Luis Carlos Vaz’s Post

🚀 Learning Python OOP – Hands-on Practice! 🐍 Today, I implemented a BankAccount class using Object-Oriented Programming in Python. This small project helped me clearly understand how real-world concepts map into code. 🔑 Concepts practiced: Classes & Objects __init__ constructor Instance variables Methods (deposit, withdraw, balance) Conditional logic Object interaction 💡 Seeing how a simple banking system can be modeled using OOP makes learning Python even more exciting. Step by step, building a strong foundation! On to more practice and real-world mini projects 🚀 #Python #PythonProgramming #OOP #ObjectOrientedProgramming #LearningByDoing #CodingJourney #DeveloperInProgress #VSCode #PythonBasics

Here’s something I used to debate with my friends 😅 Is Python just a programming language, or not ? Over time, I realized that Python’s biggest strength is its flexibility. From data science and machine learning to web development, automation, and scripting — Python makes many complex tasks easier to implement compared to some lower-level languages. For example, working with very large numbers is straightforward in Python because of its built-in support for arbitrary-precision integers, while in some languages it requires extra handling or libraries. That’s why I like to think of Python as the “potato” of programming languages it goes well with almost everything! 🥔

Python OOP Concepts | 14th Feb Learning Update Today’s Python session focused on Encapsulation, one of the key pillars of Object-Oriented Programming, and how Python handles data visibility and access control. 🔹 Concepts practiced: Public members (no underscore) and direct access Protected members using a single underscore (_variable) Accessing protected data within parent and child classes Understanding how protected members behave outside the class Private members using double underscore (__variable) Exploring name mangling to access private data safely Implementing encapsulation using parent–child class relationships 💡 This session helped me clearly understand how Python enforces data protection by convention and how encapsulation improves code security and maintainability. Building stronger OOP foundations, one concept at a time 🐍💻 Excited to move towards applying these ideas in real projects! #Python #OOP #Encapsulation #DataHiding #ObjectOrientedProgramming #PythonLearning #StudentDeveloper #CodingPractice #LearningJourney Pooja Chinthakayala

Why does Python start counting from 0, not 1? "Why is the first item at index [0]?" — Every Python beginner asks this. 🐍 fruits = ['apple', 'banana', 'cherry'] print(fruits[0]) # Prints 'apple', not fruits[1] The reason: It's not random! It's based on memory offsets. Think of it like house addresses on a street: • Index 0 = "0 steps from the start" • Index 1 = "1 step from the start" • Index 2 = "2 steps from the start" This makes math in programming faster and is why almost all modern languages (C, Java, JavaScript) follow the same rule. Fun fact: Some older languages like MATLAB start from 1, which causes confusion when switching! Did this finally make sense? Or do you still prefer counting from 1? 😄 #Python #ZeroIndexing #ProgrammingLogic #PythonBasics #LearnToCode #TechExplained #CodingFundamentals

Python Data Structures: Lists vs Tuples vs Sets vs Dictionaries...🔥 Understanding data structures is the foundation of writing efficient and clean Python code. Each structure has its own purpose and strengths: 🔹 **List** – Ordered, mutable, allows duplicates 🔹 **Tuple** – Ordered, immutable, faster than lists 🔹 **Set** – Unordered, unique elements only 🔹 **Dictionary** – Key-value pairs for structured data Choosing the right data structure improves performance, readability, and problem-solving efficiency. As I continue strengthening my Python fundamentals, I’m revisiting these core concepts to build a stronger base for advanced topics like data analysis and backend development. 💡 Strong basics = Strong future in programming. #Python #DataStructures #Coding #Programming #PythonDeveloper #LearningJourney

