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Welcome to issue 220 of Python Weekly. Let's get straight to the links this week.

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News

DjangoCon Europe 2016 Call for Speakers
 

Articles, Tutorials and Talks

Episode #36: Python IDEs with the PyCharm team
As a software developer, what's the most important application on your computer? If your answer is Microsoft Outlook, my heart goes out to you - stay strong! But for most of us, it's probably a toss up between your web browser and code editor. For editors, there are basically two camps: lightweight smart, text editors such as vim, emacs, and sublime text and heavy weight but highly functional IDEs such as PyDev and PyCharm. This week you'll meet Dmitry Trofimov who is one of the main developers behind one of my favorite editors: PyCharm. 

Emacs - the Best Python Editor?
Thus far, in our series of posts on Python development environments we've looked at Sublime Text and VIM. In this post we'll present another powerful editor for Python development - Emacs. While it's an indisputable fact that Emacs is the best editor, we'll (try to) keep an open mind and present Emacs objectively, from a fresh installation to a complete Python IDE so that you can make an informed decision when choosing your go-to Python IDE.

Web Summit 2015 meets Natural Language Processing - Part 1: A Map of the Social Media Lands
Imagine you're organizing a big tech conference, and you want to understand what people thought of your conference, so you can run it even better next year. Over a 5 day period, we collected about 77,000 tweets about the Web Summit 2015 using the Twitter Streaming API and in this blog series, we're going to explore them and see what we can extract.

Data analysis of Surfing Conditions on Irish East Coast
Explore the less well known side of Irish Surfing, using data analysis techiniques with Python and Pandas. Through the use of nearby weather buoy data, we investigate: factors influencing conditions, wave and wind trends, and look to develop a simple criterion of surfable waves.

Python List Comprehensions: Explained Visually
List comprehensions in Python are great, but mastering them can be tricky because they don't solve a new problem: they just provide a new syntax to solve an existing problem. Let's learn what list comprehensions are and how to identify when to use them.

The Development of Python HDBSCAN Compared to the Reference Implementation in Java
Python is a great high level language for easily expressing ideas, but people don't tend to think of it as a high performance language; for that you would want a compiled language -- ideally C or C++ but Java would do. This notebook started out as a simple benchmarking of the hdbscan clustering library written in Python against the reference implementation written in Java. It still does that, but it has expanded into an explanation of why I choose to use Python for performance critical scientific computing code.

Persona Research in Under 5 Minutes
Well-researched personas can be a useful tool for marketers, but to do it correctly takes time. But what if you don't have extra time? Using a mix of Followerwonk, Twitter, and the AIchemy language API, it's possible to do top-level persona research very quickly.

Survival of the fittest: Experimenting with a high performing strategy in other environments
A common misconception about evolution is that "The fittest organisms in a population are those that are strongest, healthiest, fastest, and/or largest." However, as that link indicates, survival of the fittest is implied at the genetic level: and implies that evolution favours genes that are most able to continue in the next generation for a given environment. In this post, I'm going to take a look at a high performing strategy from the Iterated Prisoner's dilemma that was obtained through an evolutionary algorithm.

Create Doodle Jump in Python [Time Lapse]
 
Plotly plot of chord diagrams


Books

Python Data Science Cookbook
This book will walk you through the various steps, starting from simple to the most complex algorithms available in the Data Science arsenal, to effectively mine data and derive intelligence from it. At every step, we provide simple and efficient Python recipes that will not only show you how to implement these algorithms, but also clarify the underlying concept thoroughly.


Interesting Projects, Tools and Libraries

Vocabulary
Python Module to get Meanings, Synonyms and what not for a given word.

Cinema 3
Cinema 3 is an example project which demonstrates the use of microservices for a fictional movie theater. The Cinema 3 backend is powered by 4 microservices, all of which happen to be written in Python using Flask.

django-package-monitor
A Django app for keeping track of dependency updates.

musicbot
This Telegram bot maintains a user generated catalog of music. How does it work? You simply send an audio file (from Telegram Desktop, Web or OSX) to the bot and it's added to the public catalog. All tracks are indexed and available for everyone from any Telegram client.

aiotg
Asynchronous Python framework for building Telegram bots.

PokeAPI
A RESTful API for Pokemon.

ripozo-oasis
A package and tutorial for turning your database into a HATEOAS ReSTful application.

Shablona
Shablona is a template project for small scientific python projects. The recommendations we make here follow the standards and conventions of much of the scientific Python eco-system. Following these standards and recommendations will make it easier for others to use your code, and can make it easier for you to port your code into other projects and collaborate with other users of this eco-system.


New Releases

Django 1.9 released
Some of the major highlights are:

  • Support for performing actions after a transaction commit.
  • Support for password validation.
  • Permission mixins for class-based views.
  • New styling for contrib.admin.
  • Support for running tests in parallel.

 

Upcoming Events and Webinars    

Webinar: Data analysis with Python
In this free, hour-long Webinar, I'll introduce some of the basic tools in Python's data-science toolkit. What do they provide, and what can we do with them?  What questions can we ask, and what answers can we expect to get?  This is likely the first in a series of Webinars I'll be doing on the subject of Python and data science. 

Python Presentation Night #35 - Minneapolis, MN
There will be following talks

  • Using LSXML to extract data from MS Word documents 
  • Automating File Management 


Austin Python Meetup December 2015 - Austin, TX

PyAtl Meetup December 2015 - Atlanta, GA

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