Skip to content
geeksforgeeks
  • Tutorials
    • Python
    • Java
    • Data Structures & Algorithms
    • ML & Data Science
    • Interview Corner
    • Programming Languages
    • Web Development
    • CS Subjects
    • DevOps And Linux
    • School Learning
    • Practice Coding Problems
  • Courses
    • DSA to Development
    • Get IBM Certification
    • Newly Launched!
      • Master Django Framework
      • Become AWS Certified
    • For Working Professionals
      • Interview 101: DSA & System Design
      • Data Science Training Program
      • JAVA Backend Development (Live)
      • DevOps Engineering (LIVE)
      • Data Structures & Algorithms in Python
    • For Students
      • Placement Preparation Course
      • Data Science (Live)
      • Data Structure & Algorithm-Self Paced (C++/JAVA)
      • Master Competitive Programming (Live)
      • Full Stack Development with React & Node JS (Live)
    • Full Stack Development
    • Data Science Program
    • All Courses
  • DSA
  • Interview Problems on Graph
  • Practice Graph
  • MCQs on Graph
  • Graph Tutorial
  • Graph Representation
  • Graph Properties
  • Types of Graphs
  • Graph Applications
  • BFS on Graph
  • DFS on Graph
  • Graph VS Tree
  • Transpose Graph
  • Dijkstra's Algorithm
  • Minimum Spanning Tree
  • Prim’s Algorithm
  • Topological Sorting
  • Floyd Warshall Algorithm
  • Strongly Connected Components
  • Advantages & Disadvantages
Open In App
Next Article:
Types of Graphs with Examples
Next article icon

Graph and its representations

Last Updated : 21 Jun, 2025
Comments
Improve
Suggest changes
Like Article
Like
Report

A Graph is a non-linear data structure consisting of vertices and edges. The vertices are sometimes also referred to as nodes and the edges are lines or arcs that connect any two nodes in the graph. More formally a Graph is composed of a set of vertices( V ) and a set of edges( E ). The graph is denoted by G(V, E).

Representations of Graph

Here are the two most common ways to represent a graph : For simplicity, we are going to consider only unweighted graphs in this post.

  1. Adjacency Matrix
  2. Adjacency List

Adjacency Matrix Representation

An adjacency matrix is a way of representing a graph as a matrix of boolean (0's and 1's)

Let's assume there are n vertices in the graph So, create a 2D matrix adjMat[n][n] having dimension n x n.

  • If there is an edge from vertex i to j, mark adjMat[i][j] as 1.
  • If there is no edge from vertex i to j, mark adjMat[i][j] as 0.

Representation of Undirected Graph as Adjacency Matrix:

The below figure shows an undirected graph. Initially, the entire Matrix is ​​initialized to 0. If there is an edge from source to destination, we insert 1 to both cases (adjMat[source][destination] and adjMat[destination][source]) because we can go either way.

