\u002Fgithub.com\u002Fnanobrowser\u002Fnanobrowser) - 一个开源的 AI 网络自动化工具，采用多智能体系统，直接在浏览器中运行。\n- [Best of Jupyter](https:\u002F\u002Fgithub.com\u002Fml-tooling\u002Fbest-of-jupyter) - 一份列出值得关注的 Jupyter Notebook、Hub 和 Lab 项目的排名列表。\n- [Deepnote](https:\u002F\u002Fgithub.com\u002Fdeepnote\u002Fdeepnote) - 一个兼容 Jupyter 的 AI 原生数据科学笔记本平台，具备实时协作、环境管理和集成功能。\n- [AFFiNE](https:\u002F\u002Fgithub.com\u002Ftoeverything\u002FAFFiNE) - 一个集笔记、文档和数据可视化于一体的全能工作空间。\n- [Marimo](https:\u002F\u002Fgithub.com\u002Fmarimo-team\u002Fmarimo) - 一个响应式的 Python 笔记本，适用于可重复且交互式的数据科学。\n- [ChatGPT 数据科学提示](https:\u002F\u002Fgithub.com\u002Ftravistangvh\u002FChatGPT-Data-Science-Prompts) - 一组专为使用 ChatGPT 的数据科学家设计的实用提示。\n- [Gamma.app](https:\u002F\u002Fgamma.app\u002F) - 一个由 AI 驱动的平台，用于创建和分享演示文稿及文档。\n- [Cookiecutter Data Science](https:\u002F\u002Fgithub.com\u002Fdrivendataorg\u002Fcookiecutter-data-science) - 一个用于数据科学项目的标准化项目结构。\n- [Learn Regex](https:\u002F\u002Fgithub.com\u002Fziishaned\u002Flearn-regex) - 一本包含示例和练习的全面正则表达式学习指南。\n- [Awesome Regex](https:\u002F\u002Fgithub.com\u002Faloisdg\u002Fawesome-regex) - 一个精选的正则表达式工具、库和学习资源集合。\n- [The Markdown Guide](https:\u002F\u002Fwww.markdownguide.org\u002F) - 一本全面的 Markdown 学习指南。\n- [Readme-AI](https:\u002F\u002Fgithub.com\u002Feli64s\u002Freadme-ai) - 一个可以自动为你的项目生成 README.md 文件的工具。\n- [Markdown Here](https:\u002F\u002Fgithub.com\u002Fadam-p\u002Fmarkdown-here) - 一个允许用户以 Markdown 格式撰写邮件并在发送前渲染的浏览器扩展。\n- [MarkText](https:\u002F\u002Fgithub.com\u002Fmarktext\u002Fmarktext) - 一个简单而优雅的 Markdown 编辑器，适用于文档编写。\n- [QuarkDown](https:\u002F\u002Fgithub.com\u002Fiamgio\u002Fquarkdown) - 一个轻量级的 Markdown 处理器，用于快速渲染文档。\n- [screenshot-to-code](https:\u002F\u002Fgithub.com\u002Fabi\u002Fscreenshot-to-code) - 一个 AI 工具，能够将截图转换为各种前端技术栈的代码。\n- [Codebeautify](https:\u002F\u002Fcodebeautify.org\u002F) - 一个一体化的在线代码格式化和美化工具，支持 Python、SQL、JSON 等多种语言。\n- [Notion](https:\u002F\u002Fwww.notion.com\u002F) - 一个集笔记记录和任务管理于一体的全能工作空间。\n- [Trello](https:\u002F\u002Ftrello.com\u002Fhome) - 一个可视化的项目管理工具。\n- [Habitica](https:\u002F\u002Fgithub.com\u002FHabitRPG\u002Fhabitica) - 一个习惯养成和提高生产力的应用，将你的生活视为角色扮演游戏。\n- [Bujo](https:\u002F\u002Fbulletjournal.com\u002F) - 一套帮助你改变工作和生活方式的工具。\n- [Parabola](https:\u002F\u002Fparabola.io\u002F) - 一个由 AI 驱动的工作流构建工具，用于整理数据。\n- [Asana](https:\u002F\u002Fasana.com\u002F) - 一个用于跟踪工作和项目的项目管理平台。\n- [Puter](https:\u002F\u002Fgithub.com\u002FHeyPuter\u002Fputer) - 一个开源的基于浏览器的计算环境和云操作系统。\n- [Milkdown](https:\u002F\u002Fgithub.com\u002FMilkdown\u002Fmilkdown) - 一个受 Typora 启发的插件驱动、所见即所得的 Markdown 编辑器框架。\n- [PDFMathTranslate](https:\u002F\u002Fgithub.com\u002FPDFMathTranslate\u002FPDFMathTranslate) - 一个 AI 工具，用于双语科学 PDF 翻译，同时保留公式、图表和版面布局。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"productivity-useful-linux-tools\">\u003C\u002Fa>\n\n### 有用的 Linux 工具\n\n一系列用于提升 Linux 环境下生产力和功能性的工具。\n\n- [tldr-pages](https:\u002F\u002Fgithub.com\u002Ftldr-pages\u002Ftldr) - 基于社区维护的简化版手册页，附带实用示例。\n- [Bat](https:\u002F\u002Fgithub.com\u002Fsharkdp\u002Fbat) - 具有语法高亮功能的 `cat` 替代工具。\n- [Exa](https:\u002F\u002Fgithub.com\u002Fogham\u002Fexa) - 现代化的 `ls` 替代工具。\n- [Ripgrep](https:\u002F\u002Fgithub.com\u002FBurntSushi\u002Fripgrep) - 更快的 `grep` 替代工具。\n- [Zoxide](https:\u002F\u002Fgithub.com\u002Fajeetdsouza\u002Fzoxide) - 智能化的 `cd` 命令。\n- [Peek](https:\u002F\u002Fgithub.com\u002Fphw\u002Fpeek) - 简单易用的动画 GIF 屏幕录制工具。\n- [CopyQ](https:\u002F\u002Fgithub.com\u002Fhluk\u002FCopyQ) - 具有高级功能的剪贴板管理器。\n- [Translate Shell](https:\u002F\u002Fgithub.com\u002Fsoimort\u002Ftranslate-shell) - 使用 Google Translate、Bing Translator、Yandex.Translate 等的命令行翻译工具。\n- [Espanso](https:\u002F\u002Fgithub.com\u002Fespanso\u002Fespanso) - 用 Rust 编写的跨平台文本扩展工具。\n- [Flameshot](https:\u002F\u002Fgithub.com\u002Fflameshot-org\u002Fflameshot) - 功能强大且易于使用的截图软件。\n- [DrawIO Desktop](https:\u002F\u002Fgithub.com\u002Fjgraph\u002Fdrawio-desktop) - 开源流程图、工艺流程图等绘图软件。\n- [Inkscape](https:\u002F\u002Fgithub.com\u002Finkscape\u002Finkscape) - 强大、免费且开源的矢量图形编辑器，用于创建和编辑可视化内容。\n- [Rclone](https:\u002F\u002Frclone.org\u002F) - 用于管理云存储文件的命令行工具。\n- [Rsync](https:\u002F\u002Frsync.samba.org\u002F) - 快速且多功能的文件复制工具，可在网络或本地同步两个位置之间的文件和目录。\n- [Timeshift](https:\u002F\u002Fgithub.com\u002Flinuxmint\u002Ftimeshift) - Linux 系统还原工具，使用 rsync+硬链接或 BTRFS 快照来创建文件系统快照。\n- [Backintime](https:\u002F\u002Fgithub.com\u002Fbit-team\u002Fbackintime) - 方便且可高度配置的增量备份图形界面工具。\n- [Fzf](https:\u002F\u002Fgithub.com\u002Fjunegunn\u002Ffzf) - 命令行模糊查找工具。\n- [Osquery](https:\u002F\u002Fgithub.com\u002Fosquery\u002Fosquery) - 基于 SQL 的操作系统监控、分析与仪表化工具。\n- [GNU Parallel](https:\u002F\u002Fwww.gnu.org\u002Fsoftware\u002Fparallel\u002F) - 用于并行执行任务的工具。\n- [HTop](https:\u002F\u002Fhtop.dev\u002F) - 交互式进程查看器。\n- [Ncdu](https:\u002F\u002Fdev.yorhel.nl\u002Fncdu) - 带有 ncurses 界面的磁盘使用情况分析工具。\n- [Thefuck](https:\u002F\u002Fgithub.com\u002Fnvbn\u002Fthefuck) - 用于纠正之前输入的错误命令的工具。\n- [Miller](https:\u002F\u002Fgithub.com\u002Fjohnkerl\u002Fmiller) - 用于查询、处理和格式化多种文件格式（如 CSV、JSON 等）数据的工具，类似于 `awk`\u002F`sed`\u002F`cut` 对数据的操作。\n- [jq](https:\u002F\u002Fgithub.com\u002Fjqlang\u002Fjq) - 命令行 JSON 处理器，用于解析和操作 JSON 数据。\n- [yq](https:\u002F\u002Fgithub.com\u002Fmikefarah\u002Fyq) - 可移植的命令行 YAML 处理器（类似于 jq 用于 YAML 和 XML）。\n- [q](https:\u002F\u002Fgithub.com\u002Fharelba\u002Fq) - 直接在命令行上对 CSV 或 TSV 文件运行 SQL 查询。\n- [VisiData](https:\u002F\u002Fgithub.com\u002Fsaulpw\u002Fvisidata) - 终端中用于表格数据探索的交互式多功能工具。\n- [csvkit](https:\u002F\u002Fgithub.com\u002Fwireservice\u002Fcsvkit) - 一套用于处理 CSV 数据的命令行工具。\n- [httpie](https:\u002F\u002Fgithub.com\u002Fhttpie\u002Fcli) - 用于 API 测试和调试的现代化命令行 HTTP 客户端。\n- [glances](https:\u002F\u002Fgithub.com\u002Fnicolargo\u002Fglances) - 跨平台的系统监控工具，用于资源使用情况分析。