Issue #208


March 18, 2016

 Django Channels, "for humans" controversy and much more.

Issue #208: Tesla

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Scalable reverse image search built on Kubernetes and Elasticsearch
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Developer-friendly asynchrony for Django.
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A framework designed for building highly available microservices. Alchemy includes service discovery, easy deployment, smart load balancing, and is also designed to be polyglot.
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reddit as file storage, a command-line script for storing files on private subreddits.
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Tidy Data In Python
Using Python to tidy your messy datasets.
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An automated algorithm/library translation for Python, JS, Go, C#, Ruby. Given an algorithm, it generate the idiomatic code for it in the language of your choice.
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Best Python Libraries
A collection of "great" Python libraries from the crew over at Cactus consulting.
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Buildout/setuptools speed improvements
A simple tip/script to improve buildout build times.
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Python 201: namedtuple
Nice tutorial for Python's namedtuple. Everything you need to know to get started with this feature is right here!
Shared by @myusuf3

Python for geospatial data processing.
Shared by @mgrouchy

Idiomatic Python: comprehensions
From the python engineering team at Microsoft, core Python developer Brett Cannon teaches us about idiomatic python and in this case specifically about list comprehensions.
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Bayes theorem in simple words and Bayesian expected returns estimation using Python and Markov Chain Monte Carlo
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Finally, Real-Time Django Is Here: Get Started with Django Channels
Jacob Kaplan-Moss talks about Django, Django Channels and how to get started doing an app with Django channels.
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Adventures in Signal Processing with Python
No more explanation here is necessary, dive into signal processing in Python with matplotlib, numpy and scipy.
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Diving Into Other People's Code
Reading other people's code is a huge part of being a professional programmer. This post is a nice guide for how to tackle this process and some tools you should consider if you are.
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Scientific Python for Raspberry Pi
A guide for setting up a Python scientific stack on the Raspberry Pi 3.
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Analyzing Golden State Warriors' passing network using GraphFrames in Spark
This is super cool! Learn a little bit about graphframes in spark and the Golden State Warriors.
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Describing projects as "For Humans" must stop
It is starting to lose its meaning now.
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