What Is Tree Data Structure in Python?


Angela Bailey

A Tree Data Structure is a widely used data structure in computer science. It represents a hierarchical structure of data, similar to how a tree looks in nature. In Python, you can implement a tree data structure using various techniques.

Understanding Trees

In a tree data structure, each element is called a node. The topmost node is called the root, and it serves as the starting point of the tree. Each node can have zero or more child nodes connected to it.

Node Structure and Terminology

Each node in a tree consists of two parts: the data and the list of references to its child nodes. The data holds information associated with the node, while the list of references represents the connections between nodes.

The nodes that are not connected to any other node are called leaves. Nodes that have at least one child are known as internal or non-leaf nodes.

Types of Trees

There are different types of trees based on their characteristics and purposes:

  • Binary Tree: A binary tree is a type of tree where each node has at most two children. The left child represents the smaller value, while the right child represents the larger value.
  • Binary Search Tree: A binary search tree (BST) is a binary tree with an additional constraint: for each node, all elements in its left subtree must have smaller values, and all elements in its right subtree must have larger values.
  • Balanced Tree: A balanced tree ensures that the height difference between left and right subtrees is minimal.

    It provides efficient searching, inserting, and deleting operations.

  • AVL Tree: An AVL tree is a type of balanced tree where the height difference between left and right subtrees is limited to one. It guarantees efficient operations by automatically adjusting itself.

Implementing Trees in Python

Python provides several ways to implement a tree data structure. One common approach is to create a Node class that represents each node in the tree. Each Node object holds the data and references to its child nodes.

Creating a Node Class

Let’s create a Node class in Python:

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

With this class, we can create nodes and assign values to them. The `left` and `right` attributes can be used to connect child nodes.

Building a Tree

To build a tree using the Node class, we need to create instances of the class and establish connections between them.

# Creating nodes
root = Node(“A”)
node1 = Node(“B”)
node2 = Node(“C”)

# Connecting nodes
root.left = node1
root.right = node2

In this example, we created three nodes: `root`, `node1`, and `node2`. We connected `node1` as the left child of `root` and `node2` as the right child of `root`.

Traversing a Tree

Traversing or visiting each node in a tree is an essential operation. There are different traversal algorithms available, including pre-order, in-order, and post-order traversals.

Here’s an example of an in-order traversal using recursion:

def inorder_traversal(node):
if node:

In this code snippet, we recursively traverse the left subtree, visit the current node, and then recursively traverse the right subtree.


Trees are powerful data structures that find applications in various domains. Understanding their structure and implementing them in Python can enhance your programming skills. With the help of proper tree traversal algorithms, you can perform efficient operations on tree data structures.

Now that you have a grasp of trees in Python, you can explore more advanced topics such as binary search trees, balanced trees, and self-balancing trees like AVL trees. Remember to practice implementing and traversing trees to strengthen your understanding.

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