Sliding Window Problems: M vs P in Range

🚨 If you're solving LeetCode but still not improving… read this. Most people just solve questions ❌ Top candidates understand patterns + solutions ✅ That’s the difference. --- 🔥 Complete LeetCode Solutions – FREE PDF This is not just questions… This is a full solution guide to master DSA step-by-step 💯 --- 📘 What’s inside: ✔ Linked List problems (cycle, reverse, merge, etc.) ✔ Trees & BST (traversals, depth, LCA, validation) ✔ Graphs (connected components, course schedule) ✔ Arrays & Strings (2 sum, 3 sum, sliding window) ✔ Recursion & Backtracking ✔ Dynamic Programming concepts ✔ Bit Manipulation & Maths ✔ Clean Python solutions for each problem --- 💡 From the document: You’ll find structured solutions starting from basics like Linked List Cycle to advanced problems like Binary Tree Maximum Path Sum, helping you build real problem-solving skills --- ⚠️ Hard truth: LeetCode alone won’t help… 👉 Understanding solutions WILL. --- 🎯 Best way to use this: 1. Try question yourself 2. Check solution 3. Understand logic deeply 4. Re-implement without seeing Repeat this → You’ll become dangerous in DSA 🔥 --- If this helps you: ❤️ Like 🔁 Repost 💬 Comment “LEETCODE” → I’ll share roadmap + more PDFs --- #leetcode #dsa #coding #interviewprep #developers #programming #python #placements #softwareengineer #learncoding

Every developer has lived this exact moment. Your code is working perfectly. You make one small change just to test. Everything breaks completely. This is not a beginner problem. This is a coding problem. It happens at every level. Here is what I have learned when this happens: Step 1 — Do not panic. Breathe. Step 2 — Undo the change. Get back to what worked. Step 3 — Test one change at a time, never multiple at once. Step 4 — Read the error message carefully. It always tells you something. Step 5 — Google is not cheating. It is a tool. Use it. The best developers are not the ones who never break their code. They are the ones who know how to fix it. Breaking things is how you truly learn to build things. #Python #Coding #DataScience #LearnToCode #BeginnerCoder #Debugging #StudentLife

🚀 Just Completed My Project: Library Management System 📚 So, I built a Library Management System — a simple yet powerful project that solves a real-world problem. 💡 What this project does: -Manage book records efficiently -Track issued and returned books -Store user and library data -Reduce manual work and errors 🛠 What I learned: -How to structure a real-world project -Working with data (using Python & databases) -Writing clean and organized code -Problem-solving and logical thinking 📊 Why this matters: In real life, libraries still struggle with manual systems. This project shows how technology can make processes faster, smarter. 🔥 This is just the beginning. Next step → Improving it with a trained model & deploying it as a web app. If you're also learning, don’t just watch tutorials — build something real. Code And Output:-https://lnkd.in/dkkn3p_i #Python #Projects #LibraryManagementSystem #CodingJourney #DataScience #Beginners #LearningByDoing

DSA LeetCode Solutions Handbook – From Linked List to Trees Most people solve problems on LeetCode… But don’t organize their learning. That’s where real improvement gets missed. Found this structured handbook and it’s worth exploring . It covers key patterns across: 1) Linked List – pointers, cycles, reversal. 2) Trees – traversals, recursion, BSTs. 3) Graphs – BFS, DFS, shortest paths. 4) Heaps – Top K, priority queues. 5) Arrays & Strings – sliding window, two pointers. 6) Bit Manipulation – tricks that save time in interviews. 7) Maths – number theory, edge cases. 8) Matrix – grid-based problems & patterns. While building this, one thing became clear: - Solving problems is good, but organizing patterns is what truly improves speed and confidence. - This is something I’ll keep updating as I solve more problems. If you're preparing for coding interviews, structured notes like this can really help. Do you maintain your own notes while solving DSA problems? #DSA #LeetCode #CodingInterview #Python #ProblemSolving #SoftwareEngineering #TechLearning #PythonDeveloper

🚀 DSA Day 5 — Recursion (Core Problems) Today I focused on strengthening my recursion fundamentals by solving some classic problems from scratch. 🔹 Topics Covered: • Printing numbers (1 → 10 and 10 → 1 using recursion) • Factorial using recursion • Sum of digits (two approaches: accumulator & return-based) • Reverse a number (recursive approach) • Palindrome check using recursion 🔹 Key Learnings: Recursion is not about memorizing patterns — it’s about understanding: Base condition (where to stop) State change (how the problem reduces) If you don’t get these right, recursion will break. 🔹 Two practical insights I learned: • Example 1: In factorial, missing the correct base case (n == 1 or n == 0) leads to infinite recursion. • Example 2: In reverse number, carrying state (rev) properly is critical — otherwise, you lose digits. 🔹 What I improved today: • Writing cleaner recursive functions • Avoiding unnecessary parameters • Thinking in terms of smaller subproblems 📌 Code available here: [🚀 DSA Day 5 — Recursion (Core Problems) Today I focused on strengthening my recursion fundamentals by solving some classic problems from scratch. 🔹 Topics Covered: • Printing numbers (1 → 10 and 10 → 1 using recursion) • Factorial using recursion • Sum of digits (two approaches: accumulator & return-based) • Reverse a number (recursive approach) • Palindrome check using recursion 🔹 Key Learnings: Recursion is not about memorizing patterns — it’s about understanding: Base condition (where to stop) State change (how the problem reduces) If you don’t get these right, recursion will break. 🔹 Two practical insights I learned: • Example 1: In factorial, missing the correct base case (n == 1 or n == 0) leads to infinite recursion. • Example 2: In reverse number, carrying state (rev) properly is critical — otherwise, you lose digits. 🔹 What I improved today: • Writing cleaner recursive functions • Avoiding unnecessary parameters • Thinking in terms of smaller subproblems 📌 Code available here: [Add your GitHub link] #DSA #Recursion #Python #CodingJourney #ProblemSolving #100DaysOfCode] #DSA #Recursion #Python #CodingJourney #ProblemSolving #100DaysOfCode 10000 Coders sanjeev ch

