Python Weekly (Issue 424 November 21 2019)

Python Weekly - Issue 424

November 21, 2019

Python Weekly

Welcome to issue 424 of Python Weekly. Let's get straight to the links this week.

From Our Sponsor 

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News

PyCon will take place April 14-23, 2020 in Pittsburgh, PA. The early bird pricing is $550 for corporate, $350 for individuals, and $100 for students. Once we sell the first 800 tickets, regular prices will go into effect. Regular pricing will be $700 for corporate, $400 for individuals, and $125 for students.

Chan Zuckerberg Initiative has given $195,000 grant to NumPy and OpenBLAS through its Essential Open Source Software for Science (EOSS) program! The full $195,000 will go to NumFOCUS, the fiscal sponsor of NumPy. Of that grant, $55,000 is reserved for the OpenBLAS work and the other $140,000 is for NumPy.

Articles, Tutorials and Talks

In this video, we will be learning how to create and extract zip archives. We will start by using the zipfile module, and then we will see how to do this using the shutil module. We will learn how to do this with single files and directories, as well as learning how to use gzip as well.

This tutorial shows you how easy it is to build a chatbot for WhatsApp using the Twilio API for WhatsApp and the Flask framework for Python. 

This is a short introduction on methods that use neural networks in an offensive manner (bug hunting, shellcode obfuscation, etc.) and how to exploit neural networks found in the wild (information extraction, malware injection, backdooring, etc.).

Learn Tkinter in this full course for beginners. Tkinter is the fastest and easiest way to create the Graphic User Interfaces (GUI applications) with Python. Tkinter comes with Python already, so there's nothing to install!  

In this tutorial, you will learn how to detect fire and smoke using Computer Vision, OpenCV, and the Keras Deep Learning library.

In this video, you'll learn how to use OneHotEncoder and ColumnTransformer to encode your categorical features and prepare your feature matrix in a single step. You'll also learn how to include this step within a Pipeline so that you can cross-validate your model and preprocessing steps simultaneously. Finally, you'll learn why you should use scikit-learn (rather than pandas) for preprocessing your dataset.

How to build secure asynchronous APIs in Python using Korv and AsyncSSH that listen on TCP sockets over SSH sessions.

Everything you need to know to get started with NumPy.

Learn how to master lists with this Python list tutorial. Analyze app store data and learn how to use loops to automate repetitive tasks.

The official documentation has a chapter about performance and optimization with good advice. In this article I want to build on that and show tools and methods I've used in the past to reduce page load time.

Porting an awk script to Python is more about code style than transliteration.

Think of all the downtime in your daily life that goes to waste. That hour on the subway, that afternoon at the dmv, that 20 minutes at work when the servers are down and you’re waiting for the IT department to get things up and running again. Would’nt it be great if you could use that time to get some data science work in? Lets get started on setting up a remote server running Jupyter!

This article discusses GPT-2 and BERT models, as well using knowledge distillation to create highly accurate models with fewer parameters than their teachers.

Learn to use boolean (True and False) values in your Python 3 code.

Learn how to deploy flask web application on glitch.com in this video.

TensorFlow Lite is a framework for running lightweight machine learning models, and it's perfect for low-power devices like the Raspberry Pi! This video shows how to set up TensorFlow Lite on the Raspberry Pi for running object detection models to locate and identify objects in real-time webcam feeds, videos, or images.

Interesting Projects, Tools and Libraries

Build, Store, and Distribute your Applications and Containers.

End-to-end simulation for self-driving cars.

Great Expectations is a tool for profiling, validating, and documenting your data to maintain quality and improve communication between teams.

There are various mesh formats available for representing unstructured meshes. meshio can read and write multiple formats and smoothly converts between them.

Clusterman (the Cluster Manager) is an autoscaling engine for Mesos and Kubernetes clusters. It looks at metrics and can launch or terminate compute to meet the needs of your workloads, similarly to the official Kubernetes Cluster Autoscaler 

A toolkit for testing, tweaking and cracking JSON Web Tokens.

A CLI tool to clear up your email!

An Open-Source Package for Neural Relation Extraction (NRE).

A simple script to get songs from youtube in mp3 format with all tags from itunes.

A Python toolkit for unsupervised anomaly detection in time series.

A PyTorch Library for Accelerating 3D Deep Learning Research.

rsh is a tool purely written in python to easily generate reverse shell command for linux as well as windows.

Open-Source Admin Dashboard coded in Flask Web Framework on top of ModularAdmin Dashboard design.

A Statistical Data Validation Toolkit for Pandas.

New Releases

In this release we focused mostly on product quality. We closed a total of 60 issues, 39 of them being bug fixes. However, we’re also pleased to deliver delightful features such as: 

  • Add imports “quick fix” when using the Python Language Server 

  • Altair plot support  

  • Line numbers in the Notebook Editor. 

Upcoming Events and Webinars

Natural Language Processing has never been so easy thanks to the Python community! We will have a look at tools for NLP and compare them for different uses, including some playful generated writing examples! We will also introduce a few algorithms regarding language and cross language analysis.

There are many AI libraries available in Python, but they are designed to be used by programmers. We have been building some tools in Python to lower the barrier to entry for those existing libraries. We detect common errors early, make evaluation of learner performance easy, and visualization of learner behavior and the underlying data more accessible. We'll talk about what drove these decisions and walk through a simple example of using this tool on a readily-available dataset.

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