Grumpy is an experimental Python runtime for Go. It translates Python code into Go programs, and those transpiled programs run seamlessly within the Go runtime. We needed to support a large existing Python codebase, so it was important to have a high degree of compatibility with CPython (quirks and all). The goal is for Grumpy to be a drop-in replacement runtime for any pure-Python project. Curator's note - If you are a Go Programming Language Developer do checkout http://importgolang.com/newsletter/
Discover how you can automate & optimize your projects using a DevOps toolchain architecture that enables a continuous delivery process.
Take the twitter poll, Do you see yourself using 3.x in 2017.
I am a seasoned python developer, I have seen many UnicodeDecodeError myself, I have seen many new pythonista experience problems related to unicode strings. Actually understanding and handling text data in computer is never easy. Sometimes the programming language makes it even harder. In this post, I will try to explain everything about text and unicode handling in python.
An improved asyncio module, Pyjion for speed, and moving to Python 3 will make for a rich Python ecosystem.
In this Tutorial I will describe how you can get started with Machine Learning on Linux using Scikit-Learn and Python 3.
A library for managing setuptools packaging needs in a consistent manner. pbr reads and then filters the setup.cfg data through a setup hook to fill in default values and provide more sensible behaviors, and then feeds the results in as the arguments to a call to setup.py - so the heavy lifting of handling python packaging needs is still being done by setuptools.
This is an old article written by Guido van van Rossum himself. Occasionally people ask me about the origins of my nickname BDFL (Benevolent Dictator For Life). At some point, Wikipedia claimed it was from a Monty Python skit, which is patently false, although it has sometimes been called a Pythonesque title. I recently trawled through an old mailbox of mine, and found a message from 1995 that pinpoints the origin exactly. I'm including the entire message here, to end any doubts that the term originated in the Python community.
This talk explores how you can build applications and APIs with Flask step by step by being easy to test and scale to larger and more complex scenarios. The talk will also go a bit into the history of some design decisions in Flask and what works well and in which areas you might want to mix it with other technologies for better results.
Copy-paste this into your Python 3 interpreter to see a human-readable version of the raw SQL queries that your Django code is running.
Aisha (@AishaXBello) joined the Django community when she attended a Django Girls workshop during EuroPython in 2015. From that point on, Aisha's trajectory in the Django world was unstoppable. She is not only a talented developer but her desire to keep learning and sharing her knowledge with others is simply inspiring. She organized or helped organize a huge number of Django Girls workshop in her home country of Nigeria. Thanks to her, Nigeria is on its way to be the world-record holder of most Django Girls events organized.
Detect lanes on video frames, using NumPy and SciPy. My goal is not to achieve better performance or speed then with OpenCV. Rather, I’m going to implement some techniques learned at the Computer Vision course.
Machine learning can be intimidating for a newcomer. The concept of a machine learning things alone is quite abstract. How does that work in practice ?. In order to demystify some of the magic behind machine learning algorithms, I decided to implement a simple machine learning algorithm from scratch. I will not be using a library such as scikit-learn which already has many algorithms implemented. Instead, I’ll be writing all of the code in order to have a working binary classifier algorithm. The goal of this exercise is to understand its inner workings.
vuejs and Django integration with hot code reload
AlexaBot for Asana -- Create Asana Tasks with Amazon Echo
MNIST classification using Convolutional NeuralNetwork. Various techniques such as data augmentation, dropout, batchnormalization, etc are implemented.