Binary tree

Core Implementation

Basic Binary Tree

class TreeNode:
    def __init__(self, val=0):
        self.val = val
        self.left = None
        self.right = None

class BinaryTree:
    def __init__(self):
        self.root = None
    
    # Level-order insertion
    def insert(self, val):
        if not self.root:
            self.root = TreeNode(val)
            return
        
        queue = [self.root]
        while queue:
            node = queue.pop(0)
            if not node.left:
                node.left = TreeNode(val)
                return
            if not node.right:
                node.right = TreeNode(val)
                return
            queue.append(node.left)
            queue.append(node.right)
    
    # Traversals
    def inorder(self, root):
        if not root:
            return
        self.inorder(root.left)
        print(root.val, end=' ')
        self.inorder(root.right)
    
    def preorder(self, root):
        if not root:
            return
        print(root.val, end=' ')
        self.preorder(root.left)
        self.preorder(root.right)
    
    def postorder(self, root):
        if not root:
            return
        self.postorder(root.left)
        self.postorder(root.right)
        print(root.val, end=' ')
    
    def level_order(self):
        if not self.root:
            return []
        
        result = []
        queue = [self.root]
        
        while queue:
            level = []
            level_size = len(queue)
            
            for _ in range(level_size):
                node = queue.pop(0)
                level.append(node.val)
                
                if node.left:
                    queue.append(node.left)
                if node.right:
                    queue.append(node.right)
            
            result.append(level)
        
        return result

Implementation Variations

1. Complete Binary Tree

class CompleteBinaryTree:
    def __init__(self):
        self.root = None
        self.size = 0
    
    def insert(self, val):
        self.size += 1
        if not self.root:
            self.root = TreeNode(val)
            return
        
        # Convert size to binary and use it as path
        binary = bin(self.size)[3:]  # Skip '0b1'
        node = self.root
        
        for bit in binary[:-1]:
            if bit == '0':
                node = node.left
            else:
                node = node.right
        
        if binary[-1] == '0':
            node.left = TreeNode(val)
        else:
            node.right = TreeNode(val)

2. Perfect Binary Tree

class PerfectBinaryTree:
    def __init__(self):
        self.root = None
    
    def build_perfect_tree(self, height):
        def build(level):
            if level == height:
                return None
            node = TreeNode(0)  # Value can be customized
            node.left = build(level + 1)
            node.right = build(level + 1)
            return node
        
        self.root = build(0)

3. Threaded Binary Tree

class ThreadedNode:
    def __init__(self, val=0):
        self.val = val
        self.left = None
        self.right = None
        self.is_threaded = False

class ThreadedBinaryTree:
    def __init__(self):
        self.root = None
    
    def inorder_successor(self, node):
        if not node:
            return None
        
        if node.is_threaded:
            return node.right
        
        node = node.right
        while node and node.left:
            node = node.left
        return node

Time Complexities

Operation	Perfect/Complete	Skewed	Threaded	Average
Insert	O(log n)	O(n)	O(n)	O(n)
Search	O(log n)	O(n)	O(n)	O(n)
Delete	O(log n)	O(n)	O(n)	O(n)
Inorder	O(n)	O(n)	O(n)	O(n)
Preorder	O(n)	O(n)	O(n)	O(n)
Postorder	O(n)	O(n)	O(n)	O(n)
Level Order	O(n)	O(n)	O(n)	O(n)
Height	O(log n)	O(n)	O(n)	O(n)

Space Complexities

Implementation	Space Complexity	Additional Notes
Perfect	O(2^h - 1)	h is height, all levels filled
Complete	O(n)	Last level filled from left to right
Skewed	O(n)	Degenerates to linked list
Threaded	O(n)	Extra space for thread pointers

Common Data Structure Patterns

Tree Traversal Patterns
- DFS (Inorder, Preorder, Postorder)
- BFS (Level Order)
- Vertical Order
- Boundary Traversal
Path Patterns
- Root to Leaf Paths
- Maximum Path Sum
- Path with Given Sum
- Longest Path
View Patterns
- Left/Right View
- Top/Bottom View
- Vertical View
Level-based Patterns
- Level Order
- Level Sum
- Level Average
- Connect Level Nodes
Tree Construction Patterns
- From Traversal Arrays
- From Level Order
- Serialization/Deserialization
Tree Property Patterns
- Height/Depth
- Diameter
- Balance
- Symmetry

Edge Cases to Consider

Empty Tree

if not root:
    return None

Single Node

if not root.left and not root.right:
    return root.val

Full/Complete/Perfect Checks

def is_perfect(self, root):
    level = 0
    current = root
    while current.left:
        level += 1
        current = current.left
    
    return self._is_perfect_recursive(root, level, 0)

def _is_perfect_recursive(self, root, level, current_level):
    if not root:
        return True
    
    if not root.left and not root.right:
        return level == current_level
    
    if not root.left or not root.right:
        return False
    
    return (self._is_perfect_recursive(root.left, level, current_level + 1) and
            self._is_perfect_recursive(root.right, level, current_level + 1))

