Solved 3Sum Closest Problem with Python and Two-Pointer Approach

⚡ Day 83 of #100DaysOfDSA – Majority Element 💡 📌 Problem. no 169: Given an array nums, find the element that appears more than ⌊n / 2⌋ times. Implemented an efficient hash map-based counting approach to track element frequency and determine the majority element quickly. ⚡ Runtime: 13 ms – Beats 37.81% of submissions 💾 Memory: 13.60 MB – Beats 64.79% of solutions 💻 Language Used: Python ✅ Status: Accepted – All 53 test cases passed successfully! This challenge helped me better understand frequency counting, dictionary operations, and threshold-based decision making. It’s a great problem for mastering counting logic and handling arrays efficiently. 🔑 Key Learnings: ✔️ Strengthened my understanding of hash maps and element frequency counting ✔️ Learned how to use early termination for optimization ✔️ Improved confidence in solving majority and occurrence-based problems 🎯 Day 83 — A solid step forward in mastering counting logic and data structure efficiency! 🚀🔥 #100DaysOfCode #100DaysOfDSA #LeetCode #PythonProgramming #DataStructures #AlgorithmPractice #CodeNewbie #DailyCoding #ProblemSolving #TechJourney #WomenWhoCode #CodeLife #CodingChallenge #DSAChallenge #DeveloperLife #PythonDeveloper #LearnToCode #CodingCommunity #CodeEveryday #SoftwareEngineering #KathirCollegeOfEngineering #KathirCollege #BTechLife #AIDS #FutureEngineer #CodingMotivation #ProgrammingSkills

🚀 DSA Challenge – Day 86 Problem: Minimum Operations to Make Array Elements Zero ⚙️💥 This problem was a simple yet insightful exercise in greedy thinking and recognizing that we only need to count distinct positive values to reach the goal efficiently. 🧠 Problem Summary: You are given a non-negative integer array nums. In one operation, you can choose an integer x (≤ smallest non-zero element) and subtract it from all positive numbers. Your task: Find the minimum number of operations to make all elements 0. ⚙️ My Approach: 1️⃣ Sort the array to process numbers in ascending order. 2️⃣ Maintain a running subtraction value curr to track how much has already been subtracted. 3️⃣ For each new smallest element, if it’s still positive after prior subtractions, perform one operation and update curr. 4️⃣ Each operation corresponds to discovering a new distinct positive number. 📈 Complexity: Time: O(n log n) → Sorting dominates the time complexity. Space: O(1) → Only a few extra variables used. ✨ Key Takeaway: Every distinct positive number in a sorted array represents a new operation — a clean and intuitive greedy solution that avoids overcomplication. ⚡ 🔖 #DSA #100DaysOfCode #LeetCode #ProblemSolving #GreedyAlgorithm #Sorting #CodingChallenge #Python #Algorithms #EfficientCode #Optimization #TechCommunity #InterviewPrep #CodeEveryday #LearningByBuilding

🚀 DSA Challenge – Day 80 Problem: Maximum Distance Between Valid Pairs 🌊📏 Today’s problem tested the combination of binary search and array monotonicity — finding the farthest valid pair between two non-increasing arrays! 🧠 Problem Summary: We’re given two non-increasing arrays, nums1 and nums2. A pair (i, j) is valid if: i ≤ j, and nums1[i] ≤ nums2[j]. The goal is to find the maximum distance (j - i) among all valid pairs. ⚙️ My Approach: 1️⃣ Iterate through each element in nums1. 2️⃣ Use binary search on nums2 to find the farthest valid index satisfying the condition. 3️⃣ Keep track of the maximum j - i distance encountered. This solution leverages the sorted (non-increasing) property of arrays for logarithmic efficiency. 📈 Complexity: Time: O(n log m) → For each element in nums1, a binary search on nums2. Space: O(1) → Only a few variables used. ✨ Key Takeaway: Sometimes, monotonic properties allow you to blend binary search with iteration — turning what looks like a brute-force problem into a clean and efficient search-based solution. ⚡ 🔖 #DSA #100DaysOfCode #LeetCode #ProblemSolving #Algorithms #BinarySearch #TwoPointers #CodingChallenge #Python #InterviewPrep #TechCommunity #Optimization #EfficientCode #CodeEveryday

