Issue #121


June 27, 2014

PyPy3 2.3.1, Django 1.7 RC1, battleschool and much more.

Issue #121: Wiggins

Weekly dose of all things Python.
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PyPy3 2.3.1 Fulcrum released
This is the first stable release of PyPy3, targeting support for Python 3.2.5 compatibility.
Shared by @mgrouchy

PyCon PL 2014 Call for Proposals is Open
First time speaker? Here is your chance, propose away!
Shared by @myusuf3

Jannis Leidel joins the DSF Board
The Django Software Foundation has elected long-standing Django community member Jannis Leidel to the DSF Board. Congrats Jannis!
Shared by @mgrouchy

Django 1.7 RC1 has been Released
Django 1.7 is coming soon, the first release candidate came out last night. Check it out and report any bugs!
Shared by @mgrouchy


Is Python an awesome language to move to?
Shared by @mgrouchy

What Are The Top 10 built-in Python Modules That You Must Know?
Shared by @myusuf3


Senior Server Engineer (New York, NY) 
Posted By Oyster. Promoted

Python Web Developer (New York, NY) 
Posted By SFX Entertainment Promoted


Python library for UnQLite, a lightweight, embedded NoSQL database and JSON document store.
Shared by @myusuf3

Data Science 45 minute intros.
45 minute Ipython notebook presentations for getting starting with basic programming, statistics and machine learning techniques.
Shared by @mgrouchy

This is neat. Set up your development environment using ansible in a similar way you would set up your machine with something like Boxen.
Shared by @mgrouchy

A library that provides the infrastructure for writing single-threaded concurrent code using coroutines, multiplexing I/O access over sockets and other resources. It is a portage of the Tulip project (asyncio module) on Python 2.
Shared by @mgrouchy

Awesome project, I have been huge fan of writing down daily happens, so you never forget. jrnl is a simple journal application for your command line, which works well with DayOne too.
Shared by @myusuf3

Really nice collection of Sys Admin tools that plugin to the Panic Statusboard iPad application.
Shared by @mgrouchy

Modoboa is a mail hosting and management platform including a modern and simplified web user interface. Worth a look if you want to host your own mail.
Shared by @myusuf3

Green is a Python test runner that strives to give a better user experience than its competitors like nose, pytest, etc. Version 1.0 of green was recently released.
Shared by @nathanstocks

DynamoDB integration for Flask.
Shared by @rdegges


Why Every Language Needs Its Underscore
Interesting article about the funcy library and how it is analogous to the Underscore.js library. Its purpose is to make your life easier when writing code and give you a series of functional tools to write cleaner, more understandable code.
Shared by @mgrouchy

Python Classes and Object Oriented Programming
Everything you wanted to know about Object Oriented Programming in Python. Great introduction.
Shared by @mgrouchy

Applying deep learning and a RBM to MNIST using Python
If you are interested in Machine Learning this is for you. Author gives a nice breakdown on how to use Scikit-learn to apply a Restricted Boltzmann machine to the MNIST dataset. The author shows how small changes to this dataset, when even indistinguishable to the human eye can hurt performance of a classification pipeline.
Shared by @mgrouchy

Python packaging pitfalls
There are lots of common mistakes to be made when packaging your Python project or library. Check out this list of common pitfalls and how to avoid or fix them.
Shared by @mgrouchy

Optimizing your tests in Django
Some tips for Optimizing your Django tests so they run quickly.
Shared by @mgrouchy

SimPy: Realtime Simulations
SimPy is one of those tool I have been wanting to create an excuse to use for something real, great package. This article covers how to create realtime simulations.
Shared by @myusuf3

Uploading to PyPI
Excellent and extensive survival guide to uploading to PyPI, covering some topics that are often left to people to Google.
Shared by @myusuf3

Using Python's sci-packages To Prepare Data
Nice article on how to prepare and clean up data for analysis and visualization.
Shared by @myusuf3

Parzen–Rosenblatt Kernel density estimation
Very thorough explanation of the Parzen-window method.The author gives a nice breakdown of the math involved as well as the Python code to implement.
Shared by @mgrouchy
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