[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-Tanu-N-Prabhu--Python":3,"tool-Tanu-N-Prabhu--Python":62},[4,18,26,36,46,54],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":10,"last_commit_at":24,"category_tags":25,"status":17},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,"2026-04-05T11:01:52",[14,15,13],{"id":27,"name":28,"github_repo":29,"description_zh":30,"stars":31,"difficulty_score":32,"last_commit_at":33,"category_tags":34,"status":17},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",159267,2,"2026-04-17T11:29:14",[14,13,35],"语言模型",{"id":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":42,"last_commit_at":43,"category_tags":44,"status":17},8272,"opencode","anomalyco\u002Fopencode","OpenCode 是一款开源的 AI 编程助手（Coding Agent），旨在像一位智能搭档一样融入您的开发流程。它不仅仅是一个代码补全插件，而是一个能够理解项目上下文、自主规划任务并执行复杂编码操作的智能体。无论是生成全新功能、重构现有代码，还是排查难以定位的 Bug，OpenCode 都能通过自然语言交互高效完成，显著减少开发者在重复性劳动和上下文切换上的时间消耗。\n\n这款工具专为软件开发者、工程师及技术研究人员设计，特别适合希望利用大模型能力来提升编码效率、加速原型开发或处理遗留代码维护的专业人群。其核心亮点在于完全开源的架构，这意味着用户可以审查代码逻辑、自定义行为策略，甚至私有化部署以保障数据安全，彻底打破了传统闭源 AI 助手的“黑盒”限制。\n\n在技术体验上，OpenCode 提供了灵活的终端界面（Terminal UI）和正在测试中的桌面应用程序，支持 macOS、Windows 及 Linux 全平台。它兼容多种包管理工具，安装便捷，并能无缝集成到现有的开发环境中。无论您是追求极致控制权的资深极客，还是渴望提升产出的独立开发者，OpenCode 都提供了一个透明、可信",144296,1,"2026-04-16T14:50:03",[13,45],"插件",{"id":47,"name":48,"github_repo":49,"description_zh":50,"stars":51,"difficulty_score":32,"last_commit_at":52,"category_tags":53,"status":17},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。",108322,"2026-04-10T11:39:34",[14,15,13],{"id":55,"name":56,"github_repo":57,"description_zh":58,"stars":59,"difficulty_score":32,"last_commit_at":60,"category_tags":61,"status":17},6121,"gemini-cli","google-gemini\u002Fgemini-cli","gemini-cli 是一款由谷歌推出的开源 AI 命令行工具，它将强大的 Gemini 大模型能力直接集成到用户的终端环境中。对于习惯在命令行工作的开发者而言，它提供了一条从输入提示词到获取模型响应的最短路径，无需切换窗口即可享受智能辅助。\n\n这款工具主要解决了开发过程中频繁上下文切换的痛点，让用户能在熟悉的终端界面内直接完成代码理解、生成、调试以及自动化运维任务。无论是查询大型代码库、根据草图生成应用，还是执行复杂的 Git 操作，gemini-cli 都能通过自然语言指令高效处理。\n\n它特别适合广大软件工程师、DevOps 人员及技术研究人员使用。其核心亮点包括支持高达 100 万 token 的超长上下文窗口，具备出色的逻辑推理能力；内置 Google 搜索、文件操作及 Shell 命令执行等实用工具；更独特的是，它支持 MCP（模型上下文协议），允许用户灵活扩展自定义集成，连接如图像生成等外部能力。此外，个人谷歌账号即可享受免费的额度支持，且项目基于 Apache 2.0 协议完全开源，是提升终端工作效率的理想助手。",100752,"2026-04-10T01:20:03",[45,13,15,14],{"id":63,"github_repo":64,"name":65,"description_en":66,"description_zh":67,"ai_summary_zh":67,"readme_en":68,"readme_zh":69,"quickstart_zh":70,"use_case_zh":71,"hero_image_url":72,"owner_login":73,"owner_name":74,"owner_avatar_url":75,"owner_bio":76,"owner_company":77,"owner_location":78,"owner_email":79,"owner_twitter":79,"owner_website":80,"owner_url":81,"languages":82,"stars":97,"forks":98,"last_commit_at":99,"license":79,"difficulty_score":42,"env_os":100,"env_gpu":100,"env_ram":100,"env_deps":101,"category_tags":110,"github_topics":111,"view_count":32,"oss_zip_url":79,"oss_zip_packed_at":79,"status":17,"created_at":129,"updated_at":130,"faqs":131,"releases":162},8517,"Tanu-N-Prabhu\u002FPython","Python","This repository helps you learn Python and Machine Learning from scratch.","Python 是一个专为零基础学习者打造的开源编程资源库，旨在帮助用户从零开始系统掌握 Python 语言、数据科学及机器学习核心技能。它解决了初学者在面对海量碎片化教程时难以构建完整知识体系的痛点，提供了一条清晰、连贯的学习路径。\n\n该资源库特别适合编程新手、希望转型的数据分析爱好者以及需要巩固基础的学生用户。其内容结构严谨，从基础的输入输出、变量管理、字符串处理，到列表、元组、字典等核心数据结构，再到各类运算符的使用，均配有详细的章节讲解与实战代码示例。\n\n独特的亮点在于其“一站式”学习体验：不仅涵盖了从入门到进阶的完整课程目录，还集成了活跃的社区互动元素（如 LinkedIn 内容同步）和便捷的在线开发环境支持（Gitpod 就绪），让用户无需复杂配置即可立即动手练习。作为一个持续更新且完全开放的项目，Python 资源库致力于成为你探索编程世界最可靠的伙伴，陪伴你在代码的海洋中稳步成长。","\u003Ch1 align = \"center\"> 🐍📊 \u003Ca href = \"https:\u002F\u002Ftanu-n-prabhu.github.io\u002FPython\u002F\">Welcome to the Python Programming Hub\u003C\u002Fa> 📊🐍\u003C\u002Fh1>\n\n\u003Ch2 align = \"center\">\u003Ci>The Best Place to Learn Python, Data Science and Machine Learning!\u003C\u002Fi>\u003C\u002Fh2>\n\n\u003Cp align=\"center\">\n\n  \u003C!-- Social Metrics -->\n  \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fforks\u002FTanu-N-Prabhu\u002FPython?style=social\" \u002F>\n  \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002FTanu-N-Prabhu\u002FPython?style=social\" \u002F>\n\n  \u003C!-- Repo Health -->\n  \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Frepo-size\u002FTanu-N-Prabhu\u002FPython?label=Repo%20Size\" \u002F>\n  \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fcontributors\u002FTanu-N-Prabhu\u002FPython?label=Contributors\" \u002F>\n\n  \u003C!-- Dev Tools -->\n  \u003Ca href=\"https:\u002F\u002Fgitpod.io\u002F#https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitpod-Ready%20to%20Code-blue?logo=gitpod\" \u002F>\n  \u003C\u002Fa>\n\n \n  \u003C!-- Commits This Year -->\n  \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fcommit-activity\u002Fy\u002FTanu-N-Prabhu\u002FPython?label=Commits%20This%20Year\" \u002F>\n\n  \u003C!-- Commits This Week -->\n  \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fcommit-activity\u002Fw\u002FTanu-N-Prabhu\u002FPython?label=Commits%20This%20Week\" \u002F>\n  \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPython-3.x-blue?logo=python\" \u002F>\n\n  \u003C!-- Last Commit -->\n  \u003Ca href=\".\u002FVERSION_HISTORY.md\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flast-commit\u002FTanu-N-Prabhu\u002FPython\" \u002F>\n  \u003C\u002Fa>\n\n  \u003C!-- Open Source -->\n  \u003Ca href=\"https:\u002F\u002Fopensource.org\u002F\">\n    \u003Cimg src=\"https:\u002F\u002Fbadges.frapsoft.com\u002Fos\u002Fv1\u002Fopen-source.svg?v=103\" \u002F>\n  \u003C\u002Fa>\n\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FRelease%20Notes\">\n  \u003Cimg\n    src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FRelease-Notes-blue\"\n    alt=\"Release Notes\"\n  \u002F>\n\u003C\u002Fa>\n\n\n  \n\n\u003C\u002Fp>\n\n\n\n\n\n\u003Cp align = \"right\">\u003Cb>\u003Ci>Last updated\u003C\u002Fi>\u003C\u002Fb>: \u003C!-- LAST_UPDATED -->Apr 17, 2026\u003C!-- END_LAST_UPDATED -->\u003C\u002Fp>\n\n\n| ![space-1.jpg](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FTanu-N-Prabhu_Python_readme_23386adf8790.jpg) | \n|:--:| \n| Image Credits [Wallpaper Flare](https:\u002F\u002Fwww.wallpaperflare.com\u002Fprogramming-is-an-art-text-code-python-computer-python-programming-wallpaper-srfia) |\n\n![Views Counter](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FTanu-N-Prabhu_Python_readme_37436185970b.png)\n\n\nWelcome to a treasure trove of Python programming expertise, Data Science mastery, and essential survival skills for navigating the dynamic world of programming. Dive into the depths of this repository to unlock the knowledge and tools you need to thrive in your coding journey.\n\n## Table of Contents\n\n#### [1. Introduction](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FREADME.md)\n1. [What](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster#about) \n2. [Why](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster#why-choose-this-repository) \n3. [How](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster#why-choose-this-repository)  \n\n#### [2. LinkedIn Content Overview](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FLinkedIn)  \n1. [Current Post](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FLinkedIn\u002Fpost_4_nov_23.md)\n2. [Purpose](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FLinkedIn#purpose-of-this-folder)\n\n#### [3. Python Materials](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FREADME.md#pythonic-materials)\n\n1. **Chapter 1 - Basic Concepts**\n   - [Python Input, Output and Import functions](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython_Input%2C_Output_and_Import.ipynb)\n   - [Python Variables](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython_Variables.ipynb)\n   - [Python Global, Local and Nonlocal Variables](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FGlobal%2C_Local_and_Nonlocal_variables_in_Python.ipynb)\n   - [Python Strings](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FStrings)\n   - [Python Lists](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FLists)\n   - [Python Tuples](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FTuples)\n   - [Python Dictionary](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FDictionary%20)\n   - [Python Operators](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython_Operators.ipynb)\n   - [Mastering Python Decorators](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMastering_Python_Decorators.ipynb)\n\n\n2. **Chapter 2 - Built-in Functions**\n   - [Python Input, Output and Import built-in-functions](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython_Input%2C_Output_and_Import.ipynb)\n   - [Eval built-in-function](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FEval_built_in_function.ipynb)\n   - [Range built-in-function](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FRange_built_in_function.ipynb)\n   - [Python Lambda Function](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython_Lambda_Function.ipynb)\n   - [Python Enumerate Function](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython_enumerate()_built_in_function.ipynb)\n   - [Python len function](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython_len()_built_in_function.ipynb)\n\n3. **Chapter 3 - Libraries**\n   - [Numpy library](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FNumpy)\n   - [Pandas library](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FPandas)\n   - [Math Module](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FLearn_the_Python_Math_Module.ipynb)\n   - [JSON library](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FHow_to_handle_JSON_in_Python%3F.ipynb)\n\n\n4. **Chapter 4 - APIs**\n   - [Google Translate API for Python](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FGoogle%20Translate%20API)\n   - [Google Trends API for Python](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FGoogle_Trends_API.ipynb)\n   - [Wikipedia API for Python](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FWikipedia_API_for_Python.ipynb)\n   - [Google Search API for Python](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FThe_two_Google_Search_Python_Libraries_you_should_never_miss.ipynb)\n   - [General Transit Feed Specification (GTFS)](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002F387a2cdd5bcfc4afbae2319d017a850bdaeb772c\u002FTransit_Data_Calgary_2025.ipynb)\n   - [Time Series Forecasting with Facebook Prophet](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FUnlocking_Time_Series_Forecasting_with_Facebook_Prophet.ipynb)\n\n\n5. **Chapter 5 - Additional Materials**\n   - [How to get started coding in Python?](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FHow_to_get_started_coding_in_Python%3F.ipynb)\n   - [Is Python Object Oriented?](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FIs_Python_object_oriented%3F.ipynb)\n   - [Speech Recognition using Python](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FSpeech_Recognition_using_Python.ipynb)\n   - [One-Hot Encoding](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FLearning_One_Hot_Encoding_in_Python_the_Easy_Way.ipynb)\n   - [Reading an Image Without Libraries](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FReading_An_Image_In_Python_(Without_Using_Special_Libraries).ipynb)\n   - [Render Images in Pandas DataFrame](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FRendering_Images_inside_a_Pandas_DataFrame.ipynb)\n   - [Using Pandas DataFrame as a Database](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FUsing_the_Pandas_Data_Frame_as_a_Database_.ipynb)\n   - [Using Pandas in Daily Life](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FUsing_the_Pandas_DataFrame_in_Day_To_Day_Life.ipynb)\n   - [Presenting Python Code Using RISE](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPresenting_Python_code_using_RISE.ipynb)\n   - [Google Colab Cheat Sheet](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FCheat_sheet_for_Google_Colab.ipynb)\n   - [Pick-Up Line Generator](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPick_up_Line_Generator.ipynb)\n   - [Optimizing Code with List Comprehensions](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FOptimizing_Python_Code_with_List_Comprehensions.ipynb)\n   - [Understanding Virtual Environments](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FUnderstanding_Virtual_Environments_in_Python.ipynb)\n   - [Hidden Markov Models](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FHidden_Markov_Models_in_Python.ipynb)\n   - [Feature Maps in CNNs](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FHidden_Layers_of_Understanding_CNN.ipynb)\n   - [Rule-Based System in Python](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FRule_Based_System_with_Python.ipynb)\n\n\n6. **Chapter 6 - Exercises**\n   - [String Concatenation Questions](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FString_Concatenation_Exercise_Questions.ipynb)  \n   - [String Concatenation Answers](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FString_Concatenation_Exercise_Answers.ipynb)\n   - [Built-In Functions Exercise Questions](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FBuilt_In_Functions_Exercise_Questions.ipynb)\n\n\n7. **Chapter 7 - Quiz**\n   - [Quiz 1](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FQuiz\u002FPython_Quiz_1.ipynb)\n   - [Quiz 2](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FQuiz\u002FPython_Quiz_2.ipynb)\n   - [Quiz 3](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FQuiz\u002FPython_Quiz_3.ipynb)\n\n8. **[Chapter 8 - Interview Preparation](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FPython%20Coding%20Interview%20Prep)**\n   - [Python Coding Interview Questions (Beginner → Advanced)](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FPython%20Coding%20Interview%20Questions%20(Beginner%20to%20Advanced).md)\n   - [Crack Python Interviews Like a Pro!](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FCrack%20Python%20Interviews%20Like%20a%20Pro!.md)\n   - [35 Python Interview Questions (Experienced)](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002F35%20Python%20interview%20questions%20for%20experienced.md)\n   - [Python Interview Questions - Strings](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FPython_Interview_Questions_and_Answers_Strings.md)\n   - [Python Theoretical Interview Questions](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FPython_Theoritical_Interview_Questions.md)\n   - [15 Python Interview Q&A](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FPython_Interview_Questions_and_Answers.md)\n   - [Assigning Candies to Children](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FChildren_with_candy.ipynb)\n   - [Basic Calculator](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FBasic_calculator.ipynb)\n   - [Text Justification](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FText_Justification.ipynb)\n   - [Removing an Element](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FRemove_Element.ipynb)\n   - [Vowel Count](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FVowel_Count.ipynb)\n   - [Pick-Up Line Generator (Sentiment-Based)](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002Fpick_up_line_generator_sentiments.ipynb)\n   - [Sentimental Analysis](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FSentimental_Analysis.ipynb)\n   - [Regular Polygon Visualizer](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FDraw_polygon.ipynb)\n\n\n9. **Chapter 9 - Design Principles**\n   - [Modular Pipeline \u002F Clean Architecture](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FHow_to_Structure_Machine_Learning_Projects_with_Clean_Code_Principles_in_Python.ipynb)\n   - [Dependency Inversion Principle](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FDependency_Inversion_Principle_in_Python.ipynb)\n   - [Open-Closed Principle](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FOpen_Closed_Principle_in_Python.ipynb)\n   - [Liskov Substitution Principle](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FLiskov_Substitution_Principle_in_Python.ipynb)\n\n\u003C\u002Fdetails>\n\n\n\n#### [4. Machine Learning Materials]()\n1. [Foundations](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FMachine%20Learning\u002F00_foundations)\n2. [Supervised Learning](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FMachine%20Learning\u002F01_supervised_learning)\n3. [Unsupervised Learning](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FMachine%20Learning\u002F02_unsupervised_learning)\n4. [Neural Networks](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FMachine%20Learning\u002F03_neural_networks)\n5. [MLOps](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FMachine%20Learning\u002F04_mlops)\n6. [Projects](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FMachine%20Learning\u002F05_projects)\n\n#### [5. Data Science Materials](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002F713e5814c15caf6ee4640f7b4cec04a68b4b899e\u002FData%20Analysis)\n1. [Level 0](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FData%20Analysis\u002FLevel%200)\n2. [Level 1](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FData%20Analysis\u002FLevel%201)\n3. [Level 2](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FData%20Analysis\u002FLevel%202)\n4. [Level 3](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FData%20Analysis\u002FLevel%203)\n5. [Level 4](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FData%20Analysis\u002FLevel%204)\n6. [Level 5](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FData%20Analysis\u002FLevel%205)\n7. [EDA Techniques](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FExploratory%20Data%20Analysis)\n8. [25 Real Data Analysis Questions With Clear Python Answers](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FData%20Analysis\u002F25%20Real%20Questions%20With%20Clear%20Python%20Answers)\n\n#### [6. Contribution Workflow](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002Fcontribution.md)\n1. [Code Snippet Guidelines](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster?tab=coc-ov-file#)\n\n#### [7. Release Notes](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002F7ef79b9098d5c82862669cf61b7b413864dfad83\u002FRelease%20Notes)\n1. [Current Release](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FRelease%20Notes\u002Fv1.2.0.md)  \n2. [Archive](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FRelease%20Notes\u002FREADME.md)\n\n#### [8. Attribution](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster?tab=readme-ov-file#reviews)\n1. [Credit & Acknowledgment](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster?tab=readme-ov-file#reviews)\n\n---\n\n## About\n\nThis repository isn’t just a collection of code snippets; it’s a comprehensive resource designed to empower learners and professionals alike. You’ll find invaluable insights and guidance for:\n\n* **Novices** - Explore the fundamentals of Python programming.\n* **Data Enthusiasts** - Delve into the intricacies of data science.\n* **Seasoned Developers** - Hone your craft with advanced techniques and tips.\n\n\n## What the repository offers\n\n* **Python Proficiency** - Elevate your Python skills from beginner to advanced levels with my curated tutorials, exercises, and real-world examples.\n* **Data Science Expertise** - Harness the power of data with our in-depth guides, projects, and best practices in data analysis, machine learning, and beyond.\n* **Survival Toolkit** - Navigate the complexities of the programming world with confidence, thanks to our tips, tricks, and advice on career development, productivity, and staying ahead of the curve.\n  \n## Why Choose This Repository\n\n| Feature | Description |\n|--------|-------------|\n| Comprehensive Learning | A structured path with beginner-to-advanced resources designed for long-term mastery. |\n| Community Support | A collaborative environment where learners and mentors help each other grow. |\n| Practical Application | Real-world examples and hands-on exercises that make concepts easy to apply. |\n\n\n### Get involved \n\nReady to embark on your Python journey? Explore our repository, contribute your expertise, and connect with fellow enthusiasts. Together, we'll sharpen our skills, unravel the mysteries of programming, and unlock new opportunities in the ever-evolving tech landscape.\n\n## Join Us on This Adventure\n\nThe Python Mastery Repository is more than just a collection of code; it's a gateway to endless possibilities. Start your exploration today and discover the boundless potential of Python programming, data science, and beyond.\n\n## Installation Tools\n\nBefore starting your Python journey, it is important to install a few helpful tools. These tools make it easier to write, run, and understand Python programs. You can choose the ones that best fit your learning style.\n\n---\n\n## Recommended Tools\n\n> These are the essential tools you’ll need to get started, from writing Python code to running it effortlessly in the cloud.\n\n| Tool | Description |\n|------|--------------|\n| [![Python](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPython-3776AB?logo=python&logoColor=white)](https:\u002F\u002Fwww.python.org\u002F) | The core language of this repository. |\n| [![VS Code](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FVS_Code-007ACC?logo=visualstudiocode&logoColor=white)](https:\u002F\u002Fcode.visualstudio.com\u002F) | A powerful, extensible editor for Python and more. |\n| [![Jupyter Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FJupyter-F37626?logo=jupyter&logoColor=white)](https:\u002F\u002Fjupyter.org\u002F) | Perfect for interactive coding and data visualization. |\n| [![Google Colab](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGoogle_Colab-F9AB00?logo=googlecolab&logoColor=white)](https:\u002F\u002Fcolab.research.google.com\u002F) | Run Python notebooks in the cloud, no installation needed. |\n\n > You can use VS Code with Jupyter and Colab notebooks directly for a seamless workflow.\n\n---\n\n# Repository Contents\n\n#### This repository is divided into two parts such as Python Coding and Data Science for Beginners.\n\n## Python Coding\n\nFollow the steps below to get started coding in Python!!!\n\n\u003Cp align=\"center\"> \n\u003Cimg src = \"https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FImg\u002FPython.PNG\">\n\u003C\u002Fp>\n\n### Pythonic Materials\n\n> **Expand** and **Collapse** the above Chapters for more details\n\n\n\u003Cdetails>\n  \u003Csummary>Chapter 1️⃣ ⮕ Basic Concepts\u003C\u002Fsummary>\n\n - \u003Cb>[Python Input, Output and Import functions](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython_Input%2C_Output_and_Import.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[Python Variables](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython_Variables.ipynb)\u003C\u002Fb>\n   * \u003Cb>[Python Global, Local and Nonlocal Variables](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FGlobal%2C_Local_and_Nonlocal_variables_in_Python.ipynb)\u003C\u002Fb>\n   \n - \u003Cb>[Python Strings](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FStrings)\u003C\u002Fb>\n \n - \u003Cb>[Python Lists](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FLists)\u003C\u002Fb> \n \n - \u003Cb>[Python Tuples](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FTuples)\u003C\u002Fb>\n \n -  \u003Cb>[Python Dictionary](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FDictionary%20)\u003C\u002Fb>\n \n - \u003Cb>[Python Operators](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython_Operators.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[Mastering Python Decorators](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMastering_Python_Decorators.ipynb)\u003C\u002Fb>\n\n\n\u003C\u002Fdetails>\n\n\n\u003Cdetails>\n  \u003Csummary>Chapter 2️⃣ ⮕ Built-in Functions\u003C\u002Fsummary>\n\n - \u003Cb>[Python Input, Output and Import built-in-functions](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython_Input%2C_Output_and_Import.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[Eval built-in-function](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FEval_built_in_function.ipynb)\u003C\u002Fb>\n   \n - \u003Cb>[Range built-in-function](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FRange_built_in_function.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[Python Lambda Function](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython_Lambda_Function.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[Python Enumerate Function](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython_enumerate()_built_in_function.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[Python len function](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython_len()_built_in_function.ipynb)\u003C\u002Fb>  \n \n\u003C\u002Fdetails>\n\n\u003Cdetails>\n  \u003Csummary>Chapter 3️⃣ ⮕ Libraries\u003C\u002Fsummary>\n\n - \u003Cb>[Numpy library](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FNumpy)\u003C\u002Fb>\n \n - \u003Cb>[Pandas library](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FPandas)\u003C\u002Fb>\n   \n - \u003Cb>[Math Module](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FLearn_the_Python_Math_Module.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[JSON library](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FHow_to_handle_JSON_in_Python%3F.ipynb)\u003C\u002Fb>\n\n\u003C\u002Fdetails>\n\n\u003Cdetails>\n  \u003Csummary>Chapter 4️⃣ ⮕ API's\u003C\u002Fsummary>\n\n - \u003Cb>[Google Translate API for Python](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FGoogle%20Translate%20API)\u003C\u002Fb>\n \n - \u003Cb>[Google Trends API for Python](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FGoogle_Trends_API.ipynb)\u003C\u002Fb>\n   \n - \u003Cb>[Wikipedia API for Python](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FWikipedia_API_for_Python.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[Google Search API for Python](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FThe_two_Google_Search_Python_Libraries_you_should_never_miss.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[General Transit Feed Specification - General Transit Feed Specification (GTFS)](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002F387a2cdd5bcfc4afbae2319d017a850bdaeb772c\u002FTransit_Data_Calgary_2025.ipynb)\u003C\u002Fb>\n\n  - \u003Cb>[Unlocking Time Series Forecasting with Facebook Prophet](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FUnlocking_Time_Series_Forecasting_with_Facebook_Prophet.ipynb)\u003C\u002Fb>\n\n\u003C\u002Fdetails>\n\n\u003Cdetails>\n  \u003Csummary>Chapter 5️⃣ ⮕ Additional Materials\u003C\u002Fsummary>\n\n - \u003Cb>[How to get started coding in Python?](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FHow_to_get_started_coding_in_Python%3F.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[Is Python Object Oriented?](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FIs_Python_object_oriented%3F.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[Speech Recognition using Python](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FSpeech_Recognition_using_Python.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[One-Hot encoding in Python](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FLearning_One_Hot_Encoding_in_Python_the_Easy_Way.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[Reading An Image In Python (Without Using Special Libraries)](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FReading_An_Image_In_Python_(Without_Using_Special_Libraries).ipynb)\u003C\u002Fb>\n \n - \u003Cb>[Rendering Images inside a Pandas DataFrame](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FRendering_Images_inside_a_Pandas_DataFrame.