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✅ Day 13 — Effective Python Coding Series 🔥 Today’s focus: Closures & Properties 🐍 Closures are functions that remember variables from their enclosing scope, even after the outer function has returned. ✨ They enable reusable & modular code, maintain state across calls, and allow custom behavior without rewriting logic. Properties let you define methods that look like attributes, while still giving you the flexibility of methods. ✨ Perfect for validating input, computing derived values on the fly, and keeping your code clean, readable, and maintainable. Closures = functions with memory 🧠 Properties = methods in disguise 🎭 👉 Whether you’re a Developer, Data Scientist, ML/AI Engineer, or simply someone who loves Python 🐍 — these features are must-know tools to write cleaner, smarter, and more Pythonic code. 🚀 Follow me for daily Effective Python Coding posts and sharpen your Pythonic thinking every day! #Python #100DaysOfCode #DataScience #ML #AI

The living legend , the brain behind Linux and git , the true open source guy, The man who has no haters, every software developer , backend engineer or system design favourite guy the one and only "Linus Torvalds" is cooking something amazing I think. Some words for this living legend 👇 #Linux #git #SoftwareEngineering

✅ Day 10 — Effective Python Coding Series 🔥 Today’s focus: *args and **kwargs 🐍 In Python, these are powerful tools to make your functions flexible and Pythonic. 🔹 *args → lets a function accept any number of positional arguments. Perfect when you don’t know how many inputs you’ll get. 🔹**kwargs → lets a function accept any number of keyword arguments as a dictionary. Great for passing named parameters dynamically. 🔹 Combine them to handle both positional and keyword arguments ✨ Why it matters: Makes your functions more reusable Handles dynamic inputs elegantly Keeps your code clean and Pythonic 👉 Whether you’re a Developer, Data Scientist, ML/AI Engineer, Data Analyst — or simply someone who loves Python 🐍, this series is for you. 🚀 Follow along for daily Python tips and sharpen your coding skills every day! #Python #EffectivePython #CodingSeries #DataScience #ML #AI #100DaysOfCode

🔥 Day 18 — Effective Python Coding Series Today’s focus: Impure Functions & Mutable Arguments 🐍 Not all functions are pure — some modify data outside their scope. These are called impure functions, and while sometimes necessary, they can introduce hidden bugs if not handled carefully. What makes a function impure? 1️⃣ Modifying arguments in place → e.g., appending to a list directly. 2️⃣ Relying on mutable arguments → lists, dicts, sets can be changed, and those changes persist outside the function. 3️⃣ Mixing responsibilities → e.g., modifying and returning new values together. Safer alternatives: ✅ Instead of modifying arguments → return a new object. ✅ Make functions focused → either mutate OR return new data, but avoid mixing both. ✅ Be explicit in naming → if a function mutates, its name should clearly reflect that. ✨ Why this matters: ✔️ Avoids side effects that are hard to trace ✔️ Makes debugging much easier ✔️ Keeps your functions predictable & modular 👉 Whether you’re a beginner or experienced Pythonista, understanding impure functions helps you write cleaner, maintainable, and bug-free code 🚀 #Python #EffectivePython #CodingSeries #100DaysOfCode #CleanCode #SoftwareEngineering #DataScience #AI

🐍 Day 11 – Effective Python Coding 🔥 Today’s focus: Decorators 🐍 In Python, decorators are powerful tools that let you modify the behavior of a function or class without changing its source code. 🔹 A decorator is a callable that takes a function or class and returns a new function or class with added behavior. 🔹 Use @decorator_name above a function to apply it — shorthand for: my_function = decorator_name(my_function) 🔹 Decorators are perfect for logging, caching, authentication, timing, and more. 🔹 They can also be applied to classes, not just functions. ✨ Why it matters: Makes your code more expressive and Pythonic Helps you reuse functionality across multiple functions Simplifies complex tasks without touching the original code 👉 Whether you’re a Developer, Data Scientist, ML/AI Engineer, or simply love Python 🐍, this series is for you. 🚀 Follow along for daily Python tips and sharpen your coding skills every day! #Python #EffectivePython #CodingSeries #DataScience #ML #AI #100DaysOfCode

