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Print the elements of an array in the decreasing frequency if 2 numbers have the same frequency then print the one which came first

Examples:  

Input:  arr[] = {2, 5, 2, 8, 5, 6, 8, 8}
Output: arr[] = {8, 8, 8, 2, 2, 5, 5, 6}

Input: arr[] = {2, 5, 2, 6, -1, 9999999, 5, 8, 8, 8}
Output: arr[] = {8, 8, 8, 2, 2, 5, 5, 6, -1, 9999999}

 

Sort elements by frequency using sorting:

Follow the given steps to solve the problem:

  • Use a sorting algorithm to sort the elements
  • Iterate the sorted array and construct a 2D array of elements and count
  • Sort the 2D array according to the count 

Below is the illustration of the above approach:

Input: arr[] = {2 5 2 8 5 6 8 8}

Step1: Sort the array, 
After sorting we get: 2 2 5 5 6 8 8 8

Step 2: Now construct the 2D array to maintain the count of every element as
{{2, 2}, {5, 2}, { 6, 1}, {8, 3}}

Step 3: Sort the array by count
{{8, 3}, {2, 2}, {5, 2}, {6, 1}}

How to maintain the order of elements if the frequency is the same?

The above approach doesn’t make sure the order of elements remains the same if the frequency is the same. To handle this, we should use indexes in step 3, if two counts are the same then we should first process(or print) the element with a lower index. In step 1, we should store the indexes instead of elements. 

Input: arr[] = {2 5 2 8 5 6 8 8}

Step1: Sort the array, 
After sorting we get: 2 2 5 5 6 8 8 8
indexes:                    0 2 1 4 5 3 6 7
 

Step 2: Now construct the 2D array to maintain the count of every element as
Index, Count
0,      2
1,      2
5,      1
3,      3

Step 3: Sort the array by count (consider indexes in case of tie)
{{3, 3}, {0, 2}, {1, 2}, {5, 1}}
Print the elements using indexes in the above 2D array

 

Below is the implementation of the above approach:

CPP

// C++ program for the above approach
#include <bits/stdc++.h>
using namespace std;
 
// Used for sorting
struct ele {
    int count, index, val;
};
 
// Used for sorting by value
bool mycomp(struct ele a, struct ele b)
{
    return (a.val < b.val);
}
 
// Used for sorting by frequency. And if frequency is same,
// then by appearance
bool mycomp2(struct ele a, struct ele b)
{
    if (a.count != b.count)
        return (a.count < b.count);
    else
        return a.index > b.index;
}
 
void sortByFrequency(int arr[], int n)
{
    struct ele element[n];
    for (int i = 0; i < n; i++) {
 
        // Fill Indexes
        element[i].index = i;
 
        // Initialize counts as 0
        element[i].count = 0;
 
        // Fill values in structure
        // elements
        element[i].val = arr[i];
    }
 
    /* Sort the structure elements according to value,
       we used stable sort so relative order is maintained.
     */
    stable_sort(element, element + n, mycomp);
 
    /* initialize count of first element as 1 */
    element[0].count = 1;
 
    /* Count occurrences of remaining elements */
    for (int i = 1; i < n; i++) {
 
        if (element[i].val == element[i - 1].val) {
            element[i].count += element[i - 1].count + 1;
 
            /* Set count of previous element as -1, we are
               doing this because we'll again sort on the
               basis of counts (if counts are equal than on
               the basis of index)*/
            element[i - 1].count = -1;
 
            /* Retain the first index (Remember first index
               is always present in the first duplicate we
               used stable sort. */
            element[i].index = element[i - 1].index;
        }
 
        /* Else If previous element is not equal to current
          so set the count to 1 */
        else
            element[i].count = 1;
    }
 
    /* Now we have counts and first index for each element
       so now sort on the basis of count and in case of tie
       use index to sort.*/
    stable_sort(element, element + n, mycomp2);
    for (int i = n - 1, index = 0; i >= 0; i--)
        if (element[i].count != -1)
            for (int j = 0; j < element[i].count; j++)
                arr[index++] = element[i].val;
}
 
// Driver code
int main()
{
    int arr[] = { 2, 5, 2, 6, -1, 9999999, 5, 8, 8, 8 };
    int n = sizeof(arr) / sizeof(arr[0]);
 
    // Function call
    sortByFrequency(arr, n);
 
    for (int i = 0; i < n; i++)
        cout << arr[i] << " ";
    return 0;
}

                    

Java

// Java program to sort elements by frequency. If two
// elements have same count, then put the elements that
// appears first Importing required classes
import java.util.*;
 
class ele {
  int count, index, val;
 