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[Using the Pandas Data Frame as a Database](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FUsing_the_Pandas_Data_Frame_as_a_Database_.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[Using the Pandas Data Frame in Day-To-Day Life](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FUsing_the_Pandas_DataFrame_in_Day_To_Day_Life.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[Presenting Python code using RISE](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPresenting_Python_code_using_RISE.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[Cheat Sheet for Google Colab](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FCheat_sheet_for_Google_Colab.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[Pick-Up Line Generator using Python Dictionaries](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPick_up_Line_Generator.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[Optimizing Python Code with List Comprehensions](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FOptimizing_Python_Code_with_List_Comprehensions.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[Understanding Virtual Environments in Python](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FUnderstanding_Virtual_Environments_in_Python.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[Hidden Markov Models in Python](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FHidden_Markov_Models_in_Python.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[Mastering Feature Maps in Convolutional Neural Networks](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FHidden_Layers_of_Understanding_CNN.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[Build a Rule-Based System with Python](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FRule_Based_System_with_Python.ipynb)\u003C\u002Fb>\n\n\n\n \n \n\u003C\u002Fdetails>\n\n\n\u003Cdetails>\n  \u003Csummary>Chapter 6️⃣ ⮕ Exercises\u003C\u002Fsummary>\n\n - \u003Cb>[String Concatenation Questions](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FString_Concatenation_Exercise_Questions.ipynb)\u003C\u002Fb>\n   * \u003Cb>[String Concatenation Answers](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FString_Concatenation_Exercise_Answers.ipynb)\u003C\u002Fb>\n   \n - \u003Cb>[Built-In Functions Exercise Questions](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FBuilt_In_Functions_Exercise_Questions.ipynb)\u003C\u002Fb>\n \n\u003C\u002Fdetails>\n\n\u003Cdetails>\n  \u003Csummary>Chapter 7️⃣ ⮕ Quiz\u003C\u002Fsummary>\n\n - \u003Cb>[Quiz - 1](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FQuiz\u002FPython_Quiz_1.ipynb)\u003C\u002Fb>\n   \n - \u003Cb>[Quiz - 2](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FQuiz\u002FPython_Quiz_2.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[Quiz - 3](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FQuiz\u002FPython_Quiz_3.ipynb)\u003C\u002Fb>\n \n\u003C\u002Fdetails>\n\n\u003Cdetails>\n  \u003Csummary>Chapter 8️⃣ ⮕ Interview Preparation\u003C\u002Fsummary>\n\n - \u003Cb>[Python Coding Interview Questions (Beginner to Advanced)](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FPython%20Coding%20Interview%20Questions%20(Beginner%20to%20Advanced).md)\u003C\u002Fb>\n\n - \u003Cb>[Crack Python Interviews Like a Pro!](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FCrack%20Python%20Interviews%20Like%20a%20Pro!.md)\u003C\u002Fb>\n \n - \u003Cb>[35 Python interview questions for experienced](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002F35%20Python%20interview%20questions%20for%20experienced.md)\u003C\u002Fb>\n\n- \u003Cb> [Python Interview Questions - Strings](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FPython_Interview_Questions_and_Answers_Strings.md)\u003C\u002Fb>\n\n - \u003Cb> [Python Theoretical Interview Questions](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FPython_Theoritical_Interview_Questions.md)\u003C\u002Fb>\n\n\n- \u003Cb> [15 Python Interview Questions and Answers](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FPython_Interview_Questions_and_Answers.md) \u003C\u002Fb>\n\n- \u003Cb>[Assigning Candies to Children Problem with Solution](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FChildren_with_candy.ipynb)\u003C\u002Fb>\n\n- \u003Cb>[Basic Calculator](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FBasic_calculator.ipynb)\u003C\u002Fb>\n\n- \u003Cb> [Text Justification](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FText_Justification.ipynb)\u003C\u002Fb>\n\n- \u003Cb>[Removing an Element](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FRemove_Element.ipynb)\u003C\u002Fb>\n\n- \u003Cb>[Vowel Count in a Sentence](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FVowel_Count.ipynb)\u003C\u002Fb>\n\n- \u003Cb>[Pick-Up Line Generator - Based on Sentiments](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002Fpick_up_line_generator_sentiments.ipynb)\u003C\u002Fb>\n\n- \u003Cb>[Sentimental Analysis](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FSentimental_Analysis.ipynb)\u003C\u002Fb>\n\n- \u003Cb>[Creating a Regular Polygon Visualizer Using Matplotlib in Python](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FDraw_polygon.ipynb)\u003C\u002Fb>\n\n\u003C\u002Fdetails>\n\n\u003Cdetails>\n  \n  \u003Csummary>Chapter 9️⃣ ⮕ Design Principles\u003C\u002Fsummary>\n\n\n - \u003Cb>[Modular Pipeline Architecture or Clean Architecture](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FHow_to_Structure_Machine_Learning_Projects_with_Clean_Code_Principles_in_Python.ipynb)\u003C\u002Fb>\n - \u003Cb>[Dependency Inversion Principle](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FDependency_Inversion_Principle_in_Python.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[Open-Closed Principle](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FOpen_Closed_Principle_in_Python.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[Liskov Substitution Principle](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FLiskov_Substitution_Principle_in_Python.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[Interface Segregation Principle](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FInterface_Segregation_Principle.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[Single Responsibility Principle](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FSingle_Responsibility_Principle.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[Law of Demeter](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FLaw_of_Demeter.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[Composition Over Inheritance](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FComposition_Over_Inheritance.ipynb)\u003C\u002Fb>\n\n\n\n\n\u003C\u002Fdetails>\n\n\n  \n---\n\n## Data Science\n\nFollow the steps below to get started learning Data Science!!!\n\n\u003Cp align=\"center\">\n\u003Cimg src = \"Img\u002FData.PNG\" >\n\u003C\u002Fp>\n\n### Data Science Materials\n\n\u003Cdetails>\n  \u003Csummary>Data Exploration\u003C\u002Fsummary>\n\n - \u003Cb>[Loading a File using Pandas](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002Fdata_load.md)\u003C\u002Fb>\n \n\u003C\u002Fdetails>\n\n\u003Cdetails>\n  \u003Csummary>Data Scraping from the Web\u003C\u002Fsummary>\n\n - \u003Cb>[Scraping Two YouTube Accounts - PewDiePie vs T-Series](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FData%20Scraping%20from%20the%20Web\u002FScraping%20YouTube%20accounts%20with%20python.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[Scraping Rate My Professor Website - My Graduate Professor](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FData%20Scraping%20from%20the%20Web\u002FWeb_Scraping_Rate_My_Professor_Website.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[Web Scraping vs API: Which Data Extraction Method is Best for Your Needs?](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FData%20Scraping%20from%20the%20Web\u002FWeb_Scraping_Vs_API.md)\u003C\u002Fb>\n \n\u003C\u002Fdetails>\n\n\u003Cdetails>\n  \u003Csummary>Cracking Transit Data, Calgary 2025\u003C\u002Fsummary>\n  \n - \u003Cb>[How to Decode and Leverage GTFS for Real-Time Transit Insights](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FTransit_Data_Calgary_2025.ipynb)\u003C\u002Fb>\n \n\u003C\u002Fdetails>\n\n\u003Cdetails>\n  \u003Csummary>Additional Materials (Projects)\u003C\u002Fsummary>\n  \n - \u003Cb>[Mastering the Art of Data Preprocessing with Pandas](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FData_Preprocessing_with_Pandas.md)\u003C\u002Fb>\n - \u003Cb>[Time Series Forecasting with Pandas](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FTime_Series_Forecasting_with_Pandas.ipynb)\u003C\u002Fb>\n - \u003Cb>[Demystifying Feature Engineering](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FDemystifying_Feature_Engineering.ipynb)\u003C\u002Fb>\n - \u003Cb>[Building Your First Machine Learning Model with scikit-learn](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FBuilding_Your_First_Machine_Learning_Model.ipynb)\u003C\u002Fb>\n - \u003Cb>[Build a Smart Resume Ranker with Python and Natural Language Processing](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FSmart_Resume_Ranker_with_Python.ipynb)\u003C\u002Fb>\n - \u003Cb>[Predicting Loan Default Using Decision Trees with Explainable AI](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPredicting_Loan_Default_Using_Decision_Trees.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[How to Efficiently Compute Euclidean Distance in Python Using NumPy (No Loops Needed)](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FHow_to_Efficiently_Compute_Euclidean_Distance_in_Python_Using_NumPy.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[How to Handle Missing Data in Pandas Like a Pro (Python for Data Science)](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FHow_to_Handle_Missing_Data_in_Pandas_Like_a_Pro.ipynb)\u003C\u002Fb>\n\n\n\n\n\u003C\u002Fdetails>\n\n\n\n\n\n\n---\n\n## Machine Learning\n\nFollow the steps below to get started on your journey to perfect Machine Learning !!!\n\n| ![space-1.jpg](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FTanu-N-Prabhu_Python_readme_8aaf9efaf33d.png) | \n|:--:| \n| Image by [Author](https:\u002F\u002Fmachinelearningmastery.com\u002Fauthor\u002Fkanwalmehreen\u002F) - Canva |\n\n### Machine Learning Materials\n\n\u003Cdetails>\n  \u003Csummary>Prerequisite\u003C\u002Fsummary>\n\n - \u003Cb>[Main roadmap and guide](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002FREADME.md)\u003C\u002Fb>\n - \u003Cb>[Python dependencies](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002Frequirements.txt)\u003C\u002Fb>\n - \u003Cb>[Contribution guidelines](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002Fcontribution.md)\u003C\u002Fb>\n - \u003Cb>[Further reading and links](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002Fresources.md)\u003C\u002Fb>\n \n\u003C\u002Fdetails>\n\n\u003Cdetails>\n \u003Csummary>Math & Python foundations\u003C\u002Fsummary>\n\n - \u003Cb>[Python Review](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F00_foundations\u002Fpython_review.ipynb)\u003C\u002Fb>\n - \u003Cb>[Linear Algebra](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F00_foundations\u002Flinear_algebra.ipynb)\u003C\u002Fb>\n - \u003Cb>[Statistics](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F00_foundations\u002Fstatistics.ipynb)\u003C\u002Fb>\n\n\u003C\u002Fdetails>\n\n\u003Cdetails>\n \u003Csummary>Regression & classification\u003C\u002Fsummary>\n\n - \u003Cb>[Regression](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F01_supervised_learning\u002Fregression.ipynb)\u003C\u002Fb>\n - \u003Cb>[Classification](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F01_supervised_learning\u002Fclassification.ipynb)\u003C\u002Fb>\n - \u003Cb>[Advanced Model Evaluation](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F01_supervised_learning\u002Fmodel_evaluation.ipynb)\u003C\u002Fb>\n\n\u003C\u002Fdetails>\n\n\u003Cdetails>\n \u003Csummary>Clustering & dimensionality reduction\u003C\u002Fsummary>\n\n - \u003Cb>[Clustering](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F02_unsupervised_learning\u002Fclustering.ipynb)\u003C\u002Fb>\n - \u003Cb>[Dimensionality Reduction](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F02_unsupervised_learning\u002Fdimensionality_reduction.ipynb)\u003C\u002Fb>\n\u003C\u002Fdetails>\n\n\n\u003Cdetails>\n \u003Csummary>Deep learning with NNs\u003C\u002Fsummary>\n\n - \u003Cb>[Perceptron](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F03_neural_networks\u002Fperceptron.ipynb)\u003C\u002Fb>\n - \u003Cb>[Deep Learning](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F03_neural_networks\u002Fdeep_learning_intro.ipynb)\u003C\u002Fb>\n - \u003Cb>[Convolutional Neural Networks](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F03_neural_networks\u002Fcnn.ipynb)\u003C\u002Fb>\n - \u003Cb>[Recurrent Neural Networks](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F03_neural_networks\u002Frnn.ipynb)\u003C\u002Fb>\n\u003C\u002Fdetails>\n\n\n\u003Cdetails>\n \u003Csummary>Deployment, tracking, versioning\u003C\u002Fsummary>\n  \n - \u003Cb>[Data Versioning](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F04_mlops\u002Fdata_versioning.md)\u003C\u002Fb>\n - \u003Cb>[Model Deployment](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F04_mlops\u002Fmodel_deployment.md)\u003C\u002Fb>\n - \u003Cb>[Monitoring Machine Learning Models in Production](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F04_mlops\u002Fmonitoring.md)\u003C\u002Fb>\n\n\u003C\u002Fdetails>\n\n\u003Cdetails>\n \u003Csummary>Real-world end-to-end projects\u003C\u002Fsummary>\n  \n - \u003Cb>[House