Difference Between Stack and Tree

What is a Stack?

A stack is a linear data structure that follows the Last In, First Out (LIFO) principle. This means that the last element added to stack is the first one to be removed. A stack can be visualized as a pile of plates where we can only add or remove the top plate.

Key Operations on a Stack

The following are key operations on a stack ?

• Push ? Add an element to the top of the stack.
• Pop ? Remove the top element from stack.
• Peek (or Top) ? View the top element without removing it.
• isEmpty ? Check if the stack is empty.

Example

The following Python program demonstrates how to use some basic stack operations.

# Simple Stack implementation in Python

stack = []

# Push elements into the stack
stack.append(10)
stack.append(20)
stack.append(30)

# Pop the top element (30) from the stack
stack.pop() # Output: 30

# Peek at the top element (20)
print(stack[-1])

Output

20

Use Cases of Stack

The following are some use cases of stack ?

• Function call management (recursion) ? The stack manages function calls in programming languages.
• Undo/Redo mechanisms ? Applications like text editors use a stack to the store states for undo and redo operations.
• Expression evaluation ? Infix to postfix conversion, postfix evaluation etc. use stacks.

What is a Tree?

A tree is a hierarchical data structure composed of nodes. Each node can have multiple children forming a branching structure with the single root node at the top. Unlike a stack in which is linear a tree is non-linear and can represent the more complex relationships.

Key Components of a Tree

The following are key components of a tree ?

• Root ? The topmost node of the tree.
• Node ? Each individual element of the tree.
• Edge ? The connection between the two nodes.
• Leaf Node ? A node with no children.
• Parent and Child ? The relationship between the nodes where one node is connected directly to another.
• Depth ? The number of edges from root to a node.

Types of Trees

The following are some commonly used types of trees ?

• Binary Tree ? A tree where each node has at most two children.
• Binary Search Tree (BST) ? A binary tree in which each left child has a value less than the parent and each right child has a value greater.
• AVL Tree ? A balanced binary search tree where the height difference between left and right subtrees is at most 1.
• B-tree ? A self-balancing tree used in the databases.

Example of a Tree (Binary Search Tree)

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

# Inserting values into a binary search tree
def insert(root, key):
    if root is None:
        return Node(key)
    
    if key < root.val:
        root.left = insert(root.left, key)
    else:
        root.right = insert(root.right, key)

    return root

# In-order traversal (prints the tree in sorted order)
def inorder_traversal(root):
    if root:
        inorder_traversal(root.left)
        print(root.val, end=" ")
        inorder_traversal(root.right)

# Example usage
root = Node(50)
insert(root, 30)
insert(root, 70)
insert(root, 20)
insert(root, 40)

# Print the in-order traversal of the BST
inorder_traversal(root)

Output

20 30 40 50 70

Use Cases of a Tree

The following are some use cases of a tree ?

• Hierarchical data storage ? Trees represent hierarchical data like file systems, DOM in HTML and organizational structures.
• Binary Search Tree (BST) ? Efficient for search, insert and delete operations with the average time complexityO(logn).
• Trie ? A tree used to store dynamic sets of strings for the fast search and prefix matching.

Differences Between Stack and Tree

The following table highlights major differences between Stack and Tree ?

Conclusion

Both stacks and trees are crucial data structures each suited for the specific tasks. The Stacks excel in managing linear operations where order matters while trees efficiently handle hierarchical data and relationships. Understanding the differences and applications of these data structures can greatly enhance problem-solving skills in computer science and software engineering.