Level of a Node in Binary Tree Last Updated : 23 Jul, 2025 Comments Improve Suggest changes Like Article Like Report Try it on GfG Practice Given a Binary Tree and a key, the task is to find the level of key in the Binary Tree.Examples:Input : key = 4Output: 3Explanation: The level of the key in above binary tree is 3.Input : key = 10Output: -1Explanation: Key is not present in the above Binary tree.Table of Content[Expected Approach - 1] Using Recursion - O(n) Time and O(h) Space[Expected Approach - 2] Using Level Order Traversal- O(n) Time and O(n) Space[Expected Approach - 1] Using Recursion - O(n) Time and O(h) SpaceThe idea is to start from the root and level as 1. If the target matches with root's data, return level. Else recursively call for left and right subtrees with level as level + 1. Below is the implementation of the above approach: C++ // C++ code to find level of a Node in Binary Tree #include <iostream> using namespace std; class Node { public: int data; Node* left; Node* right; Node(int val) { data = val; left = right = nullptr; } }; // Recursive function to find the level of the target key int getLevel(Node* root, int target, int level) { if (root == nullptr) { return -1; } // If the target key matches the current node's // data, return the level if (root->data == target) { return level; } // Recursively call for left and right subtrees int leftLevel = getLevel(root->left, target, level + 1); if (leftLevel != -1) { return leftLevel; } return getLevel(root->right, target, level + 1); } int main() { // Creating a sample binary tree: // 1 // / \ // 2 3 // / \ / \ // 4 5 6 7 Node* root = new Node(1); root->left = new Node(2); root->right = new Node(3); root->left->left = new Node(4); root->left->right = new Node(5); root->right->left = new Node(6); root->right->right = new Node(7); int target = 5; cout << getLevel(root, target, 1) << endl; return 0; } C // C code to find level of a Node in Binary Tree #include <stdio.h> #include <stdlib.h> struct Node { int data; struct Node* left; struct Node* right; }; int getLevel(struct Node* root, int target, int level) { if (root == NULL) { return -1; } // If the target key matches the current node's data, return the level if (root->data == target) { return level; } // Recursively call for left and right subtrees int leftLevel = getLevel(root->left, target, level + 1); if (leftLevel != -1) { return leftLevel; } return getLevel(root->right, target, level + 1); } struct Node* createNode(int val) { struct Node* newNode = (struct Node*)malloc(sizeof(struct Node)); newNode->data = val; newNode->left = newNode->right = NULL; return newNode; } int main() { // Creating a sample binary tree: // 1 // / \ // 2 3 // / \ / \ // 4 5 6 7 struct Node* root = createNode(1); root->left = createNode(2); root->right = createNode(3); root->left->left = createNode(4); root->left->right = createNode(5); root->right->left = createNode(6); root->right->right = createNode(7); int target = 5; printf("%d\n", getLevel(root, target, 1)); return 0; } Java // Java code to find level of a Node in Binary Tree class Node { int data; Node left, right; Node(int val) { data = val; left = right = null; } } class GfG { // Recursive function to find the level of the target key static int getLevel(Node root, int target, int level) { if (root == null) { return -1; } // If the target key matches the current node's // data, return the level if (root.data == target) { return level; } // Recursively call for left and right subtrees int leftLevel = getLevel(root.left, target, level + 1); if (leftLevel != -1) { return leftLevel; } return getLevel(root.right, target, level + 1); } public static void main(String[] args) { // Creating a sample binary tree: // 1 // / \ // 2 3 // / \ / \ // 4 5 6 7 Node root = new Node(1); root.left = new Node(2); root.right = new Node(3); root.left.left = new Node(4); root.left.right = new Node(5); root.right.left = new Node(6); root.right.right = new Node(7); int target = 5; System.out.println(getLevel(root, target, 1)); } } Python # Python code to find level of a Node in Binary Tree class Node: def __init__(self, val): self.data = val self.left = None self.right = None # Recursive function to find the level of the target key def getLevel(root, target, level): if root is None: return -1 # If the target key matches the current node's # data, return the level if root.data == target: return level # Recursively call for left and right subtrees leftLevel = getLevel(root.left, target, level + 1) if leftLevel != -1: return leftLevel return getLevel(root.right, target, level + 1) if __name__ == "__main__": # Creating a sample binary tree: # 1 # / \ # 2 3 # / \ / \ # 4 5 6 7 root = Node(1) root.left = Node(2) root.right = Node(3) root.left.left = Node(4) root.left.right = Node(5) root.right.left = Node(6) root.right.right = Node(7) target = 5 print(getLevel(root, target, 1)) C# // C# code to find level of a Node in Binary Tree using System; class Node { public int Data; public Node Left; public Node Right; public Node(int val) { Data = val; Left = Right = null; } } class GfG { // Recursive function to find the level of the target key static int GetLevel(Node root, int target, int level) { if (root == null) { return -1; } // If the target key matches the current // node's data, return the level if (root.Data == target) { return level; } // Recursively call for left and right subtrees int leftLevel = GetLevel(root.Left, target, level + 1); if (leftLevel != -1) { return leftLevel; } return GetLevel(root.Right, target, level + 1); } static void Main() { // Creating a sample binary tree: // 1 // / \ // 2 3 // / \ / \ // 4 5 6 7 Node root = new Node(1); root.Left = new Node(2); root.Right = new Node(3); root.Left.Left = new Node(4); root.Left.Right = new Node(5); root.Right.Left = new Node(6); root.Right.Right = new Node(7); int target = 5; Console.WriteLine(GetLevel(root, target, 1)); } } JavaScript // Javascript code to find level of a Node // in Binary Tree class Node { constructor(val) { this.data = val; this.left = null; this.right = null; } } // Recursive function to find the level of the target key function getLevel(root, target, level) { if (root === null) { return -1; } // If the target key matches the current node's // data, return the level if (root.data === target) { return level; } // Recursively call for left and right subtrees let leftLevel = getLevel(root.left, target, level + 1); if (leftLevel !== -1) { return leftLevel; } return getLevel(root.right, target, level + 1); } // Creating a sample binary tree: // 1 // / \ // 2 3 // / \ / \ // 4 5 6 7 const root = new Node(1); root.left = new Node(2); root.right = new Node(3); root.left.left = new Node(4); root.left.right = new Node(5); root.right.left = new Node(6); root.right.right = new Node(7); const target = 5; console.log(getLevel(root, target, 1)); Output3 Time Complexity: O(n), where n is the number of nodes in the binary tree.Auxiliary Space: O(h), where h is height of binary tree. [Expected Approach - 2] Using Level Order Traversal- O(n) Time and O(n) SpaceThe idea is to perform a level-order traversal and keep track of the current level as we traverse the tree. If the key matches with root's data, return level. C++ // C++ code to find level of a Node in Binary Tree #include <bits/stdc++.h> using namespace std; class Node { public: int data; Node* left; Node* right; Node(int val) { data = val; left = right = nullptr; } }; // Function to find the level of the target key int getLevel(Node* root, int target) { if (root == nullptr) { return -1; } // Create a queue for level-order // traversal queue<Node*> q; q.push(root); int level = 1; while (!q.empty()) { int size = q.size(); // Process all nodes at the current level for (int i = 0; i < size; i++) { Node* curr = q.front(); q.pop(); // Check if the current node matches the target if (curr->data == target) { return level; } // Push the left and right children to the queue if (curr->left != nullptr) { q.push(curr->left); } if (curr->right != nullptr) { q.push(curr->right); } } level++; } return -1; } int main() { // Creating a sample binary tree: // 1 // / \ // 2 3 // / \ / \ // 4 5 6 7 Node* root = new Node(1); root->left = new Node(2); root->right = new Node(3); root->left->left = new Node(4); root->left->right = new Node(5); root->right->left = new Node(6); root->right->right = new Node(7); int target = 5; cout << getLevel(root, target); return 0; } Java // Java code to find level of a Node // in Binary Tree import java.util.LinkedList; import java.util.Queue; class Node { int data; Node left, right; Node(int val) { data = val; left = right = null; } } // Function to find the level of // the target key class GfG { static int getLevel(Node root, int target) { if (root == null) { return -1; } // Create a queue for level-order traversal Queue<Node> q = new LinkedList<>(); q.add(root); int level = 1; while (!q.isEmpty()) { int size = q.size(); // Process all nodes at the current level for (int i = 0; i < size; i++) { Node curr = q.poll(); // Check if the current node matches the target if (curr.data == target) { return level; } // Push the left and right children to the queue if (curr.left != null) { q.add(curr.left); } if (curr.right != null) { q.add(curr.right); } } level++; } return -1; } public static void main(String[] args) { // Creating a sample binary tree: // 1 // / \ // 2 3 // / \ / \ // 4 5 6 7 Node root = new Node(1); root.left = new Node(2); root.right = new Node(3); root.left.left = new Node(4); root.left.right = new Node(5); root.right.left = new Node(6); root.right.right = new Node(7); int target = 5; System.out.println(getLevel(root, target)); } } Python # Python code to find level of a Node in Binary Tree class Node: def __init__(self, val): self.data = val self.left = None self.right = None # Function to find the level of the target key def getLevel(root, target): if root is None: return -1 # Create a queue for level-order traversal q = [] q.append(root) level = 1 while len(q) > 0: size = len(q) # Process all nodes at the current level for i in range(size): curr = q.pop(0) # Check if the current node matches the target if curr.data == target: return level # Push the left and right children to the queue if curr.left is not None: q.append(curr.left) if curr.right is not None: q.append(curr.right) level += 1 return -1 if __name__ == "__main__": # Creating a sample binary tree: # 1 # / \ # 2 3 # / \ / \ # 4 5 6 7 root = Node(1) root.left = Node(2) root.right = Node(3) root.left.left = Node(4) root.left.right = Node(5) root.right.left = Node(6) root.right.right = Node(7) target = 5 print(getLevel(root, target)) C# // C# code to find level of a Node in Binary Tree using System; using System.Collections.Generic; class Node { public int data; public Node left, right; public Node(int val) { data = val; left = right = null; } } class GfG { // Function to find the level of the target key static int GetLevel(Node root, int target) { if (root == null) { return -1; } // Create a queue for level-order traversal Queue<Node> q = new Queue<Node>(); q.Enqueue(root); int level = 1; while (q.Count > 0) { int size = q.Count; // Process all nodes at the current level for (int i = 0; i < size; i++) { Node curr = q.Dequeue(); // Check if the current node matches the target if (curr.data == target) { return level; } // Push the left and right children to the queue if (curr.left != null) { q.Enqueue(curr.left); } if (curr.right != null) { q.Enqueue(curr.right); } } level++; } return -1; } static void Main(string[] args) { // Creating a sample binary tree: // 1 // / \ // 2 3 // / \ / \ // 4 5 6 7 Node root = new Node(1); root.left = new Node(2); root.right = new Node(3); root.left.left = new Node(4); root.left.right = new Node(5); root.right.left = new Node(6); root.right.right = new Node(7); int target = 5; Console.WriteLine(GetLevel(root, target)); } } JavaScript // Javascript code to find level of // a Node in Binary Tree class Node { constructor(val) { this.data = val; this.left = this.right = null; } } // Function to find the level of the target key function getLevel(root, target) { if (root === null) { return -1; } // Create a queue for level-order traversal let q = []; q.push(root); let level = 1; while (q.length > 0) { let size = q.length; // Process all nodes at the current level for (let i = 0; i < size; i++) { let curr = q.shift(); // Check if the current node matches the target if (curr.data === target) { return level; } // Push the left and right children to the queue if (curr.left !== null) { q.push(curr.left); } if (curr.right !== null) { q.push(curr.right); } } level++; } return -1; } // Creating the binary tree // 1 // / \ // 2 3 // / \ / \ // 4 5 6 7 let root = new Node(1); root.left = new Node(2); root.right = new Node(3); root.left.left = new Node(4); root.left.right = new Node(5); root.right.left = new Node(6); root.right.right = new Node(7); let target = 5; console.log(getLevel(root, target)); Output3Time Complexity: O(n), where n is the number of nodes in the binary tree.Auxiliary Space: O(n) Get level of a node in binary tree Iterative approach Comment More infoAdvertise with us Next Article Types of Asymptotic Notations in Complexity Analysis of Algorithms K kartik Follow Improve Article Tags : Tree DSA Practice Tags : Tree Similar Reads Basics & PrerequisitesTime Complexity and Space ComplexityMany times there are more than one ways to solve a problem with different algorithms and we need a way to compare multiple ways. Also, there are situations where we would like to know how much time and resources an algorithm might take when implemented. To measure performance of algorithms, we typic 13 min read Types of Asymptotic Notations in Complexity Analysis of AlgorithmsWe have discussed Asymptotic Analysis, and Worst, Average, and Best Cases of Algorithms. The main idea of asymptotic analysis is to have a measure of the efficiency of algorithms that don't depend on machine-specific constants and don't require algorithms to be implemented and time taken by programs 8 min read Data StructuresGetting 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. Here’s 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 AlgorithmsSearching 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 AdvancedSegment 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 PreparationInterview Corner: All Resources To Crack Any Tech InterviewThis article serves as your one-stop guide to interview preparation, designed to help you succeed across different experience levels and company expectations. Here is what you should expect in a Tech Interview, please remember the following points:Tech Interview Preparation does not have any fixed s 3 min read GfG160 - 160 Days of Problem SolvingAre you preparing for technical interviews and would like to be well-structured to improve your problem-solving skills? Well, we have good news for you! GeeksforGeeks proudly presents GfG160, a 160-day coding challenge starting on 15th November 2024. In this event, we will provide daily coding probl 3 min read Practice ProblemGeeksforGeeks Practice - Leading Online Coding PlatformGeeksforGeeks Practice is an online coding platform designed to help developers and students practice coding online and sharpen their programming skills with the following features. GfG 160: This consists of most popular interview problems organized topic wise and difficulty with with well written e 6 min read Problem of The Day - Develop the Habit of CodingDo you find it difficult to develop a habit of Coding? If yes, then we have a most effective solution for you - all you geeks need to do is solve one programming problem each day without any break, and BOOM, the results will surprise you! Let us tell you how:Suppose you commit to improve yourself an 5 min read Like