Introduction to Tree Data Structure
Last Updated :
27 Jul, 2025
Tree data structure is a hierarchical structure that is used to represent and organize data in the form of parent child relationship. The following are some real world situations which are naturally a tree.
- Folder structure in an operating system.
- Tag structure in an HTML (root tag the as html tag) or XML document.
The topmost node of the tree is called the root, and the nodes below it are called the child nodes. Each node can have multiple child nodes, and these child nodes can also have their own child nodes, forming a recursive structure.

Basic Terminologies In Tree Data Structure:
- Parent Node: The node which is an immediate predecessor of a node is called the parent node of that node. {B} is the parent node of {D, E}.
- Child Node: The node which is the immediate successor of a node is called the child node of that node. Examples: {D, E} are the child nodes of {B}.
- Root Node: The topmost node of a tree or the node which does not have any parent node is called the root node. {A} is the root node of the tree. A non-empty tree must contain exactly one root node and exactly one path from the root to all other nodes of the tree.
- Leaf Node or External Node: The nodes which do not have any child nodes are called leaf nodes. {I, J, K, F, G, H} are the leaf nodes of the tree.
- Ancestor of a Node: Any predecessor nodes on the path of the root to that node are called Ancestors of that node. {A,B} are the ancestor nodes of the node {E}
- Descendant: A node x is a descendant of another node y if and only if y is an ancestor of x.
- Sibling: Children of the same parent node are called siblings. {D,E} are called siblings.
- Level of a node: The count of edges on the path from the root node to that node. The root node has level 0.
- Internal node: A node with at least one child is called Internal Node.
- Neighbour of a Node: Parent or child nodes of that node are called neighbors of that node.
- Subtree: Any node of the tree along with its descendant.
Basic Terminologies In TreeWhy Tree is considered a non-linear data structure?
The data in a tree are not stored in a sequential manner i.e., they are not stored linearly. Instead, they are arranged on multiple levels or we can say it is a hierarchical structure. For this reason, the tree is considered to be a non-linear data structure.
We strongly recommend to study a Binary Tree first as a Binary Tree has structure and code implementation compared to a general tree.
Representation of Tree Data Structure:
A tree consists of a root node, and zero or more subtrees T1, T2, ... , Tk such that there is an edge from the root node of the tree to the root node of each subtree. Subtree of a node X consists of all the nodes which have node X as the ancestor node.
Representation of Tree Data StructureRepresentation of a Node in Tree Data Structure:
A tree can be represented using a collection of nodes. Each of the nodes can be represented with the help of class or structs. Below is the representation of Node in different languages:
C++
class Node {
public:
int data;
Node* first_child;
Node* second_child;
Node* third_child;
.
.
.
Node* nth_child;
};
C
struct Node {
int data;
struct Node* first_child;
struct Node* second_child;
struct Node* third_child;
.
.
.
struct Node* nth_child;
};
Java
public static class Node {
int data;
Node first_child;
Node second_child;
Node third_child;
.
.
.
Node nth_child;
}
Python
class Node:
def __init__(self, data):
self.data = data
self.children = []
JavaScript
class Node {
constructor(data) {
this.data = data;
this.children = [];
}
}
Importance for Tree Data Structure:
One reason to use trees might be because you want to store information that naturally forms a hierarchy. For example, the file system on a computer: The DOM model of an HTML page is also tree where we have html tag as root, head and body its children and these tags, then have their own children.Please refer Applications, Advantages and Disadvantages of Tree for details.
Types of Tree data structures:
Tree data structure can be classified into three types based upon the number of children each node of the tree can have. The types are:
- Binary tree: In a binary tree, each node can have a maximum of two children linked to it. Some common types of binary trees include full binary trees, complete binary trees, balanced binary trees, and degenerate or pathological binary trees. Examples of Binary Tree are Binary Search Tree and Binary Heap.
- Ternary Tree: A Ternary Tree is a tree data structure in which each node has at most three child nodes, usually distinguished as “left”, “mid” and “right”.