}
// Used for sorting by value
class mycomp implements Comparator<ele> {
  public int compare(ele a, ele b)
  {
    return (a.val - b.val);
  }
}
 
// Used for sorting by frequency. And if frequency is same,
// then by appearance
class mycomp2 implements Comparator<ele> {
  public int compare(ele a, ele b)
  {
    if (a.count != b.count)
      return (a.count - b.count);
    return (b.index - a.index);
  }
}
 
class GFG {
 
  static void sortByFrequency(int[] arr, int n)
  {
    ArrayList<ele> element = new ArrayList<ele>();
    // ele[] element = new ele[n];
    for (int i = 0; i < n; i++) {
 
      element.add(new ele());
      // Fill Indexes
      element.get(i).index = i;
 
      // Initialize counts as 0
      element.get(i).count = 0;
 
      // Fill values in structure
      // elements
      element.get(i).val = arr[i];
    }
 
    /* Sort the structure elements according to value,
           we used stable sort so relative order is
           maintained.
         */
    Collections.sort(element, new mycomp());
 
    /* initialize count of first element as 1 */
    element.get(0).count = 1;
 
    /* Count occurrences of remaining elements */
    for (int i = 1; i < n; i++) {
 
      if (element.get(i).val == element.get(i - 1).val) {
        element.get(i).count += element.get(i - 1).count + 1;
 
        /* Set count of previous element as -1, we
                   are doing this because we'll again sort
                   on the basis of counts (if counts are
                   equal than on the basis of index)*/
        element.get(i - 1).count = -1;
 
        /* Retain the first index (Remember first
                   index is always present in the first
                   duplicate we used stable sort. */
        element.get(i).index = element.get(i - 1).index;
      }
 
      /* Else If previous element is not equal to
              current so set the count to 1 */
      else
        element.get(i).count = 1;
    }
 
    /* Now we have counts and first index for each
           element so now sort on the basis of count and in
           case of tie use index to sort.*/
    Collections.sort(element, new mycomp2());
    for (int i = n - 1, index = 0; i >= 0; i--){
      if (element.get(i).count != -1)
      {
        for (int j = 0; j < element.get(i).count;j++)
          arr[index++] = element.get(i).val;
      }
    }
  }
 
  // Driver code
  public static void main(String[] args)
  {
    int[] arr = { 2, 5, 2, 6, -1, 9999999, 5, 8, 8, 8 };
    int n = arr.length;
 
    // Function call
    sortByFrequency(arr,     n);
 
    for (int i = 0; i < n; i++)
      System.out.print(arr[i] + " ");
  }
}
 
// This code is contributed by Abhijeet Kumar(abhijeet19403)

                    

Python3

# Python3 program that performs the following
# operations: Sort elements by frequency. If two elements
# have same count, then put the elements that appears first
 
# Used for sorting
 
 
class ele:
    def __init__(self):
 
        self.count = 0
        self.index = 0
        self.val = 0
 
 
def mycomp(a):
    return a.val
 
# Used for sorting by frequency. And if frequency is same,
# then by appearance
 
 
def mycomp2(a):
    # using negative value for a.index
    # since the sorting should be in
    # descending order
    return (a.count, -a.index)
 
 
def sortByFrequency(arr, n):
    element = [None for _ in range(n)]
    for i in range(n):
 
        element[i] = ele()
 
        # Fill Indexes
        element[i].index = i
 
        # Initialize counts as 0
        element[i].count = 0
 
        # Fill values in structure
        # elements
        element[i].val = arr[i]
 
    # Sort the structure elements according to value,
    # we used stable sort so relative order is maintained.
    #
    element.sort(key=mycomp)
 
    # initialize count of first element as 1
    element[0].count = 1
 
    # Count occurrences of remaining elements
    for i in range(1, n):
 
        if (element[i].val == element[i - 1].val):
            element[i].count += element[i - 1].count + 1
 
            # Set count of previous element as -1, we are
            #  doing this because we'll again sort on the
            #  basis of counts (if counts are equal than on
            # the basis of index)*/
            element[i - 1].count = -1
 
            # Retain the first index (Remember first index
            #  is always present in the first duplicate we
            #  used stable sort. */
            element[i].index = element[i - 1].index
 
        # Else If previous element is not equal to current
        #  so set the count to 1
        else:
            element[i].count = 1
 
    # Now we have counts and first index for each element
    # so now sort on the basis of count and in case of tie
    # use index to sort.*/
    element.sort(key=mycomp2)
 
    index = 0
    for i in range(n - 1, -1, -1):
        if (element[i].count != -1):
            for j in range(element[i].count):
                arr[index] = element[i].val
                index += 1
 
