Binary TreesBinary Trees36
  1. 1Preorder Traversal of a Binary Tree using Recursion
  2. 2Preorder Traversal of a Binary Tree using Iteration
  3. 3Inorder Traversal of a Binary Tree using Recursion
  4. 4Inorder Traversal of a Binary Tree using Iteration
  5. 5Postorder Traversal of a Binary Tree Using Recursion
  6. 6Postorder Traversal of a Binary Tree using Iteration
  7. 7Level Order Traversal of a Binary Tree using Recursion
  8. 8Level Order Traversal of a Binary Tree using Iteration
  9. 9Reverse Level Order Traversal of a Binary Tree using Iteration
  10. 10Reverse Level Order Traversal of a Binary Tree using Recursion
  11. 11Find Height of a Binary Tree
  12. 12Find Diameter of a Binary Tree
  13. 13Find Mirror of a Binary Tree
  14. 14Left View of a Binary Tree
  15. 15Right View of a Binary Tree
  16. 16Top View of a Binary Tree
  17. 17Bottom View of a Binary Tree
  18. 18Zigzag Traversal of a Binary Tree
  19. 19Check if a Binary Tree is Balanced
  20. 20Diagonal Traversal of a Binary Tree
  21. 21Boundary Traversal of a Binary Tree
  22. 22Construct a Binary Tree from a String with Bracket Representation
  23. 23Convert a Binary Tree into a Doubly Linked List
  24. 24Convert a Binary Tree into a Sum Tree
  25. 25Find Minimum Swaps Required to Convert a Binary Tree into a BST
  26. 26Check if a Binary Tree is a Sum Tree
  27. 27Check if All Leaf Nodes are at the Same Level in a Binary Tree
  28. 28Lowest Common Ancestor (LCA) in a Binary Tree
  29. 29Solve the Tree Isomorphism Problem
  30. 30Check if a Binary Tree Contains Duplicate Subtrees of Size 2 or More
  31. 31Check if Two Binary Trees are Mirror Images
  32. 32Calculate the Sum of Nodes on the Longest Path from Root to Leaf in a Binary Tree
  33. 33Print All Paths in a Binary Tree with a Given Sum
  34. 34Find the Distance Between Two Nodes in a Binary Tree
  35. 35Find the kth Ancestor of a Node in a Binary Tree
  36. 36Find All Duplicate Subtrees in a Binary Tree
GraphsGraphs46
  1. 1Breadth-First Search in Graphs
  2. 2Depth-First Search in Graphs
  3. 3Number of Provinces in an Undirected Graph
  4. 4Connected Components in a Matrix
  5. 5Rotten Oranges Problem - BFS in Matrix
  6. 6Flood Fill Algorithm - Graph Based
  7. 7Detect Cycle in an Undirected Graph using DFS
  8. 8Detect Cycle in an Undirected Graph using BFS
  9. 9Distance of Nearest Cell Having 1 - Grid BFS
  10. 10Surrounded Regions in Matrix using Graph Traversal
  11. 11Number of Enclaves in Grid
  12. 12Word Ladder - Shortest Transformation using Graph
  13. 13Word Ladder II - All Shortest Transformation Sequences
  14. 14Number of Distinct Islands using DFS
  15. 15Check if a Graph is Bipartite using DFS
  16. 16Topological Sort Using DFS
  17. 17Topological Sort using Kahn's Algorithm
  18. 18Cycle Detection in Directed Graph using BFS
  19. 19Course Schedule - Task Ordering with Prerequisites
  20. 20Course Schedule 2 - Task Ordering Using Topological Sort
  21. 21Find Eventual Safe States in a Directed Graph
  22. 22Alien Dictionary Character Order
  23. 23Shortest Path in Undirected Graph with Unit Distance
  24. 24Shortest Path in DAG using Topological Sort
  25. 25Dijkstra's Algorithm Using Set - Shortest Path in Graph
  26. 26Dijkstra’s Algorithm Using Priority Queue
  27. 27Shortest Distance in a Binary Maze using BFS
  28. 28Path With Minimum Effort in Grid using Graphs
  29. 29Cheapest Flights Within K Stops - Graph Problem
  30. 30Number of Ways to Reach Destination in Shortest Time - Graph Problem
  31. 31Minimum Multiplications to Reach End - Graph BFS
  32. 32Bellman-Ford Algorithm for Shortest Paths
  33. 33Floyd Warshall Algorithm for All-Pairs Shortest Path
  34. 34Find the City With the Fewest Reachable Neighbours
  35. 35Minimum Spanning Tree in Graphs
  36. 36Prim's Algorithm for Minimum Spanning Tree
  37. 37Disjoint Set (Union-Find) with Union by Rank and Path Compression
  38. 38Kruskal's Algorithm - Minimum Spanning Tree
  39. 39Minimum Operations to Make Network Connected
  40. 40Most Stones Removed with Same Row or Column
  41. 41Accounts Merge Problem using Disjoint Set Union
  42. 42Number of Islands II - Online Queries using DSU
  43. 43Making a Large Island Using DSU
  44. 44Bridges in Graph using Tarjan's Algorithm
  45. 45Articulation Points in Graphs
  46. 46Strongly Connected Components using Kosaraju's Algorithm

Check if All Leaf Nodes are at the Same Level in a Binary Tree

Problem Statement

Given a binary tree, determine if all its leaf nodes (nodes with no children) are present at the same level. Return true if they are, otherwise return false.

