7 Habits of Great Python Developers

Focus on Concepts, Not Syntax Every year: • New languages appear • New frameworks launch • New libraries become popular So trying to remember everything is almost impossible. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝘀𝗸𝗶𝗹𝗹 𝗶𝘀 𝗻𝗼𝘁 𝘀𝘆𝗻𝘁𝗮𝘅. 𝗜𝘁’𝘀 𝗰𝗼𝗻𝗰𝗲𝗽𝘁. No matter which language you use: JavaScript Python Java Go C++ Internally they all rely on the same fundamentals: • Variables • Loops • Conditions • Functions • Data structures • Logic and problem solving Syntax can be searched in seconds. But understanding the problem and designing the solution takes real thinking. Today you can generate code with: Google Stack Overflow AI tools But if your concept is wrong, the code will also be wrong. And the biggest problems in large projects usually start from small mistakes at the beginning. That’s why good developers always: Analyze first , Research properly, Think deeply 𝗧𝗵𝗲𝗻 𝘁𝗵𝗲𝘆 𝘄𝗿𝗶𝘁𝗲 𝗰𝗼𝗱𝗲. Great software is not built by developers who type fast. It’s built by developers who think clearly before writing the first line of code. #SoftwareEngineering, #Programming #Developers #Coding #ProblemSolving #TechCareers #SoftwareDevelepment #ProgrammingMindset #softwaredesign

“If you’re always using 𝒔𝒆𝒍𝒇 in Python… you’re probably doing it wrong.” ⚠️ For the longest time, every method I wrote looked like this: def some_function(self):   ... Didn’t matter what it did, I just slapped 𝒔𝒆𝒍𝒇 on it. Until one code review changed everything. 👀 My senior asked: “Why is this an instance method?” I didn’t have an answer. That’s when I realized: 👉 I wasn’t designing… I was just coding. 💡The simple rule that changed how I think: 🔹 𝐈𝐧𝐬𝐭𝐚𝐧𝐜𝐞 𝐌𝐞𝐭𝐡𝐨𝐝 (𝐬𝐞𝐥𝐟) Use when your logic depends on object data class User:   def greet(self):     return f"Hi, {self.name}" ✔ Tied to a specific object ✔ Reads/modifies instance state 🔹 𝐂𝐥𝐚𝐬𝐬 𝐌𝐞𝐭𝐡𝐨𝐝 (𝐜𝐥𝐬) Use when logic belongs to the class as a whole class User:    count = 0   @classmethod   def get_count(cls):     return cls.count ✔ Shared across all objects ✔ Great for factory methods 🔹 𝐒𝐭𝐚𝐭𝐢𝐜 𝐌𝐞𝐭𝐡𝐨𝐝 (𝐧𝐨 𝐬𝐞𝐥𝐟, 𝐧𝐨 𝐜𝐥𝐬) Use when logic is just related, not dependent class MathUtils:   @staticmethod   def add(a, b):     return a + b ✔ Pure function ✔ Just grouped inside class for clarity 🔥 The shift that made me better: Before: “I need a method → add self” After: “What does this method actually depend on?” ⚡ Rule of thumb: Needs object data? → Instance method Needs class state? → Class method Needs neither? → Static method That one small distinction… 👉 Improved my code quality 👉 Made my design cleaner 👉 And made code reviews way easier 😄 Most devs know these concepts. Few actually apply them correctly. #Python #SoftwareEngineering #BackendDevelopment #Coding #TechGrowth #Developers #CleanCode #Programming

I’m tired of the debate about agent-based coding and drowning in technical depth. Whether you drown in abstraction is not about the tool. It is about the user. An #LLM will happily generate spaghetti architecture if you let it. It will also enforce clean modular design, strict typing, tests, and separation of concerns if you instruct it properly. The difference is not #Claude, GPT, or any other model. The difference is whether you provide architectural constraints. If you use agents to write Python code, at least force them to follow clear structure and best practices. I created a #cookiecutter project boilerplate to set up those guardrails for new Python projects, following modern best practices. You can find it here: https://lnkd.in/dk-bVZhN

Python vs Go — When to Use What? 🤔 Both Python and Go are powerful, but they solve different problems. Choosing the right one can make your system faster, simpler, and easier to maintain. 🔹 Python — Best for Speed of Development Python is easy to learn, flexible, and has a huge ecosystem. Use Python when: • You are building APIs quickly (FastAPI, Django, Flask) • Working on Data Science, AI, or Machine Learning • Writing automation scripts or internal tools • You need rapid prototyping and quick iterations Why Python? • Simple and readable syntax • Huge community support • Tons of libraries for almost everything 🔹 Go (Golang) — Best for Performance & Scalability Go is designed for concurrency and high-performance systems. Use Go when: • Building microservices or distributed systems • Handling high traffic APIs • Working with cloud-native tools (Docker, Kubernetes) • Need fast execution and low memory usage Why Go? • Built-in concurrency (goroutines) • Compiled language → faster than Python • Simple, clean, and production-friendly ⚖️ Quick Comparison: • Python = Productivity & Flexibility • Go = Performance & Scalability 🚀 Real-world tip: Many companies use BOTH. 👉 Python for data & business logic 👉 Go for high-performance services Final Thought: Don’t ask “Which is better?” Ask “Which fits my problem?” #Python #Golang #BackendDevelopment #Microservices #SoftwareEngineering #TechCareers

