-[2017-Yes, Python is Slow, and I Don’t Care](https://hackernoon.com/yes-python-is-slow-and-i-dont-care-13763980b5a1): A rant on sacrificing performance for productivity.
*[2017-Yes, Python is Slow, and I Don’t Care](https://hackernoon.com/yes-python-is-slow-and-i-dont-care-13763980b5a1): A rant on sacrificing performance for productivity.
-[2017-Writing a basic x86-64 JIT compiler from scratch in stock Python](https://csl.name/post/python-jit/):
In this post I'll show how to write a rudimentary, native x86-64 just-in-time compiler (JIT) in CPython, using only the built-in modules.
*[2017-Writing a basic x86-64 JIT compiler from scratch in stock Python](https://csl.name/post/python-jit/): In this post I'll show how to write a rudimentary, native x86-64 just-in-time compiler (JIT) in CPython, using only the built-in modules.
## CheatSheet: 速览清单
## Tutorial: 教程
*[Python Crash Course - Cheat Sheets](https://ehmatthes.github.io/pcc/cheatsheets/README.html): A cheat sheet can be really helpful when you’re trying a set of exercises related to a specific topic, or working on a project.
-[2017-Python for JavaScript Developers](https://dev.to/underdogio/python-for-javascript-developers): To serve as a quick guide for JavaScript developers who want to get up to speed quickly with Python, but without having to learn what declaring a variable means or what a function is.
*[Python Cheat Sheet](https://github.com/juliangaal/python-cheat-sheet): This rep is a growing list of Python cheat sheets, tailored for Data Science.
*[Python cheatsheet #Project#](https://www.pythonsheets.com/): This project tries to provide a lot of piece of Python code that makes life easier.
-[Interactive Coding Challenges](https://parg.co/bhs): Huge update! Interactive Python coding interview challenges (algorithms and data structures). Includes Anki Flashcards.
# Resource: 资源
-[pytudes #Project#](https://github.com/norvig/pytudes): This project contains pytudes—Python programs for perfecting programming skills. Some are in Jupyter (IPython) notebooks, some in *.py files.
## Book
## CheatSheet: 速览清单
*[简明 Python 教程 #Book#](https://www.gitbook.com/book/lenkimo/byte-of-python-chinese-edition/details):《A Byte of Python》是一本由 Swaroop C H 编写，旨在于介绍如何使用 Python 语言进行编程的自由图书。它以教材与指南的形式为入门者介绍 Python 语言。如果你对电脑知识的了解仅限于如何保存文本文件的话，那这本书就是为你准备的。
*[2017-Problem Solving with Algorithms and Data Structures using Python #Book#](http://6me.us/jgWZ)
-[Python Crash Course - Cheat Sheets](https://ehmatthes.github.io/pcc/cheatsheets/README.html): A cheat sheet can be really helpful when you’re trying a set of exercises related to a specific topic, or working on a project.
# Tutorial: 教程
-[Python Cheat Sheet](https://github.com/juliangaal/python-cheat-sheet): This rep is a growing list of Python cheat sheets, tailored for Data Science.
*[2017-Python for JavaScript Developers](https://dev.to/underdogio/python-for-javascript-developers): To serve as a quick guide for JavaScript developers who want to get up to speed quickly with Python, but without having to learn what declaring a variable means or what a function is.
-[Python cheatsheet #Project#](https://www.pythonsheets.com/): This project tries to provide a lot of piece of Python code that makes life easier.
*[Interactive Coding Challenges](https://parg.co/bhs): Huge update! Interactive Python coding interview challenges (algorithms and data structures). Includes Anki Flashcards.
-[简明 Python 教程 #Book#](https://www.gitbook.com/book/lenkimo/byte-of-python-chinese-edition/details):《A Byte of Python》是一本由 Swaroop C H 编写，旨在于介绍如何使用 Python 语言进行编程的自由图书。它以教材与指南的形式为入门者介绍 Python 语言。如果你对电脑知识的了解仅限于如何保存文本文件的话，那这本书就是为你准备的。
*[pytudes #Project#](https://github.com/norvig/pytudes): This project contains pytudes—Python programs for perfecting programming skills. Some are in Jupyter (IPython) notebooks, some in \*.py files.
-[2017-Problem Solving with Algorithms and Data Structures using Python #Book#](http://6me.us/jgWZ)
*[2018-Migrating to Python 3 with pleasure](https://github.com/arogozhnikov/python3_with_pleasure): A short guide on features of Python 3 for data scientists.
