Learning Functions in Python for Reusable Code

I don't know how much it's relevant, as many say, to learn coding, given the fact that vibe coding tools ( such as Cursor), Claude code, or other AI tools can write code for human. I, however, fulfilled my long time resolution - i.e. learning python programming - taking this python free course offered by the Programming Hero platform. Though I started learning python programming watching tutorials on the YouTube two years ago, I couldn't finish it as my learning was not organised and quit it later to give more times on GRE preparation. However, I started python programming again, three months ago. I have been obsessed with the python programming since then. As a business graduate, I would like to share some insights, which may help novice learner of python or business students to learn python programming by taking the free course. Step 1: First thing we need to do is to acknowledge that we don't need to be a computer science major to learn python. There is no pre-requisite to learn it. Step 2: It requires consistent efforts. We don't need to put 2 hours every day. If we are busy with doing other works, we can allocate 30 minutes for it each day upto a certain time period. Believe me your learning curve will follow the exponential function. What we require is to just start off without procrastination or looking for perfectionism. Step 3: While taking the course in the mobile application called Fibo ( Previously called Programming Hero), note down the important concepts and topics, and practise each programming exercise or example either in paper or python open source web (Jupyter, Pycharm). There are 19 modules in the course. I found following modules effective for a business major or a non-computer science major person: 1-6, 8, 15-19. Step 4: Using AI can make learning python easier. I sometimes found some coding problem difficult to understand or didn't understand the logic behind the syntax. I took the screenshot of the problem which I uploaded in the Claude, asking it to explain in the plain English translation. For instance, I wrote the prompt as such: " Act as a python teacher. I am a novice learner of Python programming, with non-computer science major. Explain the syntax of the code in the plain English translation as easily as possible. Provide alternative easy solutions, if any, to the problem. Be specific. If you need more clarification regarding theprompt, ask me further questions. " Step 5: The last thing, not the least, is to practise as much as possible. That is what I am doing now. As I am learning python in an effort to learn machine learning and data analysis, I am solving the problems related to the data analysis. Based on our respective field, we can practise the respective area coding problem. I am grateful to the Programming Hero platform for offering the course free. #PythonProgramming #ProgrammingHero

Why I’m Diving Deep into OOP with Python 🐍 | Building for Scalability I’m excited to share that I am currently leveling up my software engineering toolkit by mastering Object-Oriented Programming (OOP) in Python at Camerinfolks. 🚀 As I progress through this program, I’ve realized that OOP isn't just a technical requirement—it’s a mindset shift. It’s the difference between writing "scripts that work" and building "systems that last." For my fellow learners and tech enthusiasts, here is how I’ve been refining my understanding of the 4 Pillars of OOP: 🛡️ 1. Encapsulation: The "Private Vault" In Python, we use encapsulation to bundle data and methods, keeping the internal state of an object safe from unintended interference. The Value: Security & Integrity. It ensures that our objects are "self-contained," making the code much easier to debug and maintain. 🔍 2. Abstraction: The "Dashboard" Abstraction allows us to hide complex background logic and only show the essential "interface" to the user. The Value: Clarity. Just like you don't need to know how an engine works to drive a car, abstraction lets developers use complex tools without getting lost in the "how." 🧬 3. Inheritance: The "Blueprint" This is the ultimate tool for efficiency. We can create a base class and have other classes "inherit" its features. The Value: Efficiency. Why write the same code twice? Inheritance allows us to build a hierarchy where specialized objects reuse the logic of general ones. 🎭 4. Polymorphism: The "Master Key" Polymorphism allows different objects to be treated as instances of the same general class through a unified interface. The Value: Flexibility. It allows our systems to stay "open for extension but closed for modification," a core principle of high-quality software architecture. 💡 My Takeaway Learning these concepts Camerin - Indian Institute Of Upskill has completely changed how I approach problem-solving. Instead of thinking in "steps," I’m learning to think in "systems." By focusing on these values—Security, Clarity, Efficiency, and Flexibility—we create software that isn't just functional, but professional and future-proof. To my network: For those who made the switch from procedural to object-oriented programming, what was your "aha!" moment? I’d love to hear your insights! 👇 #Python #OOP #SoftwareDevelopment #Camerinfolks #CodingJourney #WebDevelopment #ContinuousLearning #TechCommunity #camerinfolks

