Daily Python: PyCoder’s Weekly: Issue #379 (July 30, 2019)

#379 – JULY 30, 2019
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What’s Coming in Python 3.8

“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.”
JAKE EDGE

PyLint False Positives

“In some recent discussion on Reddit, I claimed that, for cases where I’m already using flake8, it seemed as though 95% of Pylint’s reported problems were false positives. Others had very different experiences, so I was intrigued enough to actually do some measurements.”
LUKE PLANT

Automate Your Code Review Process With Codacy

Take the hassle out of code reviews—Codacy flags errors automatically, directly from your Git workflow. Customize standards on coverage, duplication, complexity & style violations. Use in the cloud or on private servers—free for open source projects & small teams →
CODACY sponsor

Understanding the Python Traceback

Learn how to read and understand the information you can get from a Python traceback. You’ll walk through several examples of tracebacks and see how to handle some of the most common types of exceptions in Python.
REAL PYTHON

Django vs Flask in 2019: Which Framework to Choose

In this article, you’ll take a look at the best use cases for Django and Flask along with what makes them unique, from an educational and development standpoint.
MICHAEL HERMAN • Shared by Michael Herman

Dictionaries in Python

In this course on Python dictionaries, you’ll cover the basic characteristics of dictionaries and learn how to access and manage dictionary data. Once you’ve finished this course, you’ll have a good sense of when a dictionary is the appropriate data type to use and know how to use it.
REAL PYTHON video

Python 3.8.0b3 Available for Testing

PYTHON.ORG

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Is Pipenv Dead? Why Has the Project Stopped?

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4 Attempts at Packaging Python as an Executable

Interesting recap of the author’s experience creating a single-file executable of a Python application using four different tools: Cython, Nuitka, PyOxidizer, and PyInstaller.
CHRISTIAN MEDINA • Shared by Christian Medina

Neural Networks From Scratch, With Python

A 4-post series that provides a fundamentals-oriented approach towards understanding Neural Networks.
VICTOR ZHOU

Python Developers Are in Demand on Vettery

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VETTERY sponsor

Docker Packaging Guide for Python

A detailed guide to creating production-ready Docker images for your Python applications.
ITAMAR TURNER-TRAURING

The 35 Words You Need to Python

“I’m going to try, in this post and the ones that follow, to shed some light on the meaning of – and a little of the etymological history behind – the fundamental units of Python fluency. In this first part we will start with the most basic of those units, Python’s 35 keywords.”
MICHAEL MOREHOUSE

Cyclical Learning Rates With Keras and Deep Learning

In this tutorial, you will learn how to use Cyclical Learning Rates (CLR) and Keras to train your own neural networks. Using Cyclical Learning Rates you can dramatically reduce the number of experiments required to tune and find an optimal learning rate for your model.
ADRIAN ROSEBROCK

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Tuesday, July 30, 2019

PyCoder’s Weekly: Issue #379 (July 30, 2019)

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