**What Is Postorder in Data Structure?**

Data structures are essential tools in computer science and programming. They help organize and store data efficiently, allowing for faster and more efficient algorithms. One of the fundamental concepts in data structures is traversal, which refers to visiting each element in a data structure in a specific order.

## Postorder Traversal

In postorder traversal, the left subtree is visited first, then the right subtree, and finally the root node. This means that the root node is visited last.

This type of traversal is commonly used with binary trees, where each node can have at most two children – a left child and a right child.

### Example:

Let’s consider the following binary tree:

A / \ B C / \ / \ D E F G

The postorder traversal of this tree would be: D -> E -> B -> F -> G -> C -> A.

## Applications of Postorder Traversal

__Deleting a Tree:__Postorder traversal can be used to delete all nodes of a tree. By visiting the leaf nodes first (postorder), we ensure that we delete all child nodes before deleting their parent nodes.__Evaluating Expressions:__Postfix expressions, also known as Reverse Polish Notation (RPN), can be evaluated using postorder traversal.The operands are visited first, followed by the operators.

__Expression Trees:__Postorder traversal is often used to create expression trees from postfix expressions. The operands become leaf nodes, while operators become internal nodes.

## Implementation in Code

Here’s an example of how postorder traversal can be implemented in code:

function postorderTraversal(node) { if (node !== null) { postorderTraversal(node.left); postorderTraversal(node.right); console.log(node.value); } }

This recursive function takes a node as input and performs postorder traversal by recursively calling itself on the left and right subtrees before printing the value of the current node.

By understanding postorder traversal and its applications, you can expand your knowledge of data structures and algorithms, enabling you to solve more complex problems efficiently.