Undirected_to_Adjacency_matrix
Undirected Graph to Adjacency Matrix
C++
// C++ program to demonstrate Adjacency Matrix // representation of undirected and unweighted graph #include <bits/stdc++.h> using namespace std;  void addEdge(vector<vector<int>> &mat, int i, int j) {     mat[i][j] = 1;     mat[j][i] = 1; // Since the graph is undirected }  void displayMatrix(vector<vector<int>> &mat) {     int V = mat.size();     for (int i = 0; i < V; i++)     {         for (int j = 0; j < V; j++)             cout << mat[i][j] << " ";         cout << endl;     } }  int main() {      // Create a graph with 4 vertices and no edges     // Note that all values are initialized as 0     int V = 4;     vector<vector<int>> mat(V, vector<int>(V, 0));      // Now add edges one by one     addEdge(mat, 0, 1);     addEdge(mat, 0, 2);     addEdge(mat, 1, 2);     addEdge(mat, 2, 3);      /* Alternatively we can also create using below        code if we know all edges in advacem       vector<vector<int>> mat = {{ 0, 1, 0, 0 },                                { 1, 0, 1, 0 },                                { 0, 1, 0, 1 },                                { 0, 0, 1, 0 } }; */      cout << "Adjacency Matrix Representation" << endl;     displayMatrix(mat);      return 0; } 
C
#include<stdio.h>  #define V 4  void addEdge(int mat[V][V], int i, int j) {     mat[i][j] = 1;     mat[j][i] = 1; // Since the graph is undirected }  void displayMatrix(int mat[V][V]) {     for (int i = 0; i < V; i++) {         for (int j = 0; j < V; j++)             printf("%d ", mat[i][j]);         printf("\n");     } }  int main() {     // Create a graph with 4 vertices and no edges     // Note that all values are initialized as 0     int mat[V][V] = {0};      // Now add edges one by one     addEdge(mat, 0, 1);     addEdge(mat, 0, 2);     addEdge(mat, 1, 2);     addEdge(mat, 2, 3);      /* Alternatively, we can also create using the below        code if we know all edges in advance      int mat[V][V] = {         {0, 1, 0, 0},         {1, 0, 1, 0},         {0, 1, 0, 1},         {0, 0, 1, 0}     }; */      printf("Adjacency Matrix Representation\n");     displayMatrix(mat);      return 0; } 
Java
import java.util.Arrays;  public class GfG {      public static void addEdge(int[][] mat, int i, int j) {         mat[i][j] = 1;         mat[j][i] = 1; // Since the graph is undirected     }      public static void displayMatrix(int[][] mat) {         for (int[] row : mat) {             for (int val : row) {                 System.out.print(val + " ");             }             System.out.println();         }     }      public static void main(String[] args) {          // Create a graph with 4 vertices and no edges         // Note that all values are initialized as 0         int V = 4;         int[][] mat = new int[V][V];          // Now add edges one by one         addEdge(mat, 0, 1);         addEdge(mat, 0, 2);         addEdge(mat, 1, 2);         addEdge(mat, 2, 3);          /* Alternatively we can also create using below            code if we know all edges in advance           int[][] mat = {{ 0, 1, 0, 0 },                         { 1, 0, 1, 0 },                         { 0, 1, 0, 1 },                         { 0, 0, 1, 0 } }; */          System.out.println("Adjacency Matrix Representation");         displayMatrix(mat);     } } 
Python
def add_edge(mat, i, j):        # Add an edge between two vertices     mat[i][j] = 1  # Graph is      mat[j][i] = 1  # Undirected  def display_matrix(mat):        # Display the adjacency matrix     for row in mat:         print(" ".join(map(str, row)))    # Main function to run the program if __name__ == "__main__":     V = 4  # Number of vertices     mat = [[0] * V for _ in range(V)]        # Add edges to the graph     add_edge(mat, 0, 1)     add_edge(mat, 0, 2)     add_edge(mat, 1, 2)     add_edge(mat, 2, 3)      # Optionally, initialize matrix directly     """     mat = [         [0, 1, 0, 0],         [1, 0, 1, 0],         [0, 1, 0, 1],         [0, 0, 1, 0]     ]     """      # Display adjacency matrix     print("Adjacency Matrix:")     display_matrix(mat) 
C#
using System;  public class GfG {     // Add an edge between two vertices     public static void AddEdge(int[,] mat, int i, int j)     {         mat[i, j] = 1; // Since the graph is          mat[j, i] = 1; // undirected     }      // Display the adjacency matrix     public static void DisplayMatrix(int[,] mat)     {         int V = mat.GetLength(0);          for (int i = 0; i < V; i++)         {             for (int j = 0; j < V; j++)             {                 Console.Write(mat[i, j] + " ");             }             Console.WriteLine();          }     }      // Main method to run the program     public static void Main(string[] args)     {         int V = 4; // Number of vertices         int[,] mat = new int[V, V]; // Initialize matrix          // Add edges to the graph         AddEdge(mat, 0, 1);         AddEdge(mat, 0, 2);         AddEdge(mat, 1, 2);         AddEdge(mat, 2, 3);          // Optionally, initialize matrix directly         /*         int[,] mat = new int[,]         {             { 0, 1, 0, 0 },             { 1, 0, 1, 0 },             { 0, 1, 0, 1 },             { 0, 0, 1, 0 }         };         */          // Display adjacency matrix         Console.WriteLine("Adjacency Matrix:");         DisplayMatrix(mat);     } } 
JavaScript
function addEdge(mat, i, j) {     mat[i][j] = 1; // Graph is      mat[j][i] = 1; // undirected }  function displayMatrix(mat) {     // Display the adjacency matrix     for (const row of mat) {         console.log(row.join(" "));      } }  // Main function to run the program const V = 4; // Number of vertices   // Initialize matrix let mat = Array.from({ length: V }, () => Array(V).fill(0));  // Add edges to the graph addEdge(mat, 0, 1); addEdge(mat, 0, 2); addEdge(mat, 1, 2); addEdge(mat, 2, 3);  /* Optionally, initialize matrix directly let mat = [     [0, 1, 0, 0],     [1, 0, 1, 0],     [0, 1, 0, 1],     [0, 0, 1, 0] ]; */  // Display adjacency matrix console.log("Adjacency Matrix:"); displayMatrix(mat); 

