Python is instrumental in so many data science and machine learning workflows that it can sometimes just blend into our daily rhythm; how often, after all, do you think about your office light switch or door knob? You use them all the time, too.
For our first Python-centric Variable edition of 2024, we decided to focus on some of the more interesting and off-the-beaten-path use cases we’ve published recently. We love a good Pandas or Matplotlib tutorial—and so do many of our readers—but sometimes it’s fun to take a break from bread-and-butter topics and dive into some fancier stuff. This week, let’s indulge a little! We hope you enjoy the nine Python reads we’ve selected, which cover a striking range of projects and challenges.
- Watching Storms from Space: A Python Script for Creating an Amazing View
Working with geospatial data comes with its own set of challenges; Mahyar Aboutalebi, Ph.D.’s latest guide unpacks the process of building a Python script that allows you to collect satellite images and transform them into powerful storm animations. - Python’s Most Powerful Decorator
In case you missed it, Siavash Yasini’s detailed introduction to Python’s @property decorator is one of our most-read programming articles in recent weeks. It covers several useful ways to leverage its power: from protecting data attributes from being overwritten to lazy-loading and memory optimization. - Molding the Imagination: Using AI to Create New 3D-Printable Objects
After text, image, music, and video, could 3D objects become the next frontier for generative AI? Robert A. Gonsalves shares the results of his recent experiments, which depend on Midjourney for image generation and on some good-old Python code for translating these into tangible objects. - Text Embeddings: Comprehensive Guide
If you’re new to the world of text embeddings, Mariya Mansurova’s primer is a great place to start—it’s both (very) thorough and accessible, and the hands-on sections include all the Python snippets you’ll need to start tinkering on your own. - Understanding Junctions (Chains, Forks, and Colliders) and the Role they Play in Causal Inference
In his recent deep dive on DAGs (directed acyclic graphs), Graham Harrison zooms in on junction types and their importance in causal-inference tasks. Along the way, he also demonstrates how to generate datasets, execute ordinary least squares (OLS) regression, and more, all with—you guessed it—Python.
