Python Weekly (Issue 402 June 20 2019)

Python Weekly - Issue 402

June 20, 2019

Python Weekly

Welcome to issue 402 of Python Weekly. Let's get straight to the links this week.

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Articles, Tutorials and Talks

In this Python Programming series, we will be learning how to use the Matplotlib library. Matplotlib allows us to create some great looking plots in order to visualize our data in easy to digest formats. This series covers a wide variety of topics such as: customizing our plots, different plot types, plotting live data in real-time, and more.

Whenever someone says ‘You can do that with an extension’ in the Jupyter ecosystem, it is often not clear what kind of extension they are talking about. The Jupyter ecosystem is very modular and extensible, so there are lots of ways to extend it. This post aims to provide a quick summary of the most common ways to extend Jupyter, and links to help you explore the extension ecosystem.

Tips and Tricks, especially in the programming world, can be very useful. Sometimes a little hack can be both time and life-saving. A minor shortcut or add-on can sometimes prove to be a Godsend and can be a real productivity booster. So, here are some of my favourite tips and tricks that I have used and compiled together in the form of this article.

Python’s for loops don’t work the way for loops do in other languages. In this article we’re going to dive into Python’s for loops to take a look at how they work under the hood and why they work the way they do.

Long Short-Term Memory (LSTM) Networks have been widely used to solve various sequential tasks. Let's find out how these networks work and how we can implement them.

How do you price a Broadway show?

In this tutorial, you will learn how to perform online/incremental learning with Keras and Creme on datasets too large to fit into memory.

The life changing magic of a minimal standard library.

Here’s a small tip on how to write some endpoints in Django Rest Framework.

Interesting Projects, Tools and Libraries

Tool for collaboratively building interactive lessons.

3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK).

Automagica is an open source Smart Robotic Process Automation (SRPA) platform. With Automagica, automating cross-platform processes becomes a breeze. With this open source library we want to provide a comprehensive and consistent wrapper around known and lesser known automation libraries.

Clone a voice in 5 seconds to generate arbitrary speech in real-time.

Controlling Robotic Arm with a Vision-based, Economic System.

Reads and plays mazes in augmented reality.

A user-centered Python package for differentiable probabilistic inference.

Terminal Pomodoro Timer.

New Releases

Upcoming Events and Webinars

There will be following talks

  • Teaching old CAD new tricks with Python

  • A Rhino, a Python and a Grasshopper Walk Into a Bar 

In this talk I’ll try to demystify things. I’ll review Python’s double-underscore (“dunder”) methods, which combine ideas from operator overloading, mix-ins, and inheritance, but which are implemented in a unique way by Python. We’ll discuss how they can be used to have your classes emulate the behavior of Python’s internal types like lists and dictionaries.

There will be following talks

  • Unittest'ing in Python

  • Natural Language Processing

  • Division and modulo: How Python got these right

  • The Unspeakable Horror of Discovering You Didn't Write Tests

Our next event features Reynold Xin, co-founder of Databricks, telling us how we can finally make pandas-based projects infinitely scalable using Databricks' new open source "Koalas" project. We'll follow that up with Google's Hanoi Hantrakul telling us about "Magenta", Google's initiative to use AI to enhance human creativity (with special live musical demos).

We'll talk about the practical aspects of analyzing real-world data using Python, Pandas, and Altair by exploring a data-set of traffic stops made by Maryland Police.

There will be a talk, Tools & Frameworks for High-Performance Analytics in Python.

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