Mastering OOP with Python Classes and Encapsulation

Day 38 — Introduction to Object Oriented Programming (OOP) If you have been writing Python using only functions, it's time to level up. OOP organises your code around objects — real-world entities that carry both data and behaviour. Python is fully object-oriented — even a simple string "hello" is an object! 🧩 The 4 Pillars of OOP: 🔒 Encapsulation — bundle data + methods, hide internals 🧬 Inheritance — child classes reuse parent class logic 🎭 Polymorphism — same method name, different behaviour 🎨 Abstraction — expose what matters, hide the complexity Class vs Object: A Class is a blueprint (e.g., Car) An Object is an instance of that blueprint(e.g., my_car = Car("Tesla")) Why learn OOP? ✅ Reusable code — inherit, don't repeat ✅ Organised — group related logic together ✅ Maintainable — change one class, affect the right places ✅ Scalable — model complex systems naturally #Python #OOP #Programming #LearningPython

🚀 Day 34 of #100DaysOfCode | Scenario-Based Questions in Python OOP Today I practiced solving scenario-based problems using Python Object-Oriented Programming (OOP). These problems help in understanding how OOP concepts are applied in real-world situations. Key Concepts I Practiced: • Designing classes based on real-world scenarios • Implementing Encapsulation to protect class data • Using Inheritance to reuse code between classes • Applying Polymorphism for flexible and reusable methods Practising scenario-based questions improves logical thinking and problem-solving skills, and helps in building scalable and maintainable applications using OOP concepts. GitHub Repository: https://lnkd.in/gTmfXrWP #Python #OOPS #100DaysOfCode #CodingJourney #PythonProjects

🚀 Learn Python in 30 Days (Simple Plan) Week 1: Basics 👉 Variables, data types, if-else, loops Week 2: Core Concepts 👉 Lists, dictionaries, functions, file handling Week 3: Intermediate 👉 OOP, modules, error handling + practice problems Week 4: Real Skills (choose one) 💻 Web (Flask) 📊 Data Science (Pandas, NumPy) 🤖 Automation (scripts, bots) Daily Routine (1–2 hrs): ✔ Learn → Practice → Build 💡 Tip: Don’t just watch tutorials — code every day. #Python #Coding #LearnToCode #Developer

Day 78 of my Python Journey 🐍(week 11) Taking a moment to reflect on everything I’ve learned so far in my journey from beginner to now: • Basics of Python (variables, data types, type casting) • User input and string handling (slicing, methods) • Conditional statements and operators • Loops (for, while, break, continue) • Functions (built-in, user-defined, arguments, recursion) • Data structures (lists, tuples, sets, dictionaries) • List methods, set methods, dictionary methods • F-strings, docstrings, enumerate • Exception handling, custom errors, finally • Modules (import, from, as, dir) • File I/O (read, write, modes, with statement) • Map, filter, reduce, lambda functions • is vs == • Introduction to OOP • Classes and objects • Constructors • Instance vs class variables • Class methods & static methods • Getters and setters • Inheritance & access specifiers • Decorators • dict, help(), super() Still a long way to go, but consistent effort is building strong fundamentals every day. #Python #CodingJourney #LearnInPublic #Day78 #Consistency

Day 29 of my Python journey 🐍🔧 – OOP Attributes & Methods Deep Dive! Instance/Class attrs + Instance/Class/Static methods = Complete OOP arsenal! 💪 Mastered: Instance attrs: self.items (unique per cart) Class attrs: ShoppingCart.tax_rate (shared GST!) Instance methods: add_item(self) → object-specific @classmethod: update_tax(cls) → class-wide changes @staticmethod: calc_discount(price, pct) → pure utility Practiced: Enhanced ShoppingCart with all types—tax updates apply everywhere! 🌐 Next: Inheritance magic incoming! #Python #Day29 #OOP #InstanceClassAttributes #InstanceClassStaticMethods #ShoppingCart #PythonOOP #CodingJourney #100DaysOfCode #LearnInPublic #CodeNewbie #DeveloperJourney #AndhraPradesh #PythonForBeginners

