Here is a researched and handpicked list of the top python github repos and libraries containing the essentials of learning python, from zero to hero!
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5. A python cheatsheet of python essentials such as: operators, data types, functions and more!
trekhleb
/
learn-python
📚 Playground and cheatsheet for learning Python. Collection of Python scripts that are split by topics and contain code examples with explanations.
Playground and Cheatsheet for Learning Python
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This is a collection of Python scripts that are split by topics and contain code examples with explanations, different use cases and links to further readings.
Read this in: Português, Español, Traditional Chinese.
It is a playground because you may change or add the code to see how it works and test it out using assertions. It also allows you to lint the code you've wrote and check if it fits to Python code style guide Altogether it might make your learning process to be more interactive and it might help you to keep code quality pretty high from very…
4. A python module perfect for machine learning. If you're going to be looking into machine learning further down the line in your python development journey, keep this library at the back of your head!
scikit-learn
/
scikit-learn
scikit-learn: machine learning in Python
scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license.
The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. See the About us page for a list of core contributors.
It is currently maintained by a team of volunteers.
Website: https://scikit-learn.org
Installation
Dependencies
scikit-learn requires:
- Python (>= 3.9)
- NumPy (>= 1.19.5)
- SciPy (>= 1.6.0)
- joblib (>= 1.2.0)
- threadpoolctl (>= 3.1.0)
Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4. scikit-learn 1.0 and later require Python 3.7 or newer. scikit-learn 1.1 and later require Python 3.8 or newer.
Scikit-learn plotting capabilities (i.e., functions start with plot_ and
classes end with Display) require Matplotlib (>= 3.3.4)
For running the examples Matplotlib >= 3.3.4 is required.
A few examples require scikit-image >= 0.17.2,…
3. A curated list of awesome Python frameworks, libraries, software and resources.
vinta
/
awesome-python
An opinionated list of awesome Python frameworks, libraries, software and resources.
Awesome Python 
An opinionated list of awesome Python frameworks, libraries, software and resources.
Inspired by awesome-php.
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Awesome Python
- Admin Panels
- Algorithms and Design Patterns
- ASGI Servers
- Asynchronous Programming
- Audio
- Authentication
- Build Tools
- Built-in Classes Enhancement
- Caching
- ChatOps Tools
- CMS
- Code Analysis
- Command-line Interface Development
- Command-line Tools
- Computer Vision
- Configuration Files
- Cryptography
- Data Analysis
- Data Validation
- Data Visualization
- Database Drivers
- Database
- Date and Time
- Debugging Tools
- Deep Learning
- DevOps Tools
- Distributed Computing
- Distribution
- Documentation
- Downloader
- Editor Plugins and IDEs
- Environment Management
- File Manipulation
- Functional Programming
- Game Development
- Geolocation
- GUI Development
- Hardware
- HTML Manipulation
- HTTP Clients
- Image Processing
- Implementations
- Interactive Interpreter
