Python Weekly (Issue 406 July 18 2019)

Python Weekly - Issue 406

July 18, 2019

Python Weekly

Welcome to issue 406 of Python Weekly. Let's get straight to the links this week.

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Articles, Tutorials and Talks

If you've already mastered the basics of iterating through Python lists, take it to the next level and learn to use for loops in pandas, numpy, and more!

The Python 3.8 beta cycle is already underway, with Python 3.8.0b1 released on June 4, followed by the second beta on July 4. That means that Python 3.8 is feature complete at this point, which makes it a good time to see what will be part of it when the final release is made. That is currently scheduled for October, so users don't have that long to wait to start using those new features.

This article will cover a project from data collection through exploratory data analysis. Web scraping is difficult to generalize because the code you write depends on the data that you’re seeking and the structure of the website you’re gathering from. However, the approach stays the same. The first step is to determine which links you will need to collect to have a complete scrape. Then, find common characteristics among the pages that will allow you to collect the data with a few functions. Finally, cover any edge cases and clean the data. We will follow this pattern in this article.

Training a neural network to control lights through dance.

In this new pandas video, you're going to learn 25 tricks that will help you to work faster, write better code, and impress your friends. These are the most useful tricks I've learned from 5 years of teaching Python's pandas library.

Here we will learn how to create bar, time series, box plot, heat map, correlogram, violin, and raincloud plots using Seaborn, Pandas, and ptitprince.

This post will explain how to run inference on Cloud Functions using TensorFlow 2.0. We’ll explain how to deploy a deep learning inference including: How to install and deploy Cloud Functions, How to store a model and How to use the Cloud Functions API endpoint.

In this tutorial, you will learn how to perform video classification using Keras, Python, and Deep Learning.

Interesting Projects, Tools and Libraries

A library of state-of-the-art pretrained models for Natural Language Processing (NLP).

A cross-platform browser fuzzing framework.

Bidirectionally transformed strings. The bistring library provides non-destructive versions of common string processing operations like normalization, case folding, and find/replace. Each bistring remembers the original string, and how its substrings map to substrings of the modified version.

A powerful set of Python debugging tools, based on PySnooper.

A tool to search a particular username on almost every social platform and tell , whether the user with that username exists on that site or not.

An Instagram Open Source Intelligence Tool. It gets a range of information from an Instagram account that you normally wouldn't be able to get from just looking at their profile.

This is a Java preprocessor, written in Python, which allows you to write Java using Python indentation and without semicolons.

Dlint is a tool for encouraging best coding practices and helping ensure we're writing secure Python code.

Create Bootstrap 4 web pages using purely Python. 

Huskarl is a framework for deep reinforcement learning focused on modularity and fast prototyping. It's built on TensorFlow 2.0 and uses the tf.keras API when possible for conciseness and readability.

Graph Processing with Python and GraphBLAS.

Upcoming Events and Webinars

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There will be following talks

  • Python Oddities Explained

  • Deep Learning Computer Vision on Edge Devices Made Easier

There will be following talks

  • From __past__ import print_statement

  • Sprinkle your Python with a hint of Functional Programming 

Django Channels allows developers to make real-time web applications using websockets while maintaining access to the full Django batteries-included model for web applications. This talk will focus on what it takes to run a channels application in production, what's possible with Django Channels beyond chat rooms, and what pitfalls & idiosyncrasies you can expect to run into when using Channels in practice.

This will be a hands-on demonstration of using these tools to maintain and develop new features on an existing open source project. We’ll also discuss abstracting these tools and concepts to understand how they should apply to your next project.

Mitja Nemec will talk about his experiences as a Python plugin developer for KiCad.

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