Python Exceptions: The Hidden Performance Cost

Most Python code works. Very little Python code scales. The difference? 👉 Object-Oriented Programming (OOPS). As part of rebuilding my Python foundations for Data, ML, and AI, I’m now focusing on OOPS — the layer that turns scripts into maintainable systems. Below are short, practical notes on OOPS — explained the way I wish I learned it 👇 (No theory overload, only what actually matters) 🧠 Python OOPS — Short Notes (Practical First) 🔹 1. Class & Object A class is a blueprint. An object is a real instance. class User: def __init__(self, name): self.name = name u = User("Anurag") Used to model real-world entities (User, File, Model, Pipeline) 🔹 2. __init__ (Constructor) Runs automatically when an object is created. Used to initialize data. def __init__(self, x, y): self.x = x self.y = y 🔹 3. Encapsulation Keep data + logic together. Control access using methods. class Account: def get_balance(self): return self.__balance Improves safety & maintainability 🔹 4. Inheritance Reuse existing code instead of rewriting. class Admin(User): pass Used heavily in frameworks & libraries 🔹 5. Polymorphism Same method name, different behavior. obj.process() Makes systems flexible and extensible 🔹 6. Abstraction Expose what a class does, hide how it does it. from abc import ABC, abstractmethod Critical for large codebases & APIs OOPS isn’t about syntax. It’s about thinking in systems, not scripts. #Python #OOPS #DataEngineering #LearningInPublic #SoftwareEngineering #AIJourney

Also checkout depyler which does something kind of similar, but actually very different. It single shot CONVERTS python to Rust and compiles it, so it is the ULTIMATE type check a one way PERMANENT conversion to Rust. Current status? 40% of 200 corpus of projects with sklearn, numpy, argparse, etc, "just work", and possible 80% in next few days... https://lnkd.in/ehJeTtvs

🧠 Python Concept You MUST Know: Python’s LEGB Rule (Variable Lookup Order) ✔️ Most Python developers use variables every day. ✔️ Very few understand how Python decides which variable to use. ✔️ That’s where the LEGB rule comes in. Let’s explain this simply 👇 🧒 Simple Explanation Imagine you’re looking for your toy car 🚗. You search in this order: 1️⃣ Your room 2️⃣ Your sibling’s room 3️⃣ Living room 4️⃣ Your building’s storage area Python does the same thing when looking for a variable. 🔍 LEGB = Local → Enclosed → Global → Built-in Python looks for variables in this exact order: 🔹 L — Local Inside the current function def fun(): x = 10 # local 🔹 E — Enclosed Inside an outer function (nested functions) def outer(): x = 20 def inner(): print(x) # enclosed 🔹 G — Global Defined at the top of the script x = 30 🔹 B — Built-in Python’s internal names (like len, range, print) 🧠 Example That Shows All 4 in Action x = 30 # global def outer(): x = 20 # enclosed def inner(): x = 10 # local print(x) inner() outer() Output: 10 Python stops at the first match — the local variable in inner(). ⚠️ When Python Can’t Find a Variable If Python checks: Local ❌ Enclosed ❌ Global ❌ Built-in ❌ Then you get: NameError: name 'x' is not defined 🎯 Interview Gold Line “Python resolves variables using the LEGB rule — Local, Enclosed, Global, Built-in — in that exact order.” Interviewers LOVE this answer. 🧠 One-Line Rule Python looks in the nearest scope first. ✨ Final Thought Knowing LEGB makes you better at: ✔ Debugging ✔ Writing functions ✔ Understanding closures ✔ Avoiding accidental shadowing It turns confusion into clarity. 📌 Save this post — LEGB is Python’s secret map for finding variables. #Python #LearnPython #PythonTips #Programming #DeveloperLife #SoftwareEngineering #Coding #TechLearning #CodeNewbie #Freshers

Why Is My Code So Slow? A Guide to Py-Spy Python Profiling frustrating issues to debug in data science code aren’t syntax errors or logical mistakes. Rather, they come from code that does exactly what it is supposed to do, but takes its sweet time doing it. Functional but inefficient code can be a massive bottleneck in a data science workflow. In this article, I will provide a brief introduction and walk-through of…...

In the Metasploit Wrap-Up from last week, a new Python Site-Specific Hook Persistence module was released. [1] I wrote a detailed blog about this persistence, which I think is pretty cool. [2] If you have never heard of this technique, you might want to read up on it. [1] https://lnkd.in/ei_C5TgQ [2] https://lnkd.in/eYRmWrx8

New Meta Open Source Content 🔎 Four type-narrowing patterns that make Python type checking more intuitive. 🐍✨ From tuple length narrowing to hasattr guards, see how Pyrefly helps reduce the need for explicit casts in your code. Learn more here: https://lnkd.in/eAny9r5R

Python devs, this one is a big deal 👀 If you’ve ever written a CPU-heavy Python script, watched only one core max out, and whispered “thanks, GIL” under your breath, this is for you. Python 3.12 quietly introduced something foundational for performance: Subinterpreters with per-interpreter GILs (PEP 684). What does that mean in practice? True parallelism for CPU-bound Python code Multiple interpreters inside a single process No heavyweight multiprocessing, no pickling overhead A real path toward multi-core Python without burning memory In my latest post, I walk through: Why the GIL has been the wall for years How subinterpreters change Python’s execution model An experimental example using _xxsubinterpreters Why this matters more right now than “GIL removal” headlines This is the groundwork for Python’s high-performance future — and it’s already here. 👉 Read the full breakdown here: https://lnkd.in/gcfsn2U3 Would love to hear how you’re thinking about concurrency in Python 👇 #Python #Python312 #PerformanceEngineering #Concurrency #BackendEngineering #SoftwareArchitecture #GIL #pythonInPlainEnglish

🐍 Ever wondered how Python actually works behind the scenes? We write Python like this: print("Hello World") And it just… works 🤯 But there’s a LOT happening in the background. Let me break it down simply 👇 🧠 Step 1: Python compiles your code Your .py file is NOT run directly. Python first converts it into: ➡️ Bytecode (.pyc) This is a low-level instruction format, not machine code yet. ⚙️ Step 2: Python Virtual Machine (PVM) The bytecode is executed by the PVM. Think of PVM as: 👉 Python’s engine that runs your code line by line This is why Python is called: 🟡 An interpreted language (with a twist) 🧩 Step 3: Memory & objects Everything in Python is an object. • Integers • Strings • Functions • Even classes Variables don’t store values. They store references 🔗 That’s why: a = b = 10 points to the SAME object. ⚠️ Step 4: Global Interpreter Lock (GIL) Only ONE thread executes Python bytecode at a time 😐 ✔ Simple memory management ❌ Limits CPU-bound multithreading That’s why: • Python shines in I/O • Struggles with heavy CPU tasks 💡 Why this matters Understanding this helped me: ✨ Debug performance issues ✨ Choose multiprocessing over threads ✨ Write better, scalable backend code Python feels simple on the surface. But it’s doing serious work underneath. Once you know this, Python stops feeling “magic” and starts feeling **powerful** 🚀 #Python #BackendDevelopment #SoftwareEngineering #HowItWorks #DeveloperLearning #ProgrammingConcepts #TechExplained