Price Prediction](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FHouse%20Price%20Prediction\u002Fhouse_price_prediction.ipynb)\u003C\u002Fb>\n   - \u003Cb>[README.md](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FHouse%20Price%20Prediction\u002FREADME.md)\u003C\u002Fb>\n\n - \u003Cb>[Twitter Sentiment Analysis](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FSentiment%20Analysis\u002Fsentiment_analysis.ipynb)\u003C\u002Fb>\n   - \u003Cb>[README.md](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FSentiment%20Analysis\u002FREADME.md)\u003C\u002Fb>\n\n - \u003Cb>[Credit Card Fraud Detection](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FFraud%20Detection\u002Ffraud_detection.ipynb)\u003C\u002Fb>\n   - \u003Cb>[README.md](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FFraud%20Detection\u002FREADME.md)\u003C\u002Fb>\n\n - \u003Cb>[Customer Churn Prediction](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FCustomer%20Churn%20Prediction\u002Fcustomer_churn_prediction.ipynb)\u003C\u002Fb>\n   - \u003Cb>[README.md](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FCustomer%20Churn%20Prediction\u002FREADME.md)\u003C\u002Fb>\n\n - \u003Cb>[Loan Approval Prediction](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FLoan%20Approval%20Prediction\u002Floan_approval_prediction.ipynb)\u003C\u002Fb>\n   - \u003Cb>[README.md](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FLoan%20Approval%20Prediction\u002FREADME.md)\u003C\u002Fb>\n\n  - \u003Cb>[TMDB Movie Recommendation System](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FMovie%20Recommendation%20System\u002Ftmdb_movie_recommendation.ipynb)\u003C\u002Fb>\n    - \u003Cb>[README.md](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FMovie%20Recommendation%20System\u002FREADME.md)\u003C\u002Fb>\n\n  - \u003Cb>[Retail Sales Forecasting](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FRetail%20Sales%20Forecasting\u002Fretail_sales_forecasting.ipynb)\u003C\u002Fb>\n    - \u003Cb>[README.md](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FRetail%20Sales%20Forecasting\u002FREADME.md)\u003C\u002Fb>\n\n  - \u003Cb>[Fake News Detection](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FFake%20News%20Detection\u002Ffake_news_detection.ipynb)\u003C\u002Fb>\n    - \u003Cb>[README.md](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FFake%20News%20Detection\u002FREADME.md)\u003C\u002Fb>\n\n  - \u003Cb>[Disease Prediction from Symptoms using Machine Learning](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FDisease%20Prediction%20from%20Symptoms\u002Fdisease_prediction_from_symptoms.ipynb)\u003C\u002Fb>\n    - \u003Cb>[README.md](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FDisease%20Prediction%20from%20Symptoms\u002FREADME.md)\u003C\u002Fb>\n\n\n  - \u003Cb>[Emotion Detection from Text](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FEmotion%20Detection%20from%20Text\u002Femotion_detection_from_text.ipynb)\u003C\u002Fb>\n    - \u003Cb>[README.md](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FEmotion%20Detection%20from%20Text\u002FREADME.md)\u003C\u002Fb>\n\n - \u003Cb>[Handwritten Digit Recognition (MNIST)](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FHandwritten%20Digit%20Recognition%20(MNIST)\u002Fhandwritten_digit_recognition.ipynb)\u003C\u002Fb>\n    - \u003Cb>[README.md](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FHandwritten%20Digit%20Recognition%20(MNIST)\u002FREADME.md)\u003C\u002Fb>\n    \n\u003C\u002Fdetails>\n\n\u003Cdetails>\n  \u003Csummary>Machine Learning Interview Preparation\u003C\u002Fsummary>\n\n - \u003Cb>[Fundamental ML concepts like types of learning, bias-variance, etc.](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning%20Interview%20Prep%20Questions\u002Fcore_concepts.md)\u003C\u002Fb>\n\n - \u003Cb>[Questions on algorithms like Linear Regression, SVM, Decision Trees, etc.](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning%20Interview%20Prep%20Questions\u002Fsupervised_learning.md)\u003C\u002Fb>\n\n - \u003Cb>[Covers clustering (K-Means, DBSCAN) and dimensionality reduction (PCA)](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning%20Interview%20Prep%20Questions\u002Funsupervised_learning.md)\u003C\u002Fb>\n \n - \u003Cb>[Model evaluation metrics like Precision, Recall, F1, ROC-AUC, etc.](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning%20Interview%20Prep%20Questions\u002Fevaluation_metrics.md)\u003C\u002Fb>\n \n - \u003Cb>[Covers data cleaning, encoding, handling missing values, scaling, etc.](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning%20Interview%20Prep%20Questions\u002Ffeature_engineering.md)\u003C\u002Fb>\n \n - \u003Cb>[Deep Learning, Transfer Learning, Model Deployment, and more](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning%20Interview%20Prep%20Questions\u002Fadvanced_topics.md)\u003C\u002Fb>\n \n - \u003Cb>[Project-based questions and soft-skill questions](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning%20Interview%20Prep%20Questions\u002Fbehavioral_questions.md)\u003C\u002Fb>\n \n - \u003Cb>[Scenario-based and live practice prompts](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning%20Interview%20Prep%20Questions\u002Fmock_interviews.md)\u003C\u002Fb>\n \n - \u003Cb>[Google Colab notebook with hands-on ML implementation tasks](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning%20Interview%20Prep%20Questions\u002Fcoding_questions.ipynb)\u003C\u002Fb>\n \n\u003C\u002Fdetails>\n\n---\n\n# Tech is Easy\n\nI’m Tanu Nanda Prabhu, and my passion lies in simplifying complex concepts to make them easier for others to understand. Research is something I deeply enjoy, and after exploring numerous videos, articles, and tutorials, I decided to create a repository dedicated to Google Sheets tips and tricks. I update this repository weekly with valuable insights designed to make your life simpler. I’m also open to contributions from those who wish to help enhance this resource. Feel free to fork the repository and submit updates; I’ll gladly review and consider them.\n\n> [Tech Is Easy](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FTechIsEasy)\n\n---\n\n\n# Nbviewer\n\nIf the Jupyter Notebook doesn’t load, don’t worry! Simply copy and paste the link into [nbviewer](https:\u002F\u002Fnbviewer.jupyter.org), as most of my notebooks are accessible there.\n\n---\n\n# Contributors\n\n### Currently, there are about 20 contributors for this repository. Feel free to contribute!\n\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fgraphs\u002Fcontributors\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FTanu-N-Prabhu_Python_readme_01d24b7ca94a.png\" \u002F>\n\u003C\u002Fa>\n\n\n---\n\n# Kaggle Datasets\n\n1) [Kaggle Data Sets](https:\u002F\u002Fwww.kaggle.com\u002Ftanuprabhu\u002Fdatasets)\n\n---\n\n# HackerRank Exercises - Solved\n\n1) [HackerRank Exercise Solved](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FHacker_Rank_Exercises)\n\n\n---\n\n# Reddit Communities\n\n1) [Python](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FPython\u002F)\n2) [Learn Python](https:\u002F\u002Fwww.reddit.com\u002Fr\u002Flearnpython\u002F)\n3) [Python tips](https:\u002F\u002Fwww.reddit.com\u002Fr\u002Fpythontips\u002F)\n4) [Python coding](https:\u002F\u002Fwww.reddit.com\u002Fr\u002Fpythoncoding?utm_medium=android_app&utm_source=share)\n\n\n\n---\n\n\n# GPT Librarian \u003Cimg align=\"right\" width=\"50\" height=\"50\" src=https:\u002F\u002Fgithub.com\u002FDecron\u002FPython\u002Fassets\u002F1786607\u002F5b4e7a09-a76f-40be-b87d-c6e007b7ff35>\n\nIf you have access to ChatGPT Premium, there is a GPT Librarian with access to all files here [here](https:\u002F\u002Fchat.openai.com\u002Fg\u002Fg-2HlDYwyrW-python-from-scratch)\n\n---\n\n# Contact for Help\n\n| Platform | Details |\n|---------|----------|\n| ![Gmail](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGmail-D14836?style=for-the-badge&logo=gmail&logoColor=white) | **tanunprabhu95@gmail.com** |\n| ![LinkedIn](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Flinkedin-%230077B5.svg?style=for-the-badge&logo=linkedin&logoColor=white) | [**Tanu Nanda Prabhu**](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Ftanu-nanda-prabhu-a15a091b5\u002F) |\n| ![Medium](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMedium-12100E?style=for-the-badge&logo=medium&logoColor=white) | [**Tanu N Prabhu**](https:\u002F\u002Fmedium.com\u002F@tanunprabhu95) |\n| ![Instagram](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FInstagram-%23E4405F.svg?style=for-the-badge&logo=Instagram&logoColor=white) | [**Python Coder**](https:\u002F\u002Fwww.instagram.com\u002Fpycoderr\u002F) |\n\n---\n\n\n# Reviews  \n> **Below are some of the reviews about this Python GitHub Repository:**  \n\n---\n\n### **‪Elin Uppström**  \n`Senior Lecturer • Uppsala University, Sweden`\n\n> I found your excellent exercises on your GitHub while preparing an undergraduate course in data analysis. I want to use it in my course.\n\n---\n\n### **‪Cole Striler**  \n`Data Scientist • Founder of Datafied`\n\n> I came across your GitHub and love your Jupyter Notebooks, especially the one on \"Predicting PewDiePie's daily subscribers\". I think you do a great job of explaining your work, which others can learn from.\n\n---\n\n### **Laurence Watson**  \n`Co-Founder & CEO • Treebeard`\n\n> You have a lot of great Jupyter notebook content on GitHub.\n\n---\n\n### **Poonam Gupta**  \n`Math & AP Computer Science Instructor • Brunswick School`\n\n> Thank you so much for posting such helpful posts on GitHub. Many, many thanks for all you do to spread the knowledge.\n\n---\n\n### **David Okenwa**  \n`Mechanical Engineer • Building in Data Analytics`\n\n> I recently came across your Medium, GitHub, and portfolio website serendipitously and was enthralled. Inspiring! I want to start writing articles and building my GitHub repositories.\n\n\n**Do you like the Repository? Please share your valuable reviews by sending an [email](tanunprabhu95@gmail.com).**\n\n---\n\n## Featured Articles on Medium\n\nA curated selection of tutorials, insights, and guides on programming, software development, and emerging tech trends.\n\n| Title                                                                                                                                                                                     | Read on Medium                                                                                                                  |\n| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------- |\n| **The First Step to Becoming a Data Scientist**| 🔗 [Read Here](https:\u002F\u002Fmedium.com\u002F@tanunprabhu95\u002Fthe-first-step-to-becoming-a-data-scientist-ce316b6fea5c) |\n| **10 Must-Know Pandas Tricks Every Data Science Beginner Should Learn**| 🔗 [Read Here](https:\u002F\u002Fmedium.com\u002F@tanunprabhu95\u002F10-must-know-pandas-tricks-every-data-science-beginner-should-learn-6e75ab366042) |\n| **Common Performance Pitfalls in Python ML\u002FData Projects** | 🔗 [Read Here](https:\u002F\u002Fmedium.com\u002F@tanunprabhu95\u002Fcommon-performance-pitfalls-in-python-ml-data-projects-757dcb51ff47) |\n| **Telling Stories With Data** | 🔗 [Read Here](https:\u002F\u002Fmedium.com\u002F@tanunprabhu95\u002Ftelling-stories-with-data-0c983e44ea5c) |\n\n\n\n> Explore more stories on [Medium](https:\u002F\u002Fmedium.com\u002F@tanunprabhu95)\n\n---\n\n## 🔥 Trending Tech Topics (Auto-updated daily)\n\u003C!-- START_TRENDING -->\n- [Join the OpenClaw Challenge: $1,200 Prize Pool!](https:\u002F\u002Fdev.to\u002Fdevteam\u002Fjoin-the-openclaw-challenge-1200-prize-pool-5682)\n- [Turning the Raspberry Pi Zero into a Hacking Gadget](https:\u002F\u002Fdev.to\u002Fadmantium\u002Fturning-the-raspberry-pi-zero-into-a-hacking-gadget-2ekl)\n- [Most Apps Are Slower Than They Need to Be — Here’s Why (Live Demo🛸)](https:\u002F\u002Fdev.to\u002Fsylwia-lask\u002Fmost-apps-are-slower-than-they-need-to-be-heres-why-live-demo-2hh8)\n- [Build a voice-enabled Telegram Bot with the Gemini Interactions API](https:\u002F\u002Fdev.to\u002Fgoogleai\u002Fbuild-a-voice-enabled-telegram-bot-with-the-gemini-interactions-api-nm5)\n- [What brings you by a conference booth?](https:\u002F\u002Fdev.to\u002Fmissamarakay\u002Fwhat-brings-you-by-a-conference-booth-43e3)\n\u003C!-- END_TRENDING -->\n\n---\n\n\n# Feedback\n\n[Any Feedback or Suggestions- Please Click Here](https:\u002F\u002Fform.jotform.com\u002F92847563204259)\n\n---\n\n# Star History\n\n\n[![Star History Chart](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FTanu-N-Prabhu_Python_readme_d6d4b32315fe.png)](https:\u002F\u002Fstar-history.com\u002F#Tanu-N-Prabhu\u002FPython&Date)\n\n---\n\n\n[![Maintened by - Tanu Nanda Prabhu](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMaintained%20by-Tanu%20Nanda%20Prabhu-red)](https:\u002F\u002Ftanu-n-prabhu.github.io\u002FmyWebsite.io\u002F)\n[![made-with-Markdown](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMade%20with-Markdown-1f425f.svg)](http:\u002F\u002Fcommonmark.org)\n","\u003Ch1 align = \"center\"> 🐍📊 \u003Ca href = \"https:\u002F\u002Ftanu-n-prabhu.github.io\u002FPython\u002F\">欢迎来到 Python 编程中心\u003C\u002Fa> 📊🐍\u003C\u002Fh1>\n\n\u003Ch2 align = \"center\">\u003Ci>学习 Python、数据科学和机器学习的最佳去处！\u003C\u002Fi>\u003C\u002Fh2>\n\n\u003Cp align=\"center\">\n\n  \u003C!-- 社交指标 -->\n  \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fforks\u002FTanu-N-Prabhu\u002FPython?style=social\" \u002F>\n  \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002FTanu-N-Prabhu\u002FPython?style=social\" \u002F>\n\n  \u003C!-- 仓库健康状况 -->\n  \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Frepo-size\u002FTanu-N-Prabhu\u002FPython?label=仓库%20大小\" \u002F>\n  \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fcontributors\u002FTanu-N-Prabhu\u002FPython?label=贡献者\" \u002F>\n\n  \u003C!