Output
Adjacency Matrix Representation 0 1 1 0  1 0 1 0  1 1 0 1  0 0 1 0  

Representation of Directed Graph as Adjacency Matrix:

The below figure shows a directed graph. Initially, the entire Matrix is ​​initialized to 0. If there is an edge from source to destination, we insert 1 for that particular adjMat[source][destination].

Directed_to_Adjacency_matrix
Directed Graph to Adjacency Matrix

Please refer Adjacency Matrix Representation for more details.

Adjacency List Representation

An array of Lists is used to store edges between two vertices. The size of array is equal to the number of vertices (i.e, n). Each index in this array represents a specific vertex in the graph. The entry at the index i of the array contains a linked list containing the vertices that are adjacent to vertex i. Let's assume there are n vertices in the graph So, create an array of list of size n as adjList[n].

  • adjList[0] will have all the nodes which are connected (neighbour) to vertex 0.
  • adjList[1] will have all the nodes which are connected (neighbour) to vertex 1 and so on.

Representation of Undirected Graph as Adjacency list:

The below undirected graph has 3 vertices. So, an array of list will be created of size 3, where each indices represent the vertices. Now, vertex 0 has two neighbours (i.e, 1 and 2). So, insert vertex 1 and 2 at indices 0 of array. Similarly, For vertex 1, it has two neighbour (i.e, 2 and 0) So, insert vertices 2 and 0 at indices 1 of array. Similarly, for vertex 2, insert its neighbours in array of list.

