Mastering HCF and LCM in Python

🚀 LeetCode Easy, Big Learning! 🚀 Today I solved the “Count Pairs with Given Difference” problem, and it’s a great reminder that simple logic can go a long way 👇 💡 What’s the challenge? Given a list of numbers and a value k, the task is to count how many pairs have an absolute difference exactly equal to k. 🎯 Why I liked this problem: It strengthens problem understanding Helps practice pair comparison logic Great example of how brute force thinking builds foundations Perfect for beginners stepping into DSA 🚶♂️➡️🏃♂️ 🧠 Key takeaway: Before jumping to optimization, always make sure your logic is crystal clear. Even an Easy problem can sharpen your thinking if you focus on: Conditions Pair handling Avoiding duplicate checks 📈 Problems like these build confidence and prepare you for medium and hard challenges ahead. Consistent practice > complicated shortcuts 💪 #LeetCode #DSA #ProblemSolving #CodingJourney #Python #ComputerScience #LearningEveryDay 🚀

Mastering Python Logic: From Recommendations to Real-World Analytics! Day 7 at YouExcel Training and the momentum is only building up! 🚀 After developing recommendation systems for cars and venues, today’s warm-up was about building a Student Grading System. This project allowed me to dive deeper into Python’s mathematical capabilities and multi-layered conditional logic. What I implemented today: Arithmetic Operations: Automating the calculation of total marks and percentages from multiple subjects. Precision Logic: Using if-elif-else chains to categorize academic performance into specific grades (A+ to E). Dynamic Feedback: Crafting personalized messages for students based on their results. It’s amazing how a few days of consistent practice under sir Muhammad Rafay Shaikh can turn complex logical problems into structured, executable code. Every warm-up session feels like a step closer to becoming a proficient developer! #Python #CodingJourney #StudentGradingSystem #YouExcel #MuhammadRafayShaikh #LogicBuilding #DataAnalytics #ContinuousLearning #Day7

From Spreadsheet Stress to Script Success 🎓💻 I spent way too much time manually calculating my GPA every semester. So, I did what any student learning Python would do: I automated it. I built this CGPA Calculator to handle: * Dynamic Mapping: Automatically converting letter grades (A, B, C...) into weighted scores. * Vectorized Math: Using NumPy to sum up grades and units across multiple semesters. * Real-time Feedback: Instantly calculating both the semester GPA and the overall CGPA. It’s a simple project, but it’s a great example of how a few lines of code can solve a recurring problem. What’s the first thing you ever automated? Let’s hear it in the comments! 👇 #Python #NumPy #DataAnalysis #StudentDeveloper #Automation #EngineeringStudent

🗓 Day 63 / 100 – #100DaysOfLeetCode 📌 Problem 1161: Maximum Level Sum of a Binary Tree Today’s problem focused on tree traversal and level-wise aggregation. The task was to find the level in a binary tree that has the maximum sum of node values, with levels numbered starting from 1. 🧠 My Approach: Used Breadth-First Search (BFS) to traverse the tree level by level. For each level: Calculated the sum of all node values at that level. Compared it with the maximum sum seen so far. Kept track of: the current level number the level with the maximum sum Returned the smallest level number in case of a tie, as required. This level-order traversal makes the solution both intuitive and efficient. 💡 Key Learning: This problem reinforced: ✔ how BFS naturally fits level-based tree problems ✔ careful tracking of level indices during traversal ✔ handling ties correctly based on problem constraints Tree problems often become much simpler once the right traversal strategy is chosen. Another solid step forward in mastering binary tree patterns 🌳🚀 #100DaysOfLeetCode #LeetCodeChallenge #Python #ProblemSolving #BinaryTree #BFS #TreeTraversal #Algorithms #DSA #CompetitiveProgramming #SoftwareEngineering #CodingJourney #DeveloperJourney #LearningInPublic #TechStudent #CareerGrowth #Programming #KeepLearning