🚀 My Python Learning Journey Today I explored how Python handles errors using Exception Handling ⚠️ 🔹 Exception Handling – Overview While writing programs, errors can occur due to invalid input, wrong operations, or unexpected situations. Exception handling allows us to manage these errors gracefully without stopping the program execution. Instead of crashing, the program continues running and provides meaningful messages to the user. 🔹 How Exception Handling Works 👉 The program tries to execute risky code 👉 If an error occurs, it is caught and handled 👉 If no error occurs, normal execution continues 🔹 Key Components ✔️ try → Block where error might occur ✔️ except → Handles specific errors ✔️ else → Executes if no exception occurs ✔️ finally → Always executes (cleanup tasks) 🔹 Example try: num = int(input("Enter a number: ")) result = 10 / num except ValueError: print("Invalid input! Please enter a valid number.") except ZeroDivisionError: print("Cannot divide by zero.") else: print("Result:", result) finally: print("Execution completed") 🔹 Types of Errors I Explored ✔️ ValueError → Invalid input ✔️ ZeroDivisionError → Division by zero ✔️ TypeError → Wrong data type 🔹 Why Exception Handling is Important 💡 Prevents program crashes 💡 Improves user experience 💡 Makes applications reliable and robust 💡 Helps handle unexpected real-world scenarios 🔹 Real-Life Understanding Just like we handle unexpected situations in real life, exception handling helps programs respond to errors in a controlled way 🔹 Learning Outcome This concept helped me understand how to write safer programs that can handle errors and continue execution smoothly. It also improved my ability to think about edge cases and user inputs 🚀 #Python #Teksacademy #CodingJourney #ExceptionHandling #Programming

🚀 Python Full Stack Development Journey – Day 6 Day 6 of my training at Codegnan was focused on understanding Lists and Tuples in Python — two fundamental data structures that are widely used in real-world applications 📊🐍 🔹 Lists – Mutable and Dynamic Today, I explored how lists allow us to store and manipulate collections of data. Practiced key methods such as: ✔️ append() – adding elements to the list ✔️ insert() – inserting elements at specific positions ✔️ remove() and pop() – deleting elements ✔️ sort() and reverse() – organizing data efficiently Also learned how lists are flexible and can be modified anytime, making them ideal for dynamic data handling. 🔹 Tuples – Immutable and Efficient Understood that tuples are similar to lists but immutable, meaning their values cannot be changed once created. This makes them faster and more secure for fixed data. 🔹 Key Learnings ✔️ Difference between lists and tuples ✔️ When to use mutable vs immutable data structures ✔️ How these structures are used in data storage and processing 💡 This session gave me a clear understanding of how to manage collections of data effectively in Python. Knowing when to use lists vs tuples is an important step toward writing optimized and efficient programs. 🔹 What are Sets? Sets are unordered collections of unique elements. They are particularly useful when we need to eliminate duplicates and perform mathematical operations like unions and intersections. 🔹 Key Set Methods I Learned ✔️ add() – Adds a single element to the set ✔️ update() – Adds multiple elements at once ✔️ remove() vs discard() – Removes elements (with different error handling) ✔️ pop() – Removes a random element ✔️ clear() – Empties the set completely 🔹 Mathematical Operations on Sets ✔️ union() – Combines elements from two sets ✔️ intersection() – Finds common elements ✔️ difference() – Returns elements present in one set but not the other ✔️ symmetric_difference() – Elements present in either set but not both

🚀 Starting Your Python Journey? Make It Practical, Not Overwhelming. Learning programming can feel confusing at first — syntax, logic, debugging… it’s easy to get stuck. That’s why I created a practical, beginner-friendly Python guide focused on real understanding, not rote learning. 📘 Getting Started with Python Programming — A Practical Guide for Beginners Whether you're a student, career switcher, or professional exploring tech, this guide helps you build a strong foundation step by step. 🔍 What You’ll Learn We begin with the fundamentals that truly matter: ✨ How Python code actually works (beyond just writing syntax) ✨ Core programming concepts — structure, syntax, execution flow ✨ Expressions, operators & evaluation logic ✨ Variables and data handling ✨ User input and output handling Then we level up 🚀 🔹 Conditional statements & loops (decision-making & iteration) 🔹 Data structures — lists, tuples, sets, dictionaries 🔹 Functions & higher-order functions 🔹 Object-Oriented Programming (OOP) — classes, objects, inheritance 🔹 File handling & text processing 💡 Why This Guide Stands Out Most resources focus on what to type. This guide focuses on why it works. 👉 Don’t just write code — understand it 👉 Don’t just solve problems — think like a programmer 👉 Don’t just learn — build confidence that lasts 📌 Serious about learning Python? Start here. Save it. Share it. Revisit it. 💬 I’d love your feedback — what would you add to a beginner’s Python roadmap? #Python #PythonProgramming #LearnPython #Programming #Coding #SoftwareDevelopment #DeveloperSkills #CodingForBeginners #TechEducation #ComputerScience #OOP #DataStructures #PythonForBeginners #LearnToCode #CareerGrowth #Upskilling #DigitalSkills #ITSkills #Developers #CodingJourney