 
# Driver code
arr = [2, 5, 2, 6, -1, 9999999, 5, 8, 8, 8]
n = len(arr)
 
# Function call
sortByFrequency(arr, n)
 
print(*arr)
 
# This code is contributed by phasing17

                    

C#

// Sort elements by frequency. If two elements have same
// count, then put the elements that appears first
using System;
using System.Collections.Generic;
 
// Used for sorting
class ele {
    public int count, index, val;
};
 
// Used for sorting by value
class mycomp : Comparer<ele> {
    public override int Compare(ele a, ele b)
    {
        return (a.val.CompareTo(b.val));
    }
};
 
// Used for sorting by frequency. And if frequency is same,
// then by appearance
class mycomp2 : Comparer<ele> {
    public override int Compare(ele a, ele b)
    {
        if (a.count != b.count)
            return (a.count).CompareTo(b.count);
        return (b.index).CompareTo(a.index);
    }
};
 
class GFG {
 
    static void sortByFrequency(int[] arr, int n)
    {
        ele[] element = new ele[n];
        for (int i = 0; i < n; i++) {
 
            element[i] = new ele();
            // Fill Indexes
            element[i].index = i;
 
            // Initialize counts as 0
            element[i].count = 0;
 
            // Fill values in structure
            // elements
            element[i].val = arr[i];
        }
 
        /* Sort the structure elements according to value,
           we used stable sort so relative order is
           maintained.
         */
        Array.Sort(element, new mycomp());
 
        /* initialize count of first element as 1 */
        element[0].count = 1;
 
        /* Count occurrences of remaining elements */
        for (int i = 1; i < n; i++) {
 
            if (element[i].val == element[i - 1].val) {
                element[i].count
                    += element[i - 1].count + 1;
 
                /* Set count of previous element as -1, we
                   are doing this because we'll again sort
                   on the basis of counts (if counts are
                   equal than on the basis of index)*/
                element[i - 1].count = -1;
 
                /* Retain the first index (Remember first
                   index is always present in the first
                   duplicate we used stable sort. */
                element[i].index = element[i - 1].index;
            }
 
            /* Else If previous element is not equal to
              current so set the count to 1 */
            else
                element[i].count = 1;
        }
 
        /* Now we have counts and first index for each
           element so now sort on the basis of count and in
           case of tie use index to sort.*/
        Array.Sort(element, new mycomp2());
        for (int i = n - 1, index = 0; i >= 0; i--)
            if (element[i].count != -1)
                for (int j = 0; j < element[i].count; j++)
                    arr[index++] = element[i].val;
    }
 
    // Driver code
    public static void Main(string[] args)
    {
        int[] arr = { 2, 5, 2, 6, -1, 9999999, 5, 8, 8, 8 };
        int n = arr.Length;
 
        // Function call
        sortByFrequency(arr, n);
 
        for (int i = 0; i < n; i++)
            Console.Write(arr[i] + " ");
    }
}
 
// This code is contributed by phasing17

                    

Javascript

// JavaScript program that performs the following
// operations: Sort elements by frequency. If two elements
// have same count, then put the elements that appears first
 
// Used for sorting
class ele
{
    constructor()
    {
        this.count = 0
        this.index = 0
        this.val = 0
    }
}
 
function mycomp(a, b)
{
    return a.val - b.val
}
 
// Used for sorting by frequency. And if frequency is same,
// then by appearance
function mycomp2(a, b)
{
    // using negative value for a.index
    // since the sorting should be in
    // descending order
    if (a.count == b.count)
        return b.index - a.index
    return a.count - b.count
}
 
function sortByFrequency(arr, n)
{
    let element = new Array(n)
    for (var i = 0; i < n; i++)
    {
 
        element[i] = new ele()
 
        // Fill Indexes
        element[i].index = i
 
        // Initialize counts as 0
        element[i].count = 0
 
        // Fill values in structure
        // elements
        element[i].val = arr[i]
    }
 
    // Sort the structure elements according to value,
    // we used stable sort so relative order is maintained.
    //
    element.sort(mycomp)
 
    // initialize count of first element as 1
    element[0].count = 1
 
    // Count occurrences of remaining elements
    for (var i = 1; i < n; i++)
    {
        if (element[i].val == element[i - 1].val)
        {
            element[i].count += element[i - 1].count + 1
 
            // Set count of previous element as -1, we are
            //  doing this because we'll again sort on the
            //  basis of counts (if counts are equal than on
            // the basis of index)*/
            element[i - 1].count = -1
 
