Counting Sort – Data Structures and Algorithms Tutorials

Read

Discuss(220+)

What is Counting Sort?

Counting Sort is a non-comparison-based sorting algorithm that works well when there is limited range of input values. It is particularly efficient when the range of input values is small compared to the number of elements to be sorted. The basic idea behind Counting Sort is to count the frequency of each distinct element in the input array and use that information to place the elements in their correct sorted positions.

How does Counting Sort Algorithm work?

Step1 :

Find out the maximum element from the given array.

Finding maximum element in inputArray[]

Step 2:

Initialize a countArray[] of length max+1 with all elements as 0. This array will be used for storing the occurrences of the elements of the input array.

Initialize countArray[]

Step 3:

In the countArray[], store the count of each unique element of the input array at their respective indices.
For Example: The count of element 2 in the input array is 2. So, store 2 at index 2 in the countArray[]. Similarly, the count of element 5 in the input array is 1, hence store 1 at index 5 in the countArray[].

Maintain count of each element in countArray[]

Step 4:

Store the cumulative sum or prefix sum of the elements of the countArray[] by doing countArray[i] = countArray[i – 1] + countArray[i]. This will help in placing the elements of the input array at the correct index in the output array.

Store the cumulative sum in countArray[]

Step 5:

Iterate from end of the input array and because traversing input array from end preserves the order of equal elements, which eventually makes this sorting algorithm stable.
Update outputArray[ countArray[ inputArray[i] ] – 1] = inputArray[i]. Also, update countArray[ inputArray[i] ] = countArray[ inputArray[i] ]–.

Step 6:

For i = 6, Update outputArray[ countArray[ inputArray[6] ] – 1] = inputArray[6]. Also, update countArray[ inputArray[6] ] = countArray[ inputArray[6] ]–.

Placing inputArray[6] at it correct position in outputArray[]

Step 7:

For i = 5, Update outputArray[ countArray[ inputArray[5] ] – 1] = inputArray[5]. Also, update countArray[ inputArray[5] ] = countArray[ inputArray[5] ]–.

Placing inputArray[5] at it correct position in outputArray[]

Step 8:

For i = 4, Update outputArray[ countArray[ inputArray[4] ] – 1] = inputArray[4]. Also, update countArray[ inputArray[4] ] = countArray[ inputArray[4] ]–.

Placing inputArray[4] at it correct position in outputArray[]

Step 9:

For i = 3, Update outputArray[ countArray[ inputArray[3] ] – 1] = inputArray[3]. Also, update countArray[ inputArray[3] ] = countArray[ inputArray[3] ]–.

Placing inputArray[3] at it correct position in outputArray[]

Step 10:

For i = 2, Update outputArray[ countArray[ inputArray[2] ] – 1] = inputArray[2]. Also, update countArray[ inputArray[2] ] = countArray[ inputArray[2] ]–.

Placing inputArray[2] at it correct position in outputArray[]

Step 11:

For i = 1, Update outputArray[ countArray[ inputArray[1] ] – 1] = inputArray[1]. Also, update countArray[ inputArray[1] ] = countArray[ inputArray[1] ]–.

Placing inputArray[1] at it correct position in outputArray[]

Step 12:

For i = 0, Update outputArray[ countArray[ inputArray[0] ] – 1] = inputArray[0]. Also, update countArray[ inputArray[0] ] = countArray[ inputArray[0] ]–.

Placing inputArray[0] at it correct position in outputArray[]

Counting Sort Algorithm

Declare an auxiliary array countArray[] of size max(inputArray[])+1 and initialize it with 0.
Traverse array inputArray[] and map each element of inputArray[] as an index of countArray[] array, i.e., execute countArray[inputArray[i]]++ for 0 <= i < N.
Calculate the prefix sum at every index of array inputArray[].
Create an array outputArray[] of size N.
Traverse array inputArray[] from end and update outputArray[ countArray[ inputArray[i] ] – 1] = inputArray[i]. Also, update countArray[ inputArray[i] ] = countArray[ inputArray[i] ]–.

Below is the implementation of the above algorithm:

C++14

#include <bits/stdc++.h>
using namespace std;
 
vector<int> countSort(vector<int>& inputArray)
{
 
    int N = inputArray.size();
 
    // Finding the maximum element of array inputArray[].
    int M = 0;
 
    for (int i = 0; i < N; i++)
        M = max(M, inputArray[i]);
 
    // Initializing countArray[] with 0
    vector<int> countArray(M + 1, 0);
 
    // Mapping each element of inputArray[] as an index
    // of countArray[] array
 
    for (int i = 0; i < N; i++)
        countArray[inputArray[i]]++;
 
    // Calculating prefix sum at every index
    // of array countArray[]
    for (int i = 1; i <= M; i++)
        countArray[i] += countArray[i - 1];
 
    // Creating outputArray[] from countArray[] array
    vector<int> outputArray(N);
 
    for (int i = N - 1; i >= 0; i--)
 
    {
        outputArray[countArray[inputArray[i]] - 1]
            = inputArray[i];
 
        countArray[inputArray[i]]--;
    }
 
    return outputArray;
}
 
// Driver code
int main()
 
{
 
    // Input array
    vector<int> inputArray = { 4, 3, 12, 1, 5, 5, 3, 9 };
 
    // Output array
    vector<int> outputArray = countSort(inputArray);
 
    for (int i = 0; i < inputArray.size(); i++)
        cout << outputArray[i] << " ";
 
    return 0;
}

Python3

def count_sort(input_array):
    # Finding the maximum element of input_array.
    M = max(input_array)
 
    # Initializing count_array with 0
    count_array = [0] * (M + 1)
 
    # Mapping each element of input_array as an index of count_array
    for num in input_array:
        count_array[num] += 1
 
    # Calculating prefix sum at every index of count_array
    for i in range(1, M + 1):
        count_array[i] += count_array[i - 1]
 
    # Creating output_array from count_array
    output_array = [0] * len(input_array)
 
    for i in range(len(input_array) - 1, -1, -1):
        output_array[count_array[input_array[i]] - 1] = input_array[i]
        count_array[input_array[i]] -= 1
 
    return output_array
 
# Driver code
if __name__ == "__main__":
    # Input array
    input_array = [4, 3, 12, 1, 5, 5, 3, 9]
 
    # Output array
    output_array = count_sort(input_array)
 
    for num in output_array:
        print(num, end=" ")

Output

1 3 3 4 5 5 9 12

Complexity Analysis of Counting Sort:

Time Complexity: O(N+M), where N and M are the size of inputArray[] and countArray[] respectively.
- Worst-case: O(N+M).
- Average-case: O(N+M).
- Best-case: O(N+M).
Auxiliary Space: O(N+M), where N and M are the space taken by outputArray[] and countArray[] respectively.

Advantages of Counting Sort

Counting sort generally performs faster than all comparison-based sorting algorithms, such as merge sort and quicksort, if the range of input is of the order of the number of input.
Counting sort is easy to code
Counting sort is a stable algorithm.

Disadvantages of Counting Sort

Counting sort doesn’t work on decimal values.
Counting sort is inefficient if the range of values to be sorted is very large.
Counting sort is not an In-place sorting algorithm, It uses extra space for sorting the array elements.

Recommended

Solve DSA problems on GfG Practice.

Solve Problems

Feeling lost in the world of random DSA topics, wasting time without progress? It's time for a change! Join our DSA course, where we'll guide you on an exciting journey to master DSA efficiently and on schedule.
Ready to dive in? Explore our Free Demo Content and join our DSA course, trusted by over 100,000 geeks!

Last Updated : 11 Oct, 2023