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🌳 Binary Tree

Introduction​

A binary tree is a hierarchical data structure in which each node has at most two children, referred to as the left child and the right child.

Key Properties​

  • Root: The top node of the tree
  • Leaf: A node with no children
  • Height: The length of the longest path from the root to a leaf
  • Depth: The length of the path from the root to a node

Types of Binary Trees​

  • Full Binary Tree: Every node has 0 or 2 children
  • Complete Binary Tree: All levels are filled except possibly the last, which is filled from left to right
  • Perfect Binary Tree: All internal nodes have two children and all leaves are at the same level
  • Balanced Binary Tree: The height of the tree is minimized
  • Binary Search Tree (BST): Left child < parent < right child

Example (Python)​

class Node:
    def __init__(self, data):
        self.data = data
        self.left = None
        self.right = None

def inorder_traversal(root):
    if root:
        inorder_traversal(root.left)
        print(root.data, end=" ")
        inorder_traversal(root.right)

# Usage
root = Node(10)
root.left = Node(5)
root.right = Node(15)
root.left.left = Node(2)
root.left.right = Node(7)

inorder_traversal(root)  # Output: 2 5 7 10 15

Applications​

  • Hierarchical data representation (file systems, organization charts)
  • Expression parsing and evaluation
  • Binary search trees for fast lookup, insertion, and deletion
  • Heaps for priority queues

When to Use a Binary Tree​

  • When you need to represent hierarchical relationships
  • When you need efficient searching, insertion, and deletion (BST)

Limitations​

  • Can become unbalanced, leading to poor performance (O(n) operations)
  • More complex to implement than arrays or linked lists
  • Binary Search Tree (BST): Maintains sorted order
  • AVL Tree, Red-Black Tree: Self-balancing binary search trees
  • Heap: Complete binary tree used for priority queues