Python Trees: binary trees, tree traversal algorithms, binary search trees Example and exercise

Are you interested in learning about data structures and algorithms? If so, then understanding trees can be a great starting point! In this article, we will introduce you to the concept of trees and binary trees. We will also discuss tree traversal algorithms and binary search trees with examples and exercises.

Introduction to Trees

A tree is a hierarchical structure that consists of nodes connected by edges. The topmost node is called the root node, and all other nodes are its descendants. Each node can have any number of children, but each child node can only have one parent.

Trees are used to represent hierarchical relationships between data. For example, a file system on your computer is organized as a tree where the root is the main directory and subdirectories are the children. Another example is the organization chart of a company where the CEO is at the top, and employees are the leaf nodes.

Binary Trees

A binary tree is a special type of tree where each node has at most two children, called left and right children. A node that doesn't have any children is called a leaf node.

Below is an example of a binary tree:

Here, 10 is the root node, 20 is its left child, and 30 is its right child. 40 and 50 are the left and right children of 20, respectively.

Tree Traversal Algorithms

Tree traversal refers to visiting each node in a tree exactly once in a specific order. There are two common ways to traverse a tree:

Depth-first traversal

Depth-first traversal starts from the root node and visits each branch as far as possible before backtracking. There are three different ways to perform depth-first traversal:

In-order traversal

In-order traversal visits the left subtree, then the root node, and finally the right subtree.

Below is an example of in-order traversal on the binary tree we defined earlier:

40 -> 20 -> 50 -> 10 -> 30

Pre-order traversal

Pre-order traversal visits the root node, then the left subtree, and finally the right subtree.

Below is an example of pre-order traversal on the same binary tree:

10 -> 20 -> 40 -> 50 -> 30

Post-order traversal

Post-order traversal visits the left subtree, then the right subtree, and finally the root node.

Below is an example of post-order traversal on the same binary tree:

40 -> 50 -> 20 -> 30 -> 10

Breadth-first traversal

Breadth-first traversal visits all nodes at a given depth before moving to the next level. In other words, it visits all nodes at depth 1 before moving to depth 2, and so on.

Below is an example of breadth-first traversal on the same binary tree:

10 -> 20 -> 30 -> 40 -> 50

Binary Search Trees

A binary search tree (BST) is a binary tree with the following properties:

The left subtree of a node contains only nodes with keys lesser than the node's key.
The right subtree of a node contains only nodes with keys greater than the node's key.
The left and right subtrees must also be binary search trees.

BSTs are commonly used to store data that can be compared using some ordering criteria. For example, if you have a set of numbers, you can store them in a BST where the keys are the numbers, and each node stores a value associated with the number.

Here is an example of a binary search tree:

           8
          / \
         3   10
        / \    \
       1   6    14
          / \   /
         4   7 13

Examples and Exercises

Let's write a Python program to implement the binary search tree we defined above. Here is the code:

class Node:
    def __init__(self, key):
        self.left = None
        self.right = None
        self.val = key

def insert(root, key):
    if root is None:
        return Node(key)
    else:
        if root.val < key:
            root.right = insert(root.right, key)
        else:
            root.left = insert(root.left, key)
    return root

def inorder(root):
    if root:
        inorder(root.left)
        print(root.val)
        inorder(root.right)

root = None
root = insert(root, 8)
root = insert(root, 3)
root = insert(root, 10)
root = insert(root, 1)
root = insert(root, 6)
root = insert(root, 14)
root = insert(root, 4)
root = insert(root, 7)
root = insert(root, 13)

print("Inorder traversal of the binary search tree:")
inorder(root)

When you run this code, it will output the following: Inorder traversal of the binary search tree:

This is the in-order traversal of the binary search tree we defined earlier. As you can see, the keys are sorted in ascending order. Now let's try some exercises to reinforce your understanding of trees and binary search trees:

Exercise 1

Write a Python function to calculate the height of a binary tree.

Exercise 2

Write a Python function to find the lowest common ancestor of two nodes in a binary search tree.

Exercise 3

Write a Python function to check if a binary tree is balanced. A binary tree is balanced if the heights of its left and right subtrees differ by at most one.

Exercise 4

Given a binary search tree, write a Python function to find the kth smallest element in the tree.

Exercise 5

Write a Python function to serialize a binary tree into a string and deserialize it back to a binary tree.

Conclusion

In this article, we introduced you to trees and binary trees, discussed tree traversal algorithms, and binary search trees with examples and exercises. Hopefully, this article provided a good foundation for further exploration of data structures and algorithms. Keep practicing, and see you in the next lesson!