Trees are one of the most commonly used data structures in computer science. They are used to represent many real-world situations, such as family trees, organizational charts, and computer file directories. In order to efficiently work with trees, it is essential to determine the level of each node in the given tree. This information can be used for various purposes such as finding the depth of the tree, determining the sub-trees, and many other functionalities.

In this article, we will discuss how to determine the level of each node in the given tree. We will also provide some code examples that will help better understand this concept.

Understanding Tree Levels

Before we proceed with the code examples, it is important to first understand what levels in a tree mean. In a tree, each node has a parent node and zero or more child nodes. The level of a node represents the distance between the node and the root node of the tree. The root node is always at level 0, its direct child nodes are at level 1, their children are at level 2, and so on.

For example, consider the following binary tree:

```
1
/ \
2 3
/ \ \
4 5 6
/ \
7 8
```

In this tree, node 1 is at level 0, nodes 2 and 3 are at level 1, nodes 4, 5, and 6 are at level 2, and nodes 7 and 8 are at level 3.

Method 1: Using Recursive Approach

One way to determine the level of each node in a tree is to use a recursive approach. In this approach, we start from the root node of the tree and traverse the tree in a depth-first manner. We keep track of the current level as we traverse through the tree and assign each node its corresponding level.

Here is the code example to determine the level of each node in the binary tree using a recursive approach in Python. For simplicity, we have assumed that the binary tree node has the data value only.

```
# Node class
class Node:
def __init__(self, data):
self.left = None
self.right = None
self.data = data
# Recursive function to assign levels to all nodes
def assignLevels(root, level):
# Base case
if not root:
return
# Assign level to current node
root.level = level
# Recursively assign levels to left and right child
assignLevels(root.left, level + 1)
assignLevels(root.right, level + 1)
# Driver code to test above code
if __name__ == '__main__':
# Create the binary tree
root = Node(1)
root.left = Node(2)
root.right = Node(3)
root.left.left = Node(4)
root.left.right = Node(5)
root.right.right = Node(6)
root.right.right.left = Node(7)
root.right.right.right = Node(8)
# Assign levels to all nodes
assignLevels(root, 0)
# Print the corresponding level of each node
print("Node\tLevel")
print("-----------------------")
print(" 1 \t ", root.level)
print(" 2 \t ", root.left.level)
print(" 3 \t ", root.right.level)
print(" 4 \t ", root.left.left.level)
print(" 5 \t ", root.left.right.level)
print(" 6 \t ", root.right.right.level)
print(" 7 \t ", root.right.right.left.level)
print(" 8 \t ", root.right.right.right.level)
```

Output:

```
Node Level
-----------------------
1 0
2 1
3 1
4 2
5 2
6 2
7 3
8 3
```

Method 2: Using Queue

Another way to determine the level of each node in a tree is to use a queue. In this approach, we start by adding the root node into the queue. We then iteratively remove each node from the queue and assign its corresponding level. We then add its child nodes into the queue and repeat the process until the queue becomes empty.

Here is the code example to determine the level of each node in the binary tree using a queue approach in Python:

```
# Node class
class Node:
def __init__(self, data):
self.left = None
self.right = None
self.data = data
self.level = None
# Assign level to all nodes in the binary tree
def assignLevels(root):
# Base case
if not root:
return
# Queue to store the nodes of the binary tree
queue = []
# Enqueue the root node
queue.append(root)
# Assign level to the root node
root.level = 0
# Loop to traverse through the binary tree
while queue:
# Dequeue the front node from the queue
curr_node = queue.pop(0)
# Assign level to the left child
if curr_node.left:
curr_node.left.level = curr_node.level + 1
queue.append(curr_node.left)
# Assign level to the right child
if curr_node.right:
curr_node.right.level = curr_node.level + 1
queue.append(curr_node.right)
# Driver code to test above code
if __name__ == '__main__':
# Create the binary tree
root = Node(1)
root.left = Node(2)
root.right = Node(3)
root.left.left = Node(4)
root.left.right = Node(5)
root.right.right = Node(6)
root.right.right.left = Node(7)
root.right.right.right = Node(8)
# Assign levels to all nodes
assignLevels(root)
# Print the corresponding level of each node
print("Node\tLevel")
print("-----------------------")
print(" 1 \t ", root.level)
print(" 2 \t ", root.left.level)
print(" 3 \t ", root.right.level)
print(" 4 \t ", root.left.left.level)
print(" 5 \t ", root.left.right.level)
print(" 6 \t ", root.right.right.level)
print(" 7 \t ", root.right.right.left.level)
print(" 8 \t ", root.right.right.right.level)
```

Output:

```
Node Level
-----------------------
1 0
2 1
3 1
4 2
5 2
6 2
7 3
8 3
```

Conclusion

In this article, we have discussed two approaches to determine the level of each node in the given tree. The recursive approach uses a depth-first traversal to assign levels to the nodes while the queue approach uses a breadth-first traversal. Both approaches have their own advantages and disadvantages and can be used depending on the specific requirements of the application. By understanding how to determine the level of each node in the tree, you can perform various operations such as finding the depth of the tree, determining the sub-trees, and many others.

here's some more information about the previous topic of determining the level of each node in the given tree.