- N-ary Tree or Generic Tree: Generic trees are a collection of nodes where each node is a data structure that consists of records and a list of references to its children(duplicate references are not allowed). Unlike the linked list, each node stores the address of multiple nodes.
Please refer Types of Trees in Data Structures for details.
Basic Operations Of Tree Data Structure:
- Create – create a tree in the data structure.
- Insert − Inserts data in a tree.
- Search − Searches specific data in a tree to check whether it is present or not.
- Traversal:
Implementation of Tree Data Structure:
C++
// C++ program to demonstrate some of the above
// terminologies
#include <bits/stdc++.h>
using namespace std;
// Function to add an edge between vertices x and y
void addEdge(int x, int y, vector<vector<int> >& adj)
{
adj[x].push_back(y);
adj[y].push_back(x);
}
// Function to print the parent of each node
void printParents(int node, vector<vector<int> >& adj,
int parent)
{
// current node is Root, thus, has no parent
if (parent == 0)
cout << node << "->Root" << endl;
else
cout << node << "->" << parent << endl;
// Using DFS
for (auto cur : adj[node])
if (cur != parent)
printParents(cur, adj, node);
}
// Function to print the children of each node
void printChildren(int Root, vector<vector<int> >& adj)
{
// Queue for the BFS
queue<int> q;
// pushing the root
q.push(Root);
// visit array to keep track of nodes that have been
// visited
int vis[adj.size()] = { 0 };
// BFS
while (!q.empty()) {
int node = q.front();
q.pop();
vis[node] = 1;
cout << node << "-> ";
for (auto cur : adj[node])
if (vis[cur] == 0) {
cout << cur << " ";
q.push(cur);
}
cout << endl;
}
}
// Function to print the leaf nodes
void printLeafNodes(int Root, vector<vector<int> >& adj)
{
// Leaf nodes have only one edge and are not the root
for (int i = 1; i < adj.size(); i++)
if (adj[i].size() == 1 && i != Root)
cout << i << " ";
cout << endl;
}
// Function to print the degrees of each node
void printDegrees(int Root, vector<vector<int> >& adj)
{
for (int i = 1; i < adj.size(); i++) {
cout << i << ": ";
// Root has no parent, thus, its degree is equal to
// the edges it is connected to
if (i == Root)
cout << adj[i].size() << endl;
else
cout << adj[i].size() - 1 << endl;
}
}
// Driver code
int main()
{
// Number of nodes
int N = 7, Root = 1;
// Adjacency list to store the tree
vector<vector<int> > adj(N + 1, vector<int>());
// Creating the tree
addEdge(1, 2, adj);
addEdge(1, 3, adj);
addEdge(1, 4, adj);
addEdge(2, 5, adj);
addEdge(2, 6, adj);
addEdge(4, 7, adj);
// Printing the parents of each node
cout << "The parents of each node are:" << endl;
printParents(Root, adj, 0);
// Printing the children of each node
cout << "The children of each node are:" << endl;
printChildren(Root, adj);
// Printing the leaf nodes in the tree
cout << "The leaf nodes of the tree are:" << endl;
printLeafNodes(Root, adj);
// Printing the degrees of each node
cout << "The degrees of each node are:" << endl;
printDegrees(Root, adj);
return 0;
}
Java
// java code for above approach
import java.io.*;
import java.util.*;
class GFG {
// Function to print the parent of each node
public static void
printParents(int node, Vector<Vector<Integer> > adj,
int parent)
{
// current node is Root, thus, has no parent
if (parent == 0)
System.out.println(node + "->Root");
else
System.out.println(node + "->" + parent);
// Using DFS
for (int i = 0; i < adj.get(node).size(); i++)
if (adj.get(node).get(i) != parent)
printParents(adj.get(node).get(i), adj,
node);
}
// Function to print the children of each node
public static void
printChildren(int Root, Vector<Vector<Integer> > adj)
{
// Queue for the BFS
Queue<Integer> q = new LinkedList<>();
// pushing the root
q.add(Root);
// visit array to keep track of nodes that have been
// visited
int vis[] = new int[adj.size()];
Arrays.fill(vis, 0);
// BFS
while (q.size() != 0) {
int node = q.peek();
q.remove();
vis[node] = 1;
