Python OOP: Method Overriding & Duck Typing

🚀 Full Stack Journey Day 48: Python Error Mastery - Syntax vs. Runtime Exceptions! 🛠️🐍 Day 48 of my #FullStackDevelopment series is all about understanding the "why" behind broken code! I’ve been diving into Exception Handling in Python, specifically learning how to distinguish between Syntax Errors and Runtime Errors (Exceptions). 🧠 Understanding these two categories is the first step toward building resilient, "unbreakable" applications: Syntax Errors (The Grammar Mistake): These occur when Python doesn't understand your code because it violates the language rules—like a missing colon :, mismatched parentheses (), or incorrect indentation. Python won't even start running your code if it finds a syntax error. It's like trying to drive a car with no engine! ❌ Runtime Errors / Exceptions (The Execution Problem): Your code is grammatically perfect, but something goes wrong while it's running—like dividing by zero, trying to open a file that doesn't exist, or using a variable that hasn't been defined. Unlike syntax errors, these can be caught and handled gracefully using try and except blocks. ✅ Knowing the difference allows me to fix my own typos faster and write code that can handle real-world "surprises" from users or external systems without crashing. 📂 Access my detailed notes here: 👉 GitHub: https://lnkd.in/g_WzXQKf #Python #AdvancedPython #ExceptionHandling #SyntaxError #RuntimeError #CodingFundamentals #CleanCode #FullStackDeveloper #LearningToCode #Programming #TechJourney #SoftwareDevelopment #DailyLearning #CodingChallenge #Day48 LinkedIn Samruddhi P.

I love Python. But it's slow. So I wrote C code to value an option and call it from Python. C helps Python run up to 45X faster. Here's how: Before you ask, yes I know there are better ways to do this. Here's the problem: A lot of quant code is already written in C. So instead of re-writing it from scratch, most quants wrap existing code in Python. Enjoy: https://lnkd.in/ejFiHXdy

I love Python. But it's slow. So I wrote C code to value an option and call it from Python. C helps Python run up to 45X faster. Here's how: Before you ask, yes I know there are better ways to do this. Here's the problem: A lot of quant code is already written in C. So instead of re-writing it from scratch, most quants wrap existing code in Python. Enjoy: https://lnkd.in/gDnfmtGk

Day 460: 7/1/2026 Why Python Execution Is Slow? Python is expressive, flexible, and easy to use — but when performance matters, it often struggles. This is not because Python is “badly written,” but because of how Python executes code and accesses memory. Let’s break it down. ⚙️ 1. Python Works With Objects, Not Raw Data In Python, data is not stored contiguously like in C or C++. Instead: --> Each value is a Python object --> Objects live at arbitrary locations in memory --> Variables hold pointers to those objects When Python accesses a value: --> The pointer is loaded --> The CPU jumps to that memory location --> The Python object is loaded --> Metadata is inspected --> The actual value is read This pointer-chasing happens for every operation. 🔁 2. Python Is Interpreted, Not Compiled to Machine Code Python source code is not executed directly. Execution flow: --> Python source is compiled into bytecode --> Bytecode consists of many small opcodes --> The Python interpreter: fetches an opcode, decodes it, dispatches it to the corresponding C implementation --> This repeats for every operation --> Each step adds overhead. Even a simple arithmetic operation involves: --> multiple bytecode instructions --> multiple function calls in C --> dynamic type checks at runtime ⚠️ 3. Dynamic Typing Adds Runtime Checks Because Python is dynamically typed: --> Types are not known at compile time --> Every operation checks type compatibility --> Method lookups happen at runtime This flexibility makes Python powerful — but it prevents many low-level optimizations. Stay tuned for more AI insights! 😊 #Python #Performance #SystemsProgramming

Python with Machine Learning — Chapter 8 📘 Topic: Python OOP's 🔍 Today, we're unlocking a powerful Python skill: Object-Oriented Programming (OOP). It's a way to organize your code by thinking about real-world things. WHY IT MATTERS As your projects grow, OOP helps you keep everything tidy and reusable. It's the foundation for building complex applications with confidence. Let's break it down simply: 1️⃣ **CLASSES**: Think of them as blueprints. A Class is a template that defines what something looks like and what it can do. *Example: A blueprint for a "Car" class.* 2️⃣ **OBJECTS**: Think of them as the actual things you build. An Object is an instance created from a Class. You can make many objects from one blueprint. Example: Creating a specific red car from the "Car" blueprint.* Let's see it in action! Here’s a simple example of a Class and creating an Object from it. [CODE] class Dog: def __init__(self, name): self.name = name # An attribute def bark(self): # A method print(f"{self.name} says Woof!") # Creating an OBJECT (instance) from the Dog class my_dog = Dog("Buddy") my_dog.bark() [/CODE] EXPLANATION: - `class Dog:` creates the blueprint. - `__init__` is a special method (constructor) that runs when you create a new object. - `self.name` stores the dog's name—this is an *attribute*. - `bark()` is a *method* (a function that belongs to the class). - `my_dog = Dog("Buddy")` creates an actual object called `my_dog`. - `my_dog.bark()` makes our object perform an action. You just built a Dog! 🐶 This pattern is how we model real things in code. Start small. You're building skills that will help you tackle bigger, exciting projects. I believe in you. What's one thing you'd love to model as a class? Comment below! #Python #MachineLearning #OOP #LearnToCode