Optimization Techniques

Node Cache

class CachedNode:
    def __init__(self, val=0):
        self.val = val
        self.left = None
        self.right = None
        self.height = 1
        self.size = 1
        self.depth = 0

Level Linking

class LevelLinkedNode:
    def __init__(self, val=0):
        self.val = val
        self.left = None
        self.right = None
        self.next = None  # Points to next node in same level

def connect_levels(self, root):
    if not root:
        return
    
    current = root
    while current.left:
        temp = current
        while temp:
            temp.left.next = temp.right
            if temp.next:
                temp.right.next = temp.next.left
            temp = temp.next
        current = current.left

Parent Pointers

class NodeWithParent:
    def __init__(self, val=0):
        self.val = val
        self.left = None
        self.right = None
        self.parent = None

def insert_with_parent(self, parent, val):
    node = NodeWithParent(val)
    node.parent = parent
    return node

Memory Management

Node Pool

class NodePool:
    def __init__(self, size=1000):
        self.pool = [TreeNode(0) for _ in range(size)]
        self.available = set(range(size))
    
    def get_node(self, val):
        if self.available:
            idx = self.available.pop()
            node = self.pool[idx]
            node.val = val
            node.left = node.right = None
            return node
        return TreeNode(val)
    
    def return_node(self, node):
        if node in self.pool:
            idx = self.pool.index(node)
            self.available.add(idx)

Tree Cleanup

def clear_tree(self, root):
    if not root:
        return
    
    self.clear_tree(root.left)
    self.clear_tree(root.right)
    
    root.left = None
    root.right = None
    root.val = None

Performance Considerations

Iterative vs Recursive Traversals

def inorder_iterative(self, root):
    result = []
    stack = []
    current = root
    
    while stack or current:
        while current:
            stack.append(current)
            current = current.left
        
        current = stack.pop()
        result.append(current.val)
        current = current.right
    
    return result

Level Order Optimization

def level_order_optimized(self, root):
    if not root:
        return []
    
    result = []
    current_level = [root]
    
    while current_level:
        next_level = []
        values = []
        
        for node in current_level:
            values.append(node.val)
            if node.left:
                next_level.append(node.left)
            if node.right:
                next_level.append(node.right)
        
        result.append(values)
        current_level = next_level
    
    return result

Common Pitfalls

Incorrect Height Calculation

# Wrong
def height(self, root):
    if not root:
        return 0
    return 1 + max(height(root.left), height(root.right))

# Correct (Empty tree height = -1)
def height(self, root):
    if not root:
        return -1
    return 1 + max(self.height(root.left), self.height(root.right))

Memory Leaks in Deletion

# Wrong
def delete(self, node):
    if node.left:
        node = node.left
    elif node.right:
        node = node.right
    else:
        node = None

# Correct
def delete(self, node):
    if node.parent:
        if node == node.parent.left:
            node.parent.left = None
        else:
            node.parent.right = None
    node.left = node.right = None

Incorrect Level Order Traversal

# Wrong (missing level information)
def level_order_wrong(self, root):
    if not root:
        return []
    
    result = []
    queue = [root]
    
    while queue:
        node = queue.pop(0)
        result.append(node.val)
        if node.left:
            queue.append(node.left)
        if node.right:
            queue.append(node.right)
    
    return result

# Correct (preserves level information)
def level_order_correct(self, root):
    if not root:
        return []
    
    result = []
    queue = [(root, 0)]
    current_level = []
    current_depth = 0
    
    while queue:
        node, depth = queue.pop(0)
        
        if depth > current_depth:
            result.append(current_level)
            current_level = []
            current_depth = depth
        
        current_level.append(node.val)
        
        if node.left:
            queue.append((node.left, depth + 1))
        if node.right:
            queue.append((node.right, depth + 1))
    
    result.append(current_level)
    return result

Foundations

Linear Data Structures

Core Implementation

Basic Binary Tree

Implementation Variations

1. Complete Binary Tree

2. Perfect Binary Tree

3. Threaded Binary Tree

Time Complexities

Space Complexities

Common Data Structure Patterns

Edge Cases to Consider

Optimization Techniques

Memory Management

Performance Considerations

Common Pitfalls

Foundations

Linear Data Structures

​Core Implementation

​Basic Binary Tree

​Implementation Variations

​1. Complete Binary Tree

​2. Perfect Binary Tree

​3. Threaded Binary Tree

​Time Complexities

​Space Complexities

​Common Data Structure Patterns

​Edge Cases to Consider

​Optimization Techniques

​Memory Management

​Performance Considerations

​Common Pitfalls

Core Implementation

Basic Binary Tree

Implementation Variations

1. Complete Binary Tree

2. Perfect Binary Tree

3. Threaded Binary Tree

Time Complexities

Space Complexities

Common Data Structure Patterns

Edge Cases to Consider

Optimization Techniques

Memory Management

Performance Considerations

Common Pitfalls