Learn More Python

Hi! Your skill level is beginner or intermediate, or even advanced. You want to learn more Python. There are plenty of resources out there, and this is just a curated list of links. Some for each skill level. It's not everything and the kitchen sink, but it's not all "must-read" either. You should feel free to skip around.

A note on approach: I agree with Learn X the Hard Way that the "hard way" — learning by doing — diving right in — is actually the easy way. So new or not, let's jump in.

Starters

The first key is Don't Panic. You can learn most by hands-on experience. A significant benefit of a language like Python, is how learnable it is by jumping into an interactive session. Rapid feedback.

If you have not installed Python yet, you need to pick a version of Python and then install. For both of these subjects, you should read Hitchhiker's Guide to Python. UPDATE in 2019: start with Python 3!

It's also a good idea to debunk the myths you may have heard about Python:

PayPal Engineering Blog: 10 Myths of Enterprise Python -- read this post whether are new to Python or not. It's great, and useful.

As for why to learn it (and ignore the bellyachers who are wasting their time telling you what to learn):

python.org → "Success Stories"
this Quora thread on some other famous cases
this more general Quora thread about why dynamic languages are found in startups and innovative environments. Remember, you are not saying no to other languages. You are just equipping yourself with more. And it's fun god-dammit.)

Python community

If nobody’s told you yet, Python’s community is one of the most important things about it :) Join forums, subreddits (/r/Python, /r/learnpython), chats (Slacks or IRCs) local meetups, etc., and get support. You’ll learn much better with a community.

Cheat sheets/quickstarts

Keep these handy.

The official docs! https://docs.python.org/2/
Learn X in Y Minutes: Python is a good cheat sheet.
Flow-chart to help you get past common Python error messages

Courses and books

If you like self-guided courses, try one of these:

Learn Python the Hard Way
RealPython: "Real Python teaches programming and web development through hands-on, interesting examples that are useful and fun!" (paid)
or Dive into Python (the classic). (Note that Dive into Python 3 only covers new & advanced features in Python 3 - not the language as a whole. Don't start there if you're new to Python) (free)
or Google's Python Introduction course (free)
or How to Think Like a Computer Scientist: Learning with Python, Interactive Edition (free) — if you're relatively new to programing, this is an especially good fit for you

If you like taking online courses with a group, I’ve heard this one is good:

Coursera: An Introduction to Interactive Programming in Python (free)

A good IDE will help you be more productive!

A lot of people think when you're first learning a language like Python, you'll grok it best if you use a basic text editor instead of an IDE. Still, maybe you are just here to learn a little Python for some projects at work, and you just need to get some things done. An IDE might help you.

I strongly prefer JetBrains PyCharm. So do most people at my company now. And many many people the world over … PyCharm does incredible things with your Python code.
There's a free Community Edition you can use immediately.

(Another side note on text editors vs. IDEs. Even if you like the lean text editor side of things - vim or Emacs, Sublime Text... Well, I still think you should check out PyCharm. I hated disliked IDEs for a long time until I tried PyCharm. Vim users, take a look at "Epic Review of PyCharm [...] From a Vim User's Perspective" (and "Part 2". The vim emulation is solid, I use it every day.)

Interactive tools for understanding Python better

ipython and ipython notebook - you should be using these!!!
There's also Python's built-in debugger, pdb, and the ipython variant (autocomplete etc) ipdb
Use PyCharm's debugger, it is excellent. JetBrains PyCharm docs: Debugging

Avoiding common mistakes and pitfalls

Free book (PDF): How to make mistakes in Python

Testing

One of Python's biggest strengths is its testability. Test-Driven Development and Python go together happily.

start with this concise post, http://docs.python-guide.org/en/latest/writing/tests/#the-basics
then try this comprehensive post. It's about unit testing with Python's Standard Library unit test package, but leads you up to introducing pytest — the best runner for any kind of tests (you can get started on that right away). It’s also the best framework for writing data-driven tests, when you’re ready to get gung-ho about testing your code :D
Jeff Knupp: Improve your Python Understanding - Unit Testing
When you’re ready, move onto pytest, definitely :) pytest.md

For intermediate and advanced Pythonistas

Are you intermediate or advanced? If you're not sure, one way to check:

Ask yourself Python Interview Questions and see if you know them. If you don't, try and learn more
Read some of "Good to Great Python Reads", a nice index of great Python reading (good to Ctrl+F for topics)

Style guides

You don’t need to read style guides until you’re beyond beginner status. At some point that will become very valuable and helpful to you :)

There are two main style guides:

The Hitchhiker's Guide to Python – opinionated guide by the legendary Kenneth Reitz of requests fame
Google Python Style Guide – perhaps the most widely-used Python style guide

Special topics

Not comprehensive, but when I brain-dumped some really good things to know about, I wrote down these subjects.

Unicode is pretty sad in Python 2, so if you're using Python 2 you should learn the gotchas and fixes ASAP...
- Unicode in Python, completely demystified (weird presentation format, but, you just press spacebar to proceed through slides)
- unicode frustrations
Functional programming, list comprehensions, etc.
- Python List Comprehensions: Explained Visually
- Decorators & functional Python
Security, with this bent: learning general skills to get into InfoSec or AppSec
- Step 1, listen to this: Talk Python to Me #37: Python, Cybersecurity, and Penetration Testing
- Step 2, read Gray Hat Python (book 1/2) and/or Black Hat Python (book 2/2)
- MartinFowler.com: "Web Security Basics" (regardless of programming language)
Machine Learning! Here’s my guide: Dive into Machine Learning

Scenario guide

You're probably going to use libraries to do a lot of things. When you have a new scenario and you are thinking what should I use... You should:

Ask your community :D
check Hitchhiker's Guide to Python: Scenario Guide for Python Applications
check Full Stack Python, another comprehensive but carefully curated guide, full of useful context, advice, etc.
check Awesome Python, "A curated list of awesome Python frameworks, libraries, software and resources" — or the Pythonidae list

Fast and scalable Python - Concurrency, Parallelism

Nice cheat sheet on various non-beginner topics in Python 3

https://www.pythonsheets.com/

Concurrency?

For completely out-of-the-box Python, concurrency is slightly complicated (because of the GIL). However, it's not some no-man's-land or something. You don't need to rediscover the solution on your own. The best single summary I've seen of (preferred) concurrency options –

Myth: Python Lacks Concurrency

There are loads of options... Greenlet, Twisted. I must say, while I've used those, I tend to go for parallelism with Celery if it makes sense. While it's a different solution, it can be relevant to some of the same problems you might be trying to solve.

Parallelism/ distributed task queue

For Python projects that need some horizontal scale-out and fast performance, there's a good chance a worker-and-queue setup might work better for you, compared with single-computer concurrency. There's no question: Celery is the way to go for worker-and-queue systems in Python --

Celery

Map/Reduce and beyond (Functional Reactive Programming)

Or maybe not worker-and-queue. I've maintained some pretty iffy worker-and-queue systems. These days I reach for Apache Spark instead. Batch and stream processing, in a portable fashion, with a very declarative and maintainable API (thanks to Functional Reactive Programming).

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