Day 459: 6/1/2026 Why Python Objects Are Heavy? Python is loved for its simplicity and flexibility. But that flexibility comes with a cost — Python objects are memory-heavy by design. ⚙️ What Happens When You Create a Python Object? Let’s take a simple example: a string. When you create a string in Python, you are not just storing characters. Python allocates a full object structure around that value. Every Python object carries additional metadata. 🧱 1. Object Header (Core Overhead) Every Python object has an object header that stores: --> Type Pointer Points to the object’s type (e.g., str, int, list) Required because Python is dynamically typed Enables runtime checks like: which methods are valid whether operations are allowed This is why “a” + 1 raises a TypeError Unlike C/C++, Python must always know the object’s type at runtime. --> Reference Count Tracks how many variables reference the object Used for Python’s memory management When the count drops to zero, the object is immediately deallocated This bookkeeping happens for every object, all the time. 🔐 2. Hash Cache (For Immutable Objects) Immutable objects like strings store their hash value inside the object. Why? Hashing strings is expensive Dictionaries need fast lookups So Python caches the hash: Hash computed once Reused for dictionary and set operations Enables average O(1) lookups This improves speed — but adds more memory per object. 📏 3. Length Metadata Strings also store their length internally. This allows: len(s) to run in O(1) slicing and iteration without recomputing length efficient bounds checking Again: faster execution, but extra memory. Stay tuned for more AI insights! 😊 #Python #MemoryManagement #PerformanceOptimization

How to Switch to ty from Mypy Python has supported type hinting for quite a few versions now, starting way back in 3.5. However, Python itself does not enforce type checking. Instead, you need to use an external tool or IDE. The first, and arguably the most popular, is mypy. Microsoft also has a Python type checker that you can use in VS Code called Pyright, and then there’s the lesser-known Pyrefly type checker and language server. The newest type checker on the block is Astral’s ty, the maker of Ruff. Ty is another super-fast Python utility written in Rust. In this article, you will learn how to switch your project to use ty locally and in GitHub Actions… https://lnkd.in/d3i-mgnq

🚀 GitHub Project | Morse-Like Message Decryption in Python 🐍 I’m excited to share a recent Python project that solves a Morse-like message decryption challenge using recursive backtracking and variable-length encoding logic. 🔍 Problem Summary Given an encrypted message of dots (.) and underscores (_), decode it into all possible valid plaintext messages based on a defined encoding scheme. 🧠 Solution Highlights ✔ Handles variable-length encodings ✔ Uses recursive backtracking to explore all combinations ✔ Generates all valid decodings efficiently ✔ Clean, modular Python implementation 💻 Repository Structure solution.py — Python implementation question.md — Full problem description README.md — Instructions & usage ▶ How to Run python solution.py 📌 Check out the code here: 👉 https://lnkd.in/g9HvrdBQ This project helped me reinforce core concepts such as: ✨ Recursion & Backtracking ✨ String parsing & algorithmic thinking ✨ Designing clean, maintainable Python code I’d love to hear your thoughts and feedback! 👇 #Python #Algorithms #Recursion #Backtracking #Coding #GitHub #SoftwareEngineering #ProblemSolving #DeveloperJourney

🐍 Back to Basics: The Building Blocks of Python I recently reviewed some comprehensive notes on Python fundamentals, and it’s a great reminder of why this language—developed by Guido Van Rossum—remains so user-friendly and powerful. Whether you are just starting or brushing up on your skills, mastering the core vocabulary is key. Here is a breakdown of the essentials found in these notes: 1. The "Grammar" (Keywords) These are the reserved words that give Python its structure. The notes highlight how we use logic operators like and, or, and not to control flow, or def and class to build functions and objects. Did you know? You can use import keyword and print(keyword.kwlist) to see all of them in your current version! 2. The "Tools" (Built-in Functions) Python comes batteries-included with functions that handle heavy lifting immediately. Math & Numbers: abs(), round(), float(), and int(). Interaction: input() and print(). Sequence Helpers: len(), range(), and the powerful map() function for applying logic across iterables. 3. Data Manipulation (Methods) The notes dive deep into how we handle Lists and Strings: Lists: Modifying data is easy with append(), insert(), pop(), and sort(). Strings: Text processing is a breeze using split(), join(), capitalize(), and checks like isalpha(). Python’s "platform independence" and "open source" nature make it accessible, but its these built-in tools that make it efficient. Which built-in Python function do you find yourself using the most? 👇 #Python #Programming #CodingBasics #SoftwareDevelopment #TechSkills #Learning

If your first Python code looks clean… you probably didn’t write it yourself 😄 Every beginner Python dev has written that ugly code. The one that: • has 20 if-elif lines • works only if the input is exactly right • breaks the moment someone types extra space • makes you think: “Why is this not working??” Example: • You just learned input() • You just learned int() • You combine them like a chemistry experiment • Code runs once • You feel like a hacker 🧠 • Next test case? Boom. Error. We all did this: • Copy from YouTube • Paste in editor • Change variable names • Pray it works • Screenshot if it works • No functions. • No loops. • No idea what’s happening internally. But here’s the part nobody tells: Ugly code = learning phase Confusing logic = brain growing Errors = real practice Clean code comes later. Confidence comes later. Right now, just showing up matters. So if your Python code is ugly today, Great. It means you started. #Python #BeginnerDeveloper #StudentLife #CodingJourney #LearningInPublic #TechStudents