Optimize Bracket Matching with HashMap and Stack

Valid Parentheses: HashMap + Stack for Clean Bracket Matching Nested if-else chains for each bracket type create messy code. HashMap maps closing brackets to required opening brackets. Stack tracks unmatched openers. On closing bracket, verify stack top matches via HashMap — extensible, clean solution. HashMap Simplification: Replacing conditional chains with HashMap lookup makes code extensible — new bracket types need one dict entry, not multiple conditionals. Generalizes to any multi-case validation. Time: O(n) | Space: O(n) #Stack #HashMap #BracketMatching #CodeCleanup #Python #AlgorithmDesign #SoftwareEngineering

🚀 Day 41 – LeetCode Journey Today’s problem: Add Binary ✔️ Implemented binary addition manually ✔️ Handled carry propagation efficiently ✔️ Worked with string manipulation and indexing 💡 Key Insight: Binary addition follows the same rules as decimal addition, but with base 2. Managing the carry correctly is the key to building the final result. This problem improved my understanding of bit-level operations and string handling. Step by step, mastering the fundamentals 🔥💪 #LeetCode #Day41 #Binary #Strings #Python #ProblemSolving #CodingJourney #100DaysOfCode

🚀 Day 62 of #100DaysOfCode Solved “Remove Nth Node From End of List” 🔗 💡 Today’s focus: Two Pointer Technique (Fast & Slow pointers) Instead of calculating length, I used an efficient one-pass approach to remove the target node. 🧠 Key Learnings: Dummy node helps handle edge cases (like removing head) Fast pointer moves n steps ahead Then move both pointers until fast reaches the end Slow pointer lands just before the node to delete ⚡ Clean, efficient & optimal solution (O(n) time, O(1) space) Consistency is starting to feel powerful now 💪 #DSA #LeetCode #CodingJourney #LinkedInLearning #100DaysOfCode #Day62 #Python #ProblemSolving

Top K Frequent: Bucket Sort Beats Heap with O(n) Time Heap-based solution costs O(n log k). Bucket sort achieves O(n) by leveraging frequency bounds — frequencies can't exceed array length. Index = frequency, value = list of elements with that frequency. Traverse buckets high-to-low, collecting k elements. Bucket Sort Advantage: When value range is bounded and known (frequencies ≤ n), bucket sort beats comparison-based O(n log k). Exploiting constraints transforms complexity. Time: O(n) | Space: O(n) #BucketSort #TopK #FrequencyAnalysis #ComplexityReduction #Python #AlgorithmOptimization #SoftwareEngineering

Group Anagrams: Frequency Array as Hashable Key Sorting each string costs O(k log k) per string. Character frequency array is O(k) and creates identical signature for anagrams. Fixed 26-element array converted to tuple serves as hashable HashMap key — faster, cleaner grouping. Frequency Signature: Character counts uniquely identify anagram groups without sorting. Tuple conversion makes array hashable for dict keys. Pattern applies to document clustering, duplicate detection. Time: O(n × k) vs O(n × k log k) sorting | Space: O(n × k) #FrequencyArray #Anagrams #HashMap #KeyOptimization #Python #AlgorithmOptimization #SoftwareEngineering

🚀 Day 33 – LeetCode Journey Today’s problem: Move Zeroes ✔️ Used two-pointer technique for in-place modification ✔️ Moved all non-zero elements forward efficiently ✔️ Maintained relative order of elements 💡 Key Insight: By keeping a pointer for non-zero elements, we can swap values in a single pass and avoid extra space — making the solution both clean and optimal. This problem improved my understanding of array manipulation and in-place algorithms. Consistency is building confidence day by day 💪🔥 #LeetCode #Day33 #Arrays #TwoPointers #Python #ProblemSolving #CodingJourney #100DaysOfCode

🚀 Day 32 – LeetCode Journey Today’s problem: Reverse String II ✔️ Implemented in-place string reversal using two pointers ✔️ Efficiently processed the string in chunks of 2k ✔️ Optimized approach with minimal extra space 💡 Key Insight: Instead of reversing the entire string, we only reverse the first k characters in every 2k block. Using the two-pointer technique makes this both simple and efficient. This problem strengthened my understanding of string manipulation, indexing, and in-place operations. Step by step, becoming a better problem solver 🔥💪 #LeetCode #Day32 #Strings #TwoPointers #Python #ProblemSolving #CodingJourney #100DaysOfCode

💧 Max Water Problem — Two Pointer Optimization Solved Container With Most Water using the Two Pointer technique in O(n) time. Key Idea: Move the pointer with the smaller height, since it limits the water capacity. 📈 Complexity: Time → O(n) Space → O(1) Small logic shift, big optimization. #DSA #Python #TwoPointers #CodingInterview #Algorithms

🚀 Day 40 – LeetCode Journey Today’s problem: Linked List Cycle ✔️ Solved using Floyd’s Cycle Detection Algorithm (Tortoise & Hare) ✔️ Used two pointers (slow & fast) ✔️ Efficient cycle detection without extra space 💡 Key Insight: If a cycle exists, the fast pointer will eventually meet the slow pointer. This approach avoids using extra memory and works in O(n) time. This problem strengthened my understanding of linked lists and pointer techniques. 40 days of consistency — getting stronger every day 💪🔥 #LeetCode #Day40 #LinkedList #TwoPointers #Python #ProblemSolving #CodingJourney #100DaysOfCode

Contains Nearby Duplicate: Fixed-Distance Sliding Window with HashSet Checking all pairs within k distance is O(n×k). Sliding window optimizes to O(n) — maintain window of at most k elements using HashSet. When window exceeds k, remove leftmost. Each element enters/exits set exactly once. Distance-Based Window: R - L > k ensures window never exceeds k elements. This distance constraint pattern appears in: rate limiting, time-based analytics, proximity requirements. Time: O(n) | Space: O(min(n, k)) #SlidingWindow #HashSet #DistanceConstraint #DuplicateDetection #Python #AlgorithmOptimization #SoftwareEngineering