SOLID principles in Python: reducing stress and chaos in real systems

𝐅𝐫𝐨𝐦 𝐞𝐚𝐫𝐥𝐲 𝐩𝐫𝐨𝐦𝐢𝐬𝐞 𝐭𝐨 𝐭𝐚𝐧𝐠𝐢𝐛𝐥𝐞 𝐢𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 𝐢𝐧 𝐀𝐈 Two years ago, during my secondment in Cairo with Universidade Nova de Lisboa, I had the opportunity to meet the author of this post, Nareman Darwish. Even then, it was clear that she combined technical clarity with a rare capacity to translate ideas into practice. Seeing her now share a concrete AI system she has built is not surprising, but it is genuinely satisfying. What stands out is not only the technical achievements but her ability to move from concept to implementation in a short time frame. It is precisely where many AI initiatives struggle. We often discuss models, architectures, and potential. Yet, the real impact emerges when these ideas are operationalised into working systems that can be tested, scrutinised, and improved. This kind of work reflects a broader shift in AI, particularly relevant for fields such as healthcare and data science. The emphasis is shifting from abstract performance metrics to usability, interpretability, and integration into real-world workflows. Building something tangible, even at an early stage, is a critical step in that direction. At the same time, it is worth remaining cautious. Early prototypes, however promising, still need rigorous validation, robustness checks, and careful consideration of ethical and operational constraints. The path from a working system to a reliable, deployable solution is long and often underestimated. Nonetheless, this is exactly the type of initiative that drives meaningful progress. It is encouraging to see such work emerging, and even more so from someone whose potential was already evident some years ago. Looking forward to seeing how this evolves. #ArtificialIntelligence #DataScience #Innovation #AIinPractice #DigitalHealth #WomenInTech

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

Every line of Python creates objects. But who's tracking them? And what happens when they're no longer needed? Most developers trust Python to "just handle it." The ones who understand how — write faster, leaner, and more reliable code. Here's how it actually works: 1. Pymalloc — Python's own allocator Python doesn't ask the OS for memory every time. It grabs large chunks upfront and carves them into Arenas → Pools → Blocks. Small object allocation stays blazing fast. 2. Reference Counting — First line of defense Every object silently tracks how many things point to it. Count hits zero? Object destroyed instantly. No waiting. No pausing. Most objects never survive past this stage. 3. Garbage Collector — Second line of defense Reference counting has one weakness — circular references. Two objects pointing to each other never hit zero. Python's GC catches these using a generational strategy: Gen 0 — New objects. Collected most often. Gen 1 — Survived once. Collected less often. Gen 2 — Long-lived. Collected rarely. Most garbage dies young. Python bets on it — and wins. 💡 Things worth knowing: __slots__ — Replaces per-object dictionaries with compact fixed structures. Cuts memory by 40-50% for classes with millions of instances. weakref — References that don't keep objects alive. Essential for caches and observer patterns. tracemalloc — Tracks allocations to the exact line. Your best friend for hunting memory leaks in production. Generators over lists — Constant memory vs. allocating everything upfront. Always prefer generators when iterating once. The mindset shift: Python manages memory so you don't have to think about it. But the best developers choose to understand it anyway. Because when you know how objects live and die — every line of code becomes more intentional. What's the nastiest memory bug you've tracked down in Python? #Python #SoftwareEngineering #PythonInternals #PythonTips #CleanCode #BackendDevelopment

Python for Developers | Step 4 — Terminology That Actually Matters One part of this course that looked trivial at first, but turned out to be important, was terminology. Function, method, attribute, module, package, library — these are often used interchangeably. They shouldn’t be. A built-in function is something Python gives you out of the box: -len(), type(), print() No import, no setup. Just available. A custom function is something you define yourself: def add(x, y): return x + y Both are functions. The difference is the source, not the behavior. A method is still a function, but attached to an object: lst.append(3) The key difference is not syntax — it’s binding. The method operates on the object it belongs to. Calling a method on an object that does not implement it raises an error, because methods are type-specific behavior, not universal functions. x = 10 x.append(3)  # AttributeError: 'int' object has no attribute 'append' An attribute is not something you call — it’s something you access: obj.x This is where confusion happens: obj.method() → behavior obj.attribute → data Accessing an attribute returns the value stored in that attribute, which represents the object's state or metadata. Mixing them leads to incorrect assumptions when reading code. A module is a single Python file. A package is a directory of modules. That distinction matters when imports start getting deeper: from package import module A library is what you install and use as a complete tool. For example: NumPy or Pandas. It usually contains multiple packages and modules, but from your perspective, it’s one unit of functionality. This might look like just naming things correctly, but it’s not. It affects: how you read documentation how you structure code how you understand what is actually being used Small detail that stood out: When you write: len([1, 2, 3]) and: [1, 2, 3].append(4) Both look similar in usage, but they are fundamentally different: one is a built-in function, the other is a method bound to an object Same language, different mechanisms.

