Python Weekly (Issue 430 January 2 2020)

Python Weekly - Issue 430

January 02, 2020

Python Weekly

Welcome to issue 430 of Python Weekly. I wish you all Happy New Year and I am excited to continue sending you the best Python related links in 2020.

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Articles, Tutorials and Talks

Python haters always say, that one of reasons they don't want to use it, is that it's slow. Well, whether specific program - regardless of programming language used - is fast or slow is very much dependant on developer who wrote it and their skill and ability to write optimized and fast programs. So, let's prove some people wrong and let's see how we can improve performance of our Python programs and make them really fast!

In this video I introduce the absolute minimum you need to know about Numba which is a just in time compiler for a subset of Python and Numpy. The first half of the video is dedicated to a basic intro and to highlighting a number of very common mistakes people make when using Numba. The remaining video presents a real world-ish simulation problem, shows up to a 1000x acceleration with Numba in both single and multithreaded cases, and concludes with a "reading list" for learning more about Numba.

Create asynchronous Flask application and run it with uWSGI or Gunicorn behind Nginx reverse proxy.

There's a lot of exciting stuff happening in the Python web development ecosystem right now — one of the main drivers of this endeavour is ASGI, the Asynchronous Standard Gateway Interface. This post is targeted at people interested in recent trends of Python web development. It takes you on a guided tour about what ASGI is and what it means for modern Python web development.

An imbalanced classification problem is a problem that involves predicting a class label where the distribution of class labels in the training dataset is not equal. Differences in the class distribution for an imbalanced classification problem will influence the choice of data preparation and modeling algorithms. Therefore it is critical that practitioners develop an intuition for the implications for different class distributions. In this tutorial, you will discover how to develop a practical intuition for imbalanced and highly skewed class distributions.

This post shows the available tools in the Jupyter ecosystem to build advanced visualizations in Jupyter Notebooks and standalone web apps using Voilá, and how to deploy those apps to the robotics cloud.

Here’s how we can create a video of the moon in just a few lines of python code!

In this part, we're using pandas datareader to collect real-time stock data for our insider trades dashboard. There are lots of good nuggets in here like using Pandas to calculate moving averages and to read html. 

Interesting Projects, Tools and Libraries

Typer, build great CLIs. Easy to code. Based on Python type hints. 

GUI based Smart Sudoku Solver that tries to extract a sudoku puzzle from a photo and solve it.

Mac OS notifications from Python.

Simple background task for Django 3.

Python bindings for FFmpeg - with complex filtering support.

A Computer Vision based Traffic Signal Violation Detection System from video footage using YOLOv3 & Tkinter. (GUI Included)

A light weight, zero dependency, minial functionality excel read/writer python library.

Heavy-duty and Advanced Cross Site Scripting Scanner.

Robatim is a pseudo-random music generator based on the conventions of the common practice period.

Upcoming Events and Webinars

Jupyter notebooks are excellent for exploration, particularly for analytics and visualization. However, when it comes to developing a library or self-contained code, we can find ourselves copying and pasting to our favorite text editor or IDE. In this presentation Leanne Fitzpatrick  will present nbdev, an elegant solution to this problem developed by fast.ai.

We are going to be demonstrating 1) how to setup a Continuous Integration pipeline for a Python application and 2) how to implement Continuous Deployment using Python/Boto3 on AWS.

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