Recursive Approach

The recursive approach is a simple and easy way to assign levels to each node in the binary tree. It works by starting at the root node and recursively assigning levels to each child node as you move through the tree.

One of the benefits of using the recursive approach is that it is very easy to understand. The code is simple and straightforward, and it works well for small to medium-sized trees. However, it can become inefficient for large trees as it requires a lot of recursive calls.

Queue Approach

The queue approach is a very efficient way to assign levels to each node in the binary tree. It works by using a queue data structure to store nodes as they are discovered in the breadth-first traversal. Each node that is dequeued from the queue is assigned a level that is one greater than its parent node.

One of the benefits of using the queue approach is that it is very efficient, even for large trees. It requires only one pass through the tree, and it uses a data structure that is designed for efficient processing of elements in a first-in-first-out (FIFO) order.

Depth of a Binary Tree

The depth of a binary tree refers to the number of nodes on the longest path from the root node to a leaf node. In other words, it is the maximum distance from the root node to any leaf node in the tree.

One approach to determining the depth of a binary tree is to use a recursive function that traverses the tree and returns the maximum of the left and right subtree depths. The base case for the recursive function is when the current node is a leaf node, in which case the depth is 0.

Another approach to determining the depth of a binary tree is to use the queue approach that we discussed above. In this case, we start by adding the root node into the queue, and then we iteratively remove each node from the queue and add its child nodes. We keep track of the depth as we traverse through the tree, and return the maximum depth at the end of the traversal.

Binary Tree Sub-Trees

A sub-tree of a binary tree is a tree that is formed by selecting a node as the root node and choosing all of its descendants as the child nodes. In other words, a sub-tree is a smaller tree that is formed by removing one or more nodes from the larger tree.

To determine the sub-trees of a binary tree, we can use either the recursive or queue approach that we discussed earlier. We start at the root node and traverse the tree in a depth-first or breadth-first manner, keeping track of each subtree that we encounter along the way.

We can use the subtree information to perform various operations on the tree, such as finding a specific subtree, deleting a subtree, or inserting a new subtree into the binary tree. By understanding sub-trees, we can work with binary trees in a more flexible and efficient way.

## Popular questions

Here are 5 questions and their corresponding answers related to determining the level of each node in a tree with code examples:

- What is the recursive approach to determine each node's level in a given tree?

Answer: The recursive approach involves starting from the root node and assigning a level of 0 to it. Then, as you traverse through the tree, you recursively assign a level equal to the parent node's level plus 1 to each of its child nodes. This process continues until you assign a level to every node in the tree.

- What is the queue approach to determine each node's level in a given tree?

Answer: The queue approach involves starting from the root node and adding it to a queue. Then, while the queue is not empty, you remove a node from the queue and assign a level equal to the removed node's parent's level plus 1. After that, you add the node's children to the queue. This process continues until you assign a level to every node in the tree.

- Which approach is more efficient, recursive or queue, for determining each node's level in a given tree?

Answer: The queue approach is generally more efficient than the recursive approach since it only requires one pass through the tree. The recursive approach can be less efficient for larger trees since it requires a lot of recursive calls.

- How do you determine the depth of a binary tree?

Answer: The depth of a binary tree refers to the maximum distance from the root node to any leaf node in the tree. To determine the depth of a binary tree, you can use a recursive function that returns the maximum of the left and right subtree depths, where the base case is when the current node is a leaf node with depth 0. Another approach is to use the queue approach that we discussed earlier.

- How can you use sub-trees to work with binary trees more efficiently?

Answer: Sub-trees are smaller trees that are formed by removing one or more nodes from the larger tree. By understanding sub-trees, you can perform various operations on the tree, such as finding a specific sub-tree, deleting a sub-tree, or inserting a new sub-tree into the binary tree. Working with sub-trees allows you to work with smaller portions of the tree and reduce the amount of processing needed.

### Tag

Leveling