Count number of common elements between two arrays
Last Updated :
12 Jul, 2025
Given two arrays a[] and b[], the task is to find the count of common elements in both the given arrays. Note that both the arrays contain distinct (individually) positive integers.
Examples:
Input: a[] = {1, 2, 3}, b[] = {2, 4, 3}
Output: 2
2 and 3 are common to both the arrays.
Input: a[] = {1, 4, 7, 2, 3}, b[] = {2, 11, 7, 4, 15, 20, 24}
Output: 3
Approach 1: We will use 3 bitset of same size. First we will traverse first array and set the bit 1 to position a[i] in first bitset.
After that we will traverse second array and set the bit 1 to position b[i] in second bitset.
At last we will find the bitwise AND of both the bitsets and if the ith position of the resultant bitset is 1 then it implies that ith position of first and second bitsets are also 1 and i is the common element in both the arrays.
Below is the implementation of the above approach:
C++
// C++ implementation of the approach
#include <bits/stdc++.h>
using namespace std;
#define MAX 100000
bitset<MAX> bit1, bit2, bit3;
// Function to return the count of common elements
int count_common(int a[], int n, int b[], int m)
{
// Traverse the first array
for (int i = 0; i < n; i++) {
// Set 1 at position a[i]
bit1.set(a[i]);
}
// Traverse the second array
for (int i = 0; i < m; i++) {
// Set 1 at position b[i]
bit2.set(b[i]);
}
// Bitwise AND of both the bitsets
bit3 = bit1 & bit2;
// Find the count of 1's
int count = bit3.count();
return count;
}
// Driver code
int main()
{
int a[] = { 1, 4, 7, 2, 3 };
int b[] = { 2, 11, 7, 4, 15, 20, 24 };
int n = sizeof(a) / sizeof(a[0]);
int m = sizeof(b) / sizeof(b[0]);
cout << count_common(a, n, b, m);
return 0;
}
Java
/*package whatever //do not write package name here */
import java.util.*;
public class GFG {
static int bit1 = 0;
static int bit2 = 0;
static int bit3 = 0;
// Function to return the count of common elements
static int count_common(int[] a, int n, int[] b, int m)
{
// Traverse the first array
for (int i = 0; i < n; i++) {
// Set 1 at (index)position a[i]
bit1 = bit1 | (1 << a[i]);
}
// Traverse the second array
for (int i = 0; i < m; i++) {
// Set 1 at (index)position b[i]
bit2 = bit2 | (1 << b[i]);
}
// Bitwise AND of both the bitsets
bit3 = bit1 & bit2;
// Find the count of 1's
int count = Integer.toBinaryString(bit3).split("1").length - 1;
return count;
}
// Driver code
public static void main(String[] args)
{
int[] a = { 1, 4, 7, 2, 3 };
int[] b = { 2, 11, 7, 4, 15, 20, 24 };
int n = a.length;
int m = b.length;
System.out.println(count_common(a, n, b, m));
}
}
// This code is contributed by aadityaburujwale.
Python3
# Python3 implementation of the approach
MAX = 100000
bit1 , bit2, bit3 = 0, 0, 0
# Function to return the count of common elements
def count_common(a, n, b, m) :
# Traverse the first array
for i in range(n) :
global bit1, bit2, bit3
# Set 1 at (index)position a[i]
bit1 = bit1 | (1<<a[i])
# Traverse the second array
for i in range(m) :
# Set 1 at (index)position b[i]
bit2 = bit2 | (1<<b[i])
# Bitwise AND of both the bitsets
bit3 = bit1 & bit2;
# Find the count of 1's
count = bin(bit3).count('1');
return count;
# Driver code
if __name__ == "__main__" :
a = [ 1, 4, 7, 2, 3 ];
b = [ 2, 11, 7, 4, 15, 20, 24 ];
n = len(a);
m = len(b);
print(count_common(a, n, b, m));
# This code is contributed by AnkitRai01
C#
// C# implementation of the approach
using System;
class GFG {
static int bit1 = 0;
static int bit2 = 0;
static int bit3 = 0;