\n- [hyperfine](https:\u002F\u002Fgithub.com\u002Fsharkdp\u002Fhyperfine) - 用于性能测试的命令行基准测试工具。\n- [termgraph](https:\u002F\u002Fgithub.com\u002Fmkaz\u002Ftermgraph) - 在终端中绘制基本图表，便于快速数据可视化。\n- [fd](https:\u002F\u002Fgithub.com\u002Fsharkdp\u002Ffd) - 简单、快速且用户友好的 `find` 替代工具。\n- [dust](https:\u002F\u002Fgithub.com\u002Fbootandy\u002Fdust) - 用 Rust 编写的更直观的 `du` 替代工具。\n- [bottom](https:\u002F\u002Fgithub.com\u002FClementTsang\u002Fbottom) - 跨平台的图形化进程\u002F系统监控工具。\n- [Keychain](https:\u002F\u002Fgithub.com\u002Fdanielrobbins\u002Fkeychain) - 用于管理和安全存储密码及密钥的工具。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"productivity-useful-vs-code-extensions\">\u003C\u002Fa>\n\n### 有用的 VS Code 扩展\n\n一系列用于增强 Visual Studio Code 功能和提升工作效率的扩展。\n\n- [JDBC Adapter](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems\u002F?itemName=cweijan.dbclient-jdbc) - 使用 JDBC 连接到各种数据库。\n- [DBCode - Connect](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=DBCode.dbcode) - 数据库客户端，用于管理和查询数据库。\n- [Markdown All in One](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=yzhang.markdown-all-in-one) - Markdown 编辑必备工具。\n- [Markdown Preview GitHub Styles](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=bierner.markdown-preview-github-styles) - 将 VS Code 的 Markdown 预览样式调整为与 GitHub 一致。\n- [Snippington Python Pandas Basic](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=snippington.snp-pandas-basic) - 用于在 Python 中操作 Pandas 的基础工具。\n- [PDF Viewer for Visual Studio Code](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=mathematic.vscode-pdf) - 在 VS Code 中直接查看 PDF 文件。\n- [Quick Python Print](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=WeidaWang.quick-python-print) - 快速处理 Python 中的打印操作。\n- [Rainbow CSV](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=mechatroner.rainbow-csv) - 高亮显示 CSV 和 TSV 文件，并可执行类似 SQL 的查询。\n- [Remove Blank Lines](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=thamaraiselvam.remove-blank-lines) - 用于移除文档中空行的扩展。\n- [PDF Preview in VSCode](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems\u002Ftomoki1207.pdf) - 在 VS Code 中显示 PDF 预览。\n- [CSV to Table](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=phplasma.csv-to-table) - 将 CSV\u002FTSV\u002FPSV 文件转换为 ASCII 格式的表格。\n- [Data Preview](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=RandomFractalsInc.vscode-data-preview) - 导入、查看、切片和导出数据。\n- [Data Wrangler](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=ms-toolsai.datawrangler) - 用于清理和准备表格型数据集的工具。\n- [Error Lens](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=usernamehw.errorlens) - 改善代码中错误和警告的显示效果。\n- [Indent Rainbow](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=oderwat.indent-rainbow) - 使缩进更易于阅读。\n- [Markdown Table Editor](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=TakumiI.markdowntable) - 增加编辑 Markdown 表格的功能。\n- [WYSIWYG Editor for Markdown](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=cweijan.vscode-office) - 查看 Word 和 Excel 文件，并编辑 Markdown。\n- [Prettier](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=esbenp.prettier-vscode) - VS Code 的代码格式化扩展。\n- [Project Manager](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=alefragnani.project-manager) - 轻松切换项目。\n- [Python Indent](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=KevinRose.vsc-python-indent) - 自动缩进 Python 代码。\n- [SandDance](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=msrvida.vscode-sanddance) - 可视化探索和展示您的数据。\n- [SQL Notebooks](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=cmoog.sqlnotebook) - 将 SQL 文件以 VSCode Notebook 格式打开。\n- [SQL Tools](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems\u002F?itemName=mtxr.sqltools) - VSCode 的数据库管理工具。\n- [Kanban Board](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=mkloubert.vscode-kanban) - 用于在 VS Code 内组织任务的 Kanban 板扩展。\n- [Path Autocomplete](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=ionutvmi.path-autocomplete) - 为 VS Code 中的文件和目录路径提供自动补全。\n- [Path Intellisense](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=christian-kohler.path-intellisense) - 自动补全代码中的文件名。\n- [Python Imports Utils](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=mgesbert.python-path) - 用于管理 Python 导入的实用工具。\n- [Workspace Dashboard](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=kruemelkatze.vscode-dashboard) - 以快速拨号方式组织您的工作区。\n- [Remote Development](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=ms-vscode-remote.vscode-remote-extensionpack) - 在容器、远程机器或 WSL 中打开任意文件夹。\n- [Text Power Tools](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=qcz.text-power-tools) - 包含 240 多条文本操作命令的一体化解决方案。\n- [Toggle Quotes](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=BriteSnow.vscode-toggle-quotes) - 在单引号、双引号和反引号之间切换字符串引号。\n- [Comment Translate](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=intellsmi.comment-translate) - 帮助翻译代码中的注释、字符串和变量名。\n- [Text Marker](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=ryu1kn.text-marker) - 选择代码中的文本，并用可配置的高亮颜色标记所有匹配项。\n- [Bookmarks](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=alefragnani.Bookmarks) - 在代码中添加书签并轻松跳转到这些位置。\n- [Dendron](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=dendron.dendron) - 一种层次化的笔记工具，会随着您的使用不断成长。\n- [Gitignore Generator](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=rubbersheep.gi) - 简化 .gitignore 文件的生成过程。\n- [Test Explorer UI](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=hbenl.vscode-test-explorer) - 在 Visual Studio Code 的侧边栏中运行测试。\n- [Python Test Explorer](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=LittleFoxTeam.vscode-python-test-adapter) - 在 Visual Studio Code 的侧边栏中运行 Python 测试。