As developers, we battle bugs, deadlines, and endless problem-solving every day. With that comes stress, frustration, late nights, too much caffeine, and sugar overload. But in all this hustle, we often forget our most important engine — the heart. 🫀❤️ Stress + poor habits can silently harm it. Smoking is the #1 risk factor, and unhealthy fuel (junk, sugar, energy drinks) only makes it worse. On this World Heart Day 🌍, let’s remind ourselves: Step away from the screen sometimes 🖥 Eat mindfully 🥗 Sleep well 😴 Manage stress 🧘‍♂️ Keep distance from smoking 🚭 Before checking our apps, models, or systems’ health — let’s check our own health first. Because a healthy heart = a longer, happier coding journey. 💻❤️ #WorldHeartDay2025 #WorldHeartDay #Developers

🔥 New post is live — Day 31 of the Effective Python Coding series! Learn how to boost CPU performance in Python using multiprocessing ⚙️ Check the pinned post for the full breakdown 👇 #Python #Coding #Developers

For investing in startups ,what investors see: 1. Management Team 2. Resilience of Founder 3. Accuracy and Truth ( Financial Situation) 4. Customer Base 5. Competition 6. Industry Future So when building a startup you should keep this on account. #buildinpublic

We have seen a lot of senior devs embracing vibe coding but 95% of senior devs say we spent more time fixing bugs of AI generated code. Checkout this article this explains in detail. techcrunch.com/2025/09/14/vib…

💡 Maximal Square Problem – Step by Step Problem Statement: Given a binary matrix of 0s and 1s, find the area of the largest square containing only 1s 🟦. First Approach (Brute Force): Check every possible square in the matrix to see if all cells are 1s. ✅ Works but slow → Time Complexity: O((m×n)²) Optimal Approach (Dynamic Programming – Overlapping Subproblems): We define dp[i][j] as the side length of the largest square ending at cell (i, j). Base Case: First row & first column → dp[i][j] = matrix[i][j] 📝 Core Idea: if matrix[i][j] == 1: dp[i][j] = 1 + min(dp[i-1][j], dp[i][j-1], dp[i-1][j-1]) else: dp[i][j] = 0 This ensures we only grow squares that are fully valid. ✅ Time Complexity: O(m×n) ✅ Space Complexity: O(m×n) (can be optimized to O(n)) Reflection: Whether you are a software developer, data scientist, or student, solving such problems is not just coding—it’s sharpening your mind 🧠⚔️ for more complex challenges 🚀. If you enjoy DSA and problem-solving, follow me for more challenging problems and insights. 👉 Join me on this journey and level up your coding and problem-solving skills! #DSA #ProblemSolving #SoftwareDevelopment

❤️🌍 Today is World Heart Day – a reminder to care for the engine that keeps us alive. 🫀 Keep your heart healthy by: ✅ Eating more fruits & veggies ✅ Staying active daily ✅ Managing stress ✅ Sleeping well ✅ Avoiding smoking & excess alcohol Small steps = Strong heart 💪 #WorldHeartDay

A lot of youth are in pain, there should be a solution for them it is necessary to address their issues. Members of parliament should take a step towards them and ensure them. #cji_help_us #ssc_chairman_jwab_do

#WomensWorldCup2025 Our girls will bring home this #CWC2025 cup this year. We have great performers like Sneh Rana , Deepti Sharma, Harleen, Smriti Mandhana, Harman , A kaur , Kranti Gaud and Richa Ghosh. In #INDWvPAKW they have defeated Pakistan by 88 and announced that this cup is ours. INDIAAAAA INDIA INDIAAAA INDIA!!!!! 🥳🥳🥳🥳🥳🥳🎉🎉🎉🎉🎉🎉

🔥 Day 19 — Effective Python Coding Series Today’s focus: @staticmethod and @classmethod Decorators 🐍 In Python, these two built-in decorators are used to define methods that are bound to the class rather than its instances. They help structure logic cleanly and make class design more flexible and organized. ✨ Static Method 🔹 Belongs to the class rather than the instance 🔹 Can be called directly on the class without creating an object 🔹 Doesn’t take self or cls as parameters 🔹 Used when the method’s logic is independent of class or instance data ✨ Class Method 🔸 Belongs to the class, not the instance 🔸 Takes cls as its first parameter — giving access to class-level data 🔸 Often used for creating alternative constructors 🔸 Useful for performing operations that affect the entire class rather than individual objects ✨ Why they matter: ✔️ Improve code organization and readability ✔️ Allow flexible class-level operations ✔️ Keep related functionality within the class context ✔️ Commonly used in frameworks and libraries for cleaner abstractions ✨ In short: Use @staticmethod for logic independent of class or instance data Use @classmethod for logic that needs access to the class itself 👉 This series is designed for Software Developers, Data Analysts, Data Scientists, Python Developers, and Automation Engineers — helping you write cleaner & more efficient Python code. 💡 If you found this post useful, please like and repost it so more people can benefit from it! #Python #100DaysOfCode #SoftwareDevelopement #ML #AI #DataScience #CleanCode #CodingTips

