Maximum Depth or Height Of a Binary Tree with python

Binary trees are hierarchical data structures that have widespread applications in computer science, from databases to graphics. One essential property of a binary tree is its depth or height. In this article, we'll discuss how to compute the maximum depth (or height) of a binary tree using Python.

What is the Maximum Depth or Height?

The maximum depth or height of a binary tree is the number of edges on the longest path from the root node down to the farthest leaf node. A tree with a single node (only the root) has a height of 0.

Recursive Approach to Find the Maximum Depth of a Binary Tree with Python

Here, we will be using a recursive approach to calculate the maximum depth or height of a binary tree. In this method, we make a recursive call again and again until the base case is reached.

Algorithm:

Step 1: Base Case - Empty Tree

• Check if the current node is None. If it is, this means the tree is empty or we've reached the end of a branch.
• In such a case, return 0 because an empty tree has a height of 0.

Step 2: Recursive Case - Non-Empty Tree

• If the current node is not None, calculate the depth of the left and right subtrees.
• Call maxDepth(node.left) to find the depth of the left subtree.
• Call maxDepth(node.right) to find the depth of the right subtree.

Step 3: Calculate and Return Maximum Depth

• Use the max() function to determine the greater depth between the left and right subtrees.
• Add 1 to the maximum depth value to account for the current node's depth.
• Return this value.

Implementation:

To determine the height of a binary tree, we can use a recursive approach. Starting from the root, the height of the tree is the maximum of the heights of its left and right subtrees, plus one (for the root itself).

class Node:
    def __init__(self, value=0, left=None, right=None):
        self.data = value
        self.left = None
        self.right = None

def maxDepth(node):
    if node is None:
        return 0
    else:
        left_depth = maxDepth(node.left)
        right_depth = maxDepth(node.right)
        return max(left_depth, right_depth) + 1

# Test case
if __name__ == "__main__":
    # Constructing the tree
    root = Node(1)
    root.left = Node(2)
    root.right = Node(3)
    root.left.left = Node(4)
    root.left.right = Node(5)
    root.left.left.left = Node(6)
    
    # Printing the depth
    print("Maximum Depth of the Binary Tree:", maxDepth(root))

Maximum Depth of the Binary Tree: 4

Time Complexity: Time complexity of above code is O(n) as we visit the each node of a binary search tree once.
Space Complexity: Space complexity of above code is also O(n) because of recursive call stack and the recursive calls are equal to the total numbers of nodes in a binary tree.

Find the Maximum Depth of a Binary Tree Using Level Order Traversal with Python

To find the maximum depth of a binary tree using an iterative approach in Python, we typically use a breadth-first search (BFS) strategy. This approach involves traversing the tree level by level and keeping track of the depth as we go. A common way to implement BFS is by using a queue.

Algorithm:

Step 1: Firstly we check if the root of the binary tree is "None". If it is None, return 0 because an empty tree has a depth of zero.

Step 2: Initialize a queue to store pairs of tree nodes and their corresponding depth levels. Begin with the root node of the tree, assigning it a depth of 1.

Step 3: Create a variable "max_depth" and set it to 0. This variable will be used to track the maximum depth of the tree as we iterate through it.

Step 4: While the queue is not empty, repeat the following steps:

• Remove the front element from the queue. This element will be a tuple containing a node from the tree ("current_node") and its depth ("depth").
• Update the "max_depth" variable with the depth of "current_node" if it is greater than the current "max_depth".
• If "current_node" has a left child, add this child to the queue with a depth of "depth + 1".
• If "current_node" has a right child, also add this child to the queue with a depth of "depth + 1".

Step 5: Continue the process of removing nodes from the queue, updating the maximum depth, and adding child nodes to the queue until the queue is empty.

Step 6: After the queue is empty, which means all nodes have been processed, the value of "max_depth" will represent the maximum depth of the tree.

Step 7: Return this value.

Implementation

class TreeNode:
    def __init__(self, value=0, left=None, right=None):
        self.value = value
        self.left = left
        self.right = right

def maxDepth(root):
    if not root:
        return 0
    # Initialize a queue with the root node and its depth
    queue = [(root, 1)]  
    max_depth = 0

    while queue:
        # Dequeue the front element
        current_node, depth = queue.pop(0)  

        # Update the maximum depth
        max_depth = max(max_depth, depth)

        # Enqueue the child of the current node with their corresponding depth
        if current_node.left:
            queue.append((current_node.left, depth + 1))
        if current_node.right:
            queue.append((current_node.right, depth + 1))

    return max_depth

# Test case
if __name__ == "__main__":
  
    # Constructing the tree
    root = TreeNode(1)
    root.left = TreeNode(2)
    root.right = TreeNode(3)
    root.left.left = TreeNode(4)
    root.left.right = TreeNode(5)

    print("Maximum Depth of the Binary Tree:", maxDepth(root))

Maximum Depth of the Binary Tree: 3

Time Complexity: O(n)
Space Complexity: O(n)

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Article Tags :

• Python
• Data Structures

Practice Tags :

• Data Structures
• python