\n- [VSCode Markdownlint](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=DavidAnson.vscode-markdownlint) - 用于检查和格式化 Markdown 文件的 VS Code 扩展。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"skill-development-career-resources\">\u003C\u002Fa>\n\n## 📚 技能提升与职业发展\n\n\u003Ca id=\"skill-development-career-resources-practice-resources\">\u003C\u002Fa>\n\n### 实践资源\n\n一系列资源，旨在提升数据分析及相关领域的技能并推动您的职业发展。\n\n- [LeetCode](https:\u002F\u002Fleetcode.com\u002Fproblemset\u002F) - 用于准备技术编码面试的平台。\n- [Kaggle 竞赛](https:\u002F\u002Fwww.kaggle.com\u002Fcompetitions) - 参与数据分析和机器学习竞赛的平台。\n- [Makeovermonday](https:\u002F\u002Fmakeovermonday.co.uk\u002F) - 专注于提升数据可视化实践的平台。\n- [Workout Wednesday](https:\u002F\u002Fworkout-wednesday.com\u002F) - 通过每周挑战来提高您的可视化技能。\n- [官方 TidyTuesday 资源库](https:\u002F\u002Fgithub.com\u002Frfordatascience\u002Ftidytuesday) - TidyTuesday 项目的资源库，致力于推广数据分析。\n- [DrivenData 竞赛](https:\u002F\u002Fwww.drivendata.org\u002Fcompetitions\u002F) - 注重社会影响力的数据分析竞赛。\n- [Codecademy 数据科学路径](https:\u002F\u002Fwww.codecademy.com\u002Flearn\u002Fpaths\u002Fdata-science) - 学习数据分析的互动课程。\n- [SQL 大师班](https:\u002F\u002Fgithub.com\u002Fdatawithdanny\u002Fsql-masterclass?tab=readme-ov-file#course-content) - 一门通过实际项目掌握 SQL 进行数据分析的课程。\n- [Hugging Face 任务](https:\u002F\u002Fhuggingface.co\u002Ftasks) - 使用真实模型进行自然语言处理和机器学习特定任务的实践。\n- [Awesome LeetCode 资源](https:\u002F\u002Fgithub.com\u002Fashishps1\u002Fawesome-leetcode-resources) - LeetCode 练习的精选资源和策略集合。\n- [Leetcode 公司专项题目](https:\u002F\u002Fgithub.com\u002Fliquidslr\u002Fleetcode-company-wise-problems) - 面试准备用的公司专项 Leetcode 题目。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"skill-development-career-resources-curated-jupyter-notebooks\">\u003C\u002Fa>\n\n### 精选 Jupyter 笔记本\n\n一系列精选的 Jupyter 笔记本，用于支持数据科学和分析领域的学习与探索。\n\n- [Awesome Notebooks](https:\u002F\u002Fgithub.com\u002Fjupyter-naas\u002Fawesome-notebooks) - 按工具分类的数据与 AI 笔记本模板目录。\n- [数据科学 IPython 笔记本](https:\u002F\u002Fgithub.com\u002Fdonnemartin\u002Fdata-science-ipython-notebooks) - 涵盖多个主题的数据科学 Python 笔记本。\n- [Pydata 书籍](https:\u002F\u002Fgithub.com\u002Fwesm\u002Fpydata-book) - Wes McKinney 的《利用 Python 进行数据分析》教材及 IPython 笔记本。\n- [Spark py 笔记本](https:\u002F\u002Fgithub.com\u002Fjadianes\u002Fspark-py-notebooks) - 用于大数据分析和机器学习的 Apache Spark & Python 教程。\n- [DataMiningNotebooks](https:\u002F\u002Fgithub.com\u002Feclarson\u002FDataMiningNotebooks) - 伴随南卫理公会大学课程的示例笔记本，用于数据挖掘。\n- [Pythondataanalysis](https:\u002F\u002Fgithub.com\u002Fhnawaz007\u002Fpythondataanalysis) - 包含 Jupyter 笔记本和脚本的 Python 数据仓库。\n- [Python 数据分析入门](https:\u002F\u002Fgithub.com\u002Fcuttlefishh\u002Fpython-for-data-analysis) - 使用 Jupyter 笔记本介绍基于 Python 和 Pandas 的数据科学。\n- [Jdwittenauer IPython 笔记本](https:\u002F\u002Fgithub.com\u002Fjdwittenauer\u002Fipython-notebooks) - 涵盖多种主题的 IPython 笔记本集合。\n- [DataScienceInteractivePython](https:\u002F\u002Fgithub.com\u002FGeostatsGuy\u002FDataScienceInteractivePython) - 用于学习数据科学概念的交互式 Python 笔记本集合。\n- [Unsloth 笔记本](https:\u002F\u002Fgithub.com\u002Funslothai\u002Fnotebooks) - 优化后的笔记本，可加快 AI 模型的训练和微调。\n- [Huggingface 笔记本](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fnotebooks) - Hugging Face 官方提供的自然语言处理、视觉、音频和扩散模型相关笔记本。\n- [深度学习与 Python 笔记本](https:\u002F\u002Fgithub.com\u002Ffchollet\u002Fdeep-learning-with-python-notebooks) - François Chollet 的《深度学习与 Python》一书中的官方 Jupyter 笔记本。\n- [PythonNumericalDemos](https:\u002F\u002Fgithub.com\u002FGeostatsGuy\u002FPythonNumericalDemos) - 用于地质统计学和数值演示的 Python 笔记本。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"skill-development-career-resources-data-sources-datasets\">\u003C\u002Fa>\n\n### 数据来源与数据集\n\n一系列用于访问数据集和数据来源的资源，供分析和项目使用。\n\n- [Kaggle 数据集](https:\u002F\u002Fwww.kaggle.com\u002Fdatasets) - 丰富的数据集集合，可用于数据分析练习。\n- [Opendatasets](https:\u002F\u002Fgithub.com\u002FJovianHQ\u002Fopendatasets) - 一个 Python 库，可从 Kaggle、Google Drive 等在线来源下载数据集。\n- [Datasette](https:\u002F\u002Fgithub.com\u002Fsimonw\u002Fdatasette) - 一款开源多用途工具，用于探索和发布数据。\n- [Awesome Public Datasets](https:\u002F\u002Fgithub.com\u002Fawesomedata\u002Fawesome-public-datasets) - 精选的高质量公开数据集列表。\n- [Open Data Sources](https:\u002F\u002Fgithub.com\u002Fdatasciencemasters\u002Fdata) - 各种开放数据源的集合。\n- [项目免费数据集](https:\u002F\u002Fwww.dataquest.io\u002Fblog\u002Ffree-datasets-for-projects\u002F) - Dataquest 整理的免费数据集。\n- [Data World](https:\u002F\u002Fdata.world\u002F) - 企业级数据目录，在人工智能时代深受 CIO、治理专家、数据分析师和工程师的信任。\n- [Awesome Public Real Time Datasets](https:\u002F\u002Fgithub.com\u002Fbytewax\u002Fawesome-public-real-time-datasets) - 公开可用的实时数据集列表。\n- [Google 数据集搜索](https:\u002F\u002Fdatasetsearch.research.google.com\u002F) - 用于搜索全网数据集的搜索引擎。\n- [NASA 开放数据门户](https:\u002F\u002Fdata.nasa.gov\u002F) - NASA 开放数据计划的网站，提供对 NASA 数据资源的访问。\n- [世界银行数据](https:\u002F\u002Fdata.worldbank.org\u002F) - 世界银行提供的全球发展数据，可免费公开访问。\n- [语音数据集](https:\u002F\u002Fgithub.com\u002Fjim-schwoebel\u002Fvoice_datasets) - 用于语音 AI 和机器学习的音频及语音数据集集合。\n- [HuggingFace 数据集](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fdatasets) - 一个轻量级库，便于共享和访问音频、计算机视觉和自然语言处理相关的数据集。\n- [TensorFlow 数据集](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Fdatasets) - 一系列可直接用于 TensorFlow 及其他 Python 机器学习框架的数据集。\n- [NLP 数据集](https:\u002F\u002Fgithub.com\u002Fniderhoff\u002Fnlp-datasets) - 为自然语言处理任务精心挑选的数据集列表。\n- [TorchVision 数据集](https:\u002F\u002Fgithub.com\u002Fpytorch\u002Fvision) - torchvision.datasets 模块提供了许多内置的计算机视觉数据集。\n- [LLM 数据集](https:\u002F\u002Fgithub.com\u002Fmlabonne\u002Fllm-datasets) - 用于训练和微调大型语言模型 (LLM) 的数据集和资源集合。\n- [Unsplash 数据集](https:\u002F\u002Fgithub.com\u002Funsplash\u002Fdatasets) - Unsplash 提供的一系列数据集，适用于计算机视觉和研究。\n- [Awesome JSON 数据集](https:\u002F\u002Fgithub.com\u002Fjdorfman\u002Fawesome-json-datasets?tab=readme-ov-file#bitcoin) - 无需认证即可公开访问的优秀 JSON 数据集精选列表。