I built my first Claude Code skill to learn how skills worked. That experiment became Python Mastery, and today I'm releasing it publicly for the first time. It's an opinionated, practitioner-grade Python reference covering the language from A to Z with the judgment of a senior engineer baked in. Not a cheat sheet. Twenty focused modules covering the stuff that actually trips people up in production: - When to use a class vs. a function, and why it matters - Safe refactoring without regressions - Debugging methodology, not just debugging syntax - FastAPI, SQLAlchemy, asyncio, Pydantic v2, pytest, Docker, and more - Architecture decisions, ADRs, tech debt and the architect mindset Built from real experience, not just the docs. See Readme on how to Install in one line, i tried to make it simple since its currently not nativly. Or grab the .skill file and upload it straight to Claude.ai. This is v1 and I want to make it better, so if you try it I'd love to hear what's missing, what's useful, or what could be sharper. Comments, GitHub issues, and DMs all welcome. Repo Link: https://lnkd.in/eM-cRG8A #Python #ClaudeCode

We once broke production because of a missing type. Not traffic. Not infrastructure. Not scaling. A type. It was a small refactor. A parameter that used to be int started coming as str. Nothing dramatic. CI passed. Tests were green. Deployment was smooth. Two days later, the payment service crashed. Somewhere deep in the flow, we were adding a number to a string. Classic. That’s the uncomfortable truth about Python. It doesn’t stop you. It trusts you. Until production stops you. In Go or Rust, this wouldn’t even compile. In Python? It runs. It ships. It fails later. Before someone says, “Just write better tests.” Sure. Tests are critical. But types eliminate entire categories of bugs before your code even executes. They are preventive, not reactive. The real issue isn’t Python. It’s how casually many teams treat it. I’ve seen production systems with no type hints, no static analysis in CI, no validation at service boundaries, and loose code review around contracts. At that point, you’re not engineering a system. You’re relying on runtime luck. The senior Python engineers I respect do something different. They treat Python as if it were strict. Type hints everywhere. mypy or pyright enforced in CI. Boundary validation with Pydantic or similar tools. Contract-driven thinking. They remove ambiguity early. They don’t let production be the validator. Here’s my honest take: Dynamic typing is powerful. But at scale, it demands discipline most teams underestimate. Now I’m curious. Do you enforce type checking in CI? Have you had a runtime type bug hit production? Is dynamic typing still worth it at scale? Let’s talk. #Python #SoftwareEngineering #BackendDevelopment #CleanCode #DevOps #Programming #TechLeadership #Microservices #SystemDesign

One of the biggest confusions in Python is not syntax… It’s this 👇 👉 Function vs Lambda vs List Comprehension All three can solve the same problem… But choosing the wrong one can make your code messy, unreadable, or overcomplicated. Let’s be real 👇 Most developers: • Overuse lambda because it looks “smart” • Ignore functions when logic grows • Write complex list comprehensions that no one can read 👉 And that’s where clean code breaks 💡 Here’s the actual way to think like a developer: ✔ Use Function (def) → when logic is complex or reusable ✔ Use Lambda → when logic is small and temporary ✔ Use List Comprehension → when transforming or filtering data 📌 Same problem, 3 ways (this is important): Get square of even numbers • Function → structured & readable • Lambda → compact but less readable • List Comprehension → best balance 👉 The difference is not output… 👉 The difference is code quality 💡 Real-world mindset: In production code, readability > short code Because your code is read 10x more than it is written 📌 What I’ve covered in today’s post: ✔ Clear comparison (Function vs Lambda vs List Comp) ✔ Same problem solved in 3 ways ✔ Real-world API example ✔ When to use what (decision clarity) ✔ Common mistakes developers make 💬 Let’s discuss (real dev talk): Which one do you use the most in your projects — and why? #PythonLearning #PythonDeveloper #CodingJourney #BackendDevelopment #Programming #CleanCode #LearnInPublic #DevelopersIndia #Python #PythonTutorial

