Java Python HackerRank 5 Star Problem Solving

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

Did you know Python is actually older than Java? Yes, read that again. Python’s clean, indentation-driven syntax makes it feel like a modern language, but its first public release was all the way back in 1991. Java, on the other hand, didn’t see its first public release until 1995. Modern isn’t always new, sometimes it’s the ideas that were just ahead of their time. #Java #Python

I've clearly not been paying attention to Scala lately. Only found out yesterday that Scala 3 supports syntax that makes it that much easier for Python developers to pick up (curly braces now optional, amongst other things). If you're a Python dev, check it out: you get a much, much faster runtime (the JVM), and a rich ecosystem of libraries (all Java libraries are interoperable with Scala). https://lnkd.in/gsTf5m_B

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

Switching from Python to Java: Coming from a Python-heavy background, working with Java has been a real shift in perspective. In Python, a lot is taken care of for you through powerful high-level abstractions. You can move quickly, write less code, and focus on solving problems. But Java? It makes you slow down in a good way. You start paying attention to details you might have overlooked before: type definitions, structure, and the mechanics behind what your code is actually doing. It demands more explicitness, more discipline, and a deeper level of understanding. And that’s the beauty of it. Different languages, different strengths, but stepping outside your comfort zone is where real growth happens. https://lnkd.in/deNbabM5 #Java #Python #SoftwareEngineering #CodingJourney #LearningToCode

Explore why Python is the ultimate choice for data science, engineering, and analytics compared to Java. Learn about its versatility, how it borrows the best features from other languages, and why it's the most efficient tool for building data pipelines and processing complex file formats. #python #java #dataengineering #datascience #dataanalytics #programminglanguages #datapipelines #softwaredevelopment

#TIL There is no real polymorphism in Python OOP In Python, method calls are not resolved by strict type like Java or C++. They are resolved at runtime, based on object behavior, not class hierarchy. What we call polymorphism in Python is actually: ✔ Duck Typing ✔ Dynamic dispatch ✔ Flexible method resolution If an object acts like it has the method, Python doesn’t care what class it belongs to. “If it quacks like a duck, it’s a duck.” This flexibility is powerful. But it also means Python OOP works very differently from classical OOP languages. Understanding this makes you a better Python engineer, not a confused one. #Python #OOP #SoftwareEngineering #Programming #PythonDev #CleanCode #DeveloperMindset

⚡ 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

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