Python Weekly (Issue 460 July 30 2020)

Python Weekly - Issue 460

July 30, 2020

Python Weekly

Welcome to issue 460 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 Extension for VS Code Insiders is excited to announce the new preview for Native Notebooks! Native Notebooks are VS Code’s newest implementation of notebooks and the Python Extension is leveraging the Native Notebooks API to revamp the data science experience!

Abigail Dogbe, lead organizer PyLadies Ghana and co-organizer of Pycon Africa 2019, has been awarded the Python Software Foundation Q1 2020 Community Service Award.

Articles, Tutorials and Talks

Using only a Raspberry Pi and an $11 video capture dongle, you can create your own KVM over IP device, allowing you to send keyboard input to a remote computer and capture its display.

Can we design a neural network to find a full-sized color image hidden inside another image? How might changing learning rates and other hyperparameters affect our model performance?

A deep-dive on XSS exploitations in Django applications, how to find them and how to fix them.

Handling audio data is an essential task for machine learning engineers working in the fields of speech analytics, music information retrieval and multimodal data analysis, but also for developers that simply want to edit, record and transcode sounds. This article shows the basics of handling audio data using command-line tools, and also provides a not-so-deep dive into handling sounds in Python.

This post shows you an improved way of defining indexes, and how to write zero downtime migrations.

An overview of the logging options available when using Django, Gunicorn and NGINX.

This post will describe how add translations (i18n), pdf/epub builds, and branch-specific versioned documentation to a Read-the-Docs-themed sphinx site hosted with GitHub Pages and built with GitHub's free CI/CD tools.

To-do List is a pretty basic example app that is often done as one of the first projects. In this post, we will make it a little more interesting by using few interesting technologies. As the backend we will use Django and Django Rest Framework, and Alpine.js + Axios to glue it all together easily on the frontend.

Slowsort is a sorting algorithm that is designed to be deterministically sub-optimal. The algorithm was published in 1986 by Andrei Broder and Jorge Stolfi in their paper Pessimal Algorithms and Simplexity Analysis where they expressed a bunch of very in-efficient algorithms. These techniques and algorithms are special because they never make a wrong move while solving a problem, instead, they find ways to delay the success. In this essay, we put our focus on the Slowsort algorithm based on the Multiply and Surrender paradigm.

In this final part of the series, we review the development cycle of the project and discuss in more details how to apply code updates and debug failures of the containerized Python services. The goal is to analyze how to speed up these recurrent phases of the development process such that we get a similar experience to the local development one.

TensorFlow 2.3 has been released! The focus of this release is on new tools to make it easier for you to load and preprocess data, and to solve input-pipeline bottlenecks, whether you’re working on one machine, or many.

In this tutorial, you will learn how to use OpenCV and GrabCut to perform foreground segmentation and extraction.

Interesting Projects, Tools and Libraries

The goal of this project is to enable users to create cool web demos using the newly released OpenAI GPT-3 API with just a few lines of Python. 

DeText is a Deep Text understanding framework for NLP related ranking, classification, and language generation tasks. It leverages semantic matching using deep neural networks to understand member intents in search and recommender systems. 

Satellite imagery for dummies.

Run SQL directly on CSV or TSV files.

Generates LaTeX math description from Python functions.

Gather is a notebook cleaning tool that analyzes and determines the necessary code dependencies within a notebook and performs code cleanup, automating this difficult, annoying, and time-consuming task.

Automagic shell tab completion for Python CLI applications.

A command line dashboard for monitoring stocks.

PyEyeTrack is a python-based pupil-tracking library. The library tracks eyes with the commodity webcam and gives a real-time stream of eye coordinates.

reNgine is an automated reconnaissance framework meant for gathering information during penetration testing of web applications. reNgine has customizable scan engines, which can be used to scan the websites, endpoints, and gather information.

90 Lines of code to convert your face movement into keyboard commands.

A simple web application which allows you to share your WiFi credentials instantly with your friends and family. Built using Python, Flask and Bootstrap.

Tool for making it easy to do data analysis on power system data from the New York Independent System Operator (NYISO).

A Python Library for Simulating Matrix-Form Games.

New Releases

The highlights for this release are:

  • The beta of the next-generation dependency resolver is available

  • Faster installations from wheel files

  • Improved handling of wheels containing non-ASCII file contents

  • Faster pip list using parallelized network operations

  • Installed packages now contain metadata about whether they were directly

  • requested by the user (PEP 376’s REQUESTED file)

The PyTorch 1.6 release includes a number of new APIs, tools for performance improvement and profiling, as well as major updates to both distributed data parallel (DDP) and remote procedure call (RPC) based distributed training. Also Microsoft is now maintaining Windows builds and binaries and will also be supporting the community on GitHub as well as the PyTorch Windows discussion forums.

Upcoming Events and Webinars

This workshop will discuss the shortcomings, pain points, and wins of using GPT2 for the CORD19 data set for Abstractive Text Summarization. We will discuss how explainability can be applied beyond model validation, and how it fits in the iterative workflow of a data scientist, specifically, NLP.

Join Silicon Valley based python expert Moshe Zadka as he follows up on his previous training course to provide comprehensive training on mocks in an engaging free 2-hour training session. Writing quality unit tests is a skillset that every software engineer should have in their arsenal.

Come hear Claire McKay Bowen, PhD introduce what SDC is, survey how SDC methods are being implemented, and cover the current standing challenges in applying newer SDC methods. She will provide motivating examples such as a current collaboration with the Urban Institute and IRS to generate synthetic data of tax return data that is invaluable for analyzing US presidential candidates’ proposed tax policies.

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