OpenAI acquiring Astral to pull Python’s most popular dev tools into Codex is a classic platform move: stop being “a model that writes code” and start owning the toolchain developers live in. When you control formatting, dependencies, builds, and the agent that edits code, you control the workflow - and workflows are where switching costs are born. This matters because the coding layer is the gateway to enterprise agents. Today it’s Python tooling. Tomorrow it’s CI/CD, ticketing, infrastructure-as-code, and deployment permissions. That’s enormous leverage - and enormous risk if governance doesn’t keep up: supply chain attacks, license contamination, insecure defaults, and “agent drift” that quietly changes production behavior. My controversial view: this acquisition isn’t about developer happiness - it’s about lock-in. OpenAI wants to be the IDE + build system + agent brain, so leaving becomes as painful as migrating clouds. Best practices for teams adopting Codex-style platforms: require signed outputs, enforce human review on critical paths, run dependency scanning, keep reproducible builds, and maintain an escape hatch (toolchain portability, data export, model routing). If one vendor owns your coding agent and your Python toolchain, who really owns your software supply chain - and what happens when that vendor’s incentives shift? #DevTools #SoftwareSupplyChain #AIGovernance https://lnkd.in/gq7GHJg5

𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 𝗩𝗮𝗿𝗶𝗮𝗯𝗹𝗲𝘀 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻 — 𝗪𝗵𝗶𝗰𝗵 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝗗𝗼 𝗬𝗼𝘂 𝗨𝘀𝗲? Every Python project deals with environment variables. Not every project handles them well.   I'll be honest, for a long time, I was sprinkling 𝗼𝘀.𝗲𝗻𝘃𝗶𝗿𝗼𝗻.𝗴𝗲𝘁() calls all over my codebase. It worked, so I didn't think twice about it.   Then I had to debug a production issue caused by a missing environment variable that was being accessed five files deep. That was a fun afternoon.   𝗟𝗼𝗼𝗸 𝗮𝘁 𝘁𝗵𝗲 𝘁𝘄𝗼 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵𝗲𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗶𝗺𝗮𝗴𝗲.   ➝ 𝗢𝗽𝘁𝗶𝗼𝗻 𝟭 reads env vars directly wherever they're needed. ➝ 𝗢𝗽𝘁𝗶𝗼𝗻 𝟮 centralizes them in one place and imports from there.   Here's why I made the switch:   ➝ With Option 1, missing variables fail silently or deep in the stack, hard to catch early. ➝ With Option 2, your app fails fast at startup if a required variable is missing which is much easier to debug. ➝ Centralizing config also makes it easy to see all your environment dependencies in one glance. ➝ Tools like 𝗽𝘆𝘁𝗵𝗼𝗻-𝗱𝗲𝗰𝗼𝘂𝗽𝗹𝗲 and 𝗽𝘆𝗱𝗮𝗻𝘁𝗶𝗰-𝘀𝗲𝘁𝘁𝗶𝗻𝗴𝘀 add type casting and validation for free.   𝗧𝗵𝗶𝘀 𝗶𝘀𝗻'𝘁 𝗮𝗯𝗼𝘂𝘁 𝗷𝘂𝘀𝘁 𝗯𝗲𝗶𝗻𝗴 𝗮 '𝗯𝗲𝘁𝘁𝗲𝗿' 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿. It's just a small structural change that saves real debugging time as your project grows.   𝗛𝗼𝘄 𝗮𝗿𝗲 𝘆𝗼𝘂 𝗰𝘂𝗿𝗿𝗲𝗻𝘁𝗹𝘆 𝗺𝗮𝗻𝗮𝗴𝗶𝗻𝗴 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 𝘃𝗮𝗿𝗶𝗮𝗯𝗹𝗲𝘀 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗮𝗻𝗱 𝗵𝗮𝘀 𝗶𝘁 𝗲𝘃𝗲𝗿 𝗰𝗮𝘂𝘀𝗲𝗱 𝘆𝗼𝘂 𝗮 𝗵𝗲𝗮𝗱𝗮𝗰𝗵𝗲? #Python #Django #BackendDevelopment #CleanCode #SoftwareEngineering #DevOps

Python's Zen emphasizes that "Simple is better than Complex." I've noticed that many people struggle with information overload. To become an interview-ready backend engineer as quickly as possible, here's a straightforward roadmap: 1. Start by learning Python. 2. Next, focus on learning Flask. 3. Then, move on to mastering PostgreSQL. 4. Become proficient in unit testing, specifically Test-Driven Development (TDD). 5. Familiarize yourself with Git and Github for version control. 6. Master REST API development using Python & Flask. 7. Gain some basic knowledge of bash scripting. 8. Continue to build on your Git and Github skills. 9. Last but not least, you need to learn about Docker and create a Github portfolio. This is all you need to become an interview-ready backend engineer within a few weeks. 📌 If you like my posts, please follow me here - António Sousa, and hit the 🔔 on my profile to get notifications for all my new posts. #python #development #testing #learning #interview #people #docker #github #softwareengineering #backenddeveloper #backenddevelopment