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!
5.
A python cheatsheet of python essentials such as: operators, data types, functions and more!
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 beginning.
It is a
cheatsheet
because you may get back to these code examples once you want to recap the
syntax of
standard Python statements and constructions
. Also because the
code is full of assertions you'll be able to see…
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 (>= 3.7)
NumPy (>= 1.14.6)
SciPy (>= 1.1.0)
joblib (>= 1.0.0)
threadpoolctl (>= 2.0.0)
Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4.
scikit-learn 0.23 and later require Python 3.6 or newer
scikit-learn 1.0 and later require Python 3.7 or newer.
Scikit-learn plotting capabilities (i.e., functions start with
plot_
and
classes end with "Display") require Matplotlib (>= 2.2.3)
For running the examples Matplotlib >= 2.2.3 is required.
A few examples require scikit-image >= 0.14.5, a…
Awesome Python
A curated list of awesome Python frameworks, libraries, software and resources.
Inspired by
awesome-php
.
Awesome Python
Admin Panels
Algorithms and Design Patterns
ASGI Servers
Asynchronous Programming
Audio
Authentication
Build Tools
Built-in Classes Enhancement
Caching
ChatOps Tools
Code Analysis
Command-line Interface Development
Command-line Tools
Compatibility
Computer Vision
Concurrency and Parallelism
Configuration
Cryptography
Data Analysis
Data Validation
Data Visualization
Database Drivers
Database
Date and Time
Debugging Tools
Deep Learning
DevOps Tools
Distributed Computing
Distribution
Documentation
Downloader
E-commerce
Editor Plugins and IDEs
Email
Enterprise Application Integrations
Environment Management
Files
Foreign Function Interface
Forms
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
Package Management
Package Repositories
Penetration testing
Permissions
Processes
Recommender Systems
Refactoring
RESTful API
Robotics
RPC Servers
Science
Search
Serialization
Serverless Frameworks
Shell
Specific Formats
…
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
，初学者可以关注下这个新项目。如果需要
Python基础视频
，可以在“B站”搜索
《Python零基础快速上手》
，这套视频是我讲课的时候录制的随堂视频，画质尚可、音质一般，但是对初学者应该会有些帮助，欢迎大家留言、评论、发弹幕。学习之后觉得有收获的小伙伴可以“一键三连”来支持UP主（千锋Python）。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，其他的专栏也在持续创作和更新中，欢迎大家关注并点赞评论。
创作不易，感谢大家的打赏支持，这些钱不会用于个人消费（例如：购买咖啡），而是通过腾讯公益、美团公益、水滴筹等平台捐赠给需要帮助的人（
点击
了解捐赠情况）。需要加入QQ学习群的可以扫描下面的二维码，三个群加一个即可，不要重复进群。学习群会为大家提供
学习资源
和
问题解答
，如果有
Python体验课
和
行业公开课
会提前在群里通知大家，欢迎大家加入。
项目“Day80~90”部分目前仍在创作中，因为作者平时也挤不出太多时间来写文档，因此更新的速度比较缓慢，感谢大家的理解。
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…
All algorithms implemented in Python - for education
Implementations are for learning purposes only. As 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're on
Discord
and
Gitter
! Community channels are great for you to ask questions and get help. Please join us!
List of Algorithms
See our
directory
for easier navigation and better overview of the project.
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