System.out.print(node + "-> ");
for (int i = 0; i < adj.get(node).size(); i++) {
if (vis[adj.get(node).get(i)] == 0) {
System.out.print(adj.get(node).get(i)
+ " ");
q.add(adj.get(node).get(i));
}
}
System.out.println();
}
}
// Function to print the leaf nodes
public static void
printLeafNodes(int Root, Vector<Vector<Integer> > adj)
{
// Leaf nodes have only one edge and are not the
// root
for (int i = 1; i < adj.size(); i++)
if (adj.get(i).size() == 1 && i != Root)
System.out.print(i + " ");
System.out.println();
}
// Function to print the degrees of each node
public static void
printDegrees(int Root, Vector<Vector<Integer> > adj)
{
for (int i = 1; i < adj.size(); i++) {
System.out.print(i + ": ");
// Root has no parent, thus, its degree is
// equal to the edges it is connected to
if (i == Root)
System.out.println(adj.get(i).size());
else
System.out.println(adj.get(i).size() - 1);
}
}
// Driver code
public static void main(String[] args)
{
// Number of nodes
int N = 7, Root = 1;
// Adjacency list to store the tree
Vector<Vector<Integer> > adj
= new Vector<Vector<Integer> >();
for (int i = 0; i < N + 1; i++) {
adj.add(new Vector<Integer>());
}
// Creating the tree
adj.get(1).add(2);
adj.get(2).add(1);
adj.get(1).add(3);
adj.get(3).add(1);
adj.get(1).add(4);
adj.get(4).add(1);
adj.get(2).add(5);
adj.get(5).add(2);
adj.get(2).add(6);
adj.get(6).add(2);
adj.get(4).add(7);
adj.get(7).add(4);
// Printing the parents of each node
System.out.println("The parents of each node are:");
printParents(Root, adj, 0);
// Printing the children of each node
System.out.println(
"The children of each node are:");
printChildren(Root, adj);
// Printing the leaf nodes in the tree
System.out.println(
"The leaf nodes of the tree are:");
printLeafNodes(Root, adj);
// Printing the degrees of each node
System.out.println("The degrees of each node are:");
printDegrees(Root, adj);
}
}
// This code is contributed by rj13to.
Python
from collections import deque
# Function to add an edge between vertices x and y
def addEdge(x, y, adj):
adj[x].append(y)
adj[y].append(x)
# Function to print the parent of each node
def printParents(node, adj, parent):
# current node is Root, thus, has no parent
if parent == 0:
print("{}->Root".format(node))
else:
print("{}->{}".format(node, parent))
# Using DFS
for cur in adj[node]:
if cur != parent:
printParents(cur, adj, node)
# Function to print the children of each node
def printChildren(Root, adj):
# Queue for the BFS
q = deque()
# pushing the root
q.append(Root)
# visit array to keep track of nodes that have been
# visited
vis = [0] * len(adj)
# BFS
while q:
node = q.popleft()
vis[node] = 1
print("{}->".format(node)),
for cur in adj[node]:
if vis[cur] == 0:
print(cur),
q.append(cur)
print()
# Function to print the leaf nodes
def printLeafNodes(Root, adj):
# Leaf nodes have only one edge and are not the root
for i in range(1, len(adj)):
if len(adj[i]) == 1 and i != Root:
print(i),
# Function to print the degrees of each node
def printDegrees(Root, adj):
for i in range(1, len(adj)):
print(i, ":"),
# Root has no parent, thus, its degree is equal to
# the edges it is connected to
if i == Root:
print(len(adj[i]))
else:
print(len(adj[i]) - 1)
# Driver code
N = 7
Root = 1
# Adjacency list to store the tree
adj = [[] for _ in range(N + 1)]
# Creating the tree
addEdge(1, 2, adj)
addEdge(1, 3, adj)
addEdge(1, 4, adj)
addEdge(2, 5, adj)
addEdge(2, 6, adj)
addEdge(4, 7, adj)
# Printing the parents of each node
print("The parents of each node are:")
printParents(Root, adj, 0)
# Printing the children of each node
print("The children of each node are:")
printChildren(Root, adj)
# Printing the leaf nodes in the tree
print("The leaf nodes of the tree are:")
printLeafNodes(Root, adj)
# Printing the degrees of each node
print("The degrees of each node are:")
printDegrees(Root, adj)
C#
using System;
using System.Collections.Generic;
class GfG
{
static void PrintParents(int node, List<List<int>> adj, int parent)
