LeetCode 3Sum Problem Solution in Python

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🚀 Day 12 of Consistency | #75DaysLeetCodeChallenge 🧠 Today’s Problem : 3Sum (#15) 💡 Key Learning: This problem teaches how to efficiently find triplets using sorting + two-pointer technique, while carefully handling duplicates. ⚡ Approach: Sort the array → fix one element (i) → use two pointers (l, r) → If sum == 0 → store triplet & skip duplicates If sum < 0 → move l++ If sum > 0 → move r-- 🧠 Why this works: Reduces complexity from O(n³) → O(n²) Avoids duplicate triplets Efficient use of sorting + two pointers 🔥 Result : ✔️ Runtime: 574 ms (Beats 75.21%) 📈 Problems like this build strong intuition for tackling complex array & pattern-based questions. Consistency is compounding. Keep going. 💪 #Day12 #LeetCode #DSA #CodingJourney #100DaysOfCode #Python #TwoPointers #Consistency

Repetition isn’t boring… it’s how progress is built. Today’s Python MahaRevision was all about something simple but powerful 👇 📘 Chapter 7: Loops in Python This is where things start to feel efficient. Instead of writing the same logic again and again, you let the program handle repetition: → for loops → while loops → break & continue → understanding loop flow Practice set done: Worked on problems like printing patterns, iterating through lists, basic counting logic, and controlling loops with conditions. At first, loops seem basic. But the more I practice, the more I see how important they are in real programs. Small concepts. Big impact. #Python #LearningInPublic #CodingJourney #Programming

🔥 Solved a classic Sliding Window problem today 👉 Find a subarray with given sum in O(n) time 💡 Idea: Expand window → add elements Shrink window → when sum exceeds target Stop when sum == target 🧠 Key Insight: Control the window based on sum, not size 🚀 Simple trick, powerful optimization! #DSA #Python #Coding #ProblemSolving #Learning

Limits are a core concept in calculus, and SymPy makes working with them surprisingly intuitive. Whether you're calculating the behavior of sequences, evaluating function limits, or deriving differential quotients, SymPy’s method gives you a powerful way to explore mathematical behavior symbolically. This post walks through practical examples of limits for sequences, functions, and difference quotients, complete with SymPy code you can run and adapt. #Python #SymPy #SymbolicMath #Calculus #RheinwerkComputingBlog #Limits Read here: https://hubs.la/Q048SdKL0

If your code is getting longer… ................................................................. you’re probably doing something wrong. That hit me hard while doing today’s Python MahaRevision 👇 📘 Chapter 8: Functions & Recursion Instead of writing the same logic again and again, I learned how to simplify everything: → Functions: write once, reuse anytime → Parameters & return values → Breaking big problems into smaller pieces → Recursion: when a function calls itself Practice set done: Built functions for repeated tasks, passed inputs, returned outputs, and tried recursion problems (confusing at first, but interesting). Not gonna lie—recursion felt weird in the beginning. But it also showed me a different way of thinking. The real shift? It’s not about writing more code… it’s about writing smarter code. Slowly starting to see the bigger picture. #Python #LearningInPublic #CodingJourney #Programming #100DaysOfCode

Day 9 of My LeetCode Journey Today, I solved the Longest Consecutive Sequence problem Approach I Used (Optimal Solution) Converted the array into a set for O(1) lookups Iterated through each number and checked if it’s the start of a sequence (i.e., num - 1 not in set) Expanded the sequence using a while loop (current + 1) Tracked the maximum length of consecutive elements Why this works efficiently? Avoids unnecessary checks by only starting from sequence beginnings Reduces time complexity to O(n) Key Learning: Using sets can drastically optimize problems involving lookups Identifying the starting point of a pattern is a powerful technique Writing clean and logical loops improves performance Consistency is the key — learning something new every day! #LeetCode #Day9 #CodingJourney #Python #DSA #ProblemSolving #Consistency

📌 Problem: Sum of Digits in Base K 💡 Approach: Convert the given number into base k by repeatedly dividing it by k. At each step, take the remainder (n % k) which represents the digit in base k, and add it to the sum. Continue until the number becomes zero. ⚙️ Key Insight: Base conversion using division and remainder No need to explicitly store the converted number ⏱️ Time Complexity: O(logₖ n) 📦 Space Complexity: O(1) 📚 What I learned: Efficient base conversion technique Working with number systems in coding problems #LeetCode #DSA #Algorithms #Coding #ProblemSolving #Python #Math #InterviewPreparation #CodingJourney

It’s funny how even code needs to “decide” what to do next. Today’s Python MahaRevision was all about making decisions in code 👇 📘 Chapter 6: Conditional Expressions Finally getting into the part where programs start to feel a bit “smart”: • if, elif, else • using logic to control flow • combining conditions with and/or/not 🧠 Did the practice set too: Tried problems like checking numbers, comparing values, simple decision-making stuff… nothing too crazy, but really made me think. Honestly, this chapter felt different. It’s not just about syntax anymore—it’s about how you think. Taking it one chapter at a time 🚀 #Python #LearningInPublic #CodingJourney #Programming

🚀 Today I Solved… A classic problem: Count pairs with sum < target using the two-pointer technique ⚡ 💡 Key idea: Sort the array Use left and right pointers If arr[left] + arr[right] < target 👉 Add right - left in one go (counts multiple pairs instantly!) 🔥 This simple trick reduces complexity from O(n²) ➝ O(n log n) Small optimization, big impact — that’s the power of patterns in DSA! #DSA #Coding #Python #ProblemSolving #InterviewPrep