-- 开发工具 -->\n  \u003Ca href=\"https:\u002F\u002Fgitpod.io\u002F#https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitpod-Ready%20to%20Code-blue?logo=gitpod\" \u002F>\n  \u003C\u002Fa>\n\n \n  \u003C!-- 今年提交次数 -->\n  \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fcommit-activity\u002Fy\u002FTanu-N-Prabhu\u002FPython?label=今年提交次数\" \u002F>\n  \n  \u003C!-- 本周提交次数 -->\n  \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fcommit-activity\u002Fw\u002FTanu-N-Prabhu\u002FPython?label=本周提交次数\" \u002F>\n  \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPython-3.x-blue?logo=python\" \u002F>\n\n  \u003C!-- 最后一次提交 -->\n  \u003Ca href=\".\u002FVERSION_HISTORY.md\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flast-commit\u002FTanu-N-Prabhu\u002FPython\" \u002F>\n  \u003C\u002Fa>\n\n  \u003C!-- 开源 -->\n  \u003Ca href=\"https:\u002F\u002Fopensource.org\u002F\">\n    \u003Cimg src=\"https:\u002F\u002Fbadges.frapsoft.com\u002Fos\u002Fv1\u002Fopen-source.svg?v=103\" \u002F>\n  \u003C\u002Fa>\n\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FRelease%20Notes\">\n  \u003Cimg\n    src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F发布说明-蓝色\"\n    alt=\"发布说明\"\n  \u002F>\n\u003C\u002Fa>\n\n\n  \n\n\u003C\u002Fp>\n\n\n\n\n\n\u003Cp align = \"right\">\u003Cb>\u003Ci>最后更新\u003C\u002Fi>\u003C\u002Fb>: \u003C!-- LAST_UPDATED -->2026年4月17日\u003C!-- END_LAST_UPDATED -->\u003C\u002Fp>\n\n\n| ![space-1.jpg](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FTanu-N-Prabhu_Python_readme_23386adf8790.jpg) | \n|:--:| \n| 图片来源 [Wallpaper Flare](https:\u002F\u002Fwww.wallpaperflare.com\u002Fprogramming-is-an-art-text-code-python-computer-python-programming-wallpaper-srfia) |\n\n![浏览量计数器](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FTanu-N-Prabhu_Python_readme_37436185970b.png)\n\n\n欢迎来到一个汇集 Python 编程专业知识、数据科学精通以及在充满活力的编程世界中生存必备技能的宝库。深入探索这个仓库，解锁你在编程之旅中茁壮成长所需的知识与工具。\n\n## 目录\n\n#### [1. 简介](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FREADME.md)\n1. [是什么](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster#about) \n2. [为什么](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster#why-choose-this-repository) \n3. [如何使用](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster#why-choose-this-repository)  \n\n#### [2. LinkedIn 内容概览](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FLinkedIn)  \n1. [最新帖子](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FLinkedIn\u002Fpost_4_nov_23.md)\n2. [目的](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FLinkedIn#purpose-of-this-folder)\n\n#### [3. Python 学习资料](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FREADME.md#pythonic-materials)\n\n1. **第1章 - 基础概念**\n   - [Python 输入、输出和导入函数](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython_Input%2C_Output_and_Import.ipynb)\n   - [Python 变量](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython_Variables.ipynb)\n   - [Python 全局、局部和非局部变量](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FGlobal%2C_Local_and_Nonlocal_variables_in_Python.ipynb)\n   - [Python 字符串](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FStrings)\n   - [Python 列表](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FLists)\n   - [Python 元组](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FTuples)\n   - [Python 字典](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FDictionary%20)\n   - [Python 运算符](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython_Operators.ipynb)\n   - [掌握 Python 装饰器](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMastering_Python_Decorators.ipynb)\n\n\n2. **第2章 - 内置函数**\n   - [Python 输入、输出和导入内置函数](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython_Input%2C_Output_and_Import.ipynb)\n   - [eval 内置函数](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FEval_built_in_function.ipynb)\n   - [range 内置函数](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FRange_built_in_function.ipynb)\n   - [Python Lambda 函数](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython_Lambda_Function.ipynb)\n   - [Python enumerate 函数](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython_enumerate()_built_in_function.ipynb)\n   - [Python len 函数](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython_len()_built_in_function.ipynb)\n\n3. **第3章 - 库**\n   - [Numpy 库](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FNumpy)\n   - [Pandas 库](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FPandas)\n   - [数学模块](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FLearn_the_Python_Math_Module.ipynb)\n   - [JSON 库](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FHow_to_handle_JSON_in_Python%3F.ipynb)\n\n\n4. **第4章 - API**\n   - [Google 翻译 API for Python](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FGoogle%20Translate%20API)\n   - [Google Trends API for Python](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FGoogle_Trends_API.ipynb)\n   - [维基百科 API for Python](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FWikipedia_API_for_Python.ipynb)\n   - [Google 搜索 API for Python](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FThe_two_Google_Search_Python_Libraries_you_should_never_miss.ipynb)\n   - [通用交通信息交换格式 (GTFS)](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002F387a2cdd5bcfc4afbae2319d017a850bdaeb772c\u002FTransit_Data_Calgary_2025.ipynb)\n   - [使用 Facebook Prophet 进行时间序列预测](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FUnlocking_Time_Series_Forecasting_with_Facebook_Prophet.ipynb)\n\n5. **第5章 - 附加材料**\n   - [如何开始用Python编程？](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FHow_to_get_started_coding_in_Python%3F.ipynb)\n   - [Python是面向对象的吗？](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FIs_Python_object_oriented%3F.ipynb)\n   - [使用Python进行语音识别](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FSpeech_Recognition_using_Python.ipynb)\n   - [独热编码](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FLearning_One_Hot_Encoding_in_Python_the_Easy_Way.ipynb)\n   - [不使用库读取图像](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FReading_An_Image_In_Python_(Without_Using_Special_Libraries).ipynb)\n   - [在Pandas DataFrame中渲染图像](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FRendering_Images_inside_a_Pandas_DataFrame.ipynb)\n   - [将Pandas DataFrame用作数据库](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FUsing_the_Pandas_Data_Frame_as_a_Database_.ipynb)\n   - [在日常生活中使用Pandas](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FUsing_the_Pandas_DataFrame_in_Day_To_Day_Life.ipynb)\n   - [使用RISE展示Python代码](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPresenting_Python_code_using_RISE.ipynb)\n   - [Google Colab速查表](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FCheat_sheet_for_Google_Colab.ipynb)\n   - [搭讪话生成器](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPick_up_Line_Generator.ipynb)\n   - [使用列表推导式优化代码](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FOptimizing_Python_Code_with_List_Comprehensions.ipynb)\n   - [理解虚拟环境](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FUnderstanding_Virtual_Environments_in_Python.ipynb)\n   - [隐马尔可夫模型](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FHidden_Markov_Models_in_Python.ipynb)\n   - [CNN中的特征图](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FHidden_Layers_of_Understanding_CNN.ipynb)\n   - [Python中的基于规则的系统](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FRule_Based_System_with_Python.ipynb)\n\n\n6. **第6章 - 练习题**\n   - [字符串拼接练习题](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FString_Concatenation_Exercise_Questions.ipynb)  \n   - [字符串拼接练习答案](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FString_Concatenation_Exercise_Answers.ipynb)\n   - [内置函数练习题](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FBuilt_In_Functions_Exercise_Questions.ipynb)\n\n\n7. **第7章 - 测验**\n   - [测验1](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FQuiz\u002FPython_Quiz_1.ipynb)\n   - [测验2](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FQuiz\u002FPython_Quiz_2.ipynb)\n   - [测验3](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FQuiz\u002FPython_Quiz_3.ipynb)\n\n8. **[第8章 - 面试准备](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FPython%20Coding%20Interview%20Prep)**\n   - [Python编程面试题（初级→高级）](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FPython%20Coding%20Interview%20Questions%20(Beginner%20to%20Advanced).md)\n   - [像专业人士一样攻克Python面试！](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FCrack%20Python%20Interviews%20Like%20a%20Pro!.md)\n   - [35道Python面试题（有经验者）](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002F35%20Python%20interview%20questions%20for%20experienced.md)\n   - [Python面试题——字符串](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FPython_Interview_Questions_and_Answers_Strings.md)\n   - [Python理论面试题](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FPython_Theoritical_Interview_Questions.md)\n   - [15道Python面试问答](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FPython_Interview_Questions_and_Answers.md)\n   - [给孩子们分配糖果](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FChildren_with_candy.ipynb)\n   - [基础计算器](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FBasic_calculator.ipynb)\n   - [文本对齐](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FText_Justification.ipynb)\n   - [移除元素](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FRemove_Element.ipynb)\n   - [元音计数](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FVowel_Count.ipynb)\n   - [基于情感的搭讪话生成器](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002Fpick_up_line_generator_sentiments.ipynb)\n   - [情感分析](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FSentimental_Analysis.ipynb)\n   - [正多边形可视化工具](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FDraw_polygon.ipynb)\n\n\n9. **第9章 - 设计原则**\n   - [模块化流水线\u002F整洁架构](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FHow_to_Structure_Machine_Learning_Projects_with_Clean_Code_Principles_in_Python.ipynb)\n   - [依赖倒置原则](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FDependency_Inversion_Principle_in_Python.ipynb)\n   - [开闭原则](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FOpen_Closed_Principle_in_Python.ipynb)\n   - [里氏替换原则](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FLiskov_Substitution_Principle_in_Python.ipynb)\n\n\u003C\u002Fdetails>\n\n\n\n#### [4. 机器学习资料]()\n1. [基础](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FMachine%20Learning\u002F00_foundations)\n2. [监督学习](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FMachine%20Learning\u002F01_supervised_learning)\n3. [无监督学习](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FMachine%20Learning\u002F02_unsupervised_learning)\n4. [神经网络](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FMachine%20Learning\u002F03_neural_networks)\n5. [MLOps](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FMachine%20Learning\u002F04_mlops)\n6. [项目](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FMachine%20Learning\u002F05_projects)\n\n#### [5. 数据科学资料](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002F713e5814c15caf6ee4640f7b4cec04a68b4b899e\u002FData%20Analysis)\n1. [Level 0](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FData%20Analysis\u002FLevel%200)\n2. [Level 1](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FData%20Analysis\u002FLevel%201)\n3. [Level 2](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FData%20Analysis\u002FLevel%202)\n4. [Level 3](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FData%20Analysis\u002FLevel%203)\n5. [Level 4](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FData%20Analysis\u002FLevel%204)\n6. [Level 5](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FData%20Analysis\u002FLevel%205)\n7. [EDA 技术](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FExploratory%20Data%20Analysis)\n8. [25 个带有清晰 Python 答案的真实数据分析问题](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FData%20Analysis\u002F25%20Real%20Questions%20With%20Clear%20Python%20Answers)\n\n#### [6. 贡献流程](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002Fcontribution.md)\n1. [代码片段指南](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster?tab=coc-ov-file#)\n\n#### [7. 发行说明](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002F7ef79b9098d5c82862669cf61b7b413864dfad83\u002FRelease%20Notes)\n1. [当前版本](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FRelease%20Notes\u002Fv1.2.0.md)  \n2. [归档](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FRelease%20Notes\u002FREADME.md)\n\n#### [8. 致谢](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster?tab=readme-ov-file#reviews)\n1. [署名与致谢](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster?tab=readme-ov-file#reviews)\n\n---\n\n\n\n## 关于\n\n这个仓库不仅仅是一系列代码片段的集合，它更是一个全面的学习资源，旨在赋能学习者和专业人士。你将在这里找到以下方面的宝贵见解和指导：\n\n* **初学者** - 探索 Python 编程的基础知识。\n* **数据爱好者** - 深入了解数据科学的复杂性。\n* **资深开发者** - 通过高级技巧和建议提升你的专业技能。\n\n\n## 该仓库提供的内容\n\n* **Python 技能提升** - 通过我精心整理的教程、练习和实际案例，帮助你从入门到精通 Python。\n* **数据科学专业知识** - 利用我们深入的指南、项目以及数据分析、机器学习等领域的最佳实践，充分发挥数据的力量。\n* **生存工具包** - 凭借我们在职业发展、提高效率以及保持领先等方面的技巧、窍门和建议，自信地应对编程世界的复杂挑战。\n  \n## 为什么选择这个仓库\n\n| 特性 | 描述 |\n|--------|-------------|\n| 全面学习 | 结构化的学习路径，提供从初级到高级的资源，助力长期掌握技能。 |\n| 社区支持 | 协作式的学习环境，学习者和导师互相帮助共同成长。 |\n| 实践应用 | 真实世界中的示例和动手练习，让概念易于应用。 |\n\n\n### 参与进来 \n\n准备好开启你的 Python 之旅了吗？探索我们的仓库，贡献你的专业知识，并与其他爱好者建立联系。让我们一起磨练技能，揭开编程的奥秘，在不断发展的技术领域中开拓新的机遇。\n\n## 加入我们的冒险之旅\n\nPython 精英学习库不仅仅是一组代码的集合，它更是通向无限可能的门户。立即开始探索，发现 Python 编程、数据科学及其他领域的无尽潜力。\n\n## 安装工具\n\n在开始你的 Python 学习之旅之前，安装一些实用工具非常重要。这些工具能够让你更轻松地编写、运行并理解 Python 程序。你可以根据自己的学习风格选择最适合的工具。\n\n---\n\n## 推荐工具\n\n> 这些是你入门所需的必备工具，从编写 Python 代码到在云端轻松运行程序。\n\n| 工具 | 描述 |\n|------|--------------|\n| [![Python](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPython-3776AB?logo=python&logoColor=white)](https:\u002F\u002Fwww.python.org\u002F) | 本仓库的核心语言。 |\n| [![VS Code](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FVS_Code-007ACC?logo=visualstudiocode&logoColor=white)](https:\u002F\u002Fcode.visualstudio.com\u002F) | 功能强大且可扩展的编辑器，适用于 Python 等多种语言。 |\n| [![Jupyter Notebook](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FJupyter-F37626?logo=jupyter&logoColor=white)](https:\u002F\u002Fjupyter.org\u002F) | 非常适合交互式编程和数据可视化。 |\n| [![Google Colab](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGoogle_Colab-F9AB00?logo=googlecolab&logoColor=white)](https:\u002F\u002Fcolab.research.google.com\u002F) | 在云端运行 Python 笔记本，无需安装任何软件。 |\n\n > 你可以直接使用 VS Code 搭配 Jupyter 和 Colab 笔记本，实现无缝的工作流程。\n\n---\n\n# 仓库内容\n\n#### 本仓库分为两大部分：Python 编程和面向初学者的数据科学。\n\n## Python 编程\n\n请按照以下步骤开始 Python 编程吧！！！\n\n\u003Cp align=\"center\"> \n\u003Cimg src = \"https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FImg\u002FPython.PNG\">\n\u003C\u002Fp>\n\n### Python 相关资料\n\n> 展开或折叠以上章节以获取更多详细信息\n\n\n\u003Cdetails>\n  \u003Csummary>第 1 章 ⮕ 基础概念\u003C\u002Fsummary>\n\n - \u003Cb>[Python 输入、输出和导入函数](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython_Input%2C_Output_and_Import.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[Python 变量](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython_Variables.ipynb)\u003C\u002Fb>\n   * \u003Cb>[Python 全局、局部和非局部变量](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FGlobal%2C_Local_and_Nonlocal_variables_in_Python.ipynb)\u003C\u002Fb>\n   \n - \u003Cb>[Python 字符串](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FStrings)\u003C\u002Fb>\n \n - \u003Cb>[Python 列表](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FLists)\u003C\u002Fb> \n \n - \u003Cb>[Python 元组](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FTuples)\u003C\u002Fb>\n \n -  \u003Cb>[Python 字典](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FDictionary%20)\u003C\u002Fb>\n \n - \u003Cb>[Python 运算符](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython_Operators.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[掌握 Python 装饰器](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMastering_Python_Decorators.ipynb)\u003C\u002Fb>\n\n\n\u003C\u002Fdetails>\n\n\n\u003Cdetails>\n  \u003Csummary>第 2 章 ⮕ 内置函数\u003C\u002Fsummary>\n\n- \u003Cb>[Python 输入、输出和导入内置函数](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython_Input%2C_Output_and_Import.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[eval 内置函数](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FEval_built_in_function.ipynb)\u003C\u002Fb>\n   \n - \u003Cb>[range 内置函数](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FRange_built_in_function.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[Python Lambda 函数](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython_Lambda_Function.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[Python enumerate 函数](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython_enumerate()_built_in_function.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[Python len 函数](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython_len()_built_in_function.ipynb)\u003C\u002Fb>  \n \n\u003C\u002Fdetails>\n\n\u003Cdetails>\n  \u003Csummary>第3章 ⮕ 库\u003C\u002Fsummary>\n\n - \u003Cb>[Numpy 库](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FNumpy)\u003C\u002Fb>\n \n - \u003Cb>[Pandas 库](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FPandas)\u003C\u002Fb>\n   \n - \u003Cb>[Math 模块](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FLearn_the_Python_Math_Module.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[JSON 库](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FHow_to_handle_JSON_in_Python%3F.ipynb)\u003C\u002Fb>\n\n\u003C\u002Fdetails>\n\n\u003Cdetails>\n  \u003Csummary>第4章 ⮕ API\u003C\u002Fsummary>\n\n - \u003Cb>[适用于 Python 的 Google 翻译 API](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FGoogle%20Translate%20API)\u003C\u002Fb>\n \n - \u003Cb>[适用于 Python 的 Google Trends API](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FGoogle_Trends_API.ipynb)\u003C\u002Fb>\n   \n - \u003Cb>[适用于 Python 的 Wikipedia API](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FWikipedia_API_for_Python.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[适用于 Python 的 Google 搜索 API](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FThe_two_Google_Search_Python_Libraries_you_should_never_miss.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[通用公交数据交换规范 - GTFS](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002F387a2cdd5bcfc4afbae2319d017a850bdaeb772c\u002FTransit_Data_Calgary_2025.ipynb)\u003C\u002Fb>\n\n  - \u003Cb>[使用 Facebook Prophet 解锁时间序列预测](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FUnlocking_Time_Series_Forecasting_with_Facebook_Prophet.ipynb)\u003C\u002Fb>\n\n\u003C\u002Fdetails>\n\n\u003Cdetails>\n  \u003Csummary>第5章 ⮕ 附加资料\u003C\u002Fsummary>\n\n - \u003Cb>[如何开始用 Python 编程？](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FHow_to_get_started_coding_in_Python%3F.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[Python 是面向对象的吗？](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FIs_Python_object_oriented%3F.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[使用 Python 进行语音识别](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FSpeech_Recognition_using_Python.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[Python 中的独热编码](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FLearning_One_Hot_Encoding_in_Python_the_Easy_Way.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[在不使用专用库的情况下读取图像](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FReading_An_Image_In_Python_(Without_Using_Special_Libraries).ipynb)\u003C\u002Fb>\n \n - \u003Cb>[在 Pandas DataFrame 中渲染图像](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FRendering_Images_inside_a_Pandas_DataFrame.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[将 Pandas DataFrame 用作数据库](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FUsing_the_Pandas_Data_Frame_as_a_Database_.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[在日常生活中使用 Pandas DataFrame](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FUsing_the_Pandas_DataFrame_in_Day_To_Day_Life.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[使用 RISE 展示 Python 代码](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPresenting_Python_code_using_RISE.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[Google Colab 备忘录](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FCheat_sheet_for_Google_Colab.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[使用 Python 字典生成搭讪语句生成器](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPick_up_Line_Generator.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[使用列表推导优化 Python 代码](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FOptimizing_Python_Code_with_List_Comprehensions.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[理解 Python 中的虚拟环境](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FUnderstanding_Virtual_Environments_in_Python.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[Python 中的隐马尔可夫模型](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FHidden_Markov_Models_in_Python.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[掌握卷积神经网络中的特征图](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FHidden_Layers_of_Understanding_CNN.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[用 Python 构建基于规则的系统](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FRule_Based_System_with_Python.ipynb)\u003C\u002Fb>\n\n\n\n \n \n\u003C\u002Fdetails>\n\n\n\u003Cdetails>\n  \u003Csummary>第6章 ⮕ 练习题\u003C\u002Fsummary>\n\n - \u003Cb>[字符串拼接练习题](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FString_Concatenation_Exercise_Questions.ipynb)\u003C\u002Fb>\n   * \u003Cb>[字符串拼接答案](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FString_Concatenation_Exercise_Answers.ipynb)\u003C\u002Fb>\n   \n - \u003Cb>[内置函数练习题](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FBuilt_In_Functions_Exercise_Questions.ipynb)\u003C\u002Fb>\n \n\u003C\u002Fdetails>\n\n\u003Cdetails>\n  \u003Csummary>第7章 ⮕ 测验\u003C\u002Fsummary>\n\n - \u003Cb>[测验 - 1](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FQuiz\u002FPython_Quiz_1.ipynb)\u003C\u002Fb>\n   \n - \u003Cb>[测验 - 2](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FQuiz\u002FPython_Quiz_2.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[测验 - 3](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FQuiz\u002FPython_Quiz_3.ipynb)\u003C\u002Fb>\n \n\u003C\u002Fdetails>\n\n\u003Cdetails>\n  \u003Csummary>第8章 ⮕ 面试准备\u003C\u002Fsummary>\n\n - \u003Cb>[Python 编码面试题（从初级到高级）](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FPython%20Coding%20Interview%20Questions%20(Beginner%20to%20Advanced).md)\u003C\u002Fb>\n\n - \u003Cb>[像专业人士一样攻克 Python 面试！](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FCrack%20Python%20Interviews%20Like%20a%20Pro!.md)\u003C\u002Fb>\n \n - \u003Cb>[35 道面向有经验者的 Python 面试题](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002F35%20Python%20interview%20questions%20for%20experienced.md)\u003C\u002Fb>\n\n- \u003Cb> [Python 面试题 - 字符串](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FPython_Interview_Questions_and_Answers_Strings.md)\u003C\u002Fb>\n\n - \u003Cb> [Python 理论面试题](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FPython_Theoritical_Interview_Questions.md)\u003C\u002Fb>\n\n\n- \u003Cb> [15 道 Python 面试问题及答案](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FPython_Interview_Questions_and_Answers.md) \u003C\u002Fb>\n\n- \u003Cb>[给孩子们分配糖果问题及解决方案](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FChildren_with_candy.ipynb)\u003C\u002Fb>\n\n- \u003Cb>[简易计算器](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FBasic_calculator.ipynb)\u003C\u002Fb>\n\n- \u003Cb> [文本对齐](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FText_Justification.ipynb)\u003C\u002Fb>\n\n- \u003Cb>[移除数组中的元素](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FRemove_Element.ipynb)\u003C\u002Fb>\n\n- \u003Cb>[句子中元音字母的计数](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FVowel_Count.ipynb)\u003C\u002Fb>\n\n- \u003Cb>[搭讪话术生成器 - 基于情感分析](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002Fpick_up_line_generator_sentiments.ipynb)\u003C\u002Fb>\n\n- \u003Cb>[情感分析](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FSentimental_Analysis.ipynb)\u003C\u002Fb>\n\n- \u003Cb>[使用Matplotlib在Python中创建正多边形可视化工具](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FDraw_polygon.ipynb)\u003C\u002Fb>\n\n\u003C\u002Fdetails>\n\n\u003Cdetails>\n  \n  \u003Csummary>第9章️⃣ ⮕ 设计原则\u003C\u002Fsummary>\n\n\n - \u003Cb>[模块化管道架构或整洁架构](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FHow_to_Structure_Machine_Learning_Projects_with_Clean_Code_Principles_in_Python.ipynb)\u003C\u002Fb>\n - \u003Cb>[依赖倒置原则](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FDependency_Inversion_Principle_in_Python.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[开闭原则](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FOpen_Closed_Principle_in_Python.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[里氏替换原则](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FLiskov_Substitution_Principle_in_Python.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[接口隔离原则](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FInterface_Segregation_Principle.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[单一职责原则](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FSingle_Responsibility_Principle.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[迪米特法则](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FLaw_of_Demeter.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[组合优于继承](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FComposition_Over_Inheritance.ipynb)\u003C\u002Fb>\n\n\n\n\n\u003C\u002Fdetails>\n\n\n  \n---\n\n\n\n## 数据科学\n\n请按照以下步骤开始学习数据科学！！！\n\n\u003Cp align=\"center\">\n\u003Cimg src = \"Img\u002FData.PNG\" >\n\u003C\u002Fp>\n\n### 数据科学资料\n\n\u003Cdetails>\n  \u003Csummary>数据探索\u003C\u002Fsummary>\n\n - \u003Cb>[使用Pandas加载文件](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002Fdata_load.md)\u003C\u002Fb>\n \n\u003C\u002Fdetails>\n\n\u003Cdetails>\n  \u003Csummary>网络爬虫\u003C\u002Fsummary>\n\n - \u003Cb>[抓取两个YouTube账号 - PewDiePie vs T-Series](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FData%20Scraping%20from%20the%20Web\u002FScraping%20YouTube%20accounts%20with%20python.ipynb)\u003C\u002Fb>\n \n - \u003Cb>[抓取Rate My Professor网站 - 我的研究生导师](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FData%20Scraping%20from%20the%20Web\u002FWeb_Scraping_Rate_My_Professor_Website.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[网络爬虫与API：哪种数据提取方法最适合您的需求？](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FData%20Scraping%20from%20the%20Web\u002FWeb_Scraping_Vs_API.md)\u003C\u002Fb>\n \n\u003C\u002Fdetails>\n\n\u003Cdetails>\n  \u003Csummary>破解卡尔加里2025年交通数据\u003C\u002Fsummary>\n  \n - \u003Cb>[如何解码并利用GTFS获取实时交通洞察](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FTransit_Data_Calgary_2025.ipynb)\u003C\u002Fb>\n \n\u003C\u002Fdetails>\n\n\u003Cdetails>\n  \u003Csummary>附加资料（项目）\u003C\u002Fsummary>\n  \n - \u003Cb>[掌握使用Pandas进行数据预处理的艺术](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FData_Preprocessing_with_Pandas.md)\u003C\u002Fb>\n - \u003Cb>[使用Pandas进行时间序列预测](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FTime_Series_Forecasting_with_Pandas.ipynb)\u003C\u002Fb>\n - \u003Cb>[揭秘特征工程](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FDemystifying_Feature_Engineering.ipynb)\u003C\u002Fb>\n - \u003Cb>[使用scikit-learn构建您的第一个机器学习模型](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FBuilding_Your_First_Machine_Learning_Model.ipynb)\u003C\u002Fb>\n - \u003Cb>[使用Python和自然语言处理构建智能简历排名系统](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FSmart_Resume_Ranker_with_Python.ipynb)\u003C\u002Fb>\n - \u003Cb>[使用决策树结合可解释AI预测贷款违约](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPredicting_Loan_Default_Using_Decision_Trees.