Static Analysis Boosts Python Code Quality with Ruff, Mypy, and Radon

Most Python code looks simple until you realize how much is happening under the surface. Take this for example: _C = (1, 2, 3) a, b, c = _C print(a) This is iterable unpacking, more precisely Python’s way of doing positional destructuring assignment. What actually happens: _C is evaluated as an iterable Python matches elements positionally Each value is bound in a single atomic assignment step So internally: a = _C[0] b = _C[1] c = _C[2] This pattern is not just syntactic sugar, it is widely used in production code: Function return unpacking (return x, y) Iteration over structured data API responses and tuple-based records Why it matters: Removes manual indexing (less error prone) Improves intent readability Makes transformations explicit and compact One important constraint: If the structure does not match, Python fails fast with a ValueError, which is often a feature, not a bug. Clean syntax, strict alignment, predictable behavior. That is the philosophy behind Python’s design. Which Python feature felt too simple until you saw it in real systems? #Python #SoftwareEngineering #CleanCode #Programming #PythonTips #Coding #Developer #SystemDesign

𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝗣𝘆𝘁𝗵𝗼𝗻 𝗰𝗼𝗱𝗲 𝗳𝗲𝗲𝗹𝘀 𝘀𝗹𝗼𝘄 𝗱𝗲𝘀𝗽𝗶𝘁𝗲 𝘂𝘀𝗶𝗻𝗴 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝘁𝗵𝗿𝗲𝗮𝗱𝘀 ? The secret lies in how Python handles execution. I’ve put together a 12-slide deep dive into Python Concurrency, moving from absolute basics to the future of Python 3.13. What’s inside? ✅ Synchronous vs. Async: Why "𝘄𝗮𝗶𝘁𝗶𝗻𝗴" is the biggest bottleneck. ✅ The Event Loop: How 𝗮𝘀𝘆𝗻𝗰𝗶𝗼 manages thousands of tasks on a single thread. ✅ The 𝗚𝗜𝗟 (𝗚𝗹𝗼𝗯𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝗲𝗿 𝗟𝗼𝗰𝗸): Why traditional Python threading isn't always "parallel." ✅ The 𝗙𝘂𝘁𝘂𝗿𝗲 (𝗙𝗿𝗲𝗲-𝗧𝗵𝗿𝗲𝗮𝗱𝗶𝗻𝗴): How Python 3.13+ finally enables true multi-core parallelism. 🟪 𝗧𝗵𝗲 "𝗞𝗶𝘁𝗰𝗵𝗲𝗻" 𝗔𝗻𝗮𝗹𝗼𝗴𝘆: Think of a single cook (Thread) multitasking between a gas stove (I/O) and a cutting board. That’s Async. Now imagine a kitchen with multiple cooks and multiple gas stoves. That’s Modern Free-Threading. Whether you're building 𝘄𝗲𝗯 𝘀𝗰𝗿𝗮𝗽𝗲𝗿𝘀 (𝗜/𝗢-𝗯𝗼𝘂𝗻𝗱) or 𝗵𝗲𝗮𝘃𝘆 𝗱𝗮𝘁𝗮 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 (𝗖𝗣𝗨-𝗯𝗼𝘂𝗻𝗱), choosing the right model is key to performance. Check out the slides below! #Python #Programming #SoftwareEngineering #Concurrency #AsyncIO #Multithreading #Python313 #TechLearning

Just wrote about something I kept running into in real backend work — using Enums instead of plain dictionaries for fixed states like booking status, event types, and error codes. Nothing fancy. Just a pattern that reduced silent bugs and made the code easier to read and refactor. Read here: https://lnkd.in/gnYwTtzU #Python #BackendDevelopment #SoftwareEngineering #PythonTips #CleanCode #APIDesign #PythonDeveloper #CodeQuality #Programming #TechArticle

Async IO in Python is single-threaded. No mutexes, no race conditions, no surprise context switches. Coming from multi-threaded code, this felt like cheating. With threads, anything can interrupt anything. You lock shared state, hope you got it right, and debug it six months later when you didn't. With async, control only transfers at await. That's it. Your data is safe between those points by definition, not by luck. The payoff was immediate. Refactored a GitHub API client to fetch a user profile and repo list at the same time using asyncio.gather(). Two concurrent HTTP calls. No threads, no locks. The mental model shift took longer than the code change. If you've been avoiding async because threads burned you before, it's not the same thing.

Clean code isn't clever. It's clear. 5 Python patterns every developer should know: 1️⃣ Flatten nested list: flat = [x for sub in nested for x in sub] 2️⃣ Merge dicts (Python 3.9+): merged = dict_a | dict_b 3️⃣ Most frequent item: max(set(lst), key=lst.count) 4️⃣ Swap variables: a, b = b, a 5️⃣ Read + strip file lines: lines = [l.strip() for l in open("file.txt")] --------------- These aren't tricks. They're idiomatic Python. When your code communicates intent: ✅ Reviews go faster ✅ Bugs surface sooner ✅ Onboarding is smoother Write for the developer reading it at 2am before a deployment. That developer is usually you. #Python #CleanCode #Programming #CodingTips #SoftwareEngineering

If your Python scripts are making 50 API calls, synchronous code spends most of its time waiting around doing absolutely nothing. Suresh Vina has written an intro to async Python using a simple analogy: boiling a kettle and making toast at the same time. Sync code does one, then the other. Async runs both concurrently. Same two tasks: 5 seconds sync, 3 seconds async. It’s not a big deal when making breakfast. But scale that concept across dozens of API calls and the difference adds up. The Infrahub Python SDK supports both sync and async natively. Switching between them is simple. Suresh walks through the core concepts, shows side-by-side code examples, and builds up to running the same operation across multiple sites concurrently. If async Python has been sitting on your "I should learn that someday" list, now’s your chance to *get up to speed*. (See what we did there? 😉) Link in comments 👇

I’ve published my first technical article: a walkthrough of the SOLID principles—with Python examples. It started as “I’ve heard these letters everywhere—what do they actually mean in code?” Turning that into something concrete helped me more than skimming another diagram. In the post I break things down into bite-sized pieces, including: • Single Responsibility: One job per module—easier to reason about and change. • Open/Closed: Extend behavior without rewriting existing code. • Liskov Substitution: Subtypes that don’t break expectations. • Interface Segregation: Small, focused contracts instead of fat interfaces. • Dependency Inversion: Depend on abstractions, not concrete details. Beyond the theory, each section includes short Python snippets so the ideas map to something you can run and tweak—not just memorize. The full post is here: https://lnkd.in/gFXSE4d9 #SoftwareEngineering #SOLID #Python #CleanCode #OOP #DesignPatterns

📘 Web Scraping with Python ✍️ Ryan Mitchell A great book to learn how to extract data from real-world websites using Python. It covers tools like BeautifulSoup, Scrapy, and Selenium, and explains how modern websites work, including APIs and JavaScript-based content. Perfect for anyone interested in automation, data collection, or building scraping tools. 🔗 Read here: https://lnkd.in/gKeQG8We⁠ #Python #WebScraping #Automation

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

C++26 Reflection & Python Bindings Writing bindings manually is tedious: * Extra code you need to read, maintain(,and write). * Extra dependencies in the project. * Extra bugs. I’ve been exploring C++26 reflection and built a small prototype: automatic Python bindings without writing bindings. Here’s how it works 👇 Post: https://lnkd.in/gwJYhnnF Code: https://lnkd.in/gbQqPVNr #cpp #reflection #c++26