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"skill-development-career-resources-resume-and-interview-tips\">\u003C\u002Fa>\n\n### 简历与面试技巧\n\n多种资源助您准备面试并提升简历质量。\n\n- [数据科学面试题及答案](https:\u002F\u002Fgithub.com\u002FyoussefHosni\u002FData-Science-Interview-Questions-Answers) - 精选的数据科学面试题目与解答列表。\n- [数据科学面试备考资源](https:\u002F\u002Fgithub.com\u002FyoussefHosni\u002FData-Science-Interview-Preperation-Resources) - 帮助您为即将到来的数据科学面试做准备的资源。\n- [数据科学面试](https:\u002F\u002Fgithub.com\u002Falexeygrigorev\u002Fdata-science-interviews) - 一份全面的数据科学面试问题与资源合集。\n- [Interviews AI](https:\u002F\u002Fgithub.com\u002FBoltzmannEntropy\u002Finterviews.ai) - 包含问题与解答的AI面试备考指南。\n- [数据科学面试宝典](https:\u002F\u002Fbook.thedatascienceinterviewproject.com\u002F) - 一本全面的资源书，用于准备数据科学和机器学习领域的面试。\n- [机器学习面试宝典](https:\u002F\u002Fgithub.com\u002Fchiphuyen\u002Fml-interviews-book) - 一本全面的指南，帮助您准备机器学习工程师职位的面试。\n- [MLQuestions](https:\u002F\u002Fgithub.com\u002Fandrewekhalel\u002FMLQuestions) - 机器学习面试题与答案的集合。\n- [Interview](https:\u002F\u002Fgithub.com\u002FOlshansk\u002Finterview) - 您准备技术面试所需的一切。\n- [Interviews](https:\u002F\u002Fgithub.com\u002Fkdn251\u002Finterviews) - 个人技术面试学习指南，涵盖算法与数据结构。\n- [Devinterview](https:\u002F\u002Fdevinterview.io\u002F) - 让您自信地通过下一次技术面试。\n- [Interviewqs](https:\u002F\u002Fwww.interviewqs.com\u002F) - 助您顺利通过下一次数据科学面试。\n- [破解数据科学面试](https:\u002F\u002Fgithub.com\u002Fkhanhnamle1994\u002Fcracking-the-data-science-interview) - 一份包含备忘录、书籍、面试题及作品集的资料，专为数据科学\u002F机器学习面试准备。\n- [Interview Query](https:\u002F\u002Fwww.interviewquery.com\u002F) - 另一个用于准备数据科学面试的平台。\n- [超强行为面试资源](https:\u002F\u002Fgithub.com\u002Fashishps1\u002Fawesome-behavioral-interviews) - 精选资源，帮助您掌握行为面试与系统设计面试。\n- [Enhancv 数据科学家简历](https:\u002F\u002Fenhancv.com\u002Fresume-examples\u002Fdata-scientist\u002F) - 针对数据科学家量身定制的简历示例与技巧合集。\n- [数据科学作品集](https:\u002F\u002Fwww.datascienceportfol.io\u002F) - 一个创建并展示您的数据科学作品集的平台。\n- [InterviewBit - SQL面试题](https:\u002F\u002Fwww.interviewbit.com\u002Fsql-interview-questions\u002F) - SQL面试题集合。\n- [StrataScratch](https:\u002F\u002Fwww.stratascratch.com\u002F) - 提供来自顶尖公司的真实数据科学面试题的平台。\n- [LeetCode模式](https:\u002F\u002Fgithub.com\u002Fseanprashad\u002Fleetcode-patterns) - 为技术面试精心挑选的编码模式与策略合集。\n- [Bartosz Jarocki 的简历](https:\u002F\u002Fgithub.com\u002FBartoszJarocki\u002Fcv) - 现代开源技术简历模板及示例。\n- [Awesome-CV](https:\u002F\u002Fgithub.com\u002Fposquit0\u002FAwesome-CV) - 使用LaTeX构建的专业简历模板。\n- [Reactive-Resume](https:\u002F\u002Fgithub.com\u002FAmruthPillai\u002FReactive-Resume) - 开源简历生成器，提供多种模板与自定义选项。\n- [史上最佳简历](https:\u002F\u002Fgithub.com\u002Fsalomonelli\u002Fbest-resume-ever) - 现代简历模板与CV示例合集。\n\n[回到目录](#contents)\n\n---\n\n\u003Ca id=\"cheatsheets\">\u003C\u002Fa>\n\n## 📋 备忘录\n\n跨多个领域的备忘录合集，便于快速参考与学习。\n\n\u003Ca id=\"cheatsheets-goalkicker\">\u003C\u002Fa>\n\n### GoalKicker 编程笔记\n\n- [Python 专业开发者笔记](https:\u002F\u002Fbooks.goalkicker.com\u002FPythonBook\u002FPythonNotesForProfessionals.pdf) - 一份庞大的Python概念、惯用语及最佳实践合集，适合各水平开发者。\n- [SQL 专业开发者笔记](https:\u002F\u002Fbooks.goalkicker.com\u002FSQLBook\u002FSQLNotesForProfessionals.pdf) - 一本关于SQL语法、查询及数据库交互概念的权威指南。\n- [PostgreSQL 专业开发者笔记](https:\u002F\u002Fbooks.goalkicker.com\u002FPostgreSQLBook\u002FPostgreSQLNotesForProfessionals.pdf) - 一本关于PostgreSQL管理和开发的专业知识汇编。\n- [MySQL 专业开发者笔记](https:\u002F\u002Fbooks.goalkicker.com\u002FMySQLBook\u002FMySQLNotesForProfessionals.pdf) - 一份关于MySQL数据库管理系统的重要参考资料。\n- [Oracle数据库专业开发者笔记](https:\u002F\u002Fbooks.goalkicker.com\u002FOracleDatabaseBook\u002FOracleDatabaseNotesForProfessionals.pdf) - 一本介绍Oracle数据库概念、PL\u002FSQL及管理任务的指南。\n- [MongoDB 专业开发者笔记](https:\u002F\u002Fbooks.goalkicker.com\u002FMongoDBBook\u002FMongoDBNotesForProfessionals.pdf) - 一本实用指南，介绍如何在现代应用开发中使用NoSQL和MongoDB。\n- [Bash 专业开发者笔记](https:\u002F\u002Fbooks.goalkicker.com\u002FBashBook\u002FBashNotesForProfessionals.pdf) - 一本关于Shell脚本编程和命令行操作的全面指南。\n- [Git 专业开发者笔记](https:\u002F\u002Fbooks.goalkicker.com\u002FGitBook\u002FGitNotesForProfessionals.pdf) - 关于Git版本控制的全方位知识，从基础到高级工作流程。\n- [Linux 专业开发者笔记](https:\u002F\u002Fbooks.goalkicker.com\u002FLinuxBook\u002FLinuxNotesForProfessionals.pdf) - 深入探讨Linux系统管理、常用命令及环境配置。\n- [Microsoft SQL Server 专业开发者笔记](https:\u002F\u002Fbooks.goalkicker.com\u002FMicrosoftSQLServerBook\u002FMicrosoftSQLServerNotesForProfessionals.pdf) - 一份详细参考材料，用于开发和管理MS SQL Server数据库。\n- [PowerShell 专业开发者笔记](https:\u002F\u002Fbooks.goalkicker.com\u002FPowerShellBook\u002FPowerShellNotesForProfessionals.pdf) - 一本关于使用PowerShell进行任务自动化和配置管理的指南。\n\n[回到目录](#contents)\n\n---\n\n\u003Ca id=\"cheatsheets-python\">\u003C\u002Fa>\n\n### Python\n\n- [Python备忘录](https:\u002F\u002Fvivitoa.github.io\u002Fpython-cheat-sheet\u002F) - 全面的Python语法与示例。\n- [Learn Python](https:\u002F\u002Fgithub.com\u002Ftrekhleb\u002Flearn-python) - 互动式Python学习。\n- [Pythoncheatsheet](https:\u002F\u002Fwww.pythoncheatsheet.org\u002F) - Python基础知识及进阶主题的快速参考。\n- [综合Python备忘录](https:\u002F\u002Fgithub.com\u002Fgto76\u002Fpython-cheatsheet) - 详细的Python函数与库说明。\n- [Python备忘录](https:\u002F\u002Fgithub.com\u002Fwilfredinni\u002Fpython-cheatsheet) - 一份全面的Python编程语言备忘录。\n- [Pysheeet](https:\u002F\u002Fgithub.com\u002Fcrazyguitar\u002Fpysheeet) - 简洁的Python备忘录，便于快速参考和面试准备。\n\n[回到目录](#contents)\n\n---\n\n\u003Ca id=\"cheatsheets-data-science-machine-learning\">\u003C\u002Fa>\n\n### 数据科学与机器学习\n\n- [DS 备忘单](https:\u002F\u002Fgithub.com\u002FFavioVazquez\u002Fds-cheatsheets) - 数据科学备忘单列表。\n- [DS 笔记与备忘单](https:\u002F\u002Fgithub.com\u002Fmerveenoyan\u002Fmy_notes) - 数据科学、机器学习、计算机科学等领域的备忘单。\n- [数据科学备忘单（数学）](https:\u002F\u002Fgithub.com\u002Fml874\u002FData-Science-Cheatsheet) - 用于数据科学数学快速参考的备忘单。