⚙️Arnau Villoro’s Post

The moment you add a new condition to an if/else chain in your pipeline, you’ve modified existing code! That’s exactly the smell OCP is meant to catch. 🔍 Open for extension, closed for modification means adding new behavior by writing new code, not patching old one! In my latest article, I break down what OCP looks like in Python data projects: strategy patterns, abstract base classes, and the tradeoffs of when the extra structure is actually worth it. https://lnkd.in/eE9yicvC This is Part 2 of my SOLID series. What’s the most painful “just add another elif” you’ve ever had to maintain? 😅 #Python #SoftwareEngineering #SOLID #DataScience #CleanCode

".env" Files Explained: Never Commit to GitHub, Python ".env" Files - Part Two. Learn how to use .env files in Python to protect sensitive credentials and never upload them to GitHub. Discover best practices to share with your teammates.

".env" Files Explained: Never Commit to GitHub, Python ".env" Files - Part Two. Learn how to use .env files in Python to protect sensitive credentials and never upload them to GitHub. Discover best practices to share with your teammates.

🌟 New Blog Just Published! 🌟 📌 Docker Made Easy for Python Data Projects: A Beginner’s Step-by-Step Guide 🚀 📖 Ever heard the phrase “It works on my machine” and felt your blood pressure spike? One missing library, a mismatched Python version, or a hidden system package can turn a tidy notebook into a...... 🔗 Read more: https://lnkd.in/dYRvhQPi 🚀✨ #dockerpython #datascience #beginnerguide

I’ve put together a quick reference guide covering essential Python Dictionary and Set methods! 🐍 Whether you are just starting out with Python or need a quick refresher, this document walks through everything from basic dictionary operations like .get() and .update(), to mathematical set operations like .intersection() and .symmetric_difference(). It includes brief explanations and simple code snippets for each method to help you write cleaner, more efficient code. Check out the document below, and let me know your favorite or most-used method in the comments! 👇 #Python #Programming #Coding #DataStructures #PythonDeveloper #Cheatsheet

Built a simple File Reader CLI in Python today. This project takes a file path as input, opens the file, reads its contents, and prints everything directly in the terminal. What I learned while building it: • Taking user input with input() • Opening files using open() • Reading file content with read() • Using with for safe file handling • Adding error handling with try/except Small projects like this are helping me strengthen my Python fundamentals and get more comfortable with writing clean, practical code. GitHub Repository: https://lnkd.in/gud495tr #Python #PythonProjects #CLI #CodingJourney #Programming #LearningInPublic

If you want your python code to be less buggy and easier to maintain and deploy, check out the following FREE, open source tools you can build into your development workflow than can do all that and more. black, ruff, pytest, py-spy, mypy and pre-commit. In my latest article on Towards Data Science I show where to get them, install and use them. Read the article for free using the link in the first comment.

🎉 LaTeX expressions written easily from Python using Latexify library. pip install latexify_py Decorators: - @latexify.expression - @latexify.algorithmic 🍀 Check out Khuyen Tran’s blog post on how to write LaTeX using Latexify, Sympy and regular IPython: https://lnkd.in/gCtsnSDS #bookmark #python #latex #pylib #latexify #tipsandtricks

If you used Rcpp back in the R days, this will feel familiar. I vibe-coded something over the weekend that does the same thing for Python: write C++ inline, call it like a normal function, get results back as numpy arrays. No build system, no separate files, no compilation step. ``` fn = cppFunction(''' std::vector<double> simulate(int n, double mu) { ... } ''') fn(10000, 2.5) ``` Projects like pybind11 and Cython are better for production C++ projects, but this is for when you just need one fast function right next to your Python code. The code compiles on the first call and is cached forever after. Armadillo works out of the box if you have it installed. https://lnkd.in/gt6YQENq

Today’s Python topic felt like the point where code stops being one-time work and starts becoming reusable. 🐍 Day 11 of my #30DaysOfPython journey was all about the basics of function, and this one was a big reminder that good code is not just about writing more — it is about writing smarter. A function is a reusable block of code designed to do a specific task, and in Python, we define it using the def keyword. Today I explored: 1. How functions are created and called 2. How return sends values back from a function and return None when nothing is returned 3. Passing parameters and arguments 4. Passing arguments using key-value style 5. Default parameters 6. Arbitrary arguments with *args 7. Arbitrary named arguments with **kwargs What stood out to me today was how functions make code feel organized, reusable, and much easier to scale. Instead of repeating the same logic again and again, you write it once and use it wherever needed. One more day, one more topic, one more step toward writing code that is cleaner, smarter, and actually built to last. Github Link - https://lnkd.in/gUhhaW_y #Python #LearnPython #CodingJourney #30DaysOfPython #Programming #DeveloperJourney