- Internationalization
- Job Scheduler
- Logging
- Machine Learning
- Miscellaneous
- Natural Language Processing
- Network Virtualization
- News Feed
- ORM
- Package Management
- Package Repositories
- Penetration testing
- Permissions
- Processes
- Recommender Systems
- Refactoring
- RESTful API
- Robotics
- RPC Servers
- Science
- Search
- Serialization
- Serverless Frameworks
- Shell
- Specific Formats Processing
- Static Site Generator
- Tagging
- Task Queues
- Template Engine
- Testing
- …
2. A 100 day python guide packed with great projects, tutorials, info and more!(will need to be translated to english using google translate since it's written in Chinese)
jackfrued
/
Python-100-Days
Python - 100天从新手到大师
Python - 100天从新手到大师
作者：骆昊
说明：从项目上线到获得8w+星标以来，一直收到反馈说基础部分（前15天的内容）对新手来说是比较困难的，建议有配套视频进行讲解。最近把基础部分的内容重新制作了一个名为“Python-Core-50-Courses”的项目，用更为简单通俗的方式重写了这部分内容并附带了视频讲解，初学者可以看看这个新的仓库。国内用户如果访问GitHub比较慢的话，可以关注我的知乎号Python-Jack，上面的“从零开始学Python”专栏比较适合初学者，其他的专栏如“数据思维和统计思维”、“基于Python的数据分析”等也在持续创作和更新中，欢迎大家关注、点赞和评论。
想获取学习视频的小伙伴，大家可以扫描下面的二维码进入微信小程序，看看有没有适合自己的内容。大家心心念念的机器学习的内容在小程序中都可以找到，由我和我的同事为大家录制的。
大家在学习过程中如果遇到一些棘手的问题或者需要相关的学习资源，可以加入下面的QQ交流群，三个群是一样的加入一个即可，请不要重复加群，也不要在群里发布广告和其他色情、低俗或敏感内容。如果缺乏自律性，有付费学习的需求，可以添加我的微信（jackfrued）私聊，备注好自己的称呼和需求，我会给大家提供一些学习方案和职业规划方面的指导。
配套的视频在抖音和B站持续更新中，有兴趣的小伙伴可以关注我的抖音或B站账号，最近刚刚起号，还希望大家多多支持，非常感谢您！
大家一直催更的《机器学习和深度学习》因个人和公司的原因，一直处于停滞状态，近期会开始更新相关内容，感谢大家一如既往的支持和理解。
Python应用领域和职业发展分析
简单的说，Python是一个“优雅”、“明确”、“简单”的编程语言。
- 学习曲线低，非专业人士也能上手
- 开源系统，拥有强大的生态圈
- 解释型语言，完美的平台可移植性
- 动态类型语言，支持面向对象和函数式编程
- 代码规范程度高，可读性强
Python在以下领域都有用武之地。
- 后端开发 - Python / Java / Go / PHP
- DevOps - Python / Shell / Ruby
- 数据采集 - Python / C++ / Java
- 量化交易 - Python / C++ / R
- 数据科学 - Python / R / Julia / Matlab
- 机器学习 - Python / R / C++ / Julia
- 自动化测试 - Python / Shell
作为一名Python开发者，根据个人的喜好和职业规划，可以选择的就业领域也非常多。
- Python后端开发工程师（服务器、云平台、数据接口）
- Python运维工程师（自动化运维、SRE、DevOps）
- Python数据分析师（数据分析、商业智能、数字化运营）
- Python数据挖掘工程师（机器学习、深度学习、算法专家）
- Python爬虫工程师
- Python测试工程师（自动化测试、测试开发）
说明：目前，数据分析和数据挖掘是非常热门的方向，因为不管是互联网行业还是传统行业都已经积累了大量的数据，各行各业都需要数据分析师从已有的数据中发现更多的商业价值，从而为企业的决策提供数据的支撑，这就是所谓的数据驱动决策。
给初学者的几个建议：
- Make English as your working language. （让英语成为你的工作语言）
- Practice makes perfect. （熟能生巧）
- All experience comes from mistakes. （所有的经验都源于你犯过的错误）
- Don't be one of the leeches. （不要当伸手党）
- Either outstanding or out. （要么出众，要么出局）
Day01~15 - Python语言基础
Day01 - 初识Python
- Python简介 - Python的历史 / Python的优缺点 / Python的应用领域
- 搭建编程环境 - Windows环境 / Linux环境 / MacOS环境
- 从终端运行Python程序 - Hello, world /
print函数 /…
1. A python repo that implements, explains and demonstrates almost every python algorithm that you would ever need to learn!
TheAlgorithms
/
Python
All Algorithms implemented in Python
Implementations are for learning purposes only. They may be less efficient than the implementations in the Python standard library. Use them at your discretion.
Getting Started
Read through our Contribution Guidelines before you contribute.
Community Channels
We are on Discord and Gitter! Community channels are a great way for you to ask questions and get help. Please join us!
List of Algorithms
See our directory for easier navigation and a better overview of the project.
That's it for this compilation!
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Top comments (2)
This is quite interesting for people who got entry-level jobs in I.T. industries. Great article!
Thank you for sharing.