\n- [Pandas 备忘单](https:\u002F\u002Fpandas.pydata.org\u002FPandas_Cheat_Sheet.pdf) - 使用 Pandas 进行数据操作。\n- [PySpark 备忘单](https:\u002F\u002Fgithub.com\u002Fkevinschaich\u002Fpyspark-cheatsheet) - 常用的 PySpark 模式。\n- [机器学习备忘单](https:\u002F\u002Fgithub.com\u002Fsoulmachine\u002Fmachine-learning-cheat-sheet) - 简明的机器学习备忘单，涵盖关键概念和公式。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"cheatsheets-linux-git\">\u003C\u002Fa>\n\n### Linux 与 Git\n\n- [Linux 备忘单](https:\u002F\u002Fgithub.com\u002Fgto76\u002Flinux-cheatsheet) - Linux 命令和快捷键。\n- [Linux Bash 命令](https:\u002F\u002Fgithub.com\u002Ftrinib\u002FLinux-Bash-Commands) - 面向开发者和系统管理员的全面 Linux\u002FBash 命令列表。\n- [Bash 优秀备忘单](https:\u002F\u002Fgithub.com\u002FLeCoupa\u002Fawesome-cheatsheets\u002Fblob\u002Fmaster\u002Flanguages\u002Fbash.sh) - Bash 脚本编写必备知识。\n- [Unix 命令参考](https:\u002F\u002Fgithub.com\u002FAdiBro\u002FData-Science-Resources\u002Fblob\u002Fmaster\u002FCheat-Sheets\u002FCL-Git\u002FUnix-Commands-Reference.pdf) - Unix 终端基础。\n- [GitHub 备忘单](https:\u002F\u002Fgithub.com\u002Ftiimgreen\u002Fgithub-cheat-sheet) - Git\u002FGitHub 工作流及技巧。\n- [Git 优秀备忘单](https:\u002F\u002Fgithub.com\u002FLeCoupa\u002Fawesome-cheatsheets\u002Fblob\u002Fmaster\u002Ftools\u002Fgit.sh) - Git 命令及最佳实践。\n- [Git 和 Git Flow 备忘单](https:\u002F\u002Fgithub.com\u002Farslanbilal\u002Fgit-cheat-sheet) - 分支策略。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"cheatsheets-probability-statistics\">\u003C\u002Fa>\n\n### 概率与统计\n\n- [斯坦福 CME 106 备忘单](https:\u002F\u002Fgithub.com\u002Fshervinea\u002Fstanford-cme-106-probability-and-statistics) - 面向工程师的概率与统计。\n- [10 页概率备忘单](https:\u002F\u002Fgithub.com\u002Fwzchen\u002Fprobability_cheatsheet) - 深入的概率概念。\n- [统计学备忘单](https:\u002F\u002Fgithub.com\u002Fkhanhnamle1994\u002Fcracking-the-data-science-interview\u002Fblob\u002Fmaster\u002FCheatsheets\u002Fstats_cheatsheet.pdf) - 关键统计方法。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"cheatsheets-sql-databases\">\u003C\u002Fa>\n\n### SQL 与数据库\n\n- [快速 SQL 备忘单](https:\u002F\u002Fgithub.com\u002Fenochtangg\u002Fquick-SQL-cheatsheet) - 方便的 SQL 参考指南。\n- [PostgreSQL 备忘单](https:\u002F\u002Fwww.postgresonline.com\u002Fdownloads\u002Fspecial_feature\u002Fpostgresql83_psql_cheatsheet.pdf) - 最常用 PostgreSQL psql 命令和查询的实用参考。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"cheatsheets-miscellaneous\">\u003C\u002Fa>\n\n### 杂项\n\n- [备忘单的备忘单](https:\u002F\u002Fgithub.com\u002Fplusminuschirag\u002FCheatSheet-for-CheatSheets) - 备忘单的大型仓库。\n- [Dataquest - Power BI 备忘单](https:\u002F\u002Fwww.dataquest.io\u002Fcheat-sheet\u002Fpower-bi-cheat-sheet\u002F) - Power BI 用户的实用资源。\n- [数据结构备忘单](https:\u002F\u002Fwww.clear.rice.edu\u002Fcomp160\u002Fdata_cheat.html) - 常见数据结构及其特性的简明参考。\n- [Matplotlib 备忘单](https:\u002F\u002Fgithub.com\u002Fmatplotlib\u002Fcheatsheets) - Python 中 Matplotlib 绘图库的官方备忘单。\n- [VSCode 优秀备忘单](https:\u002F\u002Fgithub.com\u002FLeCoupa\u002Fawesome-cheatsheets\u002Fblob\u002Fmaster\u002Ftools\u002Fvscode.md) - VS Code 快捷键。\n- [Markdown 备忘单](https:\u002F\u002Fgithub.com\u002Ftchapi\u002Fmarkdown-cheatsheet) - GitHub README 的格式化。\n- [Emoji 备忘单](https:\u002F\u002Fgithub.com\u002Fikatyang\u002Femoji-cheat-sheet) - Markdown 中的表情符号。\n- [Docker 备忘单](https:\u002F\u002Fgithub.com\u002Fwsargent\u002Fdocker-cheat-sheet) - Docker 命令和工作流。\n- [Docker 优秀备忘单](https:\u002F\u002Fgithub.com\u002FLeCoupa\u002Fawesome-cheatsheets\u002Fblob\u002Fmaster\u002Ftools\u002Fdocker.sh) - 容器化基础知识。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"additional-python-libraries\">\u003C\u002Fa>\n\n## 📦 其他 Python 库\n\n这是一系列补充性的 Python 库，它们能够提升开发流程、自动化任务，并在核心数据分析工具之外保持项目的高质量。\n\n### 代码质量与开发\n\n- [Black](https:\u002F\u002Fgithub.com\u002Fpsf\u002Fblack) - 不妥协的 Python 代码格式化工具。\n- [Pre-commit](https:\u002F\u002Fgithub.com\u002Fpre-commit\u002Fpre-commit) - 用于管理提交前钩子的框架。\n- [Pylint](https:\u002F\u002Fgithub.com\u002Fpylint-dev\u002Fpylint) - Python 代码静态分析工具。\n- [Mypy](https:\u002F\u002Fgithub.com\u002Fpython\u002Fmypy) - Python 的可选静态类型检查。\n- [Rich](https:\u002F\u002Fgithub.com\u002FTextualize\u002Frich) - 终端中的富文本和美观格式。\n- [Icecream](https:\u002F\u002Fgithub.com\u002Fgruns\u002Ficecream) - 无需使用 print 的调试工具。\n- [Pandas-log](https:\u002F\u002Fgithub.com\u002Feyaltrabelsi\u002Fpandas-log) - 记录 Pandas 操作以追踪数据转换过程。\n- [PandasVet](https:\u002F\u002Fgithub.com\u002Fdeppen8\u002Fpandas-vet) - Pandas 代码风格验证工具。\n- [Pydeps](https:\u002F\u002Fgithub.com\u002Fthebjorn\u002Fpydeps) - Python 模块依赖关系图。\n- [PyForest](https:\u002F\u002Fgithub.com\u002F8080labs\u002Fpyforest) - 自动化数据科学中的 Python 导入。\n- [Complexipy](https:\u002F\u002Fgithub.com\u002Frohaquinlop\u002Fcomplexipy) - 由 Rust 编写的超快速 Python 认知复杂性分析工具。\n\n[⬆ 返回目录](#contents)\n\n---\n\n### 文档与文件处理\n\n- [Sphinx](https:\u002F\u002Fgithub.com\u002Fsphinx-doc\u002Fsphinx) - 文档生成工具。\n- [Pdoc](https:\u002F\u002Fgithub.com\u002Fmitmproxy\u002Fpdoc) - Python 项目的 API 文档生成工具。\n- [Mkdocs](https:\u002F\u002Fgithub.com\u002Fmkdocs\u002Fmkdocs) - 使用 Markdown 编写项目文档。\n- [OpenPyXL](https:\u002F\u002Fopenpyxl.readthedocs.io\u002Fen\u002Fstable\u002F) - 读写 Excel 文件。\n- [Tablib](https:\u002F\u002Fgithub.com\u002Fjazzband\u002Ftablib) - 将数据导出为 XLSX、JSON、CSV 格式。\n- [PyPDF2](https:\u002F\u002Fgithub.com\u002Fpy-pdf\u002FPyPDF2) - 读取和写入 PDF 文件。\n- [Python-docx](https:\u002F\u002Fgithub.com\u002Fpython-openxml\u002Fpython-docx) - 读取和写入 Word 文档。\n- [CleverCSV](https:\u002F\u002Fgithub.com\u002Falan-turing-institute\u002FCleverCSV) - 针对混乱数据的智能 CSV 读取工具。\n- [Python-markdownify](https:\u002F\u002Fgithub.com\u002Fmatthewwithanm\u002Fpython-markdownify) - 将 HTML 转换为 Markdown。\n- [Xlwings](https:\u002F\u002Fgithub.com\u002Fxlwings\u002Fxlwings) - Python 与 Excel 的集成工具。