            // Retain the first index (Remember first index
            //  is always present in the first duplicate we
            //  used stable sort. */
            element[i].index = element[i - 1].index
        }
 
        // Else If previous element is not equal to current
        //  so set the count to 1
        else
            element[i].count = 1
    }
 
    // Now we have counts and first index for each element
    // so now sort on the basis of count and in case of tie
    // use index to sort.*/
    element.sort(mycomp2)
     
     
    let index = 0
    for (var i = n - 1; i >= 0; i--)
        if (element[i].count != -1)
            for (var j = 0; j < element[i].count; j++)
            {
                arr[index] = element[i].val
                index += 1
            }
}
 
// Driver code
let arr = [2, 5, 2, 6, -1, 9999999, 5, 8, 8, 8]
let n = arr.length
 
// Function call
sortByFrequency(arr, n)
 
console.log(arr.join(" "))
 
// This code is contributed by phasing17

                    

Output
8 8 8 2 2 5 5 6 -1 9999999 




Time Complexity: O(N log N), where the N is the size of the array
Auxiliary Space: O(N)

Thanks to Gaurav Ahirwar for providing the above implementation

Sort elements by frequency using hashing and sorting:

To solve the problem follow the below idea:

Using a hashing mechanism, we can store the elements (also the first index) and their counts in a hash. Finally, sort the hash elements according to their counts

Below is the implementation of the above approach:

CPP

// CPP program for above approach
#include <bits/stdc++.h>
using namespace std;
 
// Compare function
bool fcompare(pair<int, pair<int, int> > p,
              pair<int, pair<int, int> > p1)
{
    if (p.second.second != p1.second.second)
        return (p.second.second > p1.second.second);
    else
        return (p.second.first < p1.second.first);
}
void sortByFrequency(int arr[], int n)
{
    unordered_map<int, pair<int, int> > hash; // hash map
    for (int i = 0; i < n; i++) {
        if (hash.find(arr[i]) != hash.end())
            hash[arr[i]].second++;
        else
            hash[arr[i]] = make_pair(i, 1);
    } // store the count of all the elements in the hashmap
 
    // Iterator to Traverse the Hashmap
    auto it = hash.begin();
 
    // Vector to store the Final Sortted order
    vector<pair<int, pair<int, int> > > b;
    for (it; it != hash.end(); ++it)
        b.push_back(make_pair(it->first, it->second));
 
    sort(b.begin(), b.end(), fcompare);
 
    // Printing the Sorted sequence
    for (int i = 0; i < b.size(); i++) {
        int count = b[i].second.second;
        while (count--)
            cout << b[i].first << " ";
    }
}
 
// Driver code
int main()
{
    int arr[] = { 2, 5, 2, 6, -1, 9999999, 5, 8, 8, 8 };
    int n = sizeof(arr) / sizeof(arr[0]);
 
    // Function call
    sortByFrequency(arr, n);
 
    return 0;
}

                    

Java

// Java program for above approach
import java.util.Arrays;
import java.util.Collections;
import java.util.Comparator;
import java.util.HashMap;
import java.util.List;
class GFG {
 
    static Integer[] arr
        = { 2, 5, 2, 6, -1, 9999999, 5, 8, 8, 8 };
 
    // Driver Code
    public static void main(String[] args)
    {
        List<Integer> list = Arrays.asList(arr);
 
        // Function call
        sortBasedOnFrequencyAndValue(list);
    }
 
    // Compare Function
    public static void
    sortBasedOnFrequencyAndValue(List<Integer> list)
    {
        int n = arr.length;
        final HashMap<Integer, Integer> mapCount
            = new HashMap<Integer, Integer>();
        final HashMap<Integer, Integer> mapIndex
            = new HashMap<Integer, Integer>();
        for (int i = 0; i < n; i++) {
            if (mapCount.containsKey(arr[i])) {
                mapCount.put(arr[i],
                             mapCount.get(arr[i]) + 1);
            }
            else {
                mapCount.put(
                    arr[i],
                    1); // Map to capture Count of elements
                mapIndex.put(arr[i],
                             i); // Map to capture 1st
                                 // occurrence of elements
            }
        }
 
        Collections.sort(list, new Comparator<Integer>() {
            public int compare(Integer n1, Integer n2)
            {
                int freq1 = mapCount.get(n1);
                int freq2 = mapCount.get(n2);
                if (freq1 != freq2) {
                    return freq2 - freq1;
                }
                else {
                    return mapIndex.get(n1)
                        - mapIndex.get(
                            n2); // Elements with Lesser
                                 // Index gets Higher
                                 // Priority
                }
            }
        });
        System.out.println(list);
    }
}