🚀 Solved a Classic String-Search Problem! I recently worked on the problem “Find the index of the first occurrence of a string in another string” (LeetCode 28 — strStr()), and it was a great exercise in understanding string manipulation and efficient search techniques. 🔍 Problem Summary Given two strings, haystack and needle, the goal is to return the index of the first occurrence of needle within haystack, or -1 if it doesn’t exist. 🔧 My Approach I implemented two solutions: 1️⃣ Basic Sliding Window Approach Iterate through haystack Compare each substring with needle Return the index when a match is found 2️⃣ Optimized Thought Process (KMP-ready) Explored how KMP can reduce time complexity from O(n*m) to O(n) Understood prefix functions and pattern matching fundamentals 🧠 What I Learned Strengthened understanding of string traversal Learned how to optimize search operations Practiced writing clean and readable code Got a deeper understanding of how real search algorithms (like KMP) work behind the scenes Happy to connect with others exploring DSA, algorithms, and problem-solving! 🚀 #️⃣ #dsa #leetcode #coding #programming #python #softwaredevelopment #algorithms #computerscience

⚡ Day 88 of #100DaysOfDSA – Next Permutation 🔁 📌 Problem. no 31: Implement an algorithm to rearrange numbers into the next lexicographically greater permutation of numbers. If no such arrangement exists, transform it into the lowest possible order (i.e., sorted in ascending order). 🚀 Runtime: 0 ms – Beats 💯% of Python submissions 💾 Memory: 12.43 MB – Beats 52.01% of solutions 💻 Language Used: Python ✅ Status: Accepted – All 266 test cases passed successfully! This problem was a great exercise in in-place array manipulation and understanding lexicographical ordering. It strengthened my skills in reverse traversal and efficient element swapping to achieve the next permutation sequence. 🔑 Key Learnings: ✔️ Learned how to find and swap pivot points efficiently ✔️ Improved logic-building for in-place array operations ✔️ Understood how permutations can be generated without extra space 🎯 Day 88 — A solid step forward in mastering array algorithms and improving my analytical approach to sequence transformations! 🔥💡 #100DaysOfCode #100DaysOfDSA #LeetCode #PythonProgramming #DataStructures #AlgorithmPractice #CodeNewbie #DailyCoding #ProblemSolving #TechJourney #WomenWhoCode #CodeLife #CodingChallenge #DSAChallenge #DeveloperLife #PythonDeveloper #LearnToCode #CodingCommunity #CodeEveryday #SoftwareEngineering #KathirCollegeOfEngineering #KathirCollege #BTechLife #AIDS #FutureEngineer #CodingMotivation #ProgrammingSkills

📌 Problem. no 179: Given a list of non-negative integers, arrange them such that they form the largest possible number. Solved using a custom comparator to sort numbers based on concatenation order and achieve the optimal result. ⚡ Runtime: 3 ms – Beats 78.56% of submissions 💾 Memory: 12.69 MB – Beats 24.66% of solutions 💻 Language Used: Python ✅ Status: Accepted – All 235 test cases passed successfully! This problem enhanced my understanding of string-based comparison, custom sorting, and optimization through logic-based ordering. It’s an elegant challenge that blends sorting and string manipulation seamlessly. 🔑 Key Learnings: ✔️ Learned how to design and implement custom comparators ✔️ Understood how concatenation order affects numeric outcomes ✔️ Strengthened my grasp on sorting logic and greedy approaches 🎯 Day 84 — A creative challenge that refined my problem-solving through custom sorting and logical structuring! 🚀🔥 #100DaysOfCode #100DaysOfDSA #LeetCode #PythonProgramming #DataStructures #AlgorithmPractice #CodeNewbie #DailyCoding #ProblemSolving #TechJourney #WomenWhoCode #CodeLife #CodingChallenge #DSAChallenge #DeveloperLife #PythonDeveloper #LearnToCode #CodingCommunity #CodeEveryday #SoftwareEngineering #KathirCollegeOfEngineering #KathirCollege #BTechLife #AIDS #FutureEngineer #CodingMotivation #ProgrammingSkills

