Python is central to so many data science and machine learning workflows that it can sometimes become integrated into our daily rhythm; After all, how often do you think about the light switch in your office or the doorknob? You also use them all the time.
For our first Python-focused variable edition of 2024, we decided to focus on some of the most 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 the basics and dive into more sophisticated stuff. This week, let's have some fun! We hope you enjoy the nine Python reads we've selected, which cover a surprising variety of projects and challenges.
- Watching Storms from Space: A Python Script to Create an Amazing View
Working with geospatial data comes with its own set of challenges; Mahyar Aboutalebi, Ph.D.The latest guide breaks down the process of creating 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 YasiniPython's detailed introduction to the @property decorator is one of our most read programming articles in recent weeks. It covers several useful ways to harness its power: from protecting data attributes from being overwritten to lazy loading and memory optimization. - Shaping the Imagination: Using ai to Create New 3D Printable Objects
After text, images, music and video, could 3D objects become the next frontier of generative ai? Robert A. Gonsalves shares the results of his recent experiments, which rely on Midjourney for image generation and some old Python code to translate them into tangible objects. - Text Embeds: Complete Guide
If you are new to the world of text embeds, Maria MansurovaThe manual is a great place to start – it is (very) complete and accessible, and the hands-on sections include all the Python snippets you'll need to start playing on your own. - Understand joins (chains, forks, and colliders) and the role they play in causal inference
In his recent deep dive into DAGs (directed acyclic graphs), Graham Harrison focuses on the types of unions and their importance in causal inference tasks. Along the way, he also demonstrates how to generate data sets, run ordinary least squares (OLS) regression, and more, all with (you guessed it) Python.