In 12th standard, there were topics that felt abstract and disconnected. You memorized them, practiced a few problems, and moved on, often without truly understanding why they mattered. At the time, it felt like learning for the sake of exams, not for real use. Fast forward to today. While working with the NumPy library in Python, I found myself practicing advanced slicing and indexing. Suddenly, those same ideas from school mathematics clicked. What once felt confusing now felt practical, logical, and even satisfying to implement in real code. That moment was a reminder of how learning works over time. Concepts do not always make sense when you first encounter them. But when you revisit them in the right context, with real problems to solve, they transform from theory into tools. Coding has a way of doing that. It connects abstract ideas to tangible outcomes. Experiences like this make the learning journey rewarding. Not because everything becomes easy, but because progress becomes visible. You start appreciating the foundations you once questioned. #Python #NumPy #DataScience #Programming #SoftwareDevelopment #LearningJourney #ContinuousLearning #ProblemSolving #STEM

Let's talk about something practical that saved me hours. Ever spent ages writing complex data transformations? Meet Pandas 🐼→ Polars 🚀. The transition wasn't smooth at first—new syntax, different mindset. But the performance gain? 5-10x faster on large datasets. My realization: Sometimes we stick with tools because they're comfortable. But that 2-day learning curve for Polars gave me back weeks of compute time over the year. Try this: Take one of your slower pandas scripts this week. Rewrite it in Polars. Share your speedup in the comments! What's one tool switch that significantly improved your workflow? #Python #DataScience #Programming #TechTips #CareerDevelopment #DataEngineering

📘 GATE 2026 Preparation | Day 40 Update Day 40 was about practice, revision, and understanding internals, not just surface-level learning. 🔹 What I worked on yesterday: COA: Practiced Memory Mapping questions Operating Systems: Revised CPU Scheduling Revised Process States Aptitude: Solved a few practice questions Python Programming (Class 2): Studied Functions Understood the internal working of Python’s Global Dictionary Learned how Python manages names, scopes, and execution internally 🔹 Key learning: Strong preparation is not only about solving problems, but also about knowing how systems and languages work internally. Next focus: More PYQs, deeper revision, and consistent Python practice alongside GATE prep. #GATE2026 #COA #OperatingSystems #CPUScheduling #MemoryMapping #PythonInternals #Aptitude #DailyProgress #Consistency #GATEreadywithGfG

Day 4 of DSA Practice : Today I learned about Bubble Sort and understood how we can optimize it step by step. 🔹 Approaches Learned: Basic Bubble Sort: O(n²) Optimized Bubble Sort (Early Exit using a flag): Best Case O(n), Worst Case O(n²) ✅ Key Idea (Bubble Sort): We repeatedly compare adjacent elements and swap them if they’re in the wrong order. After each pass, the largest element “bubbles up” to the end of the array. 🚀 Optimization (Early Exit): If in any pass no swaps happen, the array is already sorted — so we stop early. ⚡ Complexity: Time: Worst O(n²), Best O(n) (already sorted with optimization) Space: O(1) This sorting method is great for learning sorting basics and understanding swapping-based sorting. #DSA #Programming #CompetitiveProgramming #Java #Cplusplus #Python #Learning

Learning Stack Data Structure today 📚 Spent some time understanding Stacks in DSA today. The unique thing about this structure is the LIFO (Last In, First Out) principle—meaning the last element you push is the first one you pop. It’s a great structure to learn for scenarios like implementing 'Undo' features or managing function calls in recursion. Making steady progress on my DSA journey. 🚀 #DSA #Stack #Python #Coding #DataStructures #LearningJourney

Last week I gave a talk for my university's Successful Teaching Practices workshop on Streamlining Course Setup Using the Canvas API. Here I use Python, R, RMarkdown, the Canvas API, and the OpenAI API to automate creating and retrieving Canvas content with a live demo in Emacs! This approach has saved me a ton of time at the beginning of each semester, I'd be curious to hear how others are using the Canvas API and LLMs to make working with LMS's like Canvas easier. https://lnkd.in/grNeY4yH