Python Weekly (Issue 389 March 21 2019)

Python Weekly - Issue 389

Python Weekly

Welcome to issue 389 of Python Weekly. Let's get straight to the links this week.

From Our Sponsor 

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Articles, Tutorials and Talks

I’m going to explain to you how I’m receiving these free meals from some of the best eateries in New York City.I will briefly go over some of the technologies and programming languages I use and I will explain my use of logistic regression, random forests, AWS, and automation.

Plotly Express is a new high-level Python visualization library: it’s wrapper for Plotly.py that exposes a simple syntax for complex charts. Inspired by Seaborn and ggplot2, it was specifically designed to have a terse, consistent and easy-to-learn API: with just a single import, you can make richly interactive plots in just a single function call, including faceting, maps, animations, and trendlines. It comes with on-board datasets, color scales and themes, and just like Plotly.py, Plotly Express is totally free.

In this article we’ll talk about when None isn’t enough, I’ll show you how I create unique values when None doesn’t cut it, and we’ll see a few different uses for this technique.

Building your own disassembly tooling for — that’s right — fun and profit.

Anonymous functions are powerful, but they are hard to understand.

In this article I’ll cover some ways you can prepare for crashes in advance, so when they do occur you can quickly figure out which part of the codebase caused them.

Mailchimp is an awesome tool for email marketing related services. In this tutorial we're showing how easy it is to subscribe users to your own subscriber list, in a Django application.

If you’re working on a large Python project or just like to keep your code-base tidy and neat, Pytype is the tool for you.

Python Jobs of the Week

As a software engineer at Spire on our Maritime team, you’ll move fast, iterate quickly, and solve global problems. You might be the right fit if you love exciting technical challenges, hate getting bored, and enjoy collaborating with engineers who are changing the way the world thinks about space, satellites, and data. You will join a team responsible for the design and maintenance of our ground-side data processing pipeline, from ingesting satellite data all the way through designing and scaling the APIs that our customers depend on for global data insights.

Interesting Projects, Tools and Libraries

A static analyzer for Python code. Pytype checks and infers types for your Python code - without requiring type annotations.

Get a clean, ready-to-go Linux box in seconds. 

FastAPI framework, high performance, easy to learn, fast to code, ready for production.

An open source python library for scalable Bayesian optimisation.

Automated machine learning company report in an interactive 'PDF style' from four dimensions: employees, customers, shareholders (owners) and management.

A tool which creates a spoofed certificate of any online website and signs an Executable for AV Evasion. Works for both Windows and Linux.

A web application to transfer PowerShell modules, executables, snippets and files while bypassing AV and application whitelisting.

pocsuite3 is an open-sourced remote vulnerability testing framework developed by the Knownsec 404 Team.

A simple Prolog Interpreter written in a few lines of Python 3. It runs a limited subset of Prolog and uses backtracking and generators in order to perform its magic.

A Python web scraping framework for intelligent use cases.

Automated tool to monitor pastebin for interesting information.

New Releases

Upcoming Events and Webinars

There will be following talks

  • Clean Architecture with Django

  • Migrating from REST to GraphQL on Django

There will be following talks

  • Model Evaluation and Improvement for Supervised Learning

  • Writing a command line tool with Click

  • Taking your great tech idea and turning it into a business

  • From Chemist to Software Engineer in 5 Months

  • Web scraping with Selenium and Python

Let's talk about code reviews: where they work, where they fail, and -- most importantly -- how to make them better. In this talk, we'll go through small code samples and work together to make them better and more "pythonic." At the end of the talk we should have both better code and a better understanding of how to conduct productive code reviews. 

There will be following talks

  • Python Development with Docker and Jupyter

  • Model Deployment with Python, AWS and Terraform

There will be a talk, Simplify writing code with deliberate commits.

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