{
if (parent == 0)
{
Console.WriteLine($"{node} -> Root");
}
else
{
Console.WriteLine($"{node} -> {parent}");
}
foreach (int cur in adj[node])
{
if (cur != parent)
{
PrintParents(cur, adj, node);
}
}
}
static void PrintChildren(int Root, List<List<int>> adj)
{
Queue<int> q = new Queue<int>();
q.Enqueue(Root);
bool[] vis = new bool[adj.Count];
while (q.Count > 0)
{
int node = q.Dequeue();
vis[node] = true;
Console.Write($"{node} -> ");
foreach (int cur in adj[node])
{
if (!vis[cur])
{
Console.Write($"{cur} ");
q.Enqueue(cur);
}
}
Console.WriteLine();
}
}
static void PrintLeafNodes(int Root, List<List<int>> adj)
{
for (int i = 0; i < adj.Count; i++)
{
if (adj[i].Count == 1 && i != Root)
{
Console.Write($"{i} ");
}
}
Console.WriteLine();
}
static void PrintDegrees(int Root, List<List<int>> adj)
{
for (int i = 1; i < adj.Count; i++)
{
Console.Write($"{i}: ");
if (i == Root)
{
Console.WriteLine(adj[i].Count);
}
else
{
Console.WriteLine(adj[i].Count - 1);
}
}
}
static void Main(string[] args)
{
int N = 7;
int Root = 1;
List<List<int>> adj = new List<List<int>>();
for (int i = 0; i <= N; i++)
{
adj.Add(new List<int>());
}
adj[1].AddRange(new int[] { 2, 3, 4 });
adj[2].AddRange(new int[] { 1, 5, 6 });
adj[4].Add(7);
Console.WriteLine("The parents of each node are:");
PrintParents(Root, adj, 0);
Console.WriteLine("The children of each node are:");
PrintChildren(Root, adj);
Console.WriteLine("The leaf nodes of the tree are:");
PrintLeafNodes(Root, adj);
Console.WriteLine("The degrees of each node are:");
PrintDegrees(Root, adj);
}
}
JavaScript
// Number of nodes
let n = 7, root = 1;
// Adjacency list to store the tree
let adj = new Array(n + 1).fill(null).map(() => []);
// Creating the tree
addEdge(1, 2, adj);
addEdge(1, 3, adj);
addEdge(1, 4, adj);
addEdge(2, 5, adj);
addEdge(2, 6, adj);
addEdge(4, 7, adj);
// Function to add an edge between vertices x and y
function addEdge(x, y, arr) {
arr[x].push(y);
arr[y].push(x);
}
// Function to print the parent of each node
function printParents(node, arr, parent) {
// current node is Root, thus, has no parent
if (parent == 0)
console.log(`${node}->root`);
else
console.log(`${node}->${parent}`);
// Using DFS
for (let cur of arr[node])
if (cur != parent)
printParents(cur, arr, node);
}
// Function to print the children of each node
function printChildren(root, arr) {
// Queue for the BFS
let q = [];
// pushing the root
q.push(root);
// visit array to keep track of nodes
// that have been visited
let vis = new Array(arr.length).fill(0);
// BFS
while (q.length > 0) {
let node = q.shift();
vis[node] = 1;
console.log(`${node}-> `);
for (let cur of arr[node])
if (vis[cur] == 0) {
console.log(cur + " ");
q.push(cur);
}
console.log("\n");
}
}
// Function to print the leaf nodes
function printLeafNodes(root, arr) {
// Leaf nodes have only one edge
// and are not the root
for (let i = 1; i < arr.length; i++)
if (arr[i].length == 1 &&
i != root)
console.log(i + " ");
console.log("\n");
}
// Function to print the degrees of each node
function printDegrees(Root, arr) {
for (let i = 1; i < arr.length; i++) {
console.log(`${i}: `);
// Root has no parent, thus, its degree is equal to
// the edges it is connected to
if (i == root)
console.log(arr[i].length + "\n");
else
console.log(arr[i].length - 1 + "\n");
}
}
// Function to print the tree in a hierarchical format
function printTree(node, arr, parent, level) {
// Indent based on the level of the node
console.log(" ".repeat(level) + "└── " + node);
// Recursively print children
for (let cur of arr[node])
if (cur != parent)
printTree(cur, arr, node, level + 1);
}
// Driver code
// Printing the tree in a hierarchical format
console.log("Tree Structure:");
printTree(root, adj, 0, 0);
// Printing the parents of each node
console.log("\nThe parents of each node are:");
printParents(root, adj, 0);
// Printing the children of each node
console.log("\nThe children of each node are:");
printChildren(root, adj);