🚀 Day 28/30 – 30 Days of Python Project Challenge Consistency builds skill. Skill builds confidence. 🚀 As part of my 30-day challenge, I’m focused on solving real-world problems while strengthening core development concepts. 🧠 Today’s Project: Video to Audio (MP3) Converter I built a Python-based media utility that allows users to quickly extract audio tracks from video files through a simple graphical interface. ✨ Why this project matters: This project bridges the gap between file management and media processing. It demonstrates how Python can automate tedious tasks—like converting formats—with just a few lines of code and a user-friendly file picker. ⚙️ Key Features: Native File Explorer: Uses Tkinter's filedialog to browse and select videos easily 📂 High-Quality Extraction: Preserves audio fidelity during the MP4 to MP3 transition 🎵 Resource Efficient: Automatically closes file streams to save memory 🔋 Instant Feedback: Console confirmation once the conversion is complete ✅ 💡 Concepts Applied: Media Processing with MoviePy OS Interactivity through Tkinter's GUI components Stream Management (opening/closing file handles) Error Handling for file selection and module dependencies Environment Configuration for Linux-based development 🔗 GitHub: https://lnkd.in/djsKiuE7 📌 Takeaway: Automation is at its best when it removes friction. Turning a multi-step manual conversion into a single-click script is what makes coding so rewarding. On to Day 29. 🔥 #Python #BuildInPublic #DeveloperJourney #30DaysOfCode #MoviePy #Tkinter #Automation #SoftwareDevelopment #Coding #Learning #OpenSource #Projects

This one DSA trick changed how I solve interval problems forever Most people struggle with Merge Intervals not because it's hard… but because they overthink it. Let’s simplify it 👇 Problem: You’re given intervals like [1,3] [2,6] [8,10] [15,18] and asked to merge overlaps. The mistake most people make: They try to compare every interval with every other interval → messy and slow. The game-changing insight: 👉 Sort first. Then merge. Once sorted, overlapping intervals come next to each other, and the problem becomes simple. How it works: • Start with the first interval • Compare with the next • If they overlap → merge • Else → move forward Core logic: if(interval[0] <= current[1]) { current[1] = Math.max(current[1], interval[1]); } Why this matters: This pattern shows up in real problems like scheduling, meetings, and range merging. Master this once → unlock multiple questions. Connect with me for more learning. #DSA #CodingInterview #JavaScript #LeetCode #Programming

This resonates. The same thing happens in frontend work: you can have a React component that "works" but the moment you try to walk a colleague through your state update logic, you realize half of it is held together by coincidence. Explaining code forces precision that debuggers never demand. It also makes code reviews far more valuable, for the reviewer and the author.

🚀 One Small Coding Problem That Strengthened My Logic as a Developer Sometimes, it’s not about solving complex problems. It’s about how clearly you can think through simple ones. ❓ The Question How do you create a list that contains the maximum value from each given list? First line → Integer N Next N lines → Space-separated integers Output → A single list with maximum values from each line 💡 My Approach Instead of overcomplicating it, I focused on clarity: ✔ Read input line by line ✔ Convert each line into integers ✔ Use Python’s built-in max() ✔ Store results in a list 🧩 Example Input: 3 1 2 3 4 10 20 30 5 10 15 20 Output: [4, 30, 20] 💻 Code n = int(input()) result = [] for _ in range(n): nums = list(map(int, input().split())) result.append(max(nums)) print(result) 🧠 What I Learned 👉 Simple problems can sharpen core thinking 👉 Built-in functions are powerful when used correctly 👉 Clean logic > complex code 🔥 Final Thought Consistency in solving small problems builds the foundation for solving big ones. #Python #Coding #ProblemSolving #Developers #Learning #100DaysOfCode

Day 58 of my #100DaysOfCode challenge 🚀 Today I implemented a Python program to generate prime numbers within a given range. This is a practical extension of prime checking and useful in many DSA and real-world problems. What the program does: • Takes a range (start, end) as input • Checks each number in the range • Identifies whether it is prime or not • Returns a list of all prime numbers in that range Example Output: Prime numbers between 1 and 50: [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47] How the logic works: Start from max(2, start) For each number: • Assume it is prime • Check divisibility from 2 → √n If divisible → not prime If not divisible → add to result list 👉 Uses square root optimization for better performance Why this is important: – Builds on prime number fundamentals – Useful in: Competitive programming Number theory problems Range-based queries – Helps understand optimization using √n Time Complexity: O(n√n) Space Complexity: O(k) (number of primes) Key Takeaways: – Applying optimized prime checking – Working with ranges and loops – Improving efficiency using √n – Writing clean and scalable code #100DaysOfCode #Day58 #Python #Programming #DSA #Algorithms #PrimeNumbers #NumberTheory #CodingPractice #ProblemSolving #InterviewPrep #Optimization #DeveloperJourney #Consistency #BTech #CSE #AIandML #VITBhopal #TechJourney