Stop treating Python variables like boxes. They are actually labels. Understanding this architectural shift is the difference between writing robust code and chasing "ghost" bugs for hours. Here is how to master your data architecture in Python: Know your materials: Immutable types (like int, str, and tuple) are like stone—they cannot be changed in place. Mutable types (like list and dict) are like clay—they can be reshaped without creating a new object. The "Hashability" Price: Only immutable objects are hashable, meaning they can serve as dictionary keys or set elements. If you try to use a mutable list as a key, Python will throw a TypeError. The Default Argument Trap: Never use a mutable type (like []) as a function's default argument. These are created only once at definition, meaning every call to that function will share and modify the same list. Copy with Caution: When dealing with nested structures, a "shallow copy" only creates a new outer container while sharing the inner objects. Always be explicit and use copy.deepcopy() if you need a completely independent version. The Golden Rule: Use is None for identity checks rather than == None to ensure faster, more reliable null handling. Are you building with stone or clay today? #Python #DataArchitecture #CodingTips #SoftwareEngineering

Day 11 of #60DaysOfMiniProjects From writing simple scripts to building small automation tools — improving step by step. Today I built a CLI-based File Renamer using Python What this project does: • Takes a folder path as input from the user • Reads all files inside the folder • Automatically renames files sequentially (file_1, file_2, file_3…) • Preserves the original file extensions • Helps organize files quickly using automation Concepts I worked with: • Python os module for file operations • os.listdir() to read files in a folder • os.rename() to rename files • os.path.join() and os.path.splitext() • Loops and conditional statements This project helped me understand how Python can automate repetitive tasks like file management. Small automation. Practical learning. Real progress. Consistency builds confidence #Python #MiniProjects #BuildInPublic #CodingJourney #CSE #DeveloperGrowth #LearningInPublic #Automation #PythonProjects

🚀 Day 14 – Mastering Python Comprehensions & Lambda Functions 🔥 Today I explored one of the most powerful and Pythonic concepts – List Comprehension, Dictionary Comprehension & Lambda Functions. This session helped me understand how to write clean, concise, and efficient code by replacing traditional loops with smarter approaches. 🔹 List Comprehension ✔ Creating lists in a single line ✔ Using conditions (if, if-else, if-elif-else) ✔ Nested loops & flattening lists 🔹 Dictionary Comprehension ✔ Efficient key–value creation ✔ Filtering & conditional mapping ✔ Transforming and combining data 🔹 Lambda Functions ✔ Anonymous functions for quick operations ✔ Used with map(), filter(), sorted() ✔ Applying conditions & real-time logic 📦 Advanced Applications: ✔ Nested comprehensions for complex structures ✔ Conditional logic handling inside expressions ✔ Filtering prime numbers using optimized logic ✔ Writing clean and optimized Python code 💡 This topic made me realize how important it is to: • Write readable and optimized code • Think in a Pythonic way • Solve problems efficiently in real-world scenarios 🙏 A special thanks to my mentor Nallagoni Omkar for the continuous guidance and clear explanations. ➡️ Next Topic: Advanced Python Concepts / Class, Objects(OOPS) 🚀 #Python #ListComprehension #DictionaryComprehension #LambdaFunction #LearningJourney #DataScience #Programming #PythonDeveloper #Coding

One Python feature I wish I started using earlier: **`dataclasses`**. When you’re building backend services, you often create “data-only” objects (DTOs, request/response models, internal payloads). Instead of writing repetitive boilerplate (`__init__`, `__repr__`, comparisons), you can do this: ```python from dataclasses import dataclass @dataclass(frozen=True, slots=True) class User: id: int name: str ``` Why I like it: - **Less boilerplate** → cleaner, more readable code - **`frozen=True`** → immutability (safer, fewer accidental changes) - **`slots=True`** → lower memory usage and often better performance Small change, big improvement—especially when your codebase grows. What’s one Python feature you wish you had used earlier? #Python #BackendDevelopment #SoftwareEngineering #CleanCode #Programming

One Python question I keep thinking about lately: dataclasses vs traditional classes. Python’s @dataclass decorator is great for reducing boilerplate. With one decorator you automatically get: • __init__ • __repr__ • __eq__ • other useful dunder methods So the code becomes much shorter and cleaner. But in practice, I often still prefer writing the traditional class with an explicit __init__ method. Not because dataclasses are bad — they’re actually quite elegant — but because explicit classes sometimes feel easier to reason about when working in larger codebases. For example: • the object initialization is immediately visible • you see exactly what happens inside __init__ • new engineers reading the code don’t have to mentally expand the decorator behavior Dataclasses definitely reduce boilerplate, but sometimes they feel like syntactic abstraction rather than a functional advantage. So I’m curious: Which style do you prefer in production code? 1. @dataclass for cleaner, shorter code 2. Traditional classes with explicit __init__ 3. Depends on the use case (DTOs vs business logic objects) Would love to hear how other engineers are using this in real systems. #Python #SoftwareEngineering #BackendDevelopment #CleanCode #Programming