Python Weekly (Issue 368 October 11 2018)

Python Weekly - Issue 368

October 11, 2018

Python Weekly

Welcome to issue 368 of Python Weekly. Let's get straight to the links this week.

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Articles, Tutorials and Talks

Everyone who has been working with distributed systems or logs from such a systems, has directly or indirectly encountered Lamport Timestamps. Lamport Timestamps are used to (partially) order events in a distributed system. The algorithm is based on causal ordening of events and is the foundation of more advanced clocks such as Vector Clocks and Interval Tree Clocks (ITC). In this article we will first briefly introduce the concept of logical clocks, explain why ordering of events in distributed systems is needed and discuss some alternatives. Then we’ll go over the algorithm of Lamport Timestamps and work an example with three processes. Next, we’ll implement this example in easy-to-understand code using Python’s multiprocessing library. To top it all off, we’ll transform our code into an implementation with Vector Clocks.

Decision trees are the fundamental building block of gradient boosting machines and Random Forests(tm), probably the two most popular machine learning models for structured data. Visualizing decision trees is a tremendous aid when learning how these models work and when interpreting models. Unfortunately, current visualization packages are rudimentary and not immediately helpful to the novice. For example, we couldn't find a library that visualizes how decision nodes split up the feature space. So, we've created a general package (part of the animl library) for scikit-learn decision tree visualization and model interpretation.

Should I be using Keras or TenorFlow? Which one is better? I'll answer your questions in this Keras vs. TensorFlow deep learning tutorial.

Learn to use transfer learning, with a working Python-coded example.

Redis 5.0 contains, among lots of fixes and improvements, a new data-type and set of commands for working with persistent, append-only streams. Redis streams are a complex topic, so I won't be covering all aspects of the APIs, but hopefully after reading this post you'll have a feel for how they work and whether they might be useful in your own projects.

No need for Photoshop: make an animated chart using only Python and the command line.

Explanation of pandas crosstab function, how to use it and some of its features.

Interesting Projects, Tools and Libraries

Jupyter notebooks for teaching/learning Python 3.

WebRTC and ORTC implementation for Python using asyncio.

Hyperparameter Optimization for Keras Models.

Logquacious is a set of simple logging utilities to help you over-communicate.

Predict when you last shaved with machine learning.

Fast image augmentation library and easy to use wrapper around other libraries.

A Sorta Familar HTTP Framework for Python.

A python machine learning library for structured data.

Get from one wikipedia page to another in as few clicks as possible.

Camelot is a Python library that makes it easy for anyone to extract tables from PDF files!

Python package to plot and analyse samples from probabilistic models.

ASCII generator (image to text, image to image, video to video).

A flake8 plugin for Django.

A highly efficient and modular implementation of Gaussian Processes in PyTorch.

Library and CLI for "scrambled" printing in terminal.

Auto-generate TypeScript models for Django projects.

Upcoming Events and Webinars

JupyterDay in the Triangle is a single-day conference for Jupyter users in the Southeast. The event takes place at The Carolina Club to showcase applications of the open source software created by Project Jupyter and community. These interactive technologies are reshaping how people interact with code and data in industry and academia.

The Indiana Python User Group IndyPy invites you to participate in this one-day special event when we discuss best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization.

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