🚀 DSA Challenge – Day 85 Problem: Check if All Integers in a Range Are Covered ✅📏 This problem was an elegant use of the Prefix Sum technique, where I used range updates to efficiently check coverage over an interval. 🧠 Problem Summary: You are given several inclusive integer intervals and a target range [left, right]. You must verify if every integer within [left, right] is covered by at least one of the given intervals. ⚙️ My Approach: 1️⃣ Initialize an array line to track coverage at each integer position. 2️⃣ For every range [a, b], increment line[a] and decrement line[b + 1] — this marks the start and end of coverage. 3️⃣ Convert line into a prefix sum array, so each position reflects how many intervals cover that number. 4️⃣ Finally, iterate through [left, right] to ensure each integer has coverage (> 0). 📈 Complexity: Time: O(n + 52) → Linear scan and prefix sum computation. Space: O(52) → Fixed-size array since ranges are small. ✨ Key Takeaway: Prefix sum is not just for subarray sums — it’s a powerful trick for range marking and coverage problems, offering O(1) updates and O(n) verification. ⚡ 🔖 #DSA #100DaysOfCode #LeetCode #PrefixSum #RangeUpdate #ProblemSolving #Algorithms #CodingChallenge #Python #EfficientCode #Optimization #TechCommunity #InterviewPrep #CodeEveryday #LearningByBuilding

⚡ Day 85 of #100DaysOfDSA – Reverse Bits 💡 📌 Problem. no 190: Given a 32-bit unsigned integer, reverse its bits and return the result. Solved using bit manipulation by shifting and combining bits efficiently through iterative processing. ⚡ Runtime: 15 ms – Beats 74.65% of submissions 💾 Memory: 12.49 MB – Beats 49.36% of solutions 💻 Language Used: Python ✅ Status: Accepted – All 600 test cases passed successfully! This problem strengthened my understanding of low-level bit operations, binary manipulation, and efficient use of shifting operators. It’s a great reminder that small operations can have big effects on data representation. 🔑 Key Learnings: ✔️ Learned how to manipulate bits effectively using bitwise operators ✔️ Understood the significance of shifting and masking in binary form ✔️ Improved my grasp of algorithmic efficiency at the bit level 🎯 Day 85 — A smart bitwise challenge that sharpened my binary logic and precision-based problem-solving! ⚙️💻🔥 #100DaysOfCode #100DaysOfDSA #LeetCode #PythonProgramming #DataStructures #AlgorithmPractice #CodeNewbie #DailyCoding #ProblemSolving #TechJourney #WomenWhoCode #CodeLife #CodingChallenge #DSAChallenge #DeveloperLife #PythonDeveloper #LearnToCode #CodingCommunity #CodeEveryday #SoftwareEngineering #KathirCollegeOfEngineering #KathirCollege #BTechLife #AIDS #FutureEngineer #CodingMotivation #ProgrammingSkills

"Day 3 is a massive win for efficiency! I dedicated the morning to mastering the Two-Pointer technique, a critical step in optimizing array algorithms. I successfully tackled two key problems, including the famous Remove Duplicates (#26), which confirmed the absolute necessity of O(1) auxiliary space optimization. Key Technical Insight: O(1) Space is King I compared different approaches for removing duplicates and confirmed the core DSA lesson: 1. The Slow/Fast Pointer method is the correct, efficient approach, achieving O(1) space and O(n) time by manipulating the array in-place. 2. Brute-force solutions using helper data structures violate the memory constraint and often lead to slow O(n^2) time complexity. The lesson is clear: Master the mechanism, don't just rely on the wrapper. Understanding this difference is essential for building scalable applications. All progress is documented and pushed. Now, the plan pivots to laying the foundation for my Python OOP skills and project architecture. #DSA #TwoPointers #Python #LeetCode #SoftwareEngineering #Consistency #O1Space"

🚀 Day 40 of #100DaysOfDSA Solved LeetCode Problem #69 – Sqrt(x) 🧮 💡 Problem Insight: Given a non-negative integer x, return the square root of x rounded down to the nearest integer. For example: Input: x = 8 Output: 2 (since √8 ≈ 2.828, and floor(2.828) = 2) ✨ Key Learnings: Practiced binary search to find results efficiently without using built-in math functions. Learned how to narrow down search space based on mid-square comparisons. Reinforced understanding of integer division and rounding down behavior. Time Complexity: O(log n) — fast and efficient! Space Complexity: O(1) 💬 Lesson: Binary Search isn’t just for sorted arrays — it’s a mindset for narrowing down possibilities quickly 🚀 #LeetCode #Python #DSA #BinarySearch #ProblemSolving #100DaysOfCode #Day40 #CodingJourney