7 Days of Advanced Python — Learning Beyond Basics 𝗗𝗮𝘆 𝟮 — 𝗪𝗿𝗶𝘁𝗶𝗻𝗴 𝗰𝗹𝗲𝗮𝗻𝗲𝗿 𝗰𝗼𝗱𝗲 𝗮𝗻𝗱 𝗱𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴 𝘀𝗺𝗮𝗿𝘁𝗲𝗿 Yesterday I focused on improving how I manage Python projects. Today, I noticed something else. Even when the setup is clean, the actual coding process can still get messy — especially when debugging or maintaining code. I used to rely on: print statements for debugging basic linting (or sometimes none) and manual effort to keep code clean It worked… but not efficiently. So today I explored three tools that completely changed how I approach writing Python code: 𝗥𝘂𝗳𝗳, 𝗟𝗼𝗴𝘂𝗿𝘂, 𝗮𝗻𝗱 𝗜𝗰𝗲𝗖𝗿𝗲𝗮𝗺. --- 𝗥𝘂𝗳𝗳 — 𝗙𝗮𝘀𝘁 𝗮𝗻𝗱 𝘀𝘁𝗿𝗶𝗰𝘁 𝗰𝗼𝗱𝗲 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 Earlier, I either ignored linting or used slower tools that I didn’t run consistently. Ruff feels different. It’s extremely fast and catches issues instantly — unused imports, formatting problems, and code style inconsistencies. Compared to traditional linters: • Much faster execution • Combines linting + formatting • Helps maintain consistency without extra effort If you want to explore it: https://lnkd.in/d2DkJKn6 --- 𝗟𝗼𝗴𝘂𝗿𝘂 — 𝗟𝗼𝗴𝗴𝗶𝗻𝗴 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝘁𝗵𝗲 𝗯𝗼𝗶𝗹𝗲𝗿𝗽𝗹𝗮𝘁𝗲 Before this, I used Python’s built-in logging module. It’s powerful, but setting it up always felt a bit heavy for small projects. Loguru simplifies everything. With just a few lines, you get: • Clean and readable logs • Better formatting • Easy configuration Compared to traditional logging: • Less setup • More readable output • Faster to integrate into projects Documentation: https://lnkd.in/d-C4FKWv --- 𝗜𝗰𝗲𝗖𝗿𝗲𝗮𝗺 — 𝗗𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴 𝘁𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗵𝗲𝗹𝗽𝘀 I used to debug mostly with print statements. But the problem is: You only see values — not context. IceCream improves this in a very simple way. Instead of writing multiple prints, you get: • Variable names + values together • Cleaner debugging output • Faster understanding of what’s happening Compared to print debugging: • More informative • Less repetitive • Easier to trace issues Explore here: https://lnkd.in/dBTU5t84 --- What changed for me today: I stopped thinking of debugging and code quality as “extra effort”. With the right tools, they become part of the natural workflow. And that changes everything. Because now, instead of fixing messy code later, I can write better code from the start. Curious — what do you usually rely on for debugging and code quality in Python? #Python #AdvancedPython #CleanCode #Debugging #DevTools #LearningInPublic