Examples

Input Tree All Leaves Same Level? Description
[1, 2, 3, 4, 5, null, null]
false Leaf 3 is at level 2; leaves 4 and 5 are at level 3 — not the same.
[1, 2, 3, null, 4]
false Leaf 3 is at level 2; leaf 4 is at level 3 — different levels.
[7]
true Only one node, which is also a leaf — trivially same level.
[] true No nodes means no leaf nodes — considered valid by default.
[1, 2, null, 3, null, 4]
true Only one leaf (node 4) exists — all leaves are at the same level.
[10, 20, 30, 40, 50, 60, 70]
true All leaf nodes (40, 50, 60, 70) are at the same level (level 3).

Solution

Understanding the Problem

We are given a binary tree and our goal is to determine whether all the leaf nodes (nodes with no children) are located at the same level (i.e., same depth from the root).

For example, in the tree below:


       1
     /       2     3
   /         4         5

The leaves are nodes 4 and 5, and both are at level 2 (root is at level 0). Since all leaves are at the same level, the answer is true.

However, if the tree was:


       1
     /       2     3
   /       
  4         
 /           
6           

Then, node 3 is a leaf at level 1, while node 6 is a leaf at level 3 — the leaf nodes are not at the same level, so the answer is false.

Step-by-Step Solution with Example

step 1: Choose a Traversal Strategy

To compare the levels of all leaf nodes, we can perform a level order traversal (Breadth-First Search), or a depth-first traversal with depth tracking. We'll use DFS here, keeping track of the level of each leaf node.

step 2: Use a Helper Function to Traverse

We define a recursive function that receives the current node and its level. When it reaches a leaf node, it checks: - Is this the first leaf? Record its level. - Is this a later leaf? Check if its level matches the first leaf’s level.

step 3: Implementing with Example

Let's take the following binary tree:


       10
     /        5      20
          /          15    25

Leaves: 5, 15, 25 — all are at level 2. So the output is true.

Now, if node 5 had a left child:


       10
     /        5      20
   /      /    2     15    25

Leaf nodes: 2 (level 2), 15 (level 2), 25 (level 2) → still true.

But if node 2 had a child:


       10
     /        5      20
   /      /    2     15    25
 /
1

Now leaves: 1 (level 3), 15 (level 2), 25 (level 2) → not at same level → false.

step 4: Return True or False

We return true only if all leaf nodes encountered match the level of the first leaf node.

Edge Cases

Case 1: Empty Tree

If the tree is empty (i.e., root is null), then there are no leaf nodes. Since there's nothing to violate the rule, we return true.

Case 2: Tree with One Node

If the tree contains only the root node, then it's a leaf by itself. Since it’s the only leaf, the condition is trivially satisfied. Output: true.

Case 3: All Leaf Nodes at Same Level in Unbalanced Tree

Even if the tree structure is unbalanced, we return true if the leaf nodes all occur at the same level.

Case 4: Leaves at Different Levels

If we find any leaf at a different level than the others, we immediately return false.

Finally

This problem tests your understanding of tree traversal and level management. The key idea is to track the depth of all leaf nodes and ensure they are the same. It’s a common technique in problems related to balance and symmetry in binary trees. Edge cases like empty trees and single-node trees must be handled first to ensure correctness.

Algorithm Steps

  1. Given a binary tree, if the tree is empty, return true.
  2. Initialize a queue and enqueue the root node along with its level (0).
  3. Initialize a variable leafLevel to -1 to record the level of the first encountered leaf node.
  4. While the queue is not empty, dequeue a node along with its level.
  5. If the dequeued node is a leaf (i.e. has no left and right children), then:
    1. If leafLevel is -1, set it to the current level.
    2. Otherwise, if the current level does not equal leafLevel, return false.
  6. If the node is not a leaf, enqueue its non-null children with level incremented by 1.
  7. After processing all nodes, return true as all leaves are at the same level.

Code

Python
Java
JavaScript
C
C++
C#
Kotlin
Swift
Go
Php
class TreeNode:
    def __init__(self, val=0, left=None, right=None):
        self.val = val
        self.left = left
        self.right = right

from collections import deque

def are_leaves_at_same_level(root):
    if not root:
        return True
    queue = deque([(root, 0)])
    leaf_level = -1
    while queue:
        node, level = queue.popleft()
        if not node.left and not node.right:
            if leaf_level == -1:
                leaf_level = level
            elif level != leaf_level:
                return False
        if node.left:
            queue.append((node.left, level + 1))
        if node.right:
            queue.append((node.right, level + 1))
    return True

# Example usage:
if __name__ == '__main__':
    # Construct binary tree:
    #         1
    #       /   \
    #      2     3
    #     / \     \
    #    4   5     6
    root = TreeNode(1, TreeNode(2, TreeNode(4), TreeNode(5)), TreeNode(3, None, TreeNode(6)))
    print(are_leaves_at_same_level(root))

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