Python Weekly 239

News

PyCon Australia 2016 Call for Proposals
    
PyGotham 2016  Call for Proposals


Articles, Tutorials and Talks

What's New In Python 3.6
This article explains the new features in Python 3.6, compared to 3.5.

Episode #53: Python in Visual Studio
What's your favorite Python editor? That is one of the questions I always ask at the end of the episode. This week I want to shine a light on a fantastic answer to that question for Windows developers: Visual Studio. On this episode, you'll meet Steve Dower, from Microsoft's Python Tools for Visual Studio team. He's here to tell us all about it. We also cover Python on Windows, CPython, the 2016 BUILD conference and more. 

Podcast.__init__ Episode 52 - Hypothesis with David MacIver
Writing tests is important for the stability of our projects and our confidence when making changes. One issue that we must all contend with when crafting these tests is whether or not we are properly exercising all of the edge cases. Property based testing is a method that attempts to find all of those edge cases by generating randomized inputs to your functions until a failing combination is found. This approach has been popularized by libraries such as Quickcheck in Haskell, but now Python has an offering in this space in the form of Hypothesis. This week, the creator and maintainer of Hypothesis, David MacIver, joins us to tell us about his work on it and how it works to improve our confidence in the stability of our code.

Intro to Machine Learning & NLP with Python and Weka
In this tutorial, you'll be briefly introduced to machine learning with Python (2.x) and Weka, a data processing and machine learning tool. The activity is to build a simple spam filter for emails and learn machine learning concepts.

Raspberry Pi Internet Radio with Flask

The purpose of this tutorial is to show you how you can build a radio that can stream audio from the internet using a simple web interface to operate it.

Using Anaconda with PySpark for Distributed Language Processing on a Hadoop Cluster
In this post, we'll use Anaconda to perform distributed natural language processing with PySpark using a subset of the same data set. We'll configure different enterprise Hadoop distributions, including Cloudera CDH and Hortonworks HDP,  to work interactively on your Hadoop cluster with PySpark, Anaconda and a Jupyter Notebook.

Raspberry Pi & Python Internet 'Thing' pt. 4 with Tony D!
In this video we use SocketIO to do real time updates from the server to the web page and vice-versa.

Making Ember and Django play nicely together: a to-do app walkthrough
    
Debugging of CPython processes with gdb

Visualize Foursquare Location History

Controlling USB devices with Python and libusb 

NYC subway math

Revisiting NBA Career Predictions From Rookie Performance


Books

Designing Machine Learning Systems with Python
This book will give you a solid foundation in the machine learning design process, and enable you to build customised machine learning models to solve unique problems. You may already know about, or have worked with, some of the off-the-shelf machine learning models for solving common problems such as spam detection or movie classification, but to begin solving more complex problems, it is important to adapt these models to your own specific needs. This book will give you this understanding and more.

Composing Programs
Composing Programs is a free online introduction to programming and computer science. In the tradition of SICP, this text focuses on methods for abstraction, programming paradigms, and techniques for managing the complexity of large programs. These concepts are illustrated primarily using the Python 3.


Python Jobs of the Week

Python/Flask fullstack / backend developer at Freelancer.com
We have an application that was built using flask. It was written a couple of years ago and we need to update the code so that it integrates into some of our current infrastructure. Once this is done, we will be looking to build out and add more features.

Lead Developer at Live Better With
The Lead Developer will work closely with the CEO to improve the current product whilst also building new, innovative digital products and features to help the company continue to grow globally at a rapid rate. The role of Lead Developer is a broad, high visibility, high impact position and one suited to a truly innovative, ambitious technology executive. The opportunity ahead is significant, and the incoming candidate will have the chance to make the key decisions required to position the business for aggressive growth over the next 12 - 18 months.


Interesting Projects, Tools and Libraries

Voltron
Voltron is an extensible debugger UI toolkit written in Python. It aims to improve the user experience of various debuggers (LLDB, GDB, VDB and WinDbg) by enabling the attachment of utility views that can retrieve and display data from the debugger host. By running these views in other TTYs, you can build a customised debugger user interface to suit your needs.

deepjazz

Deep learning driven jazz generation using Keras & Theano!

PyGame Learning Environment
PyGame Learning Environment (PLE) is a learning environment, mimicking the Arcade Learning Environment interface, allowing a quick start to Reinforcement Learning in Python. The goal of PLE is allow practitioners to focus design of models and experiments instead of environment design.

Awesome Python
Awesome Python is a community effort. The primary listings are based on the official Awesome Python list at GitHub. There are additional features such as search autocomplete, a better UI, tracking projects' popularity & activity, and comparing by those etc. Also other features are in pipeline.

haxor-news
A Hacker News CLI with auto-completion, output highlighting, and comment filtering.

youtube-dl
Small command-line program to download videos from YouTube.com and other video sites.

telepot
Python framework for Telegram Bot API.

drf-url-filters
A django app to apply filters on drf querysets using query params with validations using voluptuous.

Qlearning4k
Qlearning4k is a reinforcement learning add-on for the python deep learning library Keras. Its simple, and is ideal for rapid prototyping.


New Releases

MicroPython 1.7
This release adds the MicroPython cross-compiler that can generate .mpy files (pre-compiled bytecode) which can be executed within any MicroPython runtime/VM.  The ESP8266 port is also vastly improved thanks to a hugely successful Kickstarter campaign.  This port now has an alternative event loop implementation to allow proper Berkeley sockets using lwIP built from source, and a greatly improved network module. Many extension modules are now enabled (ujson, ubinascii, uctypes, uhashlib, urandom, uheapq, ure, uzlib) and the machine module includes Timer, Pin, PWM, ADC, I2C, SPI, and basic UART.  Bignums are supported, along with 30-bit precision floating point, and normal error messages. There is FatFS support and uos.dupterm. Many other improvements and bug fixes have been made to the core and other ports.