// Printing the leaf nodes in the tree
console.log("\nThe leaf nodes of the tree are:");
printLeafNodes(root, adj);
// Printing the degrees of each node
console.log("\nThe degrees of each node are:");
printDegrees(root, adj);
OutputThe parents of each node are:
1->Root
2->1
5->2
6->2
3->1
4->1
7->4
The children of each node are:
1-> 2 3 4
2-> 5 6
3->
4-> 7
5->
6->
7->
The leaf nodes of the tree are:
3 5 6 7
The degrees o...
Properties of Tree Data Structure:
Number of edges: An edge can be defined as the connection between two nodes. If a tree has N nodes then it will have (N-1) edges. There is only one path from each node to any other node of the tree.
Depth of a node: The depth of a node is defined as the length of the path from the root to that node. Each edge adds 1 unit of length to the path. So, it can also be defined as the number of edges in the path from the root of the tree to the node.
- Height of a node: The height of a node can be defined as the length of the longest path from the node to a leaf node of the tree.
- Height of the Tree: The height of a tree is the length of the longest path from the root of the tree to a leaf node of the tree.
- Degree of a Node: The total count of subtrees attached to that node is called the degree of the node. The degree of a leaf node must be 0. The degree of a tree is the maximum degree of a node among all the nodes in the tree.
Easy Problem On Tree in JavaScript
Medium Problem On Tree in JavaScript
Similar Reads
Basics & Prerequisites
Data Structures
Getting Started with Array Data StructureArray is a collection of items of the same variable type that are stored at contiguous memory locations. It is one of the most popular and simple data structures used in programming. Basic terminologies of ArrayArray Index: In an array, elements are identified by their indexes. Array index starts fr
14 min read
String in Data StructureA string is a sequence of characters. The following facts make string an interesting data structure.Small set of elements. Unlike normal array, strings typically have smaller set of items. For example, lowercase English alphabet has only 26 characters. ASCII has only 256 characters.Strings are immut
2 min read
Hashing in Data StructureHashing is a technique used in data structures that efficiently stores and retrieves data in a way that allows for quick access. Hashing involves mapping data to a specific index in a hash table (an array of items) using a hash function. It enables fast retrieval of information based on its key. The
2 min read
Linked List Data StructureA linked list is a fundamental data structure in computer science. It mainly allows efficient insertion and deletion operations compared to arrays. Like arrays, it is also used to implement other data structures like stack, queue and deque. Hereâs the comparison of Linked List vs Arrays Linked List:
2 min read
Stack Data StructureA Stack is a linear data structure that follows a particular order in which the operations are performed. The order may be LIFO(Last In First Out) or FILO(First In Last Out). LIFO implies that the element that is inserted last, comes out first and FILO implies that the element that is inserted first
2 min read
Queue Data StructureA Queue Data Structure is a fundamental concept in computer science used for storing and managing data in a specific order. It follows the principle of "First in, First out" (FIFO), where the first element added to the queue is the first one to be removed. It is used as a buffer in computer systems
2 min read
Tree Data StructureTree Data Structure is a non-linear data structure in which a collection of elements known as nodes are connected to each other via edges such that there exists exactly one path between any two nodes. Types of TreeBinary Tree : Every node has at most two childrenTernary Tree : Every node has at most
4 min read