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[如何在Python中使用NumPy高效计算欧几里得距离（无需循环）](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FHow_to_Efficiently_Compute_Euclidean_Distance_in_Python_Using_NumPy.ipynb)\u003C\u002Fb>\n\n - \u003Cb>[如何像专业人士一样处理Pandas中的缺失数据（数据科学中的Python）](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FHow_to_Handle_Missing_Data_in_Pandas_Like_a_Pro.ipynb)\u003C\u002Fb>\n\n\n\n\n\u003C\u002Fdetails>\n\n\n\n\n\n\n---\n\n## 机器学习\n\n请按照以下步骤开始您通往完美机器学习之路的旅程！！！\n\n| ![space-1.jpg](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FTanu-N-Prabhu_Python_readme_8aaf9efaf33d.png) | \n|:--:| \n| 图片由[作者](https:\u002F\u002Fmachinelearningmastery.com\u002Fauthor\u002Fkanwalmehreen\u002F)提供 - Canva |\n\n### 机器学习资料\n\n\u003Cdetails>\n  \u003Csummary>先决条件\u003C\u002Fsummary>\n\n - \u003Cb>[主要路线图和指南](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002FREADME.md)\u003C\u002Fb>\n - \u003Cb>[Python依赖项](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002Frequirements.txt)\u003C\u002Fb>\n - \u003Cb>[贡献指南](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002Fcontribution.md)\u003C\u002Fb>\n - \u003Cb>[进一步阅读和链接](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002Fresources.md)\u003C\u002Fb>\n \n\u003C\u002Fdetails>\n\n\u003Cdetails>\n \u003Csummary>数学与Python基础\u003C\u002Fsummary>\n\n - \u003Cb>[Python复习](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F00_foundations\u002Fpython_review.ipynb)\u003C\u002Fb>\n - \u003Cb>[线性代数](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F00_foundations\u002Flinear_algebra.ipynb)\u003C\u002Fb>\n - \u003Cb>[统计学](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F00_foundations\u002Fstatistics.ipynb)\u003C\u002Fb>\n\n\u003C\u002Fdetails>\n\n\u003Cdetails>\n \u003Csummary>回归与分类\u003C\u002Fsummary>\n\n - \u003Cb>[回归](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F01_supervised_learning\u002Fregression.ipynb)\u003C\u002Fb>\n - \u003Cb>[分类](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F01_supervised_learning\u002Fclassification.ipynb)\u003C\u002Fb>\n - \u003Cb>[高级模型评估](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F01_supervised_learning\u002Fmodel_evaluation.ipynb)\u003C\u002Fb>\n\n\u003C\u002Fdetails>\n\n\u003Cdetails>\n \u003Csummary>聚类与降维\u003C\u002Fsummary>\n\n - \u003Cb>[聚类](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F02_unsupervised_learning\u002Fclustering.ipynb)\u003C\u002Fb>\n - \u003Cb>[降维](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F02_unsupervised_learning\u002Fdimensionality_reduction.ipynb)\u003C\u002Fb>\n\u003C\u002Fdetails>\n\n\n\u003Cdetails>\n \u003Csummary>使用神经网络的深度学习\u003C\u002Fsummary>\n\n - \u003Cb>[感知机](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F03_neural_networks\u002Fperceptron.ipynb)\u003C\u002Fb>\n - \u003Cb>[深度学习](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F03_neural_networks\u002Fdeep_learning_intro.ipynb)\u003C\u002Fb>\n - \u003Cb>[卷积神经网络](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F03_neural_networks\u002Fcnn.ipynb)\u003C\u002Fb>\n - \u003Cb>[循环神经网络](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F03_neural_networks\u002Frnn.ipynb)\u003C\u002Fb>\n\u003C\u002Fdetails>\n\n\n\u003Cdetails>\n \u003Csummary>部署、跟踪、版本控制\u003C\u002Fsummary>\n  \n - \u003Cb>[数据版本控制](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F04_mlops\u002Fdata_versioning.md)\u003C\u002Fb>\n - \u003Cb>[模型部署](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F04_mlops\u002Fmodel_deployment.md)\u003C\u002Fb>\n - \u003Cb>[生产环境中机器学习模型的监控](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F04_mlops\u002Fmonitoring.md)\u003C\u002Fb>\n\n\u003C\u002Fdetails>\n\n\u003Cdetails>\n \u003Csummary>真实世界的端到端项目\u003C\u002Fsummary>\n  \n - \u003Cb>[房价预测](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FHouse%20Price%20Prediction\u002Fhouse_price_prediction.ipynb)\u003C\u002Fb>\n   - \u003Cb>[README.md](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FHouse%20Price%20Prediction\u002FREADME.md)\u003C\u002Fb>\n\n - \u003Cb>[Twitter情感分析](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FSentiment%20Analysis\u002Fsentiment_analysis.ipynb)\u003C\u002Fb>\n   - \u003Cb>[README.md](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FSentiment%20Analysis\u002FREADME.md)\u003C\u002Fb>\n\n - \u003Cb>[信用卡欺诈检测](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FFraud%20Detection\u002Ffraud_detection.ipynb)\u003C\u002Fb>\n   - \u003Cb>[README.md](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FFraud%20Detection\u002FREADME.md)\u003C\u002Fb>\n\n - \u003Cb>[客户流失预测](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FCustomer%20Churn%20Prediction\u002Fcustomer_churn_prediction.ipynb)\u003C\u002Fb>\n   - \u003Cb>[README.md](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FCustomer%20Churn%20Prediction\u002FREADME.md)\u003C\u002Fb>\n\n - \u003Cb>[贷款审批预测](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FLoan%20Approval%20Prediction\u002Floan_approval_prediction.ipynb)\u003C\u002Fb>\n   - \u003Cb>[README.md](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FLoan%20Approval%20Prediction\u002FREADME.md)\u003C\u002Fb>\n\n  - \u003Cb>[TMDB电影推荐系统](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FMovie%20Recommendation%20System\u002Ftmdb_movie_recommendation.ipynb)\u003C\u002Fb>\n    - \u003Cb>[README.md](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FMovie%20Recommendation%20System\u002FREADME.md)\u003C\u002Fb>\n\n  - \u003Cb>[零售销售预测](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FRetail%20Sales%20Forecasting\u002Fretail_sales_forecasting.ipynb)\u003C\u002Fb>\n    - \u003Cb>[README.md](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FRetail%20Sales%20Forecasting\u002FREADME.md)\u003C\u002Fb>\n\n  - \u003Cb>[假新闻检测](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FFake%20News%20Detection\u002Ffake_news_detection.ipynb)\u003C\u002Fb>\n    - \u003Cb>[README.md](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FFake%20News%20Detection\u002FREADME.md)\u003C\u002Fb>\n\n  - \u003Cb>[基于症状的疾病预测（机器学习）](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FDisease%20Prediction%20from%20Symptoms\u002Fdisease_prediction_from_symptoms.ipynb)\u003C\u002Fb>\n    - \u003Cb>[README.md](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FDisease%20Prediction%20from%20Symptoms\u002FREADME.md)\u003C\u002Fb>\n\n\n  - \u003Cb>[文本情绪识别](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FEmotion%20Detection%20from%20Text\u002Femotion_detection_from_text.ipynb)\u003C\u002Fb>\n    - \u003Cb>[README.md](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FEmotion%20Detection%20from%20Text\u002FREADME.md)\u003C\u002Fb>\n\n - \u003Cb>[手写数字识别（MNIST）](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FHandwritten%20Digit%20Recognition%20(MNIST)\u002Fhandwritten_digit_recognition.ipynb)\u003C\u002Fb>\n    - \u003Cb>[README.md](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning\u002F05_projects\u002FHandwritten%20Digit%20Recognition%20(MNIST)\u002FREADME.md)\u003C\u002Fb>\n    \n\u003C\u002Fdetails>\n\n\u003Cdetails>\n  \u003Csummary>机器学习面试准备\u003C\u002Fsummary>\n\n - \u003Cb>[机器学习基础概念，如学习类型、偏差-方差权衡等](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning%20Interview%20Prep%20Questions\u002Fcore_concepts.md)\u003C\u002Fb>\n\n - \u003Cb>[关于线性回归、支持向量机、决策树等算法的面试题](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning%20Interview%20Prep%20Questions\u002Fsupervised_learning.md)\u003C\u002Fb>\n\n- \u003Cb>[涵盖聚类（K-Means、DBSCAN）和降维（PCA）](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning%20Interview%20Prep%20Questions\u002Funsupervised_learning.md)\u003C\u002Fb>\n \n - \u003Cb>[模型评估指标，如精确率、召回率、F1分数、ROC-AUC等](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning%20Interview%20Prep%20Questions\u002Fevaluation_metrics.md)\u003C\u002Fb>\n \n - \u003Cb>[涵盖数据清洗、编码、缺失值处理、标准化等](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning%20Interview%20Prep%20Questions\u002Ffeature_engineering.md)\u003C\u002Fb>\n \n - \u003Cb>[深度学习、迁移学习、模型部署等](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning%20Interview%20Prep%20Questions\u002Fadvanced_topics.md)\u003C\u002Fb>\n \n - \u003Cb>[项目相关问题及软技能问题](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning%20Interview%20Prep%20Questions\u002Fbehavioral_questions.md)\u003C\u002Fb>\n \n - \u003Cb>[场景式及实战练习提示](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning%20Interview%20Prep%20Questions\u002Fmock_interviews.md)\u003C\u002Fb>\n \n - \u003Cb>[包含动手机器学习实现任务的Google Colab笔记本](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FMachine%20Learning%20Interview%20Prep%20Questions\u002Fcoding_questions.ipynb)\u003C\u002Fb>\n \n\u003C\u002Fdetails>\n\n---\n\n\n\n# 技术很简单\n\n我是Tanu Nanda Prabhu，我的热情在于将复杂概念简化，使他人更容易理解。我非常享受研究工作，在浏览了大量视频、文章和教程后，我决定创建一个专门介绍Google Sheets技巧和窍门的仓库。我每周都会更新这个仓库，分享一些能让你的生活更简单的实用见解。我也欢迎愿意帮助完善这一资源的人贡献内容。你可以随意fork这个仓库并提交更新，我会认真审阅并考虑采纳。\n\n> [技术很简单](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FTechIsEasy)\n\n---\n\n\n# Nbviewer\n\n如果Jupyter Notebook无法加载，请不要担心！只需将链接复制并粘贴到[nbviewer](https:\u002F\u002Fnbviewer.jupyter.org)，因为我的大多数笔记本都可以在那里访问。\n\n---\n\n# 贡献者\n\n### 目前，该仓库大约有20位贡献者。欢迎你也来贡献！\n\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fgraphs\u002Fcontributors\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FTanu-N-Prabhu_Python_readme_01d24b7ca94a.png\" \u002F>\n\u003C\u002Fa>\n\n\n---\n\n# Kaggle 数据集\n\n1) [Kaggle 数据集](https:\u002F\u002Fwww.kaggle.com\u002Ftanuprabhu\u002Fdatasets)\n\n---\n\n# HackerRank 练习题 - 已解答\n\n1) [HackerRank 练习题已解答](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FHacker_Rank_Exercises)\n\n\n---\n\n# Reddit 社区\n\n1) [Python](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FPython\u002F)\n2) [学习Python](https:\u002F\u002Fwww.reddit.com\u002Fr\u002Flearnpython\u002F)\n3) [Python技巧](https:\u002F\u002Fwww.reddit.com\u002Fr\u002Fpythontips\u002F)\n4) [Python编程](https:\u002F\u002Fwww.reddit.com\u002Fr\u002Fpythoncoding?utm_medium=android_app&utm_source=share)\n\n\n\n---\n\n\n# GPT图书管理员 \u003Cimg align=\"right\" width=\"50\" height=\"50\" src=https:\u002F\u002Fgithub.com\u002FDecron\u002FPython\u002Fassets\u002F1786607\u002F5b4e7a09-a76f-40be-b87d-c6e007b7ff35>\n\n如果你有ChatGPT Premium的权限，这里有一个GPT图书管理员，可以访问所有文件，[点击这里](https:\u002F\u002Fchat.openai.com\u002Fg\u002Fg-2HlDYwyrW-python-from-scratch)。\n\n---\n\n# 联系方式\n\n| 平台 | 详情 |\n|---------|----------|\n| ![Gmail](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGmail-D14836?style=for-the-badge&logo=gmail&logoColor=white) | **tanunprabhu95@gmail.com** |\n| ![LinkedIn](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Flinkedin-%230077B5.svg?style=for-the-badge&logo=linkedin&logoColor=white) | [**Tanu Nanda Prabhu**](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Ftanu-nanda-prabhu-a15a091b5\u002F) |\n| ![Medium](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMedium-12100E?style=for-the-badge&logo=medium&logoColor=white) | [**Tanu N Prabhu**](https:\u002F\u002Fmedium.com\u002F@tanunprabhu95) |\n| ![Instagram](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FInstagram-%23E4405F.svg?style=for-the-badge&logo=Instagram&logoColor=white) | [**Python Coder**](https:\u002F\u002Fwww.instagram.com\u002Fpycoderr\u002F) |\n\n---\n\n\n# 评论  \n> **以下是关于这个Python GitHub仓库的一些评价：**\n\n---\n\n### **‪Elin Uppström**  \n`乌普萨拉大学高级讲师，瑞典`\n\n> 在准备本科数据分析课程时，我在你的GitHub上发现了你出色的练习题。我想在我的课程中使用它们。\n\n---\n\n### **‪Cole Striler**  \n`数据科学家 • Datafied创始人`\n\n> 我偶然发现了你的GitHub，非常喜欢你的Jupyter Notebooks，尤其是那篇关于“预测PewDiePie每日订阅者”的笔记。我认为你很好地解释了自己的工作，其他人可以从中学习。\n\n---\n\n### **Laurence Watson**  \n`Treebeard联合创始人兼CEO`\n\n> 你在GitHub上有许多优秀的Jupyter Notebook内容。\n\n---\n\n### **Poonam Gupta**  \n`数学与AP计算机科学教师 • 布朗斯维克学校`\n\n> 非常感谢你在GitHub上发布如此有帮助的文章。对你传播知识所做的一切，表示由衷的感谢。\n\n---\n\n### **David Okenwa**  \n`机械工程师 • 致力于数据分析领域`\n\n> 我最近偶然发现了你的Medium、GitHub和作品集网站，深受启发。太鼓舞人心了！我也想开始写文章，并建立自己的GitHub仓库。\n\n\n**你喜欢这个仓库吗？请通过发送[邮件](tanunprabhu95@gmail.com)分享你的宝贵评价。**\n\n---\n\n## Medium精选文章\n\n精心挑选的教程、见解和指南，涵盖编程、软件开发以及新兴技术趋势。\n\n| 标题                                                                                                                                                                                     | 在Medium上阅读                                                                                                                  |\n| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------- |\n| **成为数据科学家的第一步**| 🔗 [阅读此处](https:\u002F\u002Fmedium.com\u002F@tanunprabhu95\u002Fthe-first-step-to-becoming-a-data-scientist-ce316b6fea5c) |\n| **每个数据科学初学者都应掌握的10个Pandas技巧**| 🔗 [阅读此处](https:\u002F\u002Fmedium.com\u002F@tanunprabhu95\u002F10-must-know-pandas-tricks-every-data-science-beginner-should-learn-6e75ab366042) |\n| **Python机器学习\u002F数据项目中的常见性能陷阱** | 🔗 [阅读此处](https:\u002F\u002Fmedium.com\u002F@tanunprabhu95\u002Fcommon-performance-pitfalls-in-python-ml-data-projects-757dcb51ff47) |\n| **用数据讲故事** | 🔗 [阅读此处](https:\u002F\u002Fmedium.com\u002F@tanunprabhu95\u002Ftelling-stories-with-data-0c983e44ea5c) |\n\n\n\n> 更多故事请访问[Medium](https:\u002F\u002Fmedium.com\u002F@tanunprabhu95)\n\n---\n\n## 🔥 热门技术话题（每日自动更新）\n\u003C!-- START_TRENDING -->\n- [加入 OpenClaw 挑战：1200 美元奖金池！](https:\u002F\u002Fdev.to\u002Fdevteam\u002Fjoin-the-openclaw-challenge-1200-prize-pool-5682)\n- [把树莓派 Zero 变成黑客工具](https:\u002F\u002Fdev.to\u002Fadmantium\u002Fturning-the-raspberry-pi-zero-into-a-hacking-gadget-2ekl)\n- [大多数应用都比必要速度慢——原因在这里（现场演示🛸）](https:\u002F\u002Fdev.to\u002Fsylwia-lask\u002Fmost-apps-are-slower-than-they-need-to-be-heres-why-live-demo-2hh8)\n- [使用 Gemini Interactions API 构建支持语音的 Telegram 机器人](https:\u002F\u002Fdev.to\u002Fgoogleai\u002Fbuild-a-voice-enabled-telegram-bot-with-the-gemini-interactions-api-nm5)\n- [是什么让你走进会议展台？](https:\u002F\u002Fdev.to\u002Fmissamarakay\u002Fwhat-brings-you-by-a-conference-booth-43e3)\n\u003C!