Data structures play a crucial role in computer science and are used to organize and store data efficiently. When it comes to traversing or visiting all the nodes of a graph or a tree, there are two common methods: Depth First Traversal (DFS) and Breadth First Traversal (BFS). In this article, we will focus on the data structure used for Depth First Traversal.

## What is Depth First Traversal?

**Depth First Traversal** is a popular algorithm used to traverse or search through a graph or tree. It starts at the root node and explores as far as possible along each branch before backtracking. This algorithm is often implemented using recursion or a stack.

## Data Structure for Depth First Traversal

To perform __Depth First Traversal__, we need to keep track of the nodes we have visited and the order in which we visit them. The **data structure** commonly used for this purpose is a **stack**. A stack follows the Last-In-First-Out (LIFO) principle, which makes it suitable for implementing depth-first traversal.

The stack allows us to keep track of the current node being visited and temporarily store other nodes that need to be explored. Whenever we visit a node, we push it onto the stack.

Then, we explore its adjacent nodes by pushing them onto the stack as well. Once all adjacent nodes have been visited, we pop the topmost node from the stack and continue exploring its unvisited neighbors.

## An Example of Depth First Traversal using Stack

To better understand how depth-first traversal works with a stack, let’s consider an example:

A / \ B C / \ \ D E F

Starting from the root node, A, we push it onto the stack. The stack now contains [A].

Next, we visit node A and push its adjacent nodes B and C onto the stack. The stack now contains [C, B, A].

We continue by visiting node C. Since C has no unvisited neighbors, we pop it from the stack. The stack now contains [B, A].

Next, we visit node B and push its adjacent nodes D and E onto the stack.

The stack now contains [E, D, B, A].

We visit node E next and push its adjacent node F onto the stack. The stack now contains [F, D, B, A].

Since F has no unvisited neighbors, we pop it from the stack. The stack now contains [D, B, A].

We continue this process until all nodes have been visited.

## Conclusion

In conclusion, __a stack is commonly used as a data structure for Depth First Traversal__. By utilizing a stack to keep track of visited nodes and explore their adjacent nodes recursively or iteratively, we can efficiently traverse a graph or tree in a depth-first manner.

**Depth First Traversal:**Algorithm used to traverse a graph or tree.**Data Structure:**Stack is used to implement Depth First Traversal.**Stack:**Follows Last-In-First-Out (LIFO) principle.**Process:**Visit a node and push its unvisited neighbors onto the stack.

### References:

- GeeksforGeeks: Depth First Traversal – https://www.org/depth-first-search-or-dfs-for-a-graph/
- TutorialsPoint: Depth First Search or DFS for a Graph – https://www.htm

Now that you understand the data structure used for Depth First Traversal, you can apply this knowledge in various graph or tree traversal scenarios.