// Function to return the count of common elements
static int count_common(int[] a, int n, int[] b, int m)
{
// Traverse the first array
for (int i = 0; i < n; i++) {
// Set 1 at (index)position a[i]
bit1 = bit1 | (1 << a[i]);
}
// Traverse the second array
for (int i = 0; i < m; i++) {
// Set 1 at (index)position b[i]
bit2 = bit2 | (1 << b[i]);
}
// Bitwise AND of both the bitsets
bit3 = bit1 & bit2;
// Find the count of 1's
var count
= Convert.ToString(bit3, 2).Split("1").Length
- 1;
return count;
}
// Driver code
public static void Main(string[] args)
{
int[] a = { 1, 4, 7, 2, 3 };
int[] b = { 2, 11, 7, 4, 15, 20, 24 };
int n = a.Length;
int m = b.Length;
Console.WriteLine(count_common(a, n, b, m));
}
}
// This code is contributed by phasing17
JavaScript
// JavaScript implementation of the approach
const MAX = 100000;
let bit1 = 0;
let bit2 = 0;
let bit3 = 0;
// Function to return the count of common elements
function count_common(a, n, b, m)
{
// Traverse the first array
for (var i = 0; i < n; i++)
{
// Set 1 at (index)position a[i]
bit1 = bit1 | (1<<a[i]);
}
// Traverse the second array
for (var i = 0; i < m; i++)
{
// Set 1 at (index)position b[i]
bit2 = bit2 | (1<<b[i]);
}
// Bitwise AND of both the bitsets
bit3 = bit1 & bit2;
// Find the count of 1's
var count = bit3.toString(2).split("1").length - 1;
return count;
}
// Driver code
var a = [ 1, 4, 7, 2, 3 ];
var b = [ 2, 11, 7, 4, 15, 20, 24 ];
var n = a.length;
var m = b.length;
console.log(count_common(a, n, b, m));
// This code is contributed by phasing17
Time Complexity: O(n + m)
Auxiliary Space: O(MAX)
Approach 2: We can also use hashmap to store frequencies of each element of both arrays a[] and b[] and sum up the minimum value for each element's frequency.
Follow given steps to solve the problem:
1. Traverse array a[] and store all frequencies in map freq1.
2. Traverse array b[] and store all frequencies in map freq2.
3. Traverse the map freq1 and sum up the minimum value between x.second and freq2[x.first] in result.
4. Return result as the final answer.
C++14
#include <bits/stdc++.h>
using namespace std;
int count_common(int *a, int& n, int *b, int& m)
{
unordered_map<int,int>freq1,freq2;
int result=0;
for(int i=0;i<n;i++)
freq1[a[i]]++;
for(int i=0;i<m;i++)
freq2[b[i]]++;
for(auto& x:freq1)
result+=min(x.second,freq2[x.first]);
return result;
}
// driver's code
int main()
{
int a[]={1,2,3};
int n=sizeof(a)/sizeof(a[0]);
int b[]={2,4,3};
int m=sizeof(b)/sizeof(b[0]);
cout<<count_common(a,n,b,m);
return 0;
}
// this code is contributed by prophet1999
Java
import java.util.*;
public class GFG {
static int count_common(int[] a, int n, int[] b, int m)
{
HashMap<Integer, Integer> freq1 = new HashMap<>();
HashMap<Integer, Integer> freq2 = new HashMap<>();
int result = 0;
for (int i = 0; i < n; i++) {
if (!freq1.containsKey(a[i])) {
freq1.put(a[i], 1);
}
else {
freq1.put(a[i], freq1.get(a[i]) + 1);
}
}
for (int i = 0; i < m; i++) {
if (!freq2.containsKey(b[i])) {
freq2.put(b[i], 1);
}
else {
freq2.put(b[i], freq2.get(b[i]) + 1);
}
}
for (Map.Entry<Integer, Integer> x :
freq1.entrySet()) {
int p = x.getValue();
int q = 0;
if (freq2.containsKey(x.getKey())) {
q = freq2.get(x.getKey());
}
result += Math.min(p, q);
}
return result;
}
// driver's code
public static void main(String args[])
{
int[] a = { 1, 2, 3 };
int n = a.length;
int[] b = { 2, 4, 3 };
int m = b.length;
System.out.print(count_common(a, n, b, m));
}
}
// This code is contributed by Samim Hossain Mondal.
