Python vs Java: Key Differences and Implementations

Taking my learning further, I explored Polymorphism in Python and compared it with Java — and it was an interesting experience. In Python, polymorphism is much more flexible and is often achieved using duck typing and method overriding. Unlike Java, Python doesn’t strictly require inheritance — the same method name can work across different classes as long as the object supports that behavior. On the other hand, in Java, polymorphism is more structured and is mainly achieved through method overloading (compile-time) and method overriding (runtime), usually with inheritance. Working on similar logic in both languages helped me clearly understand the difference: • Java → Strict, type-based polymorphism • Python → Flexible, behavior-based polymorphism A big thank you to TAP Academy for making these concepts so easy to understand across multiple languages with practical examples. Grateful for the learning and excited to apply it in real-world projects 🚀 #Python #Java #Polymorphism #OOP #CodingJourney #Learning #SoftwareDevelopment #TAPAcademy

*Day 26 of my python learning journey* Today I learned about Getter, Setter, and 3 types of methods in Python OOP. *Getter & Setter methods:* Getters are used to access private data safely → `get_name()` Setters are used to update private data with validation → `set_age()` They help protect data and add control over how variables are read or changed. *3 Types of Methods in Python:* 1. *Instance method* → Uses `self`, works with object data. Called by objects. 2. *Class method* → Uses `@classmethod` and `cls`, works with class variables. Called by class. 3. *Static method* → Uses `@staticmethod`, doesn’t use `self` or `cls`. Like a normal function inside class. One more step toward writing clean, secure OOP code. Special thanks to the CEO G.R NARENDRA REDDY Sir for constant guidance and motivation. #Python #OOP #GetterSetter #LearningJourney #Programming

⚡ Why Java Still Wins in 2026 (Performance Reality) Is Python really “too slow” for modern systems? We’ve all heard: • Python is easier • Python has better libraries • Developer speed > machine speed That’s true… until you hit production scale. 💥 At scale: • Latency becomes visible • Infrastructure costs increase • Concurrency becomes a real bottleneck This is where Java still has a strong edge. 🧠 JVM optimizations (like JIT compilation) allow Java to handle high-load systems far more efficiently — sometimes dramatically so, depending on the workload. That said — Python is still the right choice in many scenarios (especially AI, data, and rapid prototyping). The real question isn’t “Which is better?” 👉 It’s “When should you use which?” I break this down in detail here: https://lnkd.in/dVjP3x4S Curious — what are you using in production today? #Java #Python #SoftwareEngineering #Backend #SystemDesign

In Java, private means private. In Python, it means: “I trust you not to look.” I was exploring encapsulation and discovered something interesting. In Java, access is enforced. In Python, it’s… negotiated. A double underscore (__attr) doesn’t truly hide anything. It just renames it. Which means: You *can* still access it — if you know how. That realization changed how I think about class design. Java protects the code. Python trusts the developer. Two different philosophies. Which one do you prefer? 👇 Curious to hear your perspective #Python #Java #OOP #SoftwareEngineering #LearningInPublic

🐍📰 Object-Oriented Programming (OOP) in Python 3 In this article you’ll learn the fundamentals of object-oriented programming (OOP) in Python and how to work with classes, objects, and constructors. #python

Python Learning Journey Today I explored some core fundamentals that build a strong foundation in Python development: ◆ Installed VS Code and set up the Python environment ◆ Learned about different Python flavors: CPython (default implementation) Jython (Java integration) IronPython (.NET framework) PyPy (fast execution with JIT) Anaconda Python (data science ecosystem) RubyPython (experimental) ◆ Understood Python versions and compatibility ◆ Compared Java vs Python with real examples ◆ Practiced basic syntax like printing messages using print() Key concepts covered: Identifiers in Python Data Types & their types (int, float, list, tuple, dict, etc.) Typecasting Operators in Python eval() function Conditional statements (if, else, elif) Range data type and its variants Every day is a step closer to mastering Python. Consistency is the key! #Globalquesttechnologies #G R Narendra Reddy #Python #Coding Journey #Learning Python #VSCode #Programming #Developer #100DaysOfCode #TechSkills

Today I focused on understanding the fundamentals of Object-Oriented Programming (OOP) in Python. 📌 Key concepts I learned: 🔹 What is a Class? A class is a blueprint used to create objects. An object is an instance of a class. 🔹 Reference Variables vs Instance Variables Reference variables store the address of an object Instance variables belong to each individual object 🔹 Constructor in Python The constructor method __init__() is used to initialize object values It is automatically called when an object is created 🔹 Self Variable self refers to the current instance of the class It is used to access variables and methods inside the class 💡 These concepts are the foundation for writing structured, reusable, and efficient Python code. Consistency is the key — learning something new every day Global Quest Technologies #Python #OOP #CodingJourney #LearnPython #Programming #Developers #TechLearning #SoftwareDevelopment #PythonDeveloper #StudentDeveloper #GlobalQuestTechnologies

Learn Python Programming Using Java Skills — Data Validation with PySpark & Pytest A practical, beginner-friendly guide for Java developers stepping into Python and data engineering. Real code. Real comparisons. #Python #Java #PySpark #Pytest #DataEngineering #BeginnerPython

🚀 Learning Progress: Python OOP – Encapsulation Continuing my journey in mastering Object-Oriented Programming, I worked on a Python program to demonstrate Encapsulation. In this program: I used a private variable __bal with data mangling to restrict direct access Implemented controlled access through methods to safely read and modify data Ensured proper data hiding, which is the core idea of Encapsulation One interesting observation was how simple and flexible Python makes encapsulation compared to Java. Unlike Java, Python doesn’t require strict getter and setter methods—data protection can be achieved more concisely using naming conventions and built-in mechanisms like data mangling. This hands-on implementation helped me understand how Python balances readability and functionality, making it easier to write clean and maintainable code. Looking forward to exploring more OOP concepts and applying them in real-world scenarios! #Python #OOP #Encapsulation #LearningJourney #Programming #SoftwareDevelopment #TAP Academy # kshitij kenganavar

Understanding Python Dictionaries – A Must-Know for Developers Python dictionaries are one of the most powerful data structures for handling real-world data. 🔹 Store information using key-value pairs 🔹 Enable fast data retrieval 🔹 Core component of APIs and JSON handling 🔹 Flexible due to mutability 💡 Example: Python student = {"name": "Vikas", "age": 22} print(student["name"]) Mastering dictionaries is essential for writing efficient and scalable Python applications. 👉 What’s one Python concept you found difficult at first but now love? #Python #SoftwareDevelopment #Programming #Developers #TechSkills #Coding #Learning #CareerGrowth