Graph-Representation-of-Undirected-graph-to-Adjacency-List
Undirected Graph to Adjacency list
C++
#include <iostream> #include <vector> using namespace std;  // Function to add an edge between two vertices void addEdge(vector<vector<int>>& adj, int i, int j) {     adj[i].push_back(j);     adj[j].push_back(i); // Undirected }  // Function to display the adjacency list void displayAdjList(const vector<vector<int>>& adj) {     for (int i = 0; i < adj.size(); i++) {         cout << i << ": "; // Print the vertex         for (int j : adj[i]) {             cout << j << " "; // Print its adjacent         }         cout << endl;      } }  // Main function int main() {     // Create a graph with 4 vertices and no edges     int V = 4;     vector<vector<int>> adj(V);       // Now add edges one by one     addEdge(adj, 0, 1);     addEdge(adj, 0, 2);     addEdge(adj, 1, 2);     addEdge(adj, 2, 3);      cout << "Adjacency List Representation:" << endl;     displayAdjList(adj);      return 0; } 
C
#include <stdio.h> #include <stdlib.h>  // Structure for a node in the adjacency list struct Node {     int data;     struct Node* next; };  // Function to create a new node struct Node* createNode(int data) {     struct Node* newNode =        (struct Node*)malloc(sizeof(struct Node));     newNode->data = data;     newNode->next = NULL;     return newNode; }  // Function to add an edge between two vertices void addEdge(struct Node* adj[], int i, int j) {     struct Node* newNode = createNode(j);     newNode->next = adj[i];     adj[i] = newNode;      newNode = createNode(i); // For undirected graph     newNode->next = adj[j];     adj[j] = newNode; }  // Function to display the adjacency list void displayAdjList(struct Node* adj[], int V) {     for (int i = 0; i < V; i++) {         printf("%d: ", i); // Print the vertex         struct Node* temp = adj[i];         while (temp != NULL) {             printf("%d ", temp->data); // Print its adjacent             temp = temp->next;         }         printf("\n");     } }  // Main function int main() {        // Create a graph with 4 vertices and no edges     int V = 4;     struct Node* adj[V];     for (int i = 0; i < V; i++) {         adj[i] = NULL; // Initialize adjacency list     }      // Now add edges one by one     addEdge(adj, 0, 1);     addEdge(adj, 0, 2);     addEdge(adj, 1, 2);     addEdge(adj, 2, 3);      printf("Adjacency List Representation:\n");     displayAdjList(adj, V);      return 0; } 
Java
import java.util.ArrayList; import java.util.List;  public class GfG {          // Method to add an edge between two vertices     public static void addEdge(List<List<Integer>> adj, int i, int j) {         adj.get(i).add(j);         adj.get(j).add(i); // Undirected     }      // Method to display the adjacency list     public static void displayAdjList(List<List<Integer>> adj) {         for (int i = 0; i < adj.size(); i++) {             System.out.print(i + ": "); // Print the vertex             for (int j : adj.get(i)) {                 System.out.print(j + " "); // Print its adjacent              }             System.out.println();          }     }      // Main method     public static void main(String[] args) {                // Create a graph with 4 vertices and no edges         int V = 4;         List<List<Integer>> adj = new ArrayList<>(V);          for (int i = 0; i < V; i++) {             adj.add(new ArrayList<>());         }          // Now add edges one by one         addEdge(adj, 0, 1);         addEdge(adj, 0, 2);         addEdge(adj, 1, 2);         addEdge(adj, 2, 3);          System.out.println("Adjacency List Representation:");         displayAdjList(adj);     } } 
Python
def add_edge(adj, i, j):     adj[i].append(j)     adj[j].append(i)  # Undirected  def display_adj_list(adj):     for i in range(len(adj)):         print(f"{i}: ", end="")         for j in adj[i]:             print(j, end=" ")         print()  # Create a graph with 4 vertices and no edges V = 4 adj = [[] for _ in range(V)]  # Now add edges one by one add_edge(adj, 0, 1) add_edge(adj, 0, 2) add_edge(adj, 1, 2) add_edge(adj, 2, 3)  print("Adjacency List Representation:") display_adj_list(adj) 
C#
using System; using System.Collections.Generic;  public class GfG {     // Method to add an edge between two vertices     public static void AddEdge(List<List<int>> adj, int i, int j)     {         adj[i].Add(j);         adj[j].Add(i); // Undirected     }      // Method to display the adjacency list     public static void DisplayAdjList(List<List<int>> adj)     {         for (int i = 0; i < adj.Count; i++)         {             Console.Write($"{i}: "); // Print the vertex             foreach (int j in adj[i])             {                 Console.Write($"{j} "); // Print its adjacent             }             Console.WriteLine();          }     }      // Main method     public static void Main(string[] args)     {         // Create a graph with 4 vertices and no edges         int V = 4;         List<List<int>> adj = new List<List<int>>(V);          for (int i = 0; i < V; i++)             adj.Add(new List<int>());          // Now add edges one by one         AddEdge(adj, 0, 1);         AddEdge(adj, 0, 2);         AddEdge(adj, 1, 2);         AddEdge(adj, 2, 3);          Console.WriteLine("Adjacency List Representation:");         DisplayAdjList(adj);     } } 
JavaScript
function addEdge(adj, i, j) {     adj[i].push(j);     adj[j].push(i); // Undirected }  function displayAdjList(adj) {     for (let i = 0; i < adj.length; i++) {         console.log(`${i}: `);          for (const j of adj[i]) {              console.log(`${j} `);          }         console.log();      } }  // Create a graph with 4 vertices and no edges const V = 4; const adj = Array.from({ length: V }, () => []);  // Now add edges one by one addEdge(adj, 0, 1); addEdge(adj, 0, 2); addEdge(adj, 1, 2); addEdge(adj, 2, 3);  console.log("Adjacency List Representation:"); displayAdjList(adj); 