Most Python docstring tools assume Google-style. But Python's ecosystem doesn't cook with just one recipe. Django, Flask, and the CPython stdlib season their docs with Sphinx/RST field-lists. NumPy, SciPy, and pandas plate theirs with underlined section headers. Google-style is popular, but it's one ingredient in a much bigger pantry. docvet 1.13 now handles all three. Sphinx/RST support takes one line in pyproject.toml. NumPy sections work out of the box — zero configuration. What's new: → Sphinx/RST support — :param:, :returns:, :raises: directives mapped to the same quality checks → NumPy section recognition — underlined headers parsed automatically in default mode → missing-returns rule — catches undocumented return values, smart about stubs and edge cases → overload-has-docstring — flags @typing.overload signatures missing docs Your docstrings are the ingredient list for every AI agent reading your code. Copilot, Claude Code, Cursor — they're all tasting what you've written to understand your codebase. If the recipe is incomplete, the dish they serve won't be right either. docvet keeps that recipe honest, no matter which style your kitchen prefers. 22 rules. 1,424 tests. Zero runtime dependencies beyond typer. pip install docvet

File handling is the backbone of real-world Python. Before frameworks. Before RAG. Before AI pipelines. You must understand how Python reads and writes files. I created a complete, structured guide covering everything you need to know about File Handling in Python. Here’s what it includes: • What File I/O actually means • Why file handling matters in real applications • How to open files using open() • Why with is better than close() • All file modes explained – "r" read – "w" write – "a" append – "x" create – "rb"/"wb" binary Reading files properly: • read() → entire content • readline() → one line • readlines() → list of lines • Looping → memory-efficient for large files Writing files safely: • write() • writelines() • Append vs overwrite Understanding file pointers: • tell() → current cursor position • seek() → move the cursor • seek(0) → reset to start And most importantly: Error handling in file I/O. • FileNotFoundError • PermissionError • try–except for graceful failure Most production bugs aren’t model issues. They’re: Missing files. Wrong modes. Overwritten content. Unclosed handles. Frameworks hide this. Good engineers don’t ignore it. Strong file handling is non-negotiable. Master the fundamentals. Everything else builds on it. Save this as your reference.

Python Isn’t Just “Another Programming Language” Most people describe Python with a list of features. High-level. Interpreted. Dynamic. But that doesn’t explain why it became one of the most powerful languages in the world. Here’s what Python really is. 1️⃣ High-Level — You Focus on Thinking, Not Memory Python abstracts away low-level hardware details. You don’t manually manage memory. You don’t deal with pointers. You focus on logic. That’s why beginners can learn it quickly. And experts can prototype ideas fast. ================ 2️⃣ Interpreted — Execution Happens Line by Line Unlike compiled languages that convert code into machine instructions beforehand, Python executes code through an interpreter. This means: Faster development cycles Immediate feedback Easier debugging It trades a bit of raw speed for flexibility. And in many real-world applications, that trade-off is worth it. ============== 3️⃣ Multi-Paradigm — You’re Not Locked Into One Style Python doesn’t force you into one way of thinking. You can write: Object-Oriented code (classes & objects) Procedural code (functions & steps) Functional-style expressions It adapts to the problem. Not the other way around. ============= 4️⃣ Dynamically Typed — Types Are Decided at Runtime You don’t declare variable types explicitly. Instead of: int x = 10; You simply write: x = 10 The type is determined at runtime. That reduces boilerplate. But it also means you must be disciplined. Flexibility always comes with responsibility. ==================== 5️⃣ Garbage-Collected — Memory Is Managed for You Python automatically handles memory allocation and deallocation. You don’t manually free memory. The garbage collector does that behind the scenes. This reduces memory leaks. And makes development safer — especially for large systems. ================ The Bigger Picture Python isn’t popular because it’s the fastest. It’s popular because it reduces friction. Less setup. Less syntax noise. More problem-solving. And that’s why it dominates in: Data Science AI & Machine Learning Automation Web Development Not because it’s “simple”. But because it’s powerful without being complicated. #DataSalma #python

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