Python Weekly (Issue 434 January 30 2020)

Python Weekly - Issue 434

January 30, 2020

Python Weekly

Welcome to issue 434 of Python Weekly. Let's get straight to the links this week.

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Articles, Tutorials and Talks

In 2018, LinkedIn embarked on a multi-quarter effort to fully transition to a Python 3 code base. After approximately two quarters of planning and two quarters of execution, we phased out the use of Python 2 across new products and even new builds of existing products. The transition was led by our Python Foundation team with multiple teams and departments playing an integral role to ensure the relative smoothness with which the transition was executed. In total, the effort entailed the migration of about 550 code repositories (libraries, applications, and services).

Everything you regularly Google just found its place in this list. Say hello to a growing list of Python code snippets for everyday problems.

Receipt digitization addresses the challenge of automatically extracting information from a  receipt. This post covers the theory behind receipt digitization and implement an end-to-end pipeline using OpenCV and Tesseract. It also reviews a few important papers that do Receipt Digitization using Deep Learning.

This post is a PSA on the somewhat unintuitive way Field.choices works in Django.

A Django project template is the natural solution when the default Django project format is not enough anymore. Learn how to make your own in this tutorial!

With the EoL of Python 2 being in line with development of Python 3.9 there were changes made to Python 3.9 that broke a lot of packages since many deprecation warnings became errors. This sparked

on python-dev that the changes should be postponed to 3.10 and later as Fedora is doing a rebuild of its packages with Python 3.9

Learn how Dell and Domino created a refreshingly simple approach to help companies get their data science teams and the technologies they need up and running faster, with an easy path to scale.

SPONSORJupyter Notebooks in the IDE Visual Studio Code versus PyCharm.

Sequential Exeuction, Multiprocessing, and Multithreading IO-Bound Tasks in PythonPython makes concurrency easy. It took less than an hour to add multiprocessing to my blog engine, First Crack, and I have used it often since. Everyone likes to call premature optimization the root of all evil, but architecting programs for concurrent execution from the start has saved me hundreds of hours in large data capture and processing projects. Color me a Knuth skeptic. This article compares sequential execution, multiprocessing, and multithreading for IO-Bound tasks in Python, with simple code samples along the way.How To Get Started With the Requests Library in PythonIn many web apps, it's normal to connect to various third-party services by using APIs. You can also use APIs to add functionality to your app. In this article, we'll learn about the Python Requests library, which allows you to send HTTP requests in Python.What makes “XGBoost” so Extreme?Have you ever asked how “XGBoost” works ?, or how “XGBoost “differs from gradient boosting ?, or even a simple example in python. If you have then your in the right place. “Xgboost” is one of the most powerful machine learning tools available for tabulated data. It’s efficiency and performance in learning non linear decision boundaries have made it a staple in both industry and academia alike. So what makes this algorithm so special ?.Interactive basketball data visualizations with PlotlyAnalyze sports data with hexbin shot charts and bubble charts with Plotly and Plotly Express.Create Animated Images Using Python No Photoshop? Not a problem! Learn how to easily create animated images from a list of static images or a video in Python.Plotting Sentinel 5P Data Moving to Django 3.0's Field.choices Enumeration TypesCreating FastAPI from a collage of ideas and experienceA quick-and-dirty guide on how to install packages for PythonBuilding Python Data Science Container using DockerServerless python deployments with Github ActionsRIP Pipenv: Tried Too Hard. Do what you need with pip-tools.Interesting Projects, Tools and LibrariesdonkeycarOpen source hardware and software platform to build a small scale self driving car. CyberRadioA SDR Based FM/AM Radio For Desktop. Accelerated with #cuSignal and Numba.PandaPyPandaPy has the speed of NumPy and the usability of Pandas (10x to 50x faster)PyTrackingA general python framework for training and running visual object trackers, based on PyTorch.CUPPCommon User Passwords Profiler.Yakutils Yakutils is yet another toolbox of Python helper functions.TrafficTollNetLimiter-like traffic shaping for Linux.ThreatBusThe missing link to connect open-source threat intelligence tools.PyTorch3DPyTorch3d is FAIR's library of reusable components for deep learning with 3D data.Pervane Plain text file based note taking and knowledge base building tool.MOSPA platform for creating, editing and sharing JSON objects. PySurvival PySurvival is an open source python package for Survival Analysis modeling - the modeling concept used to analyze or predict when an event is likely to happen. New Releases 

Pandas 1.0.0This is a major release from 0.25.3, and includes a number of API changes, new features, enhancements, and performance improvements along with a large number of bug fixes.Upcoming Events and WebinarsNew York Python Meetup February 2020 Meetup - New York, NYThere will be following talks

  • Concurrent Python

  • Benford's Law using pandas/numpy, or, Math-is-Fun-with-Python

  • Configuring uWSGI for Production: The defaults are all wrong

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