## Level Order Binary Tree Traversal: An Introductory Guide to Breadth-First Search

Have you ever wondered how computer programs efficiently search through vast amounts of data? This is where algorithms come in, and one of the most commonly used algorithms in computer science is the breadth-first search.

In this article, we’ll explore the level order binary tree traversal, which is an implementation of breadth-first search that explores a binary tree in a level-by-level manner. We’ll start by defining level order traversal and its algorithm, followed by its implementation in Python.

### Level Order Traversal: Definition and Algorithm

Level order traversal, also known as breadth-first search, is a graph traversal algorithm that explores the nodes of a graph/tree in a level-by-level manner. In the context of binary trees, it visits the nodes in a left-to-right order and top-to-bottom order.

This traversal algorithm uses a queue data structure to maintain nodes at each level. The algorithm for performing level order traversal on a binary tree is simple.

Starting from the root node of the tree, we visit its neighbor nodes, i.e., the left and right child nodes, and enqueue them into a queue. We then dequeue the first node in the queue and visit its neighbor nodes.

We continue this process until all the nodes have been visited. Here’s a step-by-step guide to level order traversal:

1. Enqueue the root node into a queue.
2. Dequeue the first node from the queue and visit it.
3. Enqueue the left child of the visited node into the queue if it exists.
4. Enqueue the right child of the visited node into the queue if it exists.
5. Repeat steps 2 to 4 until the queue is empty.

## Implementation of Level Order Traversal in Python

To start implementing level order traversal in Python, we first need to create a binary tree data structure. One way to create a binary tree in Python is through node insertion.

Here’s a code snippet to insert nodes in a binary tree:

``````class Node:
def __init__(self, value):
self.value = value
self.left = None
self.right = None
class BinaryTree:
def __init__(self, root):
self.root = Node(root)
def insert(self, value):
queue = []
queue.append(self.root)
while queue:
node = queue.pop(0)
if not node.left:
node.left = Node(value)
break
else:
queue.append(node.left)
if not node.right:
node.right = Node(value)
break
else:
queue.append(node.right)
``````

The `Node` class represents a single node in the binary tree. It has a `value` attribute that holds the node’s value and `left` and `right` attributes that point to its left and right children.

The `BinaryTree` class uses the `Node` class to create a tree with a `root` node. The `insert` method of the `BinaryTree` class uses a queue to insert the new node into the binary tree level-by-level.

It first appends the `root` node to the queue, and then dequeues the first node in the queue. If its left child is empty, it sets the left child node to the new node, otherwise, it appends the left child node to the queue.

Similarly, if its right child is empty, it sets the right child node to the new node, otherwise, it appends the right child node to the queue. Now that we have a binary tree data structure, we can implement the level order traversal algorithm to print the nodes of the tree in a level-by-level manner.

Here’s a code snippet for the level order traversal function in Python:

``````from queue import Queue
def level_order_traversal(root):
if not root:
return
queue = Queue()
queue.put(root)
while not queue.empty():
node = queue.get()
print(node.value)
if node.left:
queue.put(node.left)
if node.right:
queue.put(node.right)
``````

The `level_order_traversal` function takes the `root` node of a binary tree as input and prints its nodes in a level-by-level manner. First, it checks if the `root` node is empty and returns if that’s the case.

It then creates a queue using the `Queue` class from the `queue` module and puts the `root` node in the queue. While the queue is not empty, the function dequeues the first node in the queue and prints its value.

It then enqueues the left and right children of the node if they exist.

### Conclusion

In conclusion, level order traversal is a useful technique for exploring a binary tree in a level-by-level manner. It uses a queue to keep track of nodes on each level, making it an efficient algorithm for searching a tree.

With the help of Python’s built-in data structures and algorithms, we can easily implement level order traversal to explore binary trees in an efficient manner. Binary tree traversal is an essential technique in computer science used to explore and search through binary trees in a structured manner.

## Traversal of Binary Tree

Binary tree traversal is a process of visiting all the nodes present in a binary tree one after another. In computer science, there are two types of traversals, namely

### Depth-First Traversal

In the depth-first traversal, the traversal algorithm starts from the root node and explores as far as possible along each path before backtracking. Depth-first traversal explores a binary tree in a top-to-bottom manner.

There are three types of depth-first traversal: In-order Traversal, Pre-order Traversal, and Post-order Traversal.

1. In-order Traversal: In In-order Traversal, the algorithm first visits the left sub-tree, then the root node, and then the right sub-tree recursively.
2. Pre-order Traversal: In Pre-order Traversal, the algorithm first visits the root node, then the left sub-tree, and then the right sub-tree recursively.
3. Post-order Traversal: In Post-order Traversal, the algorithm first visits the left sub-tree, then the right sub-tree, and then the root node recursively.

In the breadth-first traversal, the algorithm starts visiting the nodes of a binary tree in a level-by-level manner. It explores the nodes in a left-to-right order and top-to-bottom order.

Level order traversal is an implementation of breadth-first search that visits nodes in a level-by-level manner.

### Advantages of Binary Tree Traversal

Binary tree traversal is an essential technique used in various domains. Here are some advantages of binary tree traversal:

1. Searching the binary tree: The traversal algorithm helps to search the binary tree and perform various operations on it like insertion or deletion.
2. Network Routing: Binary tree traversal is also used in network routing. It helps the algorithms in finding the best route between two points in the network graph.
3. Computer graphics and gaming: In computer graphics and gaming, binary tree traversal helps in collision detection, audio processing, and developing game engines.
4. Physics simulations: The physics simulation engine that creates realistic physical behaviors of objects also uses binary tree traversal.

### Conclusion

In conclusion, binary tree traversal is an essential algorithm that helps explore and search through binary trees in an organized manner. In this article, we have discussed level order binary tree traversal, which is an implementation of breadth-first search that explores a binary tree in a level-by-level manner.

We also looked into the details of binary tree traversal, its different types, and how it is used in various domains like network routing, gaming, and physics simulations. Binary tree traversal is a building block for many complex algorithms and is one of the fundamental algorithms that every computer scientist must know.

In conclusion, binary tree traversal is a fundamental algorithm that plays a crucial role in exploring and searching through binary trees in a structured and organized manner. The two main types of traversals for binary trees are depth-first and breadth-first.

In this article, we explored level order binary tree traversal, a type of breadth-first search that visits the nodes of a tree in left-to-right and top-to-bottom order. We also discussed the advantages of binary tree traversal, including searching binary trees and its use in network routing, gaming, and physics simulations.

Overall, binary tree traversal is an essential building block for many complex algorithms, and its understanding is crucial for every computer scientist. The algorithm provides a useful framework for searching and organizing data, and its implementation ensures efficient algorithms.