\n- [Xmltodict](https:\u002F\u002Fgithub.com\u002Fmartinblech\u002Fxmltodict) - 将 XML 转换为 Python 字典。\n- [MarkItDown](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fmarkitdown) - 用于将文件和 Office 文档转换为 Markdown 的 Python 工具。\n- [Jupyter-book](https:\u002F\u002Fgithub.com\u002Fexecutablebooks\u002Fjupyter-book) - 基于 Jupyter 笔记本构建出版级书籍。\n- [WeasyPrint](https:\u002F\u002Fgithub.com\u002FKozea\u002FWeasyPrint) - 将 HTML 转换为 PDF。\n- [PyMuPDF](https:\u002F\u002Fgithub.com\u002Fpymupdf\u002FPyMuPDF) - 高级 PDF 操作库。\n- [Camelot](https:\u002F\u002Fgithub.com\u002Fcamelot-dev\u002Fcamelot) - PDF 表格提取库。\n- [Marker](https:\u002F\u002Fgithub.com\u002Fdatalab-to\u002Fmarker) - 快速且高精度的 PDF 和文档转换工具，同时保留布局。\n\n[⬆ 返回目录](#contents)\n\n---\n\n### Web 与 API\n\n- [HTTPX](https:\u002F\u002Fgithub.com\u002Fencode\u002Fhttpx) - 新一代 Python HTTP 客户端。\n- [FastAPI](https:\u002F\u002Fgithub.com\u002Ffastapi\u002Ffastapi) - 用于构建 API 的现代 Web 框架。\n- [Flask](https:\u002F\u002Fgithub.com\u002Fpallets\u002Fflask) - 用于构建应用和 API 的轻量级 Python Web 框架。\n- [Typer](https:\u002F\u002Fgithub.com\u002Ffastapi\u002Ftyper) - 用于构建命令行应用程序的库。\n- [Requests-cache](https:\u002F\u002Fgithub.com\u002Freclosedev\u002Frequests-cache) - 为 requests 库提供持久化缓存。\n- [Aiohttp](https:\u002F\u002Fgithub.com\u002Faio-libs\u002Faiohttp) - 基于 asyncio 和 Python 的异步 HTTP 客户端\u002F服务器框架。\n\n[⬆ 返回目录](#contents)\n\n---\n\n### 其他\n\n- [UV](https:\u002F\u002Fgithub.com\u002Fastral-sh\u002Fuv) - 极其快速的 Python 包管理器和解析器。\n- [Funcy](https:\u002F\u002Fgithub.com\u002FSuor\u002Ffuncy) - 为 Python 提供的高级函数式工具。\n- [Pillow](https:\u002F\u002Fgithub.com\u002Fpython-pillow\u002FPillow) - 图像处理库。\n- [Ftfy](https:\u002F\u002Fgithub.com\u002Frspeer\u002Fpython-ftfy) - 修复损坏的 Unicode 字符串。\n- [JmesPath](https:\u002F\u002Fgithub.com\u002Fjmespath\u002Fjmespath.py) - 查询 JSON 数据（类似于 SQL 的 JSON 查询）。\n- [Glom](https:\u002F\u002Fgithub.com\u002Fmahmoud\u002Fglom) - 转换嵌套数据结构。\n- [Diagrams](https:\u002F\u002Fgithub.com\u002Fmingrammer\u002Fdiagrams) - 以代码形式绘制云架构图。\n- [Pytest](https:\u002F\u002Fgithub.com\u002Fpytest-dev\u002Fpytest) - 用于编写小型测试的框架。\n- [Pampy](https:\u002F\u002Fgithub.com\u002Fsantinic\u002Fpampy) - 用于 Python 字典的模式匹配。\n- [Pygorithm](https:\u002F\u002Fgithub.com\u002FOmkarPathak\u002Fpygorithm) - 学习所有主要算法的 Python 模块。\n- [GitPython](https:\u002F\u002Fgithub.com\u002Fgitpython-developers\u002FGitPython) - 用于与 Git 仓库交互的 Python 库。\n- [TQDM](https:\u002F\u002Fgithub.com\u002Ftqdm\u002Ftqdm) - 为循环和操作添加进度条。\n- [Loguru](https:\u002F\u002Fgithub.com\u002FDelgan\u002Floguru) - 简化的 Python 日志记录工具。\n- [Click](https:\u002F\u002Fgithub.com\u002Fpallets\u002Fclick) - 美观的命令行界面。\n- [Poetry](https:\u002F\u002Fgithub.com\u002Fpython-poetry\u002Fpoetry) - Python 依赖管理和打包工具。\n- [Hydra](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fhydra) - 优雅的配置管理工具。\n- [papermill](https:\u002F\u002Fgithub.com\u002Fnteract\u002Fpapermill) - 用于参数化并以编程方式执行 Jupyter 笔记本的工具。\n- [Python Telegram Bot](https:\u002F\u002Fgithub.com\u002Fpython-telegram-bot\u002Fpython-telegram-bot) - 支持异步的纯 Python Telegram Bot API 框架。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"more-awesome-curations\">\u003C\u002Fa>\n\n## 📝 更多精彩列表\n\n精心整理的其他主题和技术领域的精彩列表。\n\n- [Awesome](https:\u002F\u002Fgithub.com\u002Fsindresorhus\u002Fawesome) - 一个精选的优秀列表集合。\n- [Freecodecamp](https:\u002F\u002Fgithub.com\u002FfreeCodeCamp\u002FfreeCodeCamp) - 一个开源平台，提供数千节互动课程，用于学习Web开发。\n- [Awesome Big Data](https:\u002F\u002Fgithub.com\u002Foxnr\u002Fawesome-bigdata) - 一个精选的大数据框架、资源和工具列表。\n- [Awesome Geospatial](https:\u002F\u002Fgithub.com\u002Fsacridini\u002FAwesome-Geospatial) - 一个精选的地理空间库、工具和资源列表。\n- [Awesome Chatgpt Prompts](https:\u002F\u002Fgithub.com\u002Ff\u002Fawesome-chatgpt-prompts) - 一个用于整理ChatGPT提示词的仓库。\n- [Awesome Jupyter](https:\u002F\u002Fgithub.com\u002Fmarkusschanta\u002Fawesome-jupyter) - 精选的Jupyter项目、库和资源列表。\n- [Awesome Business Intelligence](https:\u002F\u002Fgithub.com\u002Fthenaturalist\u002Fawesome-business-intelligence) - 积极维护的商业智能工具优秀列表。\n- [Awesome Prompt Engineering](https:\u002F\u002Fgithub.com\u002Fpromptslab\u002FAwesome-Prompt-Engineering) - 一个精选的关于使用LLM（如ChatGPT）进行提示工程的资源列表。\n- [Awesome Product Design](https:\u002F\u002Fgithub.com\u002Fttt30ga\u002Fawesome-product-design) - 一个关于产品设计的书签、资源和文章集合。\n- [Awesome Shell](https:\u002F\u002Fgithub.com\u002Falebcay\u002Fawesome-shell) - 一个精选的命令行框架、工具包和指南列表。\n- [Awesome FastAPI](https:\u002F\u002Fgithub.com\u002Fmjhea0\u002Fawesome-fastapi) - 一个精选的FastAPI框架、库和资源列表。\n- [Awesome Linux Software](https:\u002F\u002Fgithub.com\u002Fluong-komorebi\u002FAwesome-Linux-Software) - 一个适用于Linux的优秀应用和工具列表。\n- [Awesome Product Management](https:\u002F\u002Fgithub.com\u002Fdend\u002Fawesome-product-management) - 一个为产品经理及有志于成为产品经理的人士精选的资源列表。\n- [Awesome Python Applications](https:\u002F\u002Fgithub.com\u002Fmahmoud\u002Fawesome-python-applications) - 一个用Python编写的免费软件和应用列表。\n- [Awesome AutoHotkey](https:\u002F\u002Fgithub.com\u002Fahkscript\u002Fawesome-AutoHotkey) - 一个精选的AutoHotkey库、脚本和资源列表。\n- [Awesome Productivity](https:\u002F\u002Fgithub.com\u002Fjyguyomarch\u002Fawesome-productivity) - 一个精选的高效生产力资源列表。\n- [Awesome Scientific Writing](https:\u002F\u002Fgithub.com\u002Fwriting-resources\u002Fawesome-scientific-writing) - 一个精选的科学写作、出版和研究资源列表。\n- [Awesome LaTeX](https:\u002F\u002Fgithub.com\u002Fegeerardyn\u002Fawesome-LaTeX) - 一个精选的LaTeX资源、库和工具列表。\n- [Awesome Actions](https:\u002F\u002Fgithub.com\u002Fsdras\u002Fawesome-actions) - 一个精选的GitHub Actions自动化工作流列表。\n- [Awesome Quarto](https:\u002F\u002Fgithub.com\u002Fmcanouil\u002Fawesome-quarto) - 一个精选的Quarto资源列表，包括演讲、工具、示例和文章。欢迎贡献！\n- [Awesome Vscode](https:\u002F\u002Fgithub.com\u002Fviatsko\u002Fawesome-vscode) - 一个全面的VS Code实用扩展和资源列表。