                    

Python3

# Python3 program for above approach
 
from collections import defaultdict
 
# Sort by Frequency
 
 
def sortByFreq(arr, n):
    # arr -> Array to be sorted
    # n   -> Length of Array
 
    # d is a hashmap(referred as dictionary in python)
    d = defaultdict(lambda: 0)
    for i in range(n):
        d[arr[i]] += 1
 
    # Sorting the array 'arr' where key
    # is the function based on which
    # the array is sorted
    # While sorting we want to give
    # first priority to Frequency
    # Then to value of item
    arr.sort(key=lambda x: (-d[x], x), reverse = True)
    #require Updation:- reverse = True, to sort an array in descending order (Jayesh Verma)
    return (arr)
 
 
# Driver code
if __name__ == "__main__":
    arr = [2, 5, 2, 6, -1, 9999999, 5, 8, 8, 8]
    n = len(arr)
 
    # Function call
    solution = sortByFreq(arr, n)
    print(*solution)

                    

C#

// C# program to implement the approach
using System;
using System.Collections.Generic;
 
public class GFG {
 
    static int[] arr;
 
    // Compare Function
    public static void
    sortBasedOnFrequencyAndValue(List<int> list)
    {
        int n = arr.Length;
        Dictionary<int, int> mapCount
            = new Dictionary<int, int>();
        Dictionary<int, int> mapIndex
            = new Dictionary<int, int>();
        for (int i = 0; i < n; i++) {
            if (mapCount.ContainsKey(arr[i])) {
                mapCount[arr[i]]++;
            }
            else {
                mapCount[arr[i]]
                    = 1; // Map to capture Count of elements
                mapIndex[arr[i]]
                    = i; // Map to capture 1st occurrence of
                // elements
            }
        }
 
        list.Sort(
            (int n1, int n2) => mapCount[n1] != mapCount[n2]
                    ? mapCount[n2].CompareTo(mapCount[n1])
                    : mapIndex[n1].CompareTo(mapIndex[n2])
            // Elements with Lesser
            // Index gets Higher
            // Priority
        );
 
        foreach(var i in list) Console.Write(i + " ");
    }
 
    // Driver Code
    public static void Main(string[] args)
    {
        arr = new int[] { 2,       5, 2, 6, -1,
                          9999999, 5, 8, 8, 8 };
        List<int> list = new List<int>(arr);
 
        // Function call
        sortBasedOnFrequencyAndValue(list);
    }
}
 
// This code is contributed by phasing17

                    

Javascript

<script>
 
let arr=[2, 5, 2, 6, -1, 9999999, 5, 8, 8, 8];
 
// Compare Function
function sortBasedOnFrequencyAndValue(list)
{
    let n = arr.length;
        let  mapCount
            = new Map();
        let  mapIndex
            = new Map();
        for (let i = 0; i < n; i++) {
            if (mapCount.has(arr[i])) {
                mapCount.set(arr[i],
                             mapCount.get(arr[i]) + 1);
            }
            else {
                mapCount.set(arr[i],1); // Map to capture Count of elements
                mapIndex.set(arr[i],i); // Map to capture 1st occurrence of elements
            }
        }
  
        list.sort(function(n1,n2){
             
                let freq1 = mapCount.get(n1);
                let freq2 = mapCount.get(n2);
                if (freq1 != freq2) {
                    return freq2 - freq1;
                }
                else {
                    return mapIndex.get(n1)
                        - mapIndex.get(
                            n2); // Elements with Lesser
                                 // Index gets Higher
                                 // Priority
                }
             
        });
        document.write(list.join(" "));
}
 
// Driver Code
sortBasedOnFrequencyAndValue(arr);
 
// This code is contributed by patel2127
</script>

                    

Output
8 8 8 2 2 5 5 6 -1 9999999 




Time Complexity: O(N log N), where the N is the size of the array
Auxiliary Space: O(N)

Note: This can also be solved by Using two maps, one for array element as an index and after this second map whose keys are frequency and value are array elements.

Sort elements by frequency using BST:

Follow the given steps to solve the problem:

  • Insert elements in BST one by one and if an element is already present then increment the count of the node. The node of the Binary Search Tree (used in this approach) will be as follows.