Python
def count_common(a, n, b, m):
freq1 = {}
freq2 = {}
result = 0
for element in a:
if element in freq1:
freq1[element] += 1
else:
freq1[element] = 1
for element in b:
if element in freq2:
freq2[element] += 1
else:
freq2[element] = 1
for key, value in freq1.items():
if key in freq2:
result += min(value, freq2.get(key))
return result
# driver's code
a = [1, 2, 3]
n = len(a)
b = [2, 4, 3]
m = len(b)
print(count_common(a, n, b, m))
# This code is contributed by Samim Hossain Mondal.
C#
using System;
using System.Collections.Generic;
class GFG {
static int count_common(int[] a, int n, int[] b, int m)
{
Dictionary<int, int> freq1
= new Dictionary<int, int>();
Dictionary<int, int> freq2
= new Dictionary<int, int>();
int result = 0;
for (int i = 0; i < n; i++) {
if (!freq1.ContainsKey(a[i])) {
freq1.Add(a[i], 1);
}
else {
freq1[a[i]]++;
}
}
for (int i = 0; i < m; i++) {
if (!freq2.ContainsKey(b[i])) {
freq2.Add(b[i], 1);
}
else {
freq2[b[i]]++;
}
}
foreach(KeyValuePair<int, int> x in freq1)
{
int p = x.Value;
int q = 0;
if (freq2.ContainsKey(x.Key)) {
q = freq2[x.Key];
}
result += Math.Min(p, q);
}
return result;
}
// driver's code
public static void Main()
{
int[] a = { 1, 2, 3 };
int n = a.Length;
int[] b = { 2, 4, 3 };
int m = b.Length;
Console.Write(count_common(a, n, b, m));
}
}
// This code is contributed by Samim Hossain Mondal.
JavaScript
function count_common(a, n, b, m)
{
let freq1 = new Map();
let freq2 = new Map();
let result = 0;
for(let i = 0; i < n; i++)
if(freq1.has(a[i]))
freq1.set(a[i], freq1.get(a[i])+1);
else
freq1.set(a[i], 1);
for(let i = 0; i < m; i++)
if(freq2.has(b[i]))
freq2.set(b[i], freq2.get(b[i])+1);
else
freq2.set(b[i], 1);
freq1.forEach((value, key) => {
if(freq2.has(key)){
result += Math.min(value, freq2.get(key));
}
else{
result += Math.min(value, 0);
}
});
return result;
}
// driver's code
let a = [1,2,3];
let n = a.length;
let b = [2,4,3];
let m = b.length;
console.log(count_common(a, n, b, m));
// this code is contributed by Samim Hossain Mondal.
Time Complexity: O(n+m)
Auxiliary Space: O(n+m)
Approach 3 : We can also use Binary search to check if the element of array a present in the array b or not.
1. Sort the array b in ascending order.
2. Initialize count as 0 , which we store the number of common elements from array a and array b.
3. Iterate each element in array a and use binary search to check if the element exists in array b.
4.If the element exists in array b, increase the count by 1.
5.Return the count .
Below is the implementation of the above approach :
C++
// C++ implementation of the above approach
#include <bits/stdc++.h>
using namespace std;
// Function to check that element x is present in the array
bool binarysearch(int arr[], int m, int x)
{
int l = 0, r = m - 1;
while (l <= r) {
int mid = (l + r) / 2;
// Checking if the middle element is equal to x
if (arr[mid] == x) {
return true;
}
else if (arr[mid] < x) {
l = mid + 1;
}
else {
r = mid - 1;
}
}
// return true , if element x is present in the array
// else false
return false;
}
// Function to count common element
int count_common(int a[], int n, int b[], int m)
{
sort(b, b + m);
int count = 0;
// Iterate each element of array a
for (int i = 0; i < n; i++) {
// Checking if the element of array a is present in
// array b using the binary search function
if (binarysearch(b, m, a[i])) {
count++;
}
}
// Return count of common element
return count;
}
// Drive Code
int main()
{
int a[] = { 1, 4, 7, 2, 3 };
int n = sizeof(a) / sizeof(a[0]);
int b[] = { 2, 11, 7, 4, 15, 20, 24 };
int m = sizeof(b) / sizeof(b[0]);
// Function call
cout << "Number of common elements: "
<< count_common(a, n, b, m) << "\n";
return 0;
}
// This code is contributed by nikhilsainiofficial546
Java
import java.util.Arrays;
class GFG
{
// Function to check that element x is present in the
// array
public static boolean binarysearch(int arr[], int m,
int x)
{
int l = 0;
int r = m - 1;
while (l <= r) {
int mid = (l + r) / 2;
// Checking if the middle element is equal to x
if (arr[mid] == x) {
return true;
}
else if (arr[mid] < x) {
l = mid + 1;
}
else {
r = mid - 1;
}
}
// return true , if element x is present in the
// array else false
return false;
}
// Function to count common element
public static int count_common(int a[], int n, int b[],
int m)
{
Arrays.sort(b);
int count = 0;
// Iterate each element of array a
for (int i = 0; i < n; i++) {
// Checking if the element of array a is
// present in array b using the binary search
// function
if (binarysearch(b, m, a[i])) {
count++;
}
}
// Return count of common element
return count;
}
// Drive Code
public static void main(String[] args)
{
int a[] = { 1, 4, 7, 2, 3 };
int n = a.length;
int b[] = { 2, 11, 7, 4, 15, 20, 24 };
int m = b.length;
// Function call
System.out.println("Number of common elements: "
+ count_common(a, n, b, m));
}
}
// This code is contributed by phasing17.