\n- [Awesome Readme](https:\u002F\u002Fgithub.com\u002Fmatiassingers\u002Fawesome-readme) - 一系列精心编写的README文件，供参考和启发。\n- [Awesome GitHub Profile Readme](https:\u002F\u002Fgithub.com\u002Fabhisheknaiidu\u002Fawesome-github-profile-readme) - 一个收集了优秀的GitHub个人主页README文件及相关资源的列表。\n- [Awesome Code Review](https:\u002F\u002Fgithub.com\u002Fjoho\u002Fawesome-code-review?tab=readme-ov-file#awesome-code-review-) - 一个关于代码审查实践的资源集合。\n- [Awesome Certificates](https:\u002F\u002Fgithub.com\u002FPanXProject\u002Fawesome-certificates) - 一个精选的IT和开发者认证及学习资源列表。\n- [Awesome Tunneling](https:\u002F\u002Fgithub.com\u002Fanderspitman\u002Fawesome-tunneling) - 一个包含ngrok替代方案和其他隧道软件的列表。\n- [Anomaly Detection Resources](https:\u002F\u002Fgithub.com\u002Fyzhao062\u002Fanomaly-detection-resources) - 与异常检测相关的书籍、论文、视频和工具箱。\n- [Awesome Claude Prompts](https:\u002F\u002Fgithub.com\u002Flanggptai\u002Fawesome-claude-prompts) - 一组用于Anthropic公司Claude AI的强大提示词。\n- [Awesome Linux](https:\u002F\u002Fgithub.com\u002Finputsh\u002Fawesome-linux) - 一个为用户和开发者精选的Linux应用、工具和资源列表。\n- [Awesome for Beginners](https:\u002F\u002Fgithub.com\u002FMunGell\u002Fawesome-for-beginners) - 一个面向初学者的开源软件贡献项目列表。\n- [Best websites a programmer should visit](https:\u002F\u002Fgithub.com\u002Fsdmg15\u002FBest-websites-a-programmer-should-visit) - 一个为程序员和工程师精选的实用网站列表。\n- [Awesome Creative Coding](https:\u002F\u002Fgithub.com\u002Fterkelg\u002Fawesome-creative-coding) - 一个精选的创意编程资源和库列表。\n- [Awesome AI in Finance](https:\u002F\u002Fgithub.com\u002Fgeorgezouq\u002Fawesome-ai-in-finance) - 一个精选的金融领域人工智能应用、工具和研究资源列表。\n- [Awesome Algorithms](https:\u002F\u002Fgithub.com\u002Ftayllan\u002Fawesome-algorithms) - 一个用于学习和练习算法与数据结构的资源集合。\n- [Awesome Serverless](https:\u002F\u002Fgithub.com\u002Fanaibol\u002Fawesome-serverless) - 一个精选的无服务器架构和云计算资源列表。\n- [Awesome R](https:\u002F\u002Fgithub.com\u002Fqinwf\u002Fawesome-R) - 一个精选的R语言包、框架和学习资源列表。\n- [Awesome AI System Prompts](https:\u002F\u002Fgithub.com\u002Fdontriskit\u002Fawesome-ai-system-prompts) - 一组针对不同AI模型的有效系统提示词集合。\n- [Awesome Osint](https:\u002F\u002Fgithub.com\u002Fjivoi\u002Fawesome-osint) - 一个精选的开源情报（OSINT）工具和资源列表。\n- [Awesome Telegram](https:\u002F\u002Fgithub.com\u002Febertti\u002Fawesome-telegram) - 一个为开发者提供的Telegram机器人、频道和工具集合。\n- [Free for Dev](https:\u002F\u002Fgithub.com\u002Fripienaar\u002Ffree-for-dev) - 一个包含SaaS、PaaS和IaaS服务中免费开发者层级产品的列表。\n- [Font-Awesome](https:\u002F\u002Fgithub.com\u002FFortAwesome\u002FFont-Awesome) - 一个用于网页上可缩放矢量图形的图标库和工具集。\n- [Awesome Docs](https:\u002F\u002Fgithub.com\u002Ftestthedocs\u002Fawesome-docs) - 一个精选的创建优质文档所需的关键工具和资源列表。\n- [Awesome Testing](https:\u002F\u002Fgithub.com\u002FTheJambo\u002Fawesome-testing) - 一个精选的软件测试资源列表：工具、框架、书籍以及最佳实践。\n- [Awesome Graphql](https:\u002F\u002Fgithub.com\u002Fchentsulin\u002Fawesome-graphql) - 一个全面的GraphQL相关资源、库和工具集合。\n- [Awesome Remote Job](https:\u002F\u002Fgithub.com\u002Flukasz-madon\u002Fawesome-remote-job) - 一个关于寻找远程工作并成功开展工作的资源、技巧和工具列表。\n- [Awesome Asyncio](https:\u002F\u002Fgithub.com\u002Ftimofurrer\u002Fawesome-asyncio) - 一个精选的基于asyncio的Python编程框架、库和工具列表。\n- [Awesome Zsh Plugins](https:\u002F\u002Fgithub.com\u002Funixorn\u002Fawesome-zsh-plugins) - 一个庞大的Zsh插件、主题和资源集合。\n- [Awesome Scalability](https:\u002F\u002Fgithub.com\u002Fbinhnguyennus\u002Fawesome-scalability) - 一份关于构建可扩展且可靠系统的结构化设计模式指南。\n- [Books](https:\u002F\u002Fgithub.com\u002Flinsa-io\u002Fbooks) - 一个包含免费技术书籍链接的集合，涵盖编程、数据库、DevOps和数据分析等领域。\n- [Free Programming Books](https:\u002F\u002Fgithub.com\u002FEbookFoundation\u002Ffree-programming-books) - 最大的多语言免费编程书籍和学习资料集合。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"additional-resources\">\u003C\u002Fa>\n\n\n\n## 🌐 额外资源与工具\n\n涵盖广泛领域的学习、开发和探索资源与工具。\n\n- [OSSU 计算机科学](https:\u002F\u002Fgithub.com\u002Fossu\u002Fcomputer-science) - 通往免费自学计算机科学教育的道路。\n- [加州大学伯克利分校 - Data 8](https:\u002F\u002Fgithub.com\u002Fdata-8\u002Ftextbook) - 数据科学基础课程的教学材料。\n- [PaddleOCR](https:\u002F\u002Fgithub.com\u002FPaddlePaddle\u002FPaddleOCR) - 生产就绪的 OCR 工具包，支持多语言和文档 AI。\n- [免费 API 汇总列表](https:\u002F\u002Fgithub.com\u002Fpublic-apis\u002Fpublic-apis) - 适用于各种用途的全面免费 API 列表。\n- [arXiv.org](https:\u002F\u002Farxiv.org\u002F) - 学术论文的免费分发服务及开放获取档案。\n- [Elicit](https:\u002F\u002Felicit.com\u002F) - 一款 AI 研究助手，可帮助自动化文献综述的部分工作。\n- [500+ AI\u002FML\u002FDL\u002FNLP 项目](https:\u002F\u002Fgithub.com\u002Fashishpatel26\u002F500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code) - 包含代码的海量 AI 和机器学习项目集合，适合学习和构建作品集。\n- [全栈 Fastapi 模板](https:\u002F\u002Fgithub.com\u002Ffastapi\u002Ffull-stack-fastapi-template) - 使用 FastAPI、React 和 PostgreSQL 的全栈模板。\n- [Kittl](https:\u002F\u002Fwww.kittl.com\u002F) - 用于创建和编辑图表及数据可视化的平台。\n- [Zasper](https:\u002F\u002Fgithub.com\u002Fzasper-io\u002Fzasper) - 面向 Jupyter Notebook 的高性能 IDE。\n- [Sketch](https:\u002F\u002Fwww.sketch.com\u002F) - 专为设计师设计的工具包，专注于其工作流程。\n- [Growth.Design](https:\u002F\u002Fgrowth.design\u002F) - 产品案例研究与行为心理学洞察的集合，助力数据驱动型决策。\n- [Markdown 徽章](https:\u002F\u002Fgithub.com\u002FIleriayo\u002Fmarkdown-badges) - 用于 GitHub 个人主页和 Markdown 文件的徽章集合。\n- [计算机科学视频课程](https:\u002F\u002Fgithub.com\u002FDeveloper-Y\u002Fcs-video-courses) - 精选的免费高校计算机科学视频课程列表。\n- [从零开始构建自己的 X](https:\u002F\u002Fgithub.com\u002Fcodecrafters-io\u002Fbuild-your-own-x) - 关于如何从头开始构建技术的教程。\n- [当你输入 URL 并按下回车键时会发生什么](https:\u002F\u002Fgithub.com\u002Falex\u002Fwhat-happens-when) - 对输入 URL 并按回车键后发生的技术性解释。\n- [DevOps 练习](https:\u002F\u002Fgithub.com\u002Fbregman-arie\u002Fdevops-exercises) - 大量 DevOps 和 Linux 面试准备练习及问题。\n- [免费认证课程](https:\u002F\u002Fgithub.com\u002Fcloudcommunity\u002FFree-Certifications) - 定期更新的顶级云和技术公司提供的免费认证课程列表。\n- [面向学生的 A 到 Z 资源](https:\u002F\u002Fgithub.com\u002Fdipakkr\u002FA-to-Z-Resources-for-Students) - 为学习编程和技术的学生提供的全面免费资源列表。