C++

class tree {
  public:
    int element;
    int first_index; /*To handle ties in counts*/
    int count;
}
tree BST;

                    

C

struct tree {
    int element;
    int first_index /*To handle ties in counts*/
        int count;
} BST;

                    

Java

static class tree {
    int element;
    int first_index; /*To handle ties in counts*/
    int count;
}
tree BST = new tree();
 
// This code is contributed by gauravrajput1

                    

Python3

# Python code to implement the approach
class Tree:
    element = None
 
    # to handle ties
    first_index = None
    count = None
 
 
BST = Tree()
 
# This code is contributed by phasing17

                    

C#

public class tree {
    public int element;
    public int first_index; /* To handle ties in counts */
    public int count;
}
tree BST = new tree();
 
// This code is contributed by gauravrajput1

                    

Javascript

// JS code to implement the approach
 
class tree {
     constructor()
     {
         this.element;
         this.first_index; //To handle ties in counts
         this.count;
     }
      
};
 
// This code is contributed by phasing17

                    
  • Store the first indexes and corresponding counts of BST in a 2D array.
  • Sort the 2D array according to counts (and use indexes in case of a tie).

Implementation of the this approach: Set 2

Time Complexity: O(N log N) if a Self Balancing Binary Search Tree is used.
Auxiliary Space: O(N)

Sort elements by frequency using Heap:

Follow the given steps to solve the problem:

  • Take the arr and use unordered_map to have VALUE : FREQUENCY Table
  • Then make a HEAP such that high frequency remains at TOP and when frequency is same, just keep in ascending order (Smaller at TOP)
  • Then After full insertion into Heap
  • Pop one by one and copy it into the Array

Below is the implementation of the above approach:

C++

// C++ program for the above approach
 
#include <bits/stdc++.h>
using namespace std;
#define ppi pair<int, int>
 
/*IMPORTANT: comparator works in prority_queue only if they
are a class which has
operator() overloaded in it (got this from stackoverflow) */
class Compare {
public:
    bool operator()(pair<int, int> a, pair<int, int> b)
    {
        // b is at top and a is at bottom - have that in
        // mind
        if (a.first == b.first) // when freq same
            return a.second
                   > b.second; // smaller val stays at top
        return a.first
               < b.first; // higher freq stays at top
    }
};
 
void solve(vector<int>& arr)
{
    int N = arr.size();
    unordered_map<int, int> mpp; // val, freq
    for (int a : arr) {
        mpp[a]++;
    }
    // max heap - as max wise freq elements is what needed
    priority_queue<ppi, vector<ppi>, Compare> maxH;
 
    for (auto m : mpp) {
        maxH.push({ m.second, m.first }); // freq, val
    }
    // now the max freq is at TOP of MAX heap
 
    int i = 0; // to maintain index to copy
    while (maxH.size() > 0) {
        int val = maxH.top().second; // val
        int freq = maxH.top().first; // freq
 
        while (freq--) {
            // freq - those many times make a copy
            arr[i] = val;
            i++;
        }
        maxH.pop(); // heapify happens and next top freq ele
                    // goes up
    }
}
 
// Driver code
int main()
{
    vector<int> vec{ 2, 5, 2, 8, 5, 6, 8, 8 };
    int n = vec.size();
 
    // Function call
    solve(vec);
 
    for (int i = 0; i < n; i++)
        cout << vec[i] << " ";
    cout << "\n";
    return 0;
}
 
// Code done by Balakrishnan R (rbkraj000)

                    

Java

import java.util.*;
 
class Compare
  implements Comparator<Map.Entry<Integer, Integer> > {
  public int compare(Map.Entry<Integer, Integer> a,
                     Map.Entry<Integer, Integer> b)
  {
    // b is at top and a is at bottom - have that in
    // mind
    if (a.getValue() == b.getValue()) // when freq same
      return a.getKey().compareTo(
      b.getKey()); // smaller val stays at top
    return b.getValue().compareTo(
      a.getValue()); // higher freq stays at top
  }
}
 
class Main {
  static void solve(List<Integer> arr)
  {
    int N = arr.size();
    Map<Integer, Integer> mpp
      = new HashMap<>(); // val, freq
    for (int a : arr) {
      mpp.put(a, mpp.getOrDefault(a, 0) + 1);
    }
    // max heap - as max wise freq elements is what
    // needed
    PriorityQueue<Map.Entry<Integer, Integer> > maxH
      = new PriorityQueue<>(new Compare());
 
    for (Map.Entry<Integer, Integer> m :
         mpp.entrySet()) {
      maxH.add(m); // freq, val
    }
    // now the max freq is at TOP of MAX heap
 
    int i = 0; // to maintain index to copy
    while (maxH.size() > 0) {
      int val = maxH.peek().getKey(); // val
      int freq = maxH.peek().getValue(); // freq
 
      while (freq-- > 0) {
        // freq - those many times make a copy
        arr.set(i, val);
        i++;
      }
      maxH.poll(); // heapify happens and next top
      // freq ele goes up
    }
  }
 
  public static void main(String[] args)
  {
    List<Integer> arr
      = Arrays.asList(2, 5, 2, 8, 5, 6, 8, 8);
    int n = arr.size();
 