Python
# python3 implementation of the above approach
# Function to check that element x is present in the array
def binarysearch(arr, m, x):
l, r = 0, m - 1
while l <= r:
mid = (l + r) // 2
# Checking if the middle element is equal to x
if arr[mid] == x:
return True
elif arr[mid] < x:
l = mid + 1
else:
r = mid - 1
# return true , if element x is present in the array
# else false
return False
# Function to count common element
def count_common(a, n, b, m):
b.sort()
count = 0
# Iterate each element of array a
for i in range(n):
# Checking if the element of array a is present in
# array b using the binary search function
if binarysearch(b, m, a[i]):
count += 1
# Return count of common element
return count
# Drive Code
a = [1, 4, 7, 2, 3]
n = len(a)
b = [2, 11, 7, 4, 15, 20, 24]
m = len(b)
# Function call
print("Number of common elements:", count_common(a, n, b, m))
# This code is contributed by nikhilsainiofficial546
C#
// C# program to count common elements in two arrays
using System;
using System.Collections;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
class GFG {
static void Main(string[] args)
{
int[] a = new int[] { 1, 4, 7, 2, 3 };
int n = a.Length;
int[] b = new int[] { 2, 11, 7, 4, 15, 20, 24 };
int m = b.Length;
// Function call
Console.WriteLine("Number of common elements: "
+ count_common(a, n, b, m));
Console.ReadLine();
}
// Function to count common element
public static int count_common(int[] a, int n, int[] b,
int m)
{
Array.Sort(b);
int count = 0;
// Iterate each element of array a
for (int i = 0; i < n; i++) {
// Checking if the element of array a is
// present in array b using the binary search
// function
if (binarysearch(b, m, a[i])) {
count++;
}
}
// Return count of common element
return count;
}
// Function to check that element x is present in the
// array
public static bool binarysearch(int[] arr, int m, int x)
{
int l = 0;
int r = m - 1;
while (l <= r) {
int mid = (l + r) / 2;
// Checking if the middle element is equal to x
if (arr[mid] == x) {
return true;
}
else if (arr[mid] < x) {
l = mid + 1;
}
else {
r = mid - 1;
}
}
// return true , if element x is present in the
// array else false
return false;
}
}
// This code is contributed by phasing17.
JavaScript
// JavaScript implementation of the above approach
// Function to check that element x is present in the array
function binarysearch(arr, m, x) {
let l = 0;
let r = m - 1;
while (l <= r) {
let mid = Math.floor((l + r) / 2);
// Checking if the middle element is equal to x
if (arr[mid] === x) {
return true;
} else if (arr[mid] < x) {
l = mid + 1;
} else {
r = mid - 1;
}
}
// return true , if element x is present in the array
// else false
return false;
}
// Function to count common element
function count_common(a, n, b, m) {
a.sort(function(x, y)
{
return x - y;
});
b.sort(function(x, y)
{
return x - y;
});
let count = 0;
// Iterate each element of array a
for (let i = 0; i < n; i++) {
// Checking if the element of array a is present in
// array b using the binary search function
if (binarysearch(b, m, a[i])) {
count++;
}
}
// Return count of common element
return count;
}
// Drive Code
let a = [1, 4, 7, 2, 3];
let n = a.length;
let b = [2, 11, 7, 4, 15, 20, 24];
let m = b.length;
// Function call
console.log("Number of common elements:", count_common(a, n, b, m));
// This code is contributed by phasing17
OutputNumber of common elements: 3
Time Complexity: O(mlogm + nlogm)
Auxiliary Space: O(m)
Similar Reads
Basics & Prerequisites
Data Structures
Array Data StructureIn this article, we introduce array, implementation in different popular languages, its basic operations and commonly seen problems / interview questions. An array stores items (in case of C/C++ and Java Primitive Arrays) or their references (in case of Python, JS, Java Non-Primitive) at contiguous
3 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