Python Weekly (Issue 459 July 23 2020)

Python Weekly - Issue 459

July 23, 2020

Python Weekly

Welcome to issue 459 of Python Weekly. Let's get straight to the links this week.

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Articles, Tutorials and Talks

This A* Path Finding tutorial will show you how to implement the a* search algorithm using python. We will be building a path finding visualizer tool to visualize the a* pathfinding algorithm as it runs. This astar pathfinding algorithm is an informed search algorithm which means it is much more efficient than your standard algorithms like breadth first search or depth first search.

In this codelab, you will learn how to build and train a neural network that recognises handwritten digits. Along the way, as you enhance your neural network to achieve 99% accuracy, you will also discover the tools of the trade that deep learning professionals use to train their models efficiently.

How a 5-minute hack for %reload function turned into a rabbit hole of different Python tools and techniques.

A common need of web applications is to have a periodically running task in the background. This video shows you what a very robust implementation that is based on the Flask CLI and the cron service.

Working with Django in the modern JavaScript ecosystem without giving up the things that make it great.

DockerizeMe is a technique for inferring the dependencies needed to execute a Python code snippet without import error. DockerizeMe starts with offline knowledge acquisition of the resources and dependencies for popular Python packages from the Python Package Index (PyPI). It then builds Docker specifications using a graph-based inference procedure. Our inference procedure resolves import errors in 892 out of nearly 3,000 gists from the Gistable dataset for which Gistable’s baseline approach could not find and install all dependencies.

Using Redis and Python for everything but caching!

Webscraping is often a pain. Researching, finding, and installing the libraries you need can be time consuming. Finding the content you need in the HTML can take time. Getting everything to work can be finicky. This article shows you how to use the Python pandas library to scrape HTML tables with a single line of code!

Turn invisible bugs into visible fixes with mutation testing.

In this part 2, we discuss how to set up and wire other components to a containerized Python service. We show a good way to organize project files and data and how to manage the overall project configuration with Docker Compose. We also cover the best practices for writing Compose files for speeding up our containerized development process.

Working with SQL in Python can be easy with SQLAlchemy and its hybrid properties, nested queries, table metadata, dialects and more!

Learn how to add custom data fields to multiple user types with Django in a maintainable way that lets us easily use Django forms or DRF serializers.

This post will describe how to host a sphinx-powered site (using the Read the Docs theme) on your own GitHub Pages site, built with GitHub's free CI/CD tools.

Interesting Projects, Tools and Libraries

Libra automates the end-to-end machine learning process in just one line of code. It is built for both non-technical users and software professionals of all kinds.

Fawkes is privacy preserving tool against facial recognition systems.

Extract and Visualize Data from URLs using Unfurl.

Neural Question Answering At Scale. Haystack is designed in a modular way and lets you use any models trained with FARM or Transformers.

capa detects capabilities in executable files. You run it against a PE file or shellcode and it tells you what it thinks the program can do. For example, it might suggest that the file is a backdoor, is capable of installing services, or relies on HTTP to communicate.

Elyra extends JupyterLab Notebooks with an AI centric approach.

Execute a local command using the processing power of another Linux machine.

Chia blockchain python implementation (full node, farmer, harvester, timelord, and wallet)

A library for constrained optimization and manifold optimization for deep learning in PyTorch.

Sentiment analysis neural network trained by fine-tuning BERT, ALBERT, or DistilBERT on the Stanford Sentiment Treebank.

Kubernetes multi-tenant operator. Enables multi-tenant capabilities in your Kubernetes Cluster.

Cilantropy is a Python Package Manager interface created to provide an "easy-to-use" visual and also a command-line interface for Pythonistas.

New Releases

Upcoming Events and Webinars

Starting from scratch with a blank Jupyter notebook, we will use the Pandas library to explore several data sets, including: Filtering dataframes, Creating new variables, Selecting variables, Sorting dataframes, Merging, Aggregation and Basic Visualizations using matplotlib and pandas visualizations.

There will be following talks

  • Modelling with Gaussian Processes

  • What if you put your dataset in a Blender?

There will be following talks

  • Can randomness in stochastic gradient descent provide privacy?

  • Even beautiful maps can be misleading: How decisions about spatial data visualisation affect map legibility.

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