    // Function call
    solve(arr);
 
    for (int i = 0; i < n; i++)
      System.out.print(arr.get(i) + " ");
    System.out.println();
  }
}
 
// This code is contributed by divyansh2212

                    

Python3

from collections import defaultdict
from queue import PriorityQueue
 
class Compare:
    def __init__(self, freq, val):
        self.freq = freq
        self.val = val
 
    def __lt__(self, other):
        if self.freq == other.freq:
            return self.val < other.val
        return self.freq > other.freq
 
def solve(arr):
    n = len(arr)
    mpp = defaultdict(int)
    for a in arr:
        mpp[a] += 1
    max_heap = PriorityQueue()
    for key, value in mpp.items():
        max_heap.put(Compare(value, key))
 
    i = 0
    while not max_heap.empty():
        item = max_heap.get()
        freq = item.freq
        val = item.val
        for _ in range(freq):
            arr[i] = val
            i += 1
    return arr
 
vec = [2, 5, 2, 8, 5, 6, 8, 8]
print(solve(vec))

                    

C#

using System;
using System.Collections.Generic;
using System.Linq;
 
class KVCompare : IComparer<KeyValuePair<int, int> > {
  public int Compare(KeyValuePair<int, int> a,
                     KeyValuePair<int, int> b)
  {
    // b is at top and a is at bottom - have that in
    // mind
    if (a.Value == b.Value) // when freq same
      return a.Key.CompareTo(
      b.Key); // smaller val stays at top
    return b.Value.CompareTo(
      a.Value); // higher freq stays at top
  }
}
 
class GFG {
  static void Solve(List<int> arr)
  {
    int n = arr.Count;
    Dictionary<int, int> mpp
      = new Dictionary<int, int>(); // val, freq
    foreach(int a in arr)
    {
      if (mpp.ContainsKey(a))
        mpp[a]++;
      else
        mpp[a] = 1;
    }
    // sorted list - as max wise freq elements is what
    // needed
    SortedList<KeyValuePair<int, int>, bool> maxH
      = new SortedList<KeyValuePair<int, int>, bool>(
      new KVCompare());
 
    foreach(KeyValuePair<int, int> m in mpp)
    {
      maxH.Add(m, true); // freq, val
    }
    // now the max freq is at TOP of MAX heap
 
    int i = 0; // to maintain index to copy
    while (maxH.Count > 0) {
      int val = maxH.Keys[0].Key; // val
      int freq = maxH.Keys[0].Value; // freq
 
      while (freq-- > 0) {
        // freq - those many times make a copy
        arr[i] = val;
        i++;
      }
      maxH.RemoveAt(
        0); // heapify happens and next top
      // freq ele goes up
    }
  }
 
  public static void Main(string[] args)
  {
    List<int> arr
      = new List<int>() { 2, 5, 2, 8, 5, 6, 8, 8 };
    int n = arr.Count;
 
    // Function call
    Solve(arr);
 
    for (int i = 0; i < n; i++)
      Console.Write(arr[i] + " ");
    Console.WriteLine();
  }
}
 
// This code is contributed by phasing17

                    

Javascript

// Define the `solve` function
function solve(arr) {
  // Find the length of the array
  let N = arr.length;
 
  // Create an empty map to store the frequency of each element in the array
  let mpp = {};
 
  // Loop through the array to count the frequency of each element
  for (let i = 0; i < N; i++) {
    if (mpp[arr[i]] === undefined) {
      // If the element has not been encountered before, add it to the map with a frequency of 1
      mpp[arr[i]] = 1;
    } else {
      // If the element has been encountered before, increment its frequency by 1
      mpp[arr[i]]++;
    }
  }
 
  // Create an empty array to store the frequency and value of each element
  let maxH = [];
 
  // Loop through the map to add the frequency and value of each element to the `maxH` array
  for (let key in mpp) {
    maxH.push([mpp[key], key]);
  }
 
  // Sort the `maxH` array in descending order of frequency, and ascending order of value if frequency is equal
  maxH.sort((a, b) => {
    if (a[0] === b[0]) {
      return b[1] - a[1];
    }
    return a[0] - b[0];
  });
 
