http://blog.miguelgrinberg.com/post/the-flask-mega-tutorial-part-i-hello-world

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This is the first article in a series where I will be documenting my experience writing #web_applications in Python using the #Flask microframework.

https://github.com/realpython/discover-flask/blob/master/readme.md

#Flask is a micro web #framework powered by Python. Its #API is fairly small, making it easy to learn and simple to use. But don't let this fool you, as it's powerful enough to support enterprise-level applications handling large amounts of traffic. You can start small with an app contained entirely in one file, then slowly scale up to multiple files and folders in a well-structured manner as your site becomes more and more complex.

https://www.buzzfeed.com/andrewkelleher/deep-exploration-into-python-lets-review-the-dict-module?utm_term=.rhDeZBxA8#.bgB5DM0Z9

In this series, we’ll take a look at various modules and pieces of functionality of the #Python language. We’ll look at design choices, their impact, and their evolution. We’ll also look at the design of the language itself and learn about the operations of the interpreter as it parses the language all the way to the main eval loop. Finally, we’ll attempt to give practical takeaways that fall out of a deeper understanding of the language.
The #cpython implementation of Python (which is the standard on most machines) has been ported over to GitHub from its home in Mercurial. I think it also had a time under #SVN, but the engineers managed to preserve (for the most part) the commit logs.

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#Asyncio - #coroutines

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Thinking In #Coroutines - PyCon 2016 ‏
#Asyncio

https://github.com/aio-libs/aiomysql

#aiomysql is a "driver" for accessing a #MySQL database from the #asyncio (PEP-3156/tulip) framework. It depends on and reuses most parts of #PyMySQL . aiomysql tries to be like awesome #aiopg library and preserve same api, look and feel.

Internally aiomysql is copy of PyMySQL, underlying io calls switched to async, basically yield from and asyncio.coroutine added in proper places)). sqlalchemy support ported from aiopg.

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How Do Python #Coroutines Work

https://github.com/KeepSafe/aiohttp


Supports both #client and #server side of HTTP protocol.
Supports both client and server Web-Sockets out-of-the-box.
Web-server has middlewares and pluggable #routing.

Optionally you may install the #cChardet and #aiodns libraries (highly recommended for sake of speed).

https://pypi.python.org/pypi/uvloop

#uvloop is a fast, drop-in replacement of the built-in #asyncio event loop. uvloop is released under the MIT license.

uvloop and asyncio, combined with the power of async/await in Python 3.5, makes it easier than ever to write high-performance #networking code in Python.

uvloop makes asyncio fast. In fact, it is at least 2x faster than #nodejs, #gevent, as well as any other Python #asynchronous framework. The performance of uvloop-based asyncio is close to that of Go programs.

https://docs.python.org/2/library/ctypes.html

#ctypes is a foreign function library for Python. It provides #C compatible data types, and allows calling functions in #DLLs or shared libraries. It can be used to wrap these libraries in pure Python.

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Implementing #Decorators in Python

#signal — Set handlers for #asynchronous events

This module provides mechanisms to use signal handlers in Python.

The signal.signal() function allows to define custom handlers to be executed when a signal is received. A small number of default handlers are installed: #SIGPIPE is ignored (so write errors on pipes and sockets can be reported as ordinary Python exceptions) and #SIGINT is translated into a KeyboardInterrupt exception.

#Asyncio

https://docs.python.org/3.4/library/signal.html

gc — #Garbage #Collector interface

This module provides an interface to the #optional garbage collector. It provides the ability to disable the collector, tune the collection frequency, and set #debugging options. It also provides access to unreachable #objects that the collector found but cannot free. Since the collector supplements the reference counting already used in Python, you can disable the collector if you are sure your program does not create reference cycles.

https://docs.python.org/3/library/gc.html

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#django_cms 3.0 App Integration

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#django_cms Friends don't let friends use Drupal. ‏

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Getting Started with #django_cms

#Hypothesis is a Python library for creating #unit_tests which are simpler to write and more powerful when run, finding edge cases in your code you wouldn’t have thought to look for. It is stable, powerful and easy to add to any existing #test suite.

#unittest

https://hypothesis.readthedocs.io/en/latest/

https://pypi.python.org/pypi/MutPy/0.4.0

#MutPy is a mutation #testing tool for Python 3.x source code. MutPy supports standard #unittest module, generates YAML reports and has colorful output. It’s apply mutation on AST level. You could boost your mutation testing process with high order mutations (HOM) and code coverage analysis.

An #apphook allows you to attach a #Django application to a page. For example, you might have a news application that you’d like integrated with #django_cms . In this case, you can create a normal django CMS page without any content of its own, and attach the news application to the page; the news application’s #content will be delivered at the page’s URL.

http://docs.django-cms.org/en/release-3.3.x/how_to/apphooks.html

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Getting Started with #django_cms

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#Random Module #Randin