Tree Traversal Algorithms - Programming for Machine Learning and Business

Visiting Tree Nodes Systematically¶

tree = {
    "A": ["B", "C"],
    "B": [],
    "C": []
}

print("Preorder style visit:")
for node in ["A", "B", "C"]:
    print(node)

Traversal is the rule that decides which node you visit next¶

The tree overview introduced hierarchical structure. Traversal turns that structure into action: the same tree can be visited in different orders depending on whether you want to process the root first, the leaves first, or one level at a time.

Core Explanation¶

Traversal means visiting each node in a meaningful order.

Common patterns include preorder, inorder, postorder, and breadth-first traversal.

Worked Example and Practice Lab¶

This first example acts like a simple preorder-style visit: start at the root, then move through the child nodes in the chosen order.

Comparing Visit Orders¶

These short examples keep the tree fixed but change the order in which nodes are visited.

Preorder idea¶

Visit the root before its children.

Postorder idea¶

Visit children before the root.

Breadth-first idea¶

Visit the tree level by level from top to bottom.

Exercises¶

Explain what traversal means in your own words.
Describe how preorder and postorder differ.
Give one reason why breadth-first order can be useful.
Explain why the same tree can produce different traversal lists.

Guided Practice¶

What does traversal mean in a tree context?¶

Deleting all nodesTraversal visits nodes; it does not delete them by default.

Visiting nodes in a defined orderCorrect. Traversal specifies the visiting order.

Sorting node labels alphabeticallyTraversal order is not the same as alphabetical sorting.

Turning the tree into a dictionary automaticallyTraversal does not do that by itself.

In the preorder example, which node is visited first?¶

BB comes after the root in that sequence.

ACorrect. A appears first in the preorder list.

DD appears later in the traversal.

CC is not the first node shown.

Key Takeaway¶

Traversal algorithms give you a disciplined way to walk through a tree. That visit-order mindset prepares you for binary search trees, where structure and traversal start to support efficient lookup behavior.