Output
Adjacency List Representation: 0: 1 2  1: 0 2  2: 0 1 3  3: 2  

Representation of Directed Graph as Adjacency list:

The below directed graph has 3 vertices. So, an array of list will be created of size 3, where each indices represent the vertices. Now, vertex 0 has no neighbours. For vertex 1, it has two neighbour (i.e, 0 and 2) So, insert vertices 0 and 2 at indices 1 of array. Similarly, for vertex 2, insert its neighbours in array of list.

Graph-Representation-of-Directed-graph-to-Adjacency-List

Please refer Adjacency List Representation for more details.


Next Article
Types of Graphs with Examples

K

kartik
Improve
Article Tags :
  • Graph
  • DSA
  • graph-basics
Practice Tags :
  • Graph

Similar Reads

    Graph Algorithms
    Graph 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
    Introduction to Graph Data Structure
    Graph Data Structure is a non-linear data structure consisting of vertices and edges. It is useful in fields such as social network analysis, recommendation systems, and computer networks. In the field of sports data science, graph data structure can be used to analyze and understand the dynamics of
    15+ min read
    Graph and its representations
    A Graph is a non-linear data structure consisting of vertices and edges. The vertices are sometimes also referred to as nodes and the edges are lines or arcs that connect any two nodes in the graph. More formally a Graph is composed of a set of vertices( V ) and a set of edges( E ). The graph is den
    12 min read
    Types of Graphs with Examples
    A graph is a mathematical structure that represents relationships between objects by connecting a set of points. It is used to establish a pairwise relationship between elements in a given set. graphs are widely used in discrete mathematics, computer science, and network theory to represent relation
    9 min read
    Basic Properties of a Graph
    A Graph is a non-linear data structure consisting of nodes and edges. The nodes are sometimes also referred to as vertices and the edges are lines or arcs that connect any two nodes in the graph. The basic properties of a graph include: Vertices (nodes): The points where edges meet in a graph are kn
    4 min read
    Applications, Advantages and Disadvantages of Graph
    Graph is a non-linear data structure that contains nodes (vertices) and edges. A graph is a collection of set of vertices and edges (formed by connecting two vertices). A graph is defined as G = {V, E} where V is the set of vertices and E is the set of edges. Graphs can be used to model a wide varie
    7 min read
    Transpose graph
    Transpose of a directed graph G is another directed graph on the same set of vertices with all of the edges reversed compared to the orientation of the corresponding edges in G. That is, if G contains an edge (u, v) then the converse/transpose/reverse of G contains an edge (v, u) and vice versa. Giv
    9 min read
    Difference Between Graph and Tree
    Graphs and trees are two fundamental data structures used in computer science to represent relationships between objects. While they share some similarities, they also have distinct differences that make them suitable for different applications. Difference Between Graph and Tree What is Graph?A grap
    2 min read