-- END_TRENDING -->\n\n---\n\n\n# 反馈\n\n[如有任何反馈或建议，请点击此处](https:\u002F\u002Fform.jotform.com\u002F92847563204259)\n\n---\n\n# 星标历史\n\n\n[![星标历史图表](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FTanu-N-Prabhu_Python_readme_d6d4b32315fe.png)](https:\u002F\u002Fstar-history.com\u002F#Tanu-N-Prabhu\u002FPython&Date)\n\n---\n\n\n[![由 Tanu Nanda Prabhu 维护](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMaintained%20by-Tanu%20Nanda%20Prabhu-red)](https:\u002F\u002Ftanu-n-prabhu.github.io\u002FmyWebsite.io\u002F)\n[![用 Markdown 制作](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMade%20with-Markdown-1f425f.svg)](http:\u002F\u002Fcommonmark.org)","# Python 编程中心快速上手指南\n\n本仓库是一个涵盖 Python 基础、数据科学、机器学习及面试准备的综合学习资源库。以下是快速开始使用的指南。\n\n## 环境准备\n\n在开始之前，请确保您的开发环境满足以下要求：\n\n*   **操作系统**：Windows, macOS, 或 Linux\n*   **Python 版本**：推荐安装 **Python 3.x** (本仓库内容基于 Python 3)\n*   **包管理工具**：`pip` (通常随 Python 安装)\n*   **推荐编辑器**：VS Code, PyCharm 或 Jupyter Notebook (本仓库大量使用 `.ipynb` 文件)\n\n> **国内加速建议**：\n> 在中国大陆地区，建议使用国内镜像源安装依赖，以提升下载速度。\n> *   **清华源**：`https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple`\n> *   **阿里源**：`https:\u002F\u002Fmirrors.aliyun.com\u002Fpypi\u002Fsimple\u002F`\n\n## 安装步骤\n\n### 1. 克隆仓库\n使用 Git 将本学习资源库下载到本地：\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython.git\ncd Python\n```\n\n### 2. 创建虚拟环境（推荐）\n为了避免依赖冲突，建议为项目创建独立的虚拟环境：\n\n```bash\npython -m venv venv\n```\n\n激活虚拟环境：\n*   **Windows**:\n    ```cmd\n    venv\\Scripts\\activate\n    ```\n*   **macOS \u002F Linux**:\n    ```bash\n    source venv\u002Fbin\u002Factivate\n    ```\n\n### 3. 安装依赖库\n本仓库涉及多个第三方库（如 `numpy`, `pandas`, `prophet`, `googletrans` 等）。虽然不同章节依赖不同，您可以先安装核心数据科学库：\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 numpy pandas matplotlib seaborn scikit-learn jupyter notebook\n```\n\n*注：特定章节（如 API 调用、时间序列预测）可能需要额外安装特定库，请参考对应 `.ipynb` 文件顶部的导入语句进行补充安装。例如安装 Facebook Prophet：*\n```bash\npip install -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple prophet\n```\n\n## 基本使用\n\n本仓库的核心内容是 **Jupyter Notebooks (`.ipynb`)**，它们包含了代码示例、理论解释和练习题。\n\n### 方式一：使用 Jupyter Notebook 浏览（推荐）\n这是体验本仓库内容的最佳方式，可以交互式地运行代码和查看结果。\n\n1.  在终端中启动 Jupyter：\n    ```bash\n    jupyter notebook\n    ```\n2.  浏览器会自动打开，导航至仓库目录。\n3.  点击任意章节的 `.ipynb` 文件（例如 `Python_Variables.ipynb` 或 `Numpy` 文件夹内的笔记）即可开始学习。\n\n### 方式二：直接运行 Python 脚本\n如果您想测试简单的代码片段，可以创建一个 `.py` 文件。\n\n**示例：运行一个简单的变量操作（参考 Chapter 1）**\n\n创建文件 `test_basic.py`：\n\n```python\n# Python Variables Example\nname = \"Developer\"\nversion = 3.10\n\nprint(f\"Welcome to {name}!\")\nprint(f\"Current Python version focus: {version}\")\n\n# List comprehension example (Reference: Optimizing Code with List Comprehensions)\nsquares = [x**2 for x in range(5)]\nprint(f\"Squares: {squares}\")\n```\n\n运行代码：\n```bash\npython test_basic.py\n```\n\n### 学习路径建议\n根据仓库目录结构，建议按以下顺序学习：\n1.  **Chapter 1**: 掌握基础概念（变量、字符串、列表、字典）。\n2.  **Chapter 2 & 3**: 熟悉内置函数与核心库（Numpy, Pandas）。\n3.  **Chapter 4**: 尝试调用 API（Google Translate, Wikipedia 等）。\n4.  **Chapter 6 & 7**: 通过练习和测验巩固知识。\n5.  **Chapter 8**: 针对面试进行专项训练。","一名刚转行数据分析的运营专员，急需从杂乱的 Excel 报表中自动提取销售趋势并预测下季度业绩，但缺乏系统的编程基础。\n\n### 没有 Python 时\n- **学习路径混乱**：在网上零散搜索教程，内容碎片化严重，不知道从变量定义还是数据导入开始学起，浪费大量时间试错。\n- **环境配置劝退**：在安装开发环境和依赖库时频频报错，因无法解决兼容性问题而多次放弃动手实践。\n- **实战案例缺失**：只懂理论语法，面对真实的销售数据清洗和机器学习建模需求时，完全不知道如何将代码组合应用。\n- **知识更新滞后**：难以辨别过时的写法与最新的最佳实践，导致编写的脚本效率低下且难以维护。\n\n### 使用 Python 后\n- **体系化入门**：利用该仓库清晰的章节结构（从基础概念到机器学习），按部就班地掌握了输入输出、变量作用域及数据结构等核心技能。\n- **开箱即用体验**：通过集成的 Gitpod 云端开发环境，无需本地配置即可直接运行示例代码，立即进入学习状态。\n- **场景化实战**：参照仓库中关于列表、字典及运算符的具体笔记本案例，快速写出了自动化清洗销售数据和构建预测模型的脚本。\n- **持续迭代成长**：跟随仓库活跃的提交记录和版本发布说明，始终掌握 Python 3.x 的最新特性与数据科学前沿技巧。\n\nPython 将原本漫长且充满挫折的自学过程，转变为一条结构清晰、即开即用的高效进阶之路，让初学者能迅速将代码转化为解决实际业务问题的能力。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FTanu-N-Prabhu_Python_d4f0512a.png","Tanu-N-Prabhu","Tanu Nanda Prabhu","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002FTanu-N-Prabhu_98314f48.png","MSc. in Computer Science | Tech and pet enthusiast | 157th most active GitHub user in Canada \r\n| Writing since June 19, 2019 | ","Medium","Online",null,"https:\u002F\u002Fmedium.com\u002F@tanunprabhu95","https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu",[83,87,90,93],{"name":84,"color":85,"percentage":86},"Jupyter Notebook","#DA5B0B",99.8,{"name":65,"color":88,"percentage":89},"#3572A5",0.1,{"name":91,"color":92,"percentage":89},"HTML","#e34c26",{"name":94,"color":95,"percentage":96},"CSS","#663399",0,2117,910,"2026-04-17T02:07:04","未说明",{"notes":102,"python":103,"dependencies":104},"这是一个 Python 学习资源库，包含大量 Jupyter Notebook 教程。内容涵盖基础语法、数据科学库（Numpy, Pandas）、API 调用及面试准备。部分章节涉及机器学习（如 CNN、隐马尔可夫模型）和时间序列预测，运行特定笔记可能需要安装相应的第三方库。建议使用虚拟环境管理依赖。","3.x",[105,106,107,108,109],"Numpy","Pandas","Facebook Prophet","Google Translate API","Wikipedia API",[45,14,16],[112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128],"python","jupyter-notebook","pandas-dataframe","numpy","python3","python-3","numpy-arrays","data","datascraping","dataanalysis","google-colab","google-colab-notebook","machine-learning","prediction","data-analysis","data-visualization","machine-learning-algorithms","2026-03-27T02:49:30.150509","2026-04-18T00:45:41.013871",[132,137,142,147,152,157],{"id":133,"question_zh":134,"answer_zh":135,"source_url":136},38122,"这个仓库还在维护吗？我想贡献代码该如何参与？","仓库目前仍在维护中，但由于维护者忙于其他事务，更新可能会有延迟。维护者非常欢迎社区贡献者添加内容。如果您想贡献，可以直接提交 Pull Request。此外，仓库已添加了 contribution.md 文件，请在贡献前阅读该文件以了解具体的规则和指南。","https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fissues\u002F22",{"id":138,"question_zh":139,"answer_zh":140,"source_url":141},38123,"部分 Jupyter Notebook 文件无法打开并报错，如何解决？","该问题通常是由于 .ipynb 文件中包含无效的 `_metadata.widgets` 字段导致的。解决方法是手动清理 notebook 文件，删除无效的 `_metadata.widgets` 部分。维护者已对受影响的文件（如 LLM 和扩散模型相关的 demo）进行了修复，如果再次遇到类似问题，可以尝试手动编辑 JSON 文件移除该字段。","https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fissues\u002F58",{"id":143,"question_zh":144,"answer_zh":145,"source_url":146},38124,"如何在 Python 中正确编写嵌套 if 条件判断（例如判断国家名称）？","在 Python 中，不能直接使用 `if (country == \"Canada\" or \"canada\")` 这样的写法，因为字符串 \"canada\" 本身会被视为 True，导致条件永远成立。正确的写法应该是：`if country in [\"Canada\", \"canada\"]` 或者 `if country == \"Canada\" or country == \"canada\"`。请检查您的代码逻辑，确保每个比较操作都完整指定了变量名。","https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fissues\u002F29",{"id":148,"question_zh":149,"answer_zh":150,"source_url":151},38125,"如何向该仓库添加新的代码主题（如量化金融代码）？","如果您想添加特定主题的代码（例如量化金融代码），请先在 Issue 中说明意图并请求分配任务。获得确认后，请将代码整理好并通过 Pull Request (PR) 的方式提交到仓库。维护者会在 PR 中进行审查和合并。","https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fissues\u002F36",{"id":153,"question_zh":154,"answer_zh":155,"source_url":156},38126,"仓库中找不到贡献指南（contribution.md），不知道如何遵守贡献规则怎么办？","针对用户反馈缺少贡献指南的问题，维护者已经添加了 `contribution.md` 文件到仓库根目录。所有贡献者在提交代码前，请务必阅读该文件，其中详细说明了贡献流程、代码规范以及注意事项。","https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fissues\u002F51",{"id":158,"question_zh":159,"answer_zh":160,"source_url":161},38127,"使用 Kivy 和 Firebase 开发应用时，取消文件选择器导致程序报错如何处理？","当使用 `filechooser.open_file` 时，如果用户取消选择，回调函数接收到的 `selection` 列表可能为空。在访问 `selection[0]` 之前，必须先检查列表是否为空。建议在 `selected` 函数中添加判断：`if not selection: return` 或提示用户未选择文件，然后再执行后续的 Firebase 上传逻辑，以避免索引越界错误。","https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fissues\u002F24",[163,168,173,178,183],{"id":164,"version":165,"summary_zh":166,"released_at":167},306265,"v1.4.0","## 版本 1.4.0 - 2026年2月28日\n\n> **摘要：**  \n> 本次发布推出了 Python 备忘录仓库的 **第一阶段：基础**，提供了一个结构化、适合初学者的参考文档，涵盖 Python 核心概念，并配有简洁、可直接复制粘贴的示例。\n\n---\n\n### 升级步骤\n* 无需手动操作。\n\n---\n\n### 破坏性变更\n* 无\n\n---\n\n### 新特性\n* 添加了包含模块化文件结构的 **第一阶段：基础** 目录\n* 引入了按主题划分的 Markdown 文件，用于介绍 Python 核心概念\n* 采用了可扩展的分阶段仓库布局，为未来的扩展奠定基础\n* 增加了简明扼要的说明及实用代码片段\n\n---\n\n### 重构\n\n**重构内容：** 新增了 Python 学习路径专用的 README  \n  - 引入了作用域明确的 **`Python\u002FREADME.md`**，用于记录仅针对 Python 的课程体系  \n  - 清晰定义了 Python 部分的目的与范围  \n  - 文档化了文件夹结构、学习顺序及主题覆盖范围  \n  - 改进了专注于 Python 基础知识的学习者的入门体验与导航便捷性  \n  - 进一步强化了 Python 语言概念与数据科学内容之间的分离  \n\n---\n\n### 错误修复\n* 无\n\n---\n\n### 性能改进\n* 无\n\n---\n\n### 文档与网站\n* 创建并上线了该仓库的专属 **GitHub Pages 网站**\n* 设计了一个简洁、极简且视觉风格统一的界面，与仓库结构保持一致\n* 实现了 **首页**、**发布记录** 和 **内容** 页面之间的直观导航\n* 采用分板块的内容发现方式，通过样式化的行动号召按钮直接链接到对应的 GitHub 文件夹\n* 网站直接由仓库托管和维护，确保更新无缝衔接\n\n---\n\n### 其他变更\n* 为即将推出的阶段做好了准备，首先从 **第二阶段：数据结构** 开始\n* 提升了仓库的可发现性及导航一致性","2026-02-28T17:42:27",{"id":169,"version":170,"summary_zh":171,"released_at":172},306266,"v1.3.0","## 版本 1.3.0 - 2026年1月5日\n\n> **摘要：**  \n> 本次发布通过在 LinkedIn 文件夹中新增一篇反思性的 Python 文章，并在 README 中引入专门的 **“Release Notes” 徽章**，进一步丰富了仓库的文档和行业洞察内容，从而提升仓库更新的导航性和可发现性。此外，还通过统一文件夹命名规范、重新组织核心学习内容，优化了 Python 学习资料的内部结构，使其更加清晰且易于扩展。\n\n---\n\n### 升级步骤\n* 无需手动操作。\n\n---\n\n### 破坏性变更\n* 无\n\n---\n\n### 新特性\n* **新增：** 仓库内新增 LinkedIn 文章  \n  - **“我写了五年 Python，最大的 bug 其实不在代码里。”**  \n    [原文链接](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FLinkedIn\u002Fpost_5_dec_17.md)  \n  - 探讨长期 Python 开发经验、个人成长及非技术性挑战  \n  - 聚焦于思维模式、决策过程和职业发展启示，而非语法或工具使用  \n  - 采用反思式、故事化的写作风格，贴合 LinkedIn 的专业受众  \n  - 放置在 **[LinkedIn](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FLinkedIn)** 文件夹中，实现内容的结构化管理  \n\n* **新增：** README 中的 **“Release Notes” 徽章**  \n  - 引入名为 **“Release Notes”** 的专用徽章  \n  - 直接链接到 GitHub Releases 页面，方便快速查看版本历史  \n  - 风格与现有 README 徽章保持一致，采用 Shields.io 的 `\u003Cimg>` 格式  \n  - 提升文档的可发现性，同时增强仓库的专业形象  \n\n---\n\n### 重构\n\n* **重构：** 统一 Python 学习文件夹结构  \n  - 将 `Src` 重命名为 `01_Python_Basics`，以更好地服务于初学者  \n  - 将 `Exercise` 文件夹更名为 `02_Python_Exercises_And_Practice` 并进行修正  \n  - 将所有练习内容移至主 `Python` 目录下  \n  - 将面试准备文件夹更名为 `Python_Coding_Interview_Prep`  \n  - 在所有 Python 相关文件夹中采用统一的 **Title_Case_With_Underscores** 命名规则  \n  - 添加数字前缀，以体现清晰的学习进阶顺序  \n\n* **重构：** 重新组织 Python 学习结构中的测验内容  \n  - 将 `Quiz` 文件夹更名为 **`03_Quizzes`**  \n  - 将测验内容移至主 **`Python\u002F`** 目录下  \n  - 使测验内容与早期学习流程保持一致  \n  - 进一步完善按编号排列的课程式文件夹组织方式  \n\n* **重构：** 将列表相关内容整合至 Python 基础部分  \n  - 将列表笔记本移至 **`01_Python_Basics`** 文件夹  \n  - 删除独立的 `Lists` 文件夹，避免内容重复  \n  - 加强核心 Python 数据结构的概念性归类  \n  - 简化初学者的导航路径  \n\n* **重构：** 将核心数据结构示例整合至 Python 基础部分  \n  - 将字符串相关笔记本","2026-01-05T15:45:39",{"id":174,"version":175,"summary_zh":176,"released_at":177},306267,"v1.2.0","## 版本 1.2.0 - 2025年12月7日\n\n> **摘要：**  \n> 本次发布显著提升了仓库文档的视觉质量、可读性和整体专业性。主要改进包括：全新设计的徽章区域，对齐更整齐、样式更统一；更新了“推荐工具”和“为何选择本仓库”部分；并在整个 README 中进行了额外的清晰度和格式优化。\n---\n\n### 升级步骤\n* 无需手动操作；此次更新仅为视觉和文档层面的改进。\n\n---\n\n### 破坏性变更\n* 无\n\n---\n\n### 新特性\n* **更新：** README 中的[推荐工具](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FREADME.md#recommended-tools)部分  \n  - 新增了针对 **Python**、**VS Code**、**Jupyter Notebook** 和 **Google Colab** 的专业工具徽章  \n  - 优化了表格的整体设计，提升可读性和视觉结构  \n  - 为每款工具添加了清晰、以行动为导向的说明  \n* **增强：** “[为何选择本仓库](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster?tab=readme-ov-file#why-choose-this-repository)”部分  \n  - 重新编写内容，提升清晰度和结构  \n  - 移除了表情符号和特殊字符，使语气更加专业  \n  - 增加了可选的表格式和极简文本两种呈现方式，以实现更简洁的展示效果  \n  - 进一步与整个 README 的格式和风格保持一致  \n* **改进：** 整个 README 的 Markdown 结构，确保一致性\n\n* **更新：** 重新设计了 README 顶部的徽章区域\n\n  * 优化了徽章布局，使其外观更加均衡、对齐整齐  \n  * 修复了多个未正确加载或显示的徽章  \n  * 将徽章按明确的类别分组（社交指标、仓库健康状况、开发工具、提交活动）  \n  * 使用居中的 `\u003Cp>` 块实现了更专业的间距和对齐  \n  * 确保在 GitHub 的深色和浅色主题下，徽章的尺寸、颜色使用及渲染效果保持一致  \n\n---\n\n### 文档预览\n\n#### 1. README 视觉改进 \n\n| **之前**                                                                                     | **之后**                                                                                    |\n| ---------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------- |\n| *仅包含文字说明的普通 Markdown 表格。*                                       | *采用徽章、图标和简洁说明的视觉增强版布局。*                    |\n| ![之前截图](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FImg\u002Fbeforev1.2.0.PNG) | ![之后截图](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FImg\u002Fafterv1.2.0.PNG) |\n\n#### 2. 徽章区域重新设计\n\n| **之前**                               ","2025-12-07T15:17:59",{"id":179,"version":180,"summary_zh":181,"released_at":182},306268,"v1.1.0","## [版本 1.1.0](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Ftree\u002Fmaster\u002FRelease%20Notes)（2025-11-08）\n\n> 此版本通过引入新的编码资源、交互式数据科学笔记本以及专门用于展示已发表 Medium 文章的板块，进一步拓展了仓库的教育范围。同时，还修复了主 README 文件中的少量错别字。\n\n### 升级步骤\n* 用户无需采取任何操作；所有新增内容均为文档和学习相关的内容。\n\n### 破坏性变更\n* 无\n\n### 新特性\n* 新增[高级 Python 面试问题与解答](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FAdvanced_Python_Interview_Questions.md)  \n* 新增[Python 工程设计原则](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FPython_Engineering_Design_Principles.md)  \n* 新增[探索性数据分析（EDA）](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FExploratory_Data_Analysis_(EDA).ipynb)  \n* 发表新 Medium 文章：[每个数据科学初学者都应掌握的 10 个 Pandas 技巧](https:\u002F\u002Fmedium.com\u002F@tanunprabhu95\u002F10-must-know-pandas-tricks-every-data-science-beginner-should-learn-6e75ab366042)\n* 发表新 Medium 文章：[成为数据科学家的第一步](https:\u002F\u002Fmedium.com\u002F@tanunprabhu95\u002Fthe-first-step-to-becoming-a-data-scientist-ce316b6fea5c) \n* 改进了[安装工具](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FREADME.md#installation-tools)部分，移除了过时内容，并添加了更清晰的说明及指向必备工具的直接链接。\n* 在主 README 中新增了[Medium 精选文章]板块，用于展示已发布的编程和技术文章，以提升参与度和曝光率。\n\n\n### 错误修复\n* 修复了主 README 文件中的多处错别字。\n* 更正了文档中的一些小超链接问题和 Markdown 格式不一致之处。\n\n### 性能改进\n* 优化了 Markdown 和笔记本的可读性，以提升 GitHub 预览效果及 Colab 的执行效率。\n\n### 其他变更\n* 针对初学者的指南包含了在 Google Colab 中的实际操作示例，便于动手学习。\n* 简化了视觉呈现和仓库结构，使 GitHub 页面更加整洁美观。\n\n","2025-11-08T15:21:45",{"id":184,"version":185,"summary_zh":186,"released_at":187},306269,"v1.0.0","## 新增内容\n- 添加了[用数据讲述故事](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FTelling_Stories_With_Data.md)\n- 添加了[高级Python面试题（9月21日）](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002Fpython_advanced_interview_questions_sept21.md)\n- 添加了[Python面试题（9月版）](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002FPython%20Coding%20Interview%20Prep\u002FPython_Interview_Questions_September.md)\n- 添加了[数据科学初学者必知的10个Pandas技巧](https:\u002F\u002Fgithub.com\u002FTanu-N-Prabhu\u002FPython\u002Fblob\u002Fmaster\u002F10_Must_Know_Pandas_Tricks_for_Data_Science_Beginners.ipynb)\n\n## 备注\n- 本次发布扩展了Python面试准备和数据科学板块的内容。\n- 所有新材料均包含实用示例和详细解释，以帮助更好地理解。","2025-10-05T01:38:16"]