Python Weekly (Issue 486 February 11 2020)

Python Weekly - Issue 486

February 11, 2021

Python Weekly

Welcome to issue 486 of Python Weekly. Let's get straight to the links this week.

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A weekly newsletter featuring the best hand curated news, articles, tutorials, talks, tools and libraries etc for programmers.

News

The Python steering council has, after some discussion, accepted the controversial proposal to add a pattern-matching primitive to the language.

Articles, Tutorials and Talks

The Story of a Novel Supply Chain Attack.

A songification of that most holiest of Python Enhancement Proposals, the PEP 8.

The design of built-in types has certainly contributed to the Python's success. Python integers serve as an example of a quite efficient and, at the same time, accessible bignum implementation. This post explains how integers work.

Django 3.2 had its first alpha release a couple of weeks ago and the final release will be out in April. It contains a mezcla of new features, which you can check out in the release notes. This post focuses on the changes to testing, a few of which you can get on earlier Django versions with backport packages.

Python is not the fastest language around, so any performance boost helps, especially if you’re running at scale. It turns out that depending where you install Python from, its performance can vary quite a bit: choosing the wrong version of Python can cut your speed by 10-20%

Digging into the data to learn more about key trends in Jupyter notebooks.

Have you ever wanted to automate the tedious installation and configuration of programs that you use every day? Would you like to automate it so that you can do it again and again on multiple computers without much effort, e.g. when you get a new laptop? Would you like to update your configuration once and update all laptops and servers you use at once? Then read on as we set up a development machine together, using the automation tool Ansible.

This tutorial introduces the doctest module as not only a method for testing and documenting software, but also as a way to think through programming before you begin, by first documenting it, then testing it, then writing the code.

Interesting Projects, Tools and Libraries

This is a database of 180,000+ symbols containing Equities, ETFs, Funds, Indices, Futures, Options, Currencies, Cryptocurrencies and Money Markets. 

A machine learning framework that encourages learning ML concepts instead of memorizing class functions.

Free and Open Source Machine Translation API, entirely self-hosted. Unlike other APIs, it doesn't rely on proprietary providers such as Google or Azure to perform translations.

OSV is a vulnerability database for open source projects. It exposes an API that lets users of these projects query whether or not their versions are impacted.

A decentralized cryptological network offering accessible, intuitive, and extensible runtimes and interfaces for secrets management and dynamic access control.

Collection of tasks for fast prototyping, baselining, finetuning and solving problems with deep learning.

A custom Python object inspector with a fancy style.

API mocking with Python.

EvalML is an AutoML library which builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions.

Google auto-complete wrapper.

A Python library which aims to make machine learning interpretable and understandable by everyone. It provides several types of visualization that display explicit labels that everyone can understand.

Cross-platform CLI and Python drivers for AIO liquid coolers and other devices.

Upcoming Events and Webinars

TensorFlow Everywhere is a series of global events led by TensorFlow and machine learning communities around the world.

There will be following talks

  • Debugging without print()

  • Debugging by Printing

There will be a talk, Automatic multiclass news classification using deep learning ( LSTM) and word embedding with NLP technology.

There will be following talks

  • Bulk Labelling

  • The Foundation of our Machine Learning Platform at GetYourGuide

There will be a talk, Training Deep Neural Networks on Distributed GPUs.

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