    BFS and DFS on Graph

    Breadth First Search or BFS for a Graph
    Given a undirected graph represented by an adjacency list adj, where each adj[i] represents the list of vertices connected to vertex i. Perform a Breadth First Search (BFS) traversal starting from vertex 0, visiting vertices from left to right according to the adjacency list, and return a list conta
    15+ min read
    Depth First Search or DFS for a Graph
    In Depth First Search (or DFS) for a graph, we traverse all adjacent vertices one by one. When we traverse an adjacent vertex, we completely finish the traversal of all vertices reachable through that adjacent vertex. This is similar to a tree, where we first completely traverse the left subtree and
    13 min read
    Applications, Advantages and Disadvantages of Depth First Search (DFS)
    Depth First Search is a widely used algorithm for traversing a graph. Here we have discussed some applications, advantages, and disadvantages of the algorithm. Applications of Depth First Search:1. Detecting cycle in a graph: A graph has a cycle if and only if we see a back edge during DFS. So we ca
    4 min read
    Applications, Advantages and Disadvantages of Breadth First Search (BFS)
    We have earlier discussed Breadth First Traversal Algorithm for Graphs. Here in this article, we will see the applications, advantages, and disadvantages of the Breadth First Search. Applications of Breadth First Search: 1. Shortest Path and Minimum Spanning Tree for unweighted graph: In an unweight
    4 min read
    Iterative Depth First Traversal of Graph
    Given a directed Graph, the task is to perform Depth First Search of the given graph.Note: Start DFS from node 0, and traverse the nodes in the same order as adjacency list.Note : There can be multiple DFS traversals of a graph according to the order in which we pick adjacent vertices. Here we pick
    10 min read
    BFS for Disconnected Graph
    In the previous post, BFS only with a particular vertex is performed i.e. it is assumed that all vertices are reachable from the starting vertex. But in the case of a disconnected graph or any vertex that is unreachable from all vertex, the previous implementation will not give the desired output, s
    14 min read
    Transitive Closure of a Graph using DFS
    Given a directed graph, find out if a vertex v is reachable from another vertex u for all vertex pairs (u, v) in the given graph. Here reachable means that there is a path from vertex u to v. The reach-ability matrix is called transitive closure of a graph. For example, consider below graph: GraphTr
    8 min read
    Difference between BFS and DFS
    Breadth-First Search (BFS) and Depth-First Search (DFS) are two fundamental algorithms used for traversing or searching graphs and trees. This article covers the basic difference between Breadth-First Search and Depth-First Search.Difference between BFS and DFSParametersBFSDFSStands forBFS stands fo
    2 min read

