PEP 8: 3 Simple Rules for Clean Python Code

Python scope is one of those topics that separates developers who debug fast from those who don't. The language gives you no warning when a variable resolves to an unexpected value. It simply executes, returns a result, and moves on. Tracking down the source of that behaviour - without a solid mental model of how Python resolves names - can cost hours. The LEGB rule isn't complicated. But it's rarely taught with the depth it deserves. I wrote a free guide to change that: → How Python's name resolution actually works under the hood → The LEGB lookup chain with concrete, practical examples → Enclosing scopes and closure behaviour explained clearly → When global and nonlocal are appropriate - and when they signal a design problem → The scope patterns most likely to introduce silent bugs in real codebases Download it free: https://lnkd.in/djp6HJdD #Python #SoftwareEngineering #PythonDevelopment #BackendDevelopment

Want good code? Let the 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐟𝐨𝐥𝐥𝐨𝐰 𝐫𝐮𝐥𝐞𝐬 ⚙️ There are two ways of doing that in Python: with either 𝐀𝐁𝐂 𝐈𝐧𝐭𝐞𝐫𝐟𝐚𝐜𝐞𝐬 or with 𝐏𝐫𝐨𝐭𝐨𝐜𝐨𝐥𝐬. 𝐀𝐁𝐂 𝐈𝐧𝐭𝐞𝐫𝐟𝐚𝐜𝐞𝐬 is when you (1) write a base class with methods, (2) let implementations 𝑖𝑛ℎ𝑒𝑟𝑖𝑡 from that class and 𝑓𝑜𝑟𝑐𝑒 𝑡ℎ𝑒𝑚 𝑡𝑜 𝑖𝑚𝑝𝑙𝑒𝑚𝑒𝑛𝑡 all the required methods. This way you catch errors early, but it may require more code. 🧱 𝐏𝐫𝐨𝐭𝐨𝐜𝐨𝐥𝐬 are when you (1) write a protocol class, and (2) during use 𝑎𝑐𝑐𝑒𝑝𝑡 𝑎𝑛𝑦 𝑐𝑙𝑎𝑠𝑠 𝑡ℎ𝑎𝑡 ℎ𝑎𝑠 𝑡ℎ𝑒 𝑠𝑎𝑚𝑒 𝑚𝑒𝑡ℎ𝑜𝑑𝑠 as defined in the protocol. This gives you flexibility to plug-and-play quickly. Less code, but errors may only appear at runtime. 🧩 𝐆𝐞𝐧𝐞𝐫𝐚𝐥 𝐫𝐮𝐥𝐞: - Protocols are good for 𝘦𝘹𝘵𝘦𝘳𝘯𝘢𝘭 𝘴𝘵𝘶𝘧𝘧 like APIs, LLM providers, data sources 🌍 - Interfaces are good for 𝘪𝘯𝘵𝘦𝘳𝘯𝘢𝘭 𝘴𝘵𝘶𝘧𝘧 like core logic of your system ⚙️ 𝐹𝑙𝑒𝑥𝑖𝑏𝑙𝑒 𝑎𝑡 𝑡ℎ𝑒 𝑒𝑑𝑔𝑒𝑠, 𝑠𝑡𝑟𝑖𝑐𝑡 𝑎𝑡 𝑡ℎ𝑒 𝑐𝑜𝑟𝑒. #software #engineering #best #practices

Nobody teaches you this in Python tutorials. You learn variables. You learn functions. You learn classes. But scope? You learn scope the hard way. At 2am. With a bug you can't explain. Staring at code that looks perfectly fine. Here's what's actually happening: Python doesn't look for variables the way you think it does. It follows a very specific lookup order - Local → Enclosing → Global → Built-in - and if you don't know the rules, it will surprise you in the worst moments. I wrote a free guide to fix that gap: ✔ How Python actually resolves variable names ✔ Why closures behave the way they do ✔ The global and nonlocal keywords demystified ✔ Real examples of scope bugs - and how to squash them No fluff. No theory for the sake of theory. Just the stuff that makes you a sharper Python dev. 🎁 Free download: https://lnkd.in/dY8az6hc Drop a 🐍 in the comments if scope has burned you before. #Python #PythonDeveloper #LearnPython #Debugging #Scope #Variable

🚀 Just Published My First Python Library on PyPI! Excited to share that I’ve built and published "common-fun" — a modular Python utility library designed to simplify everyday development tasks. 📦 Install: pip install common-fun 🖥️ Try CLI: common-fun help 🔗 GitHub: https://lnkd.in/gjWRyhpq 🔧 What it includes: • Number utilities (prime, gcd, factorial, etc.) • String processing (palindrome, slugify, etc.) • Array helpers (flatten, chunk, rotate) • Validators (email, URL, password) • File utilities • Performance decorators (timer, retry, caching) • 🔥 CLI support for direct terminal usage 💡 Why I built this: While working on multiple projects, I realized I was repeatedly writing similar utility functions. So I decided to consolidate everything into a clean, reusable, and structured library. ⚙️ Key highlights: • Fully modular architecture • Optimized implementations • CLI tool for quick access • PyPI-ready packaging • Clean documentation This project helped me understand: ✔️ Library design ✔️ Packaging & publishing ✔️ CLI development ✔️ Clean code practices Would love your feedback and suggestions! #Python #OpenSource #Developer #Programming #PyPI #SoftwareDevelopment

