Binary Tree Right Side View via BFS

Right Side View: Last Node Per Level via BFS Right side view = rightmost node at each level. Use level-order traversal, collect all nodes per level, then take last valid node. That's the rightmost visible from right perspective. Level Pattern Variation: Standard BFS with twist — extract specific element (last) from each level. This "level + condition" pattern appears in zigzag traversal, vertical order, boundary problems. Time: O(n) | Space: O(w) #BFS #LevelOrder #RightSideView #TreeTraversal #Python #AlgorithmDesign #SoftwareEngineering

Top K Frequent: Bucket Sort Beats Heap with O(n) Time Heap solution costs O(n log k). Bucket sort achieves O(n) by exploiting constraint — frequencies ≤ array length. Index represents frequency, value is list of elements with that frequency. Traverse high-to-low, collecting k elements. Bucket Sort Advantage: When value range is bounded (frequencies ≤ n), bucket sort beats comparison-based sorting. Exploiting constraints transforms complexity. Time: O(n) | Space: O(n) #BucketSort #TopK #FrequencyAnalysis #ComplexityReduction #Python #AlgorithmOptimization #SoftwareEngineering

Find Minimum in Rotated Array: Binary Search with Rotation Detection Rotation breaks global order but one half stays sorted. Compare mid with right endpoint — if mid > right, minimum is in right half (rotation there). Otherwise, minimum in left or at mid. Track minimum while narrowing. Rotation Point Detection: Comparing mid with endpoint determines which half contains rotation/minimum. This partial ordering enables O(log n) despite global disruption. Time: O(log n) | Space: O(1) #BinarySearch #RotatedArray #MinimumFinding #RotationPoint #Python #AlgorithmDesign #SoftwareEngineering

Find Minimum in Rotated Sorted Array: Binary Search with Rotation Point Rotation breaks global sorting but one half is always properly sorted. Compare mid with right endpoint — if mid > right, minimum is in right half (rotation point there). Otherwise, minimum is in left half or at mid. Track minimum seen while narrowing. Rotation Point Detection: Comparing mid with endpoint (not left neighbor) determines which half contains rotation. This preserved partial ordering enables O(log n) despite global disruption. Time: O(log n) | Space: O(1) #BinarySearch #RotatedArray #MinimumFinding #RotationPoint #Python #AlgorithmDesign #SoftwareEngineering

Add Two Numbers: Linked List Digit Addition with Carry Propagation Process lists simultaneously like grade-school addition. Handle different lengths by treating missing nodes as 0. Carry propagates to next iteration via loop condition carry > 0. Dummy node simplifies head handling. Continue until all lists exhausted AND carry is zero. Carry Handling: Including carry in loop condition ensures final carry digit gets added. Division/modulo elegantly extract carry and digit value. Time: O(max(m, n)) | Space: O(max(m, n)) #LinkedList #DigitAddition #CarryPropagation #SimulationAlgorithm #Python #AlgorithmDesign #SoftwareEngineering

Day 254 of #365DaysOfCode Solved Check if There is a Valid Path in a Grid using a DFS-based traversal with directional constraints. Each cell type defines allowed movement directions, and transitions are validated by ensuring bidirectional connectivity between adjacent cells. The traversal explores only feasible paths while maintaining a visited structure to avoid cycles. The solution runs in O(n × m) time and emphasizes careful handling of constrained graph traversal. Continuing to strengthen understanding of grid-based graphs and state validation. #365DaysOfCode #Day254 #DSA #LeetCode #Python #Algorithms #DFS #Graphs #ProblemSolving #Consistency

✅ Day 23 of #DSAPrep > Problem: Rotate Array > Platform: LeetCode > Concept: Array Manipulation Solved array rotation using the in-place reverse technique, avoiding extra space and improving efficiency. > Key Idea: Reverse the entire array first Reverse the first k elements Reverse the remaining elements Handle edge cases like empty array and k = 0 Use k % n for large rotations > Time Complexity: O(n) > Space Complexity: O(1) #DSAPrep #Algorithms #Python #Arrays #TwoPointers #ProblemSolving #CodingJourney

#𝐃𝐚𝐲𝟏𝟐 – 𝐃𝐞𝐭𝐞𝐜𝐭 𝐂𝐲𝐜𝐥𝐞𝐬 𝐢𝐧 𝟐𝐃 𝐆𝐫𝐢𝐝 🚀 📝 Find a cycle of same character (length ≥ 4) in grid. 💡 Approach: DFS + Parent Tracking Visit each cell, explore neighbors with same char If visited neighbor ≠ parent → cycle found ✅ 🔧 Key: Track parent to avoid false 2-step cycles. ⏱️ O(m×n) time | O(m×n) space Cycle detection made simple! 🎯 #LeetCode #Python #100DaysOfCode #DFS #CycleDetection

Binary Tree Level Order Traversal: Queue with Level Isolation BFS naturally traverses level-by-level. Key technique: snapshot queue length before processing current level. Process exactly that many nodes, collecting values into level list while enqueueing children for next iteration. This prevents mixing levels. Level Isolation: Pre-loop length snapshot prevents newly-added children from affecting current level's iteration count. This pattern enables clean level-by-level processing. Time: O(n) | Space: O(w) where w = max width #BFS #LevelOrderTraversal #QueuePattern #TreeAlgorithms #Python #AlgorithmDesign #SoftwareEngineering

Hamming Weight: Brian Kernighan's Algorithm for Bit Counting Count 1-bits in binary representation. Instead of checking all 32 bits, use n & (n-1) trick — clears rightmost 1-bit each iteration. Loop runs once per 1-bit, not once per total bit. Optimal for sparse bit patterns. Bit Trick: n & (n-1) works because n-1 flips all bits after rightmost 1 (including that 1). ANDing clears that 1. Example: n=12 (1100), n-1=11 (1011), n&(n-1)=8 (1000). This runs in O(number of 1-bits) vs O(32) for naive approach. Time: O(k) where k = number of 1-bits | Space: O(1) #BitManipulation #HammingWeight #BrianKernighan #BitCounting #Python #AlgorithmDesign #SoftwareEngineering