🚀 Python Multithreading in Action 🧵⚡ Today I practiced Multithreading in Python using the threading module. 🔹 Created two thread classes 🔹 Overrode the run() method 🔹 Executed tasks simultaneously using .start() 💡 What I Learned: ✅ Threads run concurrently ✅ start() internally calls run() ✅ sleep() controls execution timing ✅ Output may interleave because threads execute in parallel 🧠 Code Concept: Class A(Thread) → prints "Hello" Class B(Thread) → prints "Pune" Both threads run 5 times with a 2-second delay Output runs simultaneously ⚡ This is the power of concurrent execution in Python. 🔥 Why Multithreading? ✔ Improves performance ✔ Best for I/O-bound tasks ✔ Used in real-world apps like: Web servers 🌐 Background processing ⚙ APIs 🔄 💬 Small practice today… 🏆 Big step towards becoming an Advanced Python Developer. #Python #Multithreading #Threading #AdvancedPython #CodingJourney #WomenInTech #MCA #LearningDaily

I’ve been doing DSA using Python for a while, and I kept running into the same confusion, especially when seeing things differently between Python and languages like C++ or Java. Things like: Why does Python get a list instead of an array on LeetCode? Is Python using NumPy in the background? Why does C++ uses vector, Go uses slices, Java uses arrays and Python feel different? So I ended up documenting everything for myself while learning. When you’re solving DSA problems in Python, the list is your array. The goal at that stage isn’t memory layout or low-level details, it’s building the right approach, handling edge cases, and understanding time/space complexity. Python gives abstractions on purpose, and LeetCode leans into that so you can focus on logic instead of fighting the language. I’m sharing my notes in case they help someone who’s doing DSA with Python and overthinking these things (like I was). If you’re early in the journey or switching from C++/Java, this might save you some time. Happy to discuss or clarify anything. #Python #DSA #DataStructures #Algorithms #LeetCode #Programming #LearningInPublic

📅 Day 23 of My Python Full-Stack Journey — Logical Operators! Today I explored one of the most essential building blocks in programming — Logical Operators in Python 🐍 These three operators control the logic flow of your entire program: 🟠 and → Both conditions must be True 🟣 or → At least one condition must be True 🔵 not → Flips the boolean value pythonage = 20 has_id = True if age >= 18 and has_id: print("Access granted") # ✅ if age >= 18 or is_member: print("Welcome in!") # ✅ print(not False) # True Simple? Yes. But combine these and you can build powerful decision-making logic for login systems, access control, form validation, and more! The more I progress, the more I realize Python reads almost like plain English — and that's what makes it beautiful. 💡 📍 23 days down, 77 to go. Let's gooo! 🔥 #Python #LogicalOperators #Day23 #100DaysOfCode #FullStack #PythonForBeginners #LearningInPublic #CodingJourneyDay23 linkedinCode ·

📅 Day 12/30 – Requests Module in Python Today I learned how to use the Requests module to send HTTP requests and interact with APIs. What I covered: • Installing the requests library • Sending GET and POST requests • Handling response objects • Working with JSON data • Checking status codes • Basic API integration Understanding how Python communicates with web services is powerful 💪 📚 Learning resource: HackerBytez – https://lnkd.in/gzKTANVt Step by step, moving closer to real-world application development 🚀 #Day12 #PythonChallenge #30DaysOfPython #RequestsModule #Python #APIs #LearningInPublic #CodingJourney

🐍 Python Basics Every Beginner Should Master 🚀 FREE Demo Session 🎯 Live Roadmap + Q&A 🔗 Register Now: https://lnkd.in/gtHCUj_J If you're starting your journey in Python programming, understanding the fundamentals is the key to writing efficient code. Two essential concepts every beginner should learn are Strings and Conditional Statements. 🔹 Strings Strings are used to store and manipulate text in Python. Some important operations include: • Concatenation (combining text) • Indexing (accessing characters) • Slicing (extracting parts of a string) • Built-in functions like len(), find(), and replace() 🔹 Conditional Statements Conditional statements help programs make decisions based on conditions using: • if • elif • else These are commonly used in real-world programs like: ✔ Grade calculators ✔ Odd or even number detection ✔ Finding the greatest number ✔ Checking multiples 💡 Pro Tip: Mastering these basics builds the foundation for advanced Python topics like Data Science, Automation, Web Development, and AI. 📌 Save this guide if you're learning Python and want to strengthen your fundamentals. #Python #LearnPython #PythonProgramming #CodingForBeginners #Programming #SoftwareDevelopment #TechLearning #CodingTips