🚀 Day 8 to10 — Python Full Stack Training | Conditional statements 🐍 Condition statements in Python are fundamental constructs used to control the flow of execution in a program. They enable decision-making by executing specific blocks of code based on whether given conditions evaluate to True. 1️⃣ if Statement The simplest form, used to execute a block of code only when a condition is satisfied. Example: x = 10 if x > 5: print("x is greater than 5") 2️⃣ if-else Statement Provides an alternative path of execution when the condition is not satisfied. Example: x = 2 if x > 5: print("Condition is True") else: print("Condition is False") 3️⃣ if-elif-else Structure Used when multiple conditions need to be evaluated in sequence. Python executes the first block where the condition is True. Example: score = 78 if score >= 90: print("Excellent") elif score >= 75: print("Good") elif score >= 50: print("Average") else: print("Needs Improvement") 4️⃣ Multiple if Statements In some scenarios, conditions need to be evaluated independently rather than exclusively. Using multiple if statements ensures each condition is checked regardless of others. Example: x = 15 if x > 10: print("Greater than 10") if x % 5 == 0: print("Divisible by 5") 5️⃣ Nested if Statements A nested structure allows you to place one condition inside another, enabling more granular decision-making. Example: x = 18 if x > 10: if x < 20: print("x is between 10 and 20") else: print("x is 20 or more") else: print("x is 10 or less")

Stop learning Python. You don't have a Python problem. I said this to myself six months into my PhD. I was on my fourth tutorial. Everything made sense while I followed along. The moment I tried to write something from scratch, I was completely lost. So I went back to another tutorial. Because I thought it was a Python problem. It wasn't. Here's what nobody tells you before you open that first script: Python tutorials assume you already know how to think computationally. How to break a messy biological question into steps a machine can actually follow. How to ask what a function needs, what it returns, what an error is really telling you. Biology trains you to observe. To hypothesise. To interpret. It never trains you to decompose. That's the gap. Not the syntax. The logic underneath it. And you can't close that gap by doing the tutorial again. Once I understood this, I stopped blaming the code. I started asking different questions before I touched the keyboard. What am I actually trying to do? What does this step need? What should come out of it? The scripts didn't magically get easier. But I finally knew what I was trying to learn and that changed everything. Stop learning Python tutorials. Start learning to think like a computer. The tutorials will make sense after that. What made the logic finally click for you not the syntax, the logic? Drop it below. I want to write the guide that nobody wrote for us. Give a follow if you found it useful!

🚀 Day 3 of sharing my Python learning journey. After learning lists and tuples, and working on different DSA problems, I explored two important concepts — loops and recursion. 🔁 Loops (for & while) Loops help us execute a block of code multiple times. Whether it's iterating through a list or repeating a task until a condition is met, they are fundamental in problem-solving. 🔹 for loop: Best when you know how many times to iterate. 🔹 while loop: Useful when the number of iterations depends on a condition. 🔄 Recursion Recursion is when a function calls itself to solve smaller parts of a problem. It might seem tricky at first, but it becomes powerful once you understand: ✔️ Base Case – The condition where recursion stops. ✔️ Recursive Case – The part where the function calls itself. Without a proper base case, recursion can lead to infinite calls! 💡 Real Learning Insight While solving problems (like finding the largest number in a list), don’t rush to code directly. 👉 First, dry run your logic with your own examples 👉 Test with different cases — does it work everywhere? 👉 Identify mistakes yourself before looking for solutions. I used to make a common mistake: Whenever I got stuck, I immediately searched for the answer. But I realized — ⚠️ This reduces your thinking ability over time. Now I try to: ✔️ Spend time understanding the problem ✔️ Trace the solution step-by-step ✔️ Ask for help only after giving my best effort 📌 Key takeaway: Understanding basics and practicing self-thinking is far more valuable than quickly finding answers. #Python #Programming #CodingJourney #Recursion #Loops #ProblemSolving #LearnToCode

Lately, I’ve been realising something important while learning Python… It’s not just about writing code. It’s about how you think before writing it. I used to jump straight into coding. Sometimes it worked, sometimes it didn’t. But the real shift happened when I started focusing on logic building. Breaking a problem into steps like: What is the input? What should happen step by step? What is the final output? That’s when things started making sense. And that’s exactly where pseudocode comes in. It’s simply your logic written in clear, human-readable steps. For example: “Take input → check condition → process → return result." When your logic is clear, your code becomes almost automatic. What I’ve understood so far: Weak logic → confusing code Strong logic → clean and simple code And honestly, this applies beyond coding, too. Problem-solving in general becomes easier when you train your mind to think in structured steps. Still learning, still improving… But this shift in thinking has made a huge difference. Curious to know — How do you approach problem-solving before writing code?