Level-order traversal (BFS) in Data Structures Theory - Mini Project: Build & Apply

Level-order traversal (BFS)

📖 Scenario: Imagine you have a family tree represented as a simple tree structure. You want to visit each family member level by level, starting from the oldest ancestor down to the youngest generation.

🎯 Goal: Build a step-by-step understanding of how to perform a level-order traversal (Breadth-First Search) on a tree structure.

📋 What You'll Learn

Create a tree data structure with nodes and children

Set up a queue to help with the traversal

Implement the level-order traversal logic

Complete the traversal by collecting nodes in the correct order

💡 Why This Matters

🌍 Real World

Level-order traversal is used in many real-world applications like finding the shortest path in maps, organizing hierarchical data, and scheduling tasks.

💼 Career

Understanding BFS and tree traversal is important for software developers, data scientists, and anyone working with hierarchical or graph data structures.

1

Create the tree data structure

Create a dictionary called tree representing a simple tree with these nodes and their children exactly: "A": ["B", "C"], "B": ["D", "E"], "C": ["F"], "D": [], "E": [], "F": [].

Data Structures Theory

# Create the tree dictionary with nodes and children
# Your code here

Need a hint?

Use a dictionary where each key is a node and the value is a list of its children.

2

Set up the queue for traversal

Create a list called queue and initialize it with the root node "A" to start the traversal.

Data Structures Theory

tree = {
    "A": ["B", "C"],
    "B": ["D", "E"],
    "C": ["F"],
    "D": [],
    "E": [],
    "F": []
}
# Create the queue list with the root node "A"
# Your code here

Need a hint?

The queue helps us visit nodes level by level. Start it with the root node.

3

Implement the level-order traversal logic

Create an empty list called visited. Use a while loop that runs as long as queue is not empty. Inside the loop, remove the first node from queue and add it to visited. Then add all its children from tree to the end of queue.

Data Structures Theory

tree = {
    "A": ["B", "C"],
    "B": ["D", "E"],
    "C": ["F"],
    "D": [],
    "E": [],
    "F": []
}
queue = ["A"]
# Create visited list and write the while loop for BFS
# Your code here

Need a hint?

Use pop(0) to remove the first item from the queue and extend() to add children.

4

Complete the traversal by collecting nodes

Add a final variable called level_order and set it equal to the visited list to represent the order of nodes visited in level-order traversal.

Data Structures Theory

tree = {
    "A": ["B", "C"],
    "B": ["D", "E"],
    "C": ["F"],
    "D": [],
    "E": [],
    "F": []
}
queue = ["A"]
visited = []
while queue:
    node = queue.pop(0)
    visited.append(node)
    queue.extend(tree[node])
# Set level_order equal to visited
# Your code here

Need a hint?

This final step just saves the visited nodes as the level order traversal result.