\n- [暑期实习机会](https:\u002F\u002Fgithub.com\u002FSimplifyJobs\u002FSummer2026-Internships) - 最新科技领域暑期实习列表，并附带截止日期跟踪。\n- [足球数据分析](https:\u002F\u002Fgithub.com\u002Feddwebster\u002Ffootball_analytics) - 使用 Python 和 R 进行足球数据分析的开放学习课程及工具包。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"contributing\">\u003C\u002Fa>\n\n## 🤝 贡献\n\n**我们欢迎您的贡献！**\n\n请参阅 [CONTRIBUTING.md](https:\u002F\u002Fgithub.com\u002FPavelGrigoryevDS\u002Fawesome-data-analysis\u002Fblob\u002Fmain\u002FCONTRIBUTING.md) 了解如何添加资源。\n\n[⬆ 返回目录](#contents)\n\n---\n\n\u003Ca id=\"license\">\u003C\u002Fa>\n\n## 📜 许可证\n\n[![CC0](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FPavelGrigoryevDS_awesome-data-analysis_readme_b7657951a0bb.png)](http:\u002F\u002Fcreativecommons.org\u002Fpublicdomain\u002Fzero\u002F1.0\u002F)\n\n本作品已根据 [CC0 1.0 Universal](https:\u002F\u002Fcreativecommons.org\u002Fpublicdomain\u002Fzero\u002F1.0\u002F) 许可协议奉献至公有领域。\n\n[⬆ 返回目录](#contents)","# Awesome Data Analysis 快速上手指南\n\n`awesome-data-analysis` 并非一个可直接安装的软件包或库，而是一个**精选资源清单（Curated List）**。它汇集了 500+ 个数据分析与数据科学领域的高质量开源项目、学习路线图、速查表、工具库及教程。\n\n本指南将帮助你如何利用该清单快速搭建开发环境、安装核心工具库并开始第一个数据分析项目。\n\n## 环境准备\n\n在开始使用清单中的资源前，建议准备好以下基础开发环境：\n\n*   **操作系统**：Windows, macOS 或 Linux (推荐 Ubuntu\u002FCentOS)。\n*   **Python 版本**：建议安装 **Python 3.8 - 3.11** (大多数数据科学库对此版本支持最佳)。\n*   **包管理器**：推荐使用 `pip` 或 `conda`。\n    *   *国内加速建议*：配置清华源或阿里源以加快下载速度。\n        ```bash\n        # 临时使用清华源安装示例\n        pip install -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple \u003Cpackage_name>\n        ```\n*   **开发工具**：\n    *   **VS Code**：推荐安装 Python 插件及 Jupyter 插件。\n    *   **Jupyter Notebook\u002FLab**：用于交互式数据分析。\n\n## 安装步骤\n\n由于这是一个资源列表，你不需要“安装”该列表本身。你需要根据清单指引，安装具体的工具库。以下是基于清单中 **Python 数据分析核心栈** 的标准安装流程。\n\n### 1. 创建虚拟环境（推荐）\n```bash\npython -m venv data-env\n# Windows\ndata-env\\Scripts\\activate\n# macOS\u002FLinux\nsource data-env\u002Fbin\u002Factivate\n```\n\n### 2. 安装核心数据处理库\n根据清单中的 [Data Manipulation](#python-data-manipulation-with-pandas-and-numpy) 和 [Useful Python Tools](#python-useful-python-tools-for-data-analysis) 部分，安装最基础的组合：\n\n```bash\n# 使用国内镜像源加速安装\npip install -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple pandas numpy matplotlib seaborn scikit-learn jupyter\n```\n\n### 3. 获取学习资源（可选）\n你可以克隆清单中提到的优秀学习仓库到本地：\n\n```bash\n# 例如：获取微软的数据科学初学者课程\ngit clone https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FData-Science-For-Beginners.git\n\n# 例如：获取 Pandas 练习题\ngit clone https:\u002F\u002Fgithub.com\u002Fguipsamora\u002Fpandas_exercises.git\n```\n\n## 基本使用\n\n安装完成后，你可以立即开始进行数据分析。以下是一个基于清单推荐工具的最简示例，演示数据加载、清洗与可视化。\n\n### 1. 启动 Jupyter Notebook\n```bash\njupyter notebook\n```\n\n### 2. 编写代码示例\n新建一个 `.ipynb` 文件，输入以下代码：\n\n```python\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# 设置绘图风格 (参考清单推荐的 Seaborn)\nsns.set(style=\"whitegrid\")\n\n# 1. 创建模拟数据 (数据操作)\ndata = {\n    'Date': pd.date_range(start='2023-01-01', periods=100),\n    'Sales': np.random.randint(100, 1000, size=100),\n    'Region': np.random.choice(['North', 'South', 'East', 'West'], size=100)\n}\ndf = pd.DataFrame(data)\n\n# 2. 数据清洗与转换 (参考 Pandas 最佳实践)\n# 添加一列：季度\ndf['Quarter'] = df['Date'].dt.quarter\n\n# 3. 简单分析 (分组聚合)\nsummary = df.groupby('Region')['Sales'].mean().reset_index()\n\n# 4. 数据可视化 (参考清单中的可视化工具)\nplt.figure(figsize=(10, 6))\nsns.barplot(x='Region', y='Sales', data=summary, palette='viridis')\nplt.title('Average Sales by Region')\nplt.show()\n\n# 打印前 5 行数据\nprint(df.head())\n```\n\n### 3. 深入探索\n访问 `awesome-data-analysis` 的在线网页版或 GitHub 仓库，根据你的具体需求（如 **SQL**, **NLP**, **时间序列**, **MLOps** 等）查找对应的专项工具库和学习路径，继续扩展你的技术栈。","某电商公司的初级数据分析师小李，正面临紧急任务：需要在两天内完成对用户流失数据的探索性分析（EDA）并构建预测模型，但他对技术栈选型和高效工具链缺乏系统认知。\n\n### 没有 awesome-data-analysis 时\n- **资源检索低效**：在谷歌和 GitHub 上盲目搜索\"Python EDA 库”或“时间序列教程”，耗费大量时间筛选过时或质量参差不齐的内容。\n- **技术盲区明显**：不知道存在 `ydata-profiling` 等自动化 EDA 工具，仍手动编写重复的代码绘制基础分布图，严重拖慢进度。\n- **学习路径混乱**：面对机器学习、SQL 优化和 MLOps 等众多领域，缺乏清晰的路线图（Roadmap），不知该优先补充哪项技能以解决当前瓶颈。\n- **面试与实战脱节**：手头只有零散的代码片段，缺乏系统的速查表（Cheatsheets）和面试指南，难以将理论知识快速转化为可落地的解决方案。\n\n### 使用 awesome-data-analysis 后\n- **一站式精准获取**：直接通过其 curated 列表找到经过社区验证的 500+ 资源，瞬间锁定最适合电商场景的 `Pandas` 高级技巧和 `Scikit-learn` 最佳实践。\n- **工具链全面升级**：在\"Automated EDA\"板块发现并应用了自动化可视化工具，将原本需要半天的数据清洗与绘图工作压缩至 1 小时内完成。\n- **成长路径清晰**：参考\"Roadmaps\"章节规划了从数据清洗到模型部署的学习顺序，并利用\"Interview Prep\"资源快速补齐统计学假设检验的知识短板。\n- **效率显著提升**：借助详细的速查表和精选 Jupyter Notebook 案例，快速复用了成熟的特征工程代码，确保了项目按时高质量交付。\n\nawesome-data-analysis 不仅是一个资源清单，更是数据科学家从迷茫摸索走向高效实战的加速器和导航仪。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FPavelGrigoryevDS_awesome-data-analysis_917de59b.png","PavelGrigoryevDS","Pavel Grigoryev","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002FPavelGrigoryevDS_c543e873.jpg","Helping 🤝 business make data-driven decisions 🎯",null,"pavel.grigoryev.ds@gmail.com","https:\u002F\u002Fgithub.com\u002FPavelGrigoryevDS",978,128,"2026-04-05T10:18:52","CC0-1.0",1,"Linux, macOS, Windows","未说明",{"notes":90,"python":88,"dependencies":91},"该项目是一个 curated list（资源清单），本身不是一个可执行的软件工具，因此没有特定的运行环境、GPU 或内存需求。它列出了用于数据分析的各种工具、库和学习资源（主要基于 Python 生态）。用户需根据清单中具体选择的某个工具或库去查阅其各自的环境要求。",[92,93,94,95,96,97,98,99,100,101],"pandas","numpy","polars","dask","vaex","scikit-learn","matplotlib","seaborn","jupyter","sqlalchemy",[51,15,54,13,14],[104,105,92,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,93,121],"data-analysis","data-science","python","sql","statistics","big-data","eda","tutorials","datasets","ai","analytics","awesome-list","dashboard","data-visualization","jupyter-notebook","ml","resources","business-intelligence","2026-03-27T02:49:30.150509","2026-04-06T07:13:37.342099",[],[]]