    Cycle in a Graph

    Detect Cycle in a Directed Graph
    Given the number of vertices V and a list of directed edges, determine whether the graph contains a cycle or not.Examples: Input: V = 4, edges[][] = [[0, 1], [0, 2], [1, 2], [2, 0], [2, 3]]Cycle: 0 → 2 → 0 Output: trueExplanation: The diagram clearly shows a cycle 0 → 2 → 0 Input: V = 4, edges[][] =
    15+ min read
    Detect cycle in an undirected graph
    Given an undirected graph, the task is to check if there is a cycle in the given graph.Examples:Input: V = 4, edges[][]= [[0, 1], [0, 2], [1, 2], [2, 3]]Undirected Graph with 4 vertices and 4 edgesOutput: trueExplanation: The diagram clearly shows a cycle 0 → 2 → 1 → 0Input: V = 4, edges[][] = [[0,
    8 min read
    Detect Cycle in a directed graph using colors
    Given a directed graph represented by the number of vertices V and a list of directed edges, determine whether the graph contains a cycle.Your task is to implement a function that accepts V (number of vertices) and edges (an array of directed edges where each edge is a pair [u, v]), and returns true
    9 min read
    Detect a negative cycle in a Graph | (Bellman Ford)
    Given a directed weighted graph, your task is to find whether the given graph contains any negative cycles that are reachable from the source vertex (e.g., node 0).Note: A negative-weight cycle is a cycle in a graph whose edges sum to a negative value.Example:Input: V = 4, edges[][] = [[0, 3, 6], [1
    15+ min read
    Cycles of length n in an undirected and connected graph
    Given an undirected and connected graph and a number n, count the total number of simple cycles of length n in the graph. A simple cycle of length n is defined as a cycle that contains exactly n vertices and n edges. Note that for an undirected graph, each cycle should only be counted once, regardle
    10 min read
    Detecting negative cycle using Floyd Warshall
    We are given a directed graph. We need compute whether the graph has negative cycle or not. A negative cycle is one in which the overall sum of the cycle comes negative. Negative weights are found in various applications of graphs. For example, instead of paying cost for a path, we may get some adva
    12 min read
    Clone a Directed Acyclic Graph
    A directed acyclic graph (DAG) is a graph which doesn't contain a cycle and has directed edges. We are given a DAG, we need to clone it, i.e., create another graph that has copy of its vertices and edges connecting them. Examples: Input : 0 - - - > 1 - - - -> 4 | / \ ^ | / \ | | / \ | | / \ |
    12 min read
geeksforgeeks-footer-logo
Corporate & Communications Address:
A-143, 7th Floor, Sovereign Corporate Tower, Sector- 136, Noida, Uttar Pradesh (201305)
Registered Address:
K 061, Tower K, Gulshan Vivante Apartment, Sector 137, Noida, Gautam Buddh Nagar, Uttar Pradesh, 201305
GFG App on Play Store GFG App on App Store
Advertise with us
  • Company
  • About Us
  • Legal
  • Privacy Policy
  • In Media
  • Contact Us
  • Advertise with us
  • GFG Corporate Solution
  • Placement Training Program
  • Languages
  • Python
  • Java
  • C++
  • PHP
  • GoLang
  • SQL
  • R Language
  • Android Tutorial
  • Tutorials Archive
  • DSA
  • Data Structures
  • Algorithms
  • DSA for Beginners
  • Basic DSA Problems
  • DSA Roadmap
  • Top 100 DSA Interview Problems
  • DSA Roadmap by Sandeep Jain
  • All Cheat Sheets
  • Data Science & ML
  • Data Science With Python
  • Data Science For Beginner
  • Machine Learning
  • ML Maths
  • Data Visualisation
  • Pandas
  • NumPy
  • NLP
  • Deep Learning
  • Web Technologies
  • HTML
  • CSS
  • JavaScript
  • TypeScript
  • ReactJS
  • NextJS
  • Bootstrap
  • Web Design
  • Python Tutorial
  • Python Programming Examples
  • Python Projects
  • Python Tkinter
  • Python Web Scraping
  • OpenCV Tutorial
  • Python Interview Question
  • Django
  • Computer Science
  • Operating Systems
  • Computer Network
  • Database Management System
  • Software Engineering
  • Digital Logic Design
  • Engineering Maths
  • Software Development
  • Software Testing
  • DevOps
  • Git
  • Linux
  • AWS
  • Docker
  • Kubernetes
  • Azure
  • GCP
  • DevOps Roadmap
  • System Design
  • High Level Design
  • Low Level Design
  • UML Diagrams
  • Interview Guide
  • Design Patterns
  • OOAD
  • System Design Bootcamp
  • Interview Questions
  • Inteview Preparation
  • Competitive Programming
  • Top DS or Algo for CP
  • Company-Wise Recruitment Process
  • Company-Wise Preparation
  • Aptitude Preparation
  • Puzzles
  • School Subjects
  • Mathematics
  • Physics
  • Chemistry
  • Biology
  • Social Science
  • English Grammar
  • Commerce
  • World GK
  • GeeksforGeeks Videos
  • DSA
  • Python
  • Java
  • C++
  • Web Development
  • Data Science
  • CS Subjects
@GeeksforGeeks, Sanchhaya Education Private Limited, All rights reserved
We use cookies to ensure you have the best browsing experience on our website. By using our site, you acknowledge that you have read and understood our Cookie Policy & Privacy Policy
Lightbox
Improvement
Suggest Changes
Help us improve. Share your suggestions to enhance the article. Contribute your expertise and make a difference in the GeeksforGeeks portal.
geeksforgeeks-suggest-icon
Create Improvement
Enhance the article with your expertise. Contribute to the GeeksforGeeks community and help create better learning resources for all.
geeksforgeeks-improvement-icon
Suggest Changes
min 4 words, max Words Limit:1000

Thank You!

Your suggestions are valuable to us.

What kind of Experience do you want to share?

Interview Experiences
Admission Experiences
Career Journeys
Work Experiences
Campus Experiences
Competitive Exam Experiences