I pretty much start every Python project with a pyproject.toml. It keeps things simple: - One place for configs - Easy for VSCode to pick up - Easy to reuse in CI/CD pipelines - No environment variables to set up - No manual setup for installs and dependencies But I ran into a subtle issue while using it inside a DevContainer. Everything looked fine: - Project structured correctly - pyproject.toml in place - Virtual environment set up But imports started behaving inconsistently. What made it tricky was that nothing was obviously broken. So I went back to basics:   - checking where Python was actually importing from import my_package print(my_package.__file__) That’s when it clicked. The DevContainer was picking up a previously installed version of the project instead of my current code. The fix ended up being:   - installing the project in editable mode pip install -e . After that, imports consistently pointed to the working directory instead of a stale installed version. Big takeaway for me: Even with a clean pyproject.toml, how your project is installed matters just as much as how it’s defined. Lately I’ve been focusing more on making my dev environment predictable, not just “working” Curious how others handle this: Do you always install your project in editable mode during development? #DevOps #Python #Docker #VSCode #SoftwareEngineering

My biggest mistake early in my Python journey wasn’t bad code. It was ignoring the small tools that make systems reliable. After 4+ years building automation projects, I found a handful of libraries that quietly transformed my side projects into production-ready products. I wrote about the exact 8 libraries I rely on today. Check out the full breakdown on my Medium account.

🐍 Which Python package manager should you actually use in 2025? I just published a deep-dive guide covering everything from the classics to the tools reshaping the ecosystem right now: → pip, pipenv, Poetry, conda, and the blazing-fast uv → Python version managers: pyenv, asdf, and why uv is doing it all → A decision flowchart so you never have to Google this again → Migration paths between tools → Where Python packaging is headed next Whether you're a solo dev tired of dependency hell or a team lead trying to standardize your stack — this one's for you. 🔗 https://lnkd.in/gd43kMfp Would love to hear what your team is using these days. Still on pip? Already moved to uv? Drop it in the comments 👇 #Python #SoftwareDevelopment #DevTools #Programming #OpenSource

🚀 Did you know the real power of @dataclass in Python? If you're still writing boilerplate code for your classes… you're wasting time 👀 Introduced in PEP 557 (Python 3.7+), the @dataclass decorator is a game-changer for creating clean, readable, and maintainable code. Let’s break it down 👇 ✨ What makes @dataclass so powerful? 🔧 No more boilerplate Just define your variables with type hints, and Python auto-generates: __init__ __repr__ __eq__ …and more! 📦 Perfect for data-holder classes Think of it as a mutable namedtuple with defaults — simple, clean, and efficient. ⚠️ Watch out for mutable defaults Using list or dict directly as defaults can lead to shared-state bugs. ✅ Instead, use: from dataclasses import field my_list: list = field(default_factory=list) 🔒 Need immutability? Use frozen=True to make your objects read-only (and hashable 👌) 💡 Pro Tips (Production Ready) a] Always use type annotations b] Prefer default_factory for mutable fields c] Use frozen=True for safer design d] Add __post_init__() for validation logic e] Try slots=True (Python 3.10+) for memory optimization 🧠 Example: from dataclasses import dataclass @dataclass(frozen=True) class Point: x: float y: float = 0.0 p = Point(1.0) print(p) ##output- Point(x=1.0, y=0.0) Clean. Readable. Pythonic. ✅ 🔥 If you're preparing for interviews or writing production code — mastering @dataclass is a must. 💬 Have you used dataclasses in your projects? Drop your experience below! #Python #DataClasses #CleanCode #SoftwareEngineering #PEP557

Python has a dirty secret. It will let you write broken code that runs perfectly fine. No errors. No warnings. No stack traces. Just wrong answers, silently, confidently wrong. Scope bugs are the #1 reason this happens. You think you're reading a local variable. Python is reading a global one. Your function returns a result. The result is garbage. And the interpreter couldn't care less. I've seen this trip up beginners on day 3. I've seen it trip up senior engineers on year 5. The fix isn't talent. It's knowing the rules Python plays by. So I wrote them down. All of them. In plain English. Get the free Python Scope Guide: https://lnkd.in/djp6HJdD Save it. Share it. Send it to that colleague who keeps asking why their variable "has the wrong value". #Python #Programming #SoftwareEngineering #PythonTips

Log 2/100: Telnetting into the Switch Python Library: telnetlib Today, I wrote a script to Telnet into my lab switch, but it kept crashing. After some troubleshooting, I found the exact issue: Python is just too fast. The script kept pasting the username before the switch had even generated the login prompt. The Fix: I added an extra line to the script (read_until) to force Python to wait and read the output until it actually saw the word "login:", and then asked it to paste the username. I applied the same logic to the Password prompt as well. Suddenly, the script started working flawlessly. The Takeaway: Writing the code is the easy part. Anticipating timing issues and making sure the script actually works without breaking in production is the tough part! The scirpts executes the show version command on the switch. Git Hub Repo: https://lnkd.in/gFkfNyWm Git hub Profile: https://lnkd.in/gjJJJQeT #Python #NetworkAutomation #NetDevOps #NetworkEngineering #100DaysOfCode #ArubaCX #CodingJourney