🔥 Day 20 — Effective Python Coding Series Today’s focus: Partial Functions 🐍 In Python, partial functions allow you to fix a certain number of arguments of a function and create a new one with fewer parameters. They help you simplify your code by predefining some inputs while keeping the logic reusable and clean. ✨ How they work: You take an existing function and “lock in” one or more arguments — the new function remembers these fixed values. When you call it later, you only provide the remaining arguments. ✨ Why partial functions matter: ✔️ Simplify repetitive function calls ✔️ Improve code readability and maintainability ✔️ Work seamlessly with higher-order functions ✔️ Perfect for scenarios where you reuse similar logic with minor changes In short, partial functions = preset arguments → cleaner, reusable code. 👉 This series is designed for Data Analysts, Data Scientists, Python Developers, and Automation Engineers — helping you write cleaner & more efficient Python code. If this helped you understand something new, drop a ❤️ or 🔁 and stay tuned for more “Effective Python Coding” posts! #Python #EffectivePython #CodingSeries #100DaysOfCode #FunctionalProgramming #ML #AI #DataScienceTools

🔥 Day 22 — Effective Python Coding Series Today’s focus: slots in Python 🧠 In Python, slots are a way to define a fixed set of attributes for a class. Instead of creating a dynamic dictionary for each instance, Python allocates a fixed amount of memory for the specified attributes — making your objects faster and more memory-efficient. ✨ Why use slots? ✔️ Memory optimization: Reduces memory usage, especially when creating a large number of instances. ✔️ Faster attribute access: Since memory is pre-allocated, attribute lookup is quicker. ✔️ Prevents dynamic attributes: Helps avoid accidental attribute creation due to typos or logic errors. ✨ Limitations to consider: ⚠️ You must define all attributes in advance — making the class less flexible. ⚠️ Subclassing can be tricky; subclasses need to declare their own slots (including parent attributes). In short, slots = memory efficiency + speed boost 🚀, but with trade-offs in flexibility. 👉 This series is designed for Data Analysts, Data Scientists, Python Developers, and Automation Engineers — helping you write cleaner & more efficient Python code. If this helped you understand something new, drop a ❤️ or 🔁 and stay tuned for more “Effective Python Coding” posts! #Python #EffectivePython #CodingSeries #100DaysOfCode #MemoryOptimization #OOP #ML #AI #DataScience

For investing in startups ,what investors see: 1. Management Team 2. Resilience of Founder 3. Accuracy and Truth ( Financial Situation) 4. Customer Base 5. Competition 6. Industry Future So when building a startup you should keep this on account. #buildinpublic

🔥 Day 34 — Effective Python Coding Series Today’s focus: The Power of Coroutines in Python ⚙️ Coroutines are one of Python’s most powerful tools for asynchronous programming. They allow functions to pause, save their state, and resume later — enabling efficient multitasking without using multiple threads or processes. 🧩 ✨ Why Coroutines Matter: When handling I/O-bound operations (like API calls, DB queries, or network requests), traditional code blocks execution. But coroutines, powered by the asyncio module, keep your app running smoothly while waiting for responses. ⚡️ ✨ Core Concepts: ✔️ async / await — Define and pause coroutine functions. ✔️ Event Loop — The engine that schedules and runs async tasks. ✔️ asyncio.gather() — Run multiple coroutines concurrently. ⚡️ Key Benefits: ✅ Handle thousands of concurrent I/O tasks efficiently ✅ Keep your app responsive ✅ Ideal for modern backends, APIs, and data pipelines ⚠️ Remember: Coroutines don’t create true parallelism like multiprocessing — they’re for concurrent I/O-bound workloads, not CPU-heavy ones. In short — Coroutines = concurrency + responsiveness + scalability 🚀 👉 This series is for Python Developers, Data Engineers, and ML Practitioners who want to master writing efficient, async, and production-ready Python code. If this post helped you learn something new today, drop a ❤️ or 🔁 and stay tuned for more Effective Python Coding insights! #Python #AsyncProgramming #EffectivePython #CodingSeries #Developers #BackendDevelopment #DataEngineering #ML