LeetCode 572: Subtree of Another Tree Python Solution

LeetCode #105 – Construct Binary Tree from Preorder and Inorder Traversal | Python Implementation I implemented a recursive divide-and-conquer approach that exploits traversal properties to rebuild the tree. Core Insight: Preorder gives root order, inorder gives left/right boundaries. Their intersection uniquely determines tree structure. Each recursive call isolates the correct subsequences for subtree reconstruction. Time: O(n²) due to slicing and index lookup | Space: O(n) recursion depth + slices #LeetCode #DataStructures #Python #BinaryTree #DivideAndConquer #TreeReconstruction #CodingInterview #SoftwareEngineering

LeetCode #104 – Maximum Depth of Binary Tree | Python Implementation I implemented a recursive DFS approach that calculates tree depth by taking the maximum of left and right subtree depths plus one for the current node. Core Insight: Depth calculation decomposes naturally into subproblems. Each node's depth is determined by its deepest child, making recursion the cleanest solution with minimal code. Time: O(n) | Space: O(h) where h = tree height (recursion stack) #LeetCode #DataStructures #Python #BinaryTree #Recursion #DFS #CodingInterview #SoftwareEngineering

Day 13/100 – #100DaysOfCode 🚀 Solved LeetCode #219 – Contains Duplicate II (Python). Today I practiced using a HashMap to efficiently check whether two equal elements exist within a given distance k in an array. Approach: 1) Create a hashmap to store numbers and their latest index. 2) Traverse the array using index i. 3) If the current number already exists in the hashmap, check the index difference. 4) If the difference between indices is ≤ k, return True. 5) Update the hashmap with the current index. 6) If no such pair exists, return False. Time Complexity: O(n) Space Complexity: O(n) Learning how hashmaps help optimize search operations in arrays 💪 #LeetCode #Python #DSA #HashMap #Arrays #ProblemSolving #100DaysOfCode

LeetCode #226 – Invert Binary Tree | Python Implementation I implemented a recursive DFS approach that swaps left and right children at every node. Core Insight: Tree inversion is distributive — inverting a tree equals swapping its root's children and inverting each subtree independently. Recursion handles this naturally in O(n) time. Time: O(n) | Space: O(h) where h = tree height (recursion stack) #LeetCode #DataStructures #Python #BinaryTree #Recursion #DFS #CodingInterview #SoftwareEngineering

LeetCode #98 – Validate Binary Search Tree | Python Implementation I implemented a recursive DFS approach with boundary tracking to validate BST properties. Core Insight: BST validation requires tracking allowable ranges, not just comparing immediate children. Each recursive call narrows the valid interval, ensuring global ordering throughout the entire subtree. Time: O(n) | Space: O(h) where h = tree height (recursion stack) #LeetCode #DataStructures #Python #BinarySearchTree #Recursion #DFS #CodingInterview #SoftwareEngineering

When working with different data types, Python requires explicit conversion. Here, numbers and boolean values are converted into strings before combining them. This ensures that all parts are of the same type during concatenation. Answer: A) 52False #Python #TypeCasting #MathModule #PythonBasics #LearningInPublic #ProblemSolving #CodingPractice

Poll Insight: Which data type does not allow duplicate values? The correct answer is Set ✅ A Set stores only unique elements, meaning duplicate values are automatically removed. That’s why sets are useful when you want to keep only distinct values in Python. 👉 Example use cases include removing duplicates from a list or storing unique items. #Python #LearnPython #CodingQuiz #ProgrammingBasics

LeetCode #100 – Same Tree | Python Implementation I implemented a recursive DFS approach that simultaneously traverses both trees, comparing nodes at each step. Core Insight: Tree equality decomposes into three checks: both null (base case), structural equality, and value equality. Recursion naturally handles the "and both subtrees match" requirement. Time: O(n) | Space: O(h) where h = tree height (recursion stack) #LeetCode #DataStructures #Python #BinaryTree #Recursion #DFS #CodingInterview #SoftwareEngineering

Sorting is the pre-step that makes fast searches possible. In Python, use sorted() when you want a new list back, and .sort() when you want to sort the original list in place. Bonus: reverse=True flips to descending order—perfect prep for binary search and many other algorithms.#Python #Sorting #Algorithms #CodingTips

LeetCode #102 – Binary Tree Level Order Traversal | Python Implementation I implemented an iterative BFS approach using a queue to collect nodes level by level. Core Insight: The len(q) snapshot before the inner loop prevents mixing levels. New children added during iteration don't affect the current level's processing count, ensuring clean level separation. Time: O(n) | Space: O(w) where w = maximum tree width #LeetCode #DataStructures #Python #BinaryTree #BFS #LevelOrderTraversal #CodingInterview #SoftwareEngineering