Graph Data StructureGraph Data Structure is a collection of nodes connected by edges. It's used to represent relationships between different entities. If you are looking for topic-wise list of problems on different topics like DFS, BFS, Topological Sort, Shortest Path, etc., please refer to Graph Algorithms. Basics of
3 min read
Trie Data StructureThe Trie data structure is a tree-like structure used for storing a dynamic set of strings. It allows for efficient retrieval and storage of keys, making it highly effective in handling large datasets. Trie supports operations such as insertion, search, deletion of keys, and prefix searches. In this
15+ min read
Algorithms
Searching AlgorithmsSearching algorithms are essential tools in computer science used to locate specific items within a collection of data. In this tutorial, we are mainly going to focus upon searching in an array. When we search an item in an array, there are two most common algorithms used based on the type of input
2 min read
Sorting AlgorithmsA Sorting Algorithm is used to rearrange a given array or list of elements in an order. For example, a given array [10, 20, 5, 2] becomes [2, 5, 10, 20] after sorting in increasing order and becomes [20, 10, 5, 2] after sorting in decreasing order. There exist different sorting algorithms for differ
3 min read
Introduction to RecursionThe process in which a function calls itself directly or indirectly is called recursion and the corresponding function is called a recursive function. A recursive algorithm takes one step toward solution and then recursively call itself to further move. The algorithm stops once we reach the solution
14 min read
Greedy AlgorithmsGreedy algorithms are a class of algorithms that make locally optimal choices at each step with the hope of finding a global optimum solution. At every step of the algorithm, we make a choice that looks the best at the moment. To make the choice, we sometimes sort the array so that we can always get
3 min read
Graph AlgorithmsGraph is a non-linear data structure like tree data structure. The limitation of tree is, it can only represent hierarchical data. For situations where nodes or vertices are randomly connected with each other other, we use Graph. Example situations where we use graph data structure are, a social net
3 min read
Dynamic Programming or DPDynamic Programming is an algorithmic technique with the following properties.It is mainly an optimization over plain recursion. Wherever we see a recursive solution that has repeated calls for the same inputs, we can optimize it using Dynamic Programming. The idea is to simply store the results of
3 min read
Bitwise AlgorithmsBitwise algorithms in Data Structures and Algorithms (DSA) involve manipulating individual bits of binary representations of numbers to perform operations efficiently. These algorithms utilize bitwise operators like AND, OR, XOR, NOT, Left Shift, and Right Shift.BasicsIntroduction to Bitwise Algorit
4 min read
Advanced
Segment TreeSegment Tree is a data structure that allows efficient querying and updating of intervals or segments of an array. It is particularly useful for problems involving range queries, such as finding the sum, minimum, maximum, or any other operation over a specific range of elements in an array. The tree
3 min read
Pattern SearchingPattern searching algorithms are essential tools in computer science and data processing. These algorithms are designed to efficiently find a particular pattern within a larger set of data. Patten SearchingImportant Pattern Searching Algorithms:Naive String Matching : A Simple Algorithm that works i
2 min read
GeometryGeometry is a branch of mathematics that studies the properties, measurements, and relationships of points, lines, angles, surfaces, and solids. From basic lines and angles to complex structures, it helps us understand the world around us.Geometry for Students and BeginnersThis section covers key br
2 min read
Interview Preparation
Practice Problem