  // Initialize a variable `i` to 0
  let i = 0;
 
  // Loop through the sorted `maxH` array
  while (maxH.length > 0) {
    // Get the frequency and value of the current element
    let freq = maxH[maxH.length - 1][0];
    let val = maxH[maxH.length - 1][1];
 
    // Fill the `arr` with the value `freq` times, starting from the `i`-th index
    for (let j = 0; j < freq; j++) {
      arr[i] = val;
      i++;
    }
    // Remove the current element from the `maxH` array
    maxH.pop();
  }
}
 
// Test the function with an example array
let vec = [2, 5, 2, 8, 5, 6, 8, 8];
solve(vec);
console.log(vec);
 
 
// This code is contributed by Nayan Nand

                    

Output
8 8 8 2 2 5 5 6 




The code and approach is done by Balakrishnan R.

Time Complexity: O(d * log(d)) (Dominating factor O(n + 2 * d * log (d))). O(n) (unordered map insertion- as 1 insertion takes O(1)) + O(d*log(d)) (Heap insertion – as one insertion is log N complexity) + O(d*log(d)) (Heap Deletion – as one pop takes Log N complexity) 
Here d = No. of Distinct Elements, n = Total no. of elements (size of input array). (Always d<=n  depends on the array)

Auxiliary Space: O(d), As heap and map is used

Sort elements by frequency using Quicksort:

Follow the given steps to solve the problem:

  1. Make a structure called “ele” with two fields called “val” and “count” to hold the value and count of each element in the input array.
  2. Make an array of “ele” structures of size n, where n is the size of the input array, and set the “val” field of each element to the value from the input array and the “count” field to 0.
  3. Traverse through the input array and for each element,  increase the count field of the corresponding “ele” structure by 1.
  4. Using the quicksort method and a custom comparison function for elements, sort the “ele” array in decreasing order of count and rising order of value.
  5. Finally, recreate the input array by iterating over the sorted “ele” array in descending order of count and adding the value of each element to the input array count number of times equal to its count field.

Below is the implementation of the above approach:

C++

#include <bits/stdc++.h>
using namespace std;
 
struct ele {
    int val, count;
};
 
void swap(struct ele* a, struct ele* b)
{
    struct ele temp = *a;
    *a = *b;
    *b = temp;
}
 
int partition(struct ele arr[], int low, int high)
{
    struct ele pivot = arr[high];
    int i = (low - 1);
 
    for (int j = low; j <= high - 1; j++) {
        if (arr[j].count < pivot.count || (arr[j].count == pivot.count && arr[j].val > pivot.val)) {
            i++;
            swap(&arr[i], &arr[j]);
        }
    }
 
    swap(&arr[i + 1], &arr[high]);
    return (i + 1);
}
 
void quicksort(struct ele arr[], int low, int high)
{
    if (low < high) {
        int pi = partition(arr, low, high);
        quicksort(arr, low, pi - 1);
        quicksort(arr, pi + 1, high);
    }
}
 
void sortByFrequency(int arr[], int n)
{
    struct ele element[n];
    for (int i = 0; i < n; i++) {
        element[i].val = arr[i];
        element[i].count = 0;
    }
 
    for (int i = 0; i < n; i++) {
        int j;
        for (j = 0; j < i; j++)
            if (arr[i] == arr[j])
                break;
 
        if (i == j)
            element[i].count++;
        else
            element[j].count++;
    }
 
    quicksort(element, 0, n - 1);
 
    for (int i = n - 1, index = 0; i >= 0; i--) {
        for (int j = 0; j < element[i].count; j++)
            arr[index++] = element[i].val;
    }
}
 
int main()
{
    int arr[] = { 2, 5, 2, 8, 5, 6, 8, 8};
    int n = sizeof(arr) / sizeof(arr[0]);
 
    sortByFrequency(arr, n);
 
    for (int i = 0; i < n; i++)
        cout << arr[i] << " ";
    return 0;
}
 
// This code is contributed by Vaibhav Saroj

                    

Java

// Java code for the above approach
import java.util.Arrays;
 
public class GFG {
    static class ele {
        public int val;
        public int count;
    }
 
    static void swap(ele[] arr, int i, int j) {
        ele temp = arr[i];
        arr[i] = arr[j];
        arr[j] = temp;
    }
 
    static int partition(ele[] arr, int low, int high) {
        ele pivot = arr[high];
        int i = low - 1;
 
        for (int j = low; j <= high - 1; j++) {
            if (arr[j].count < pivot.count || (arr[j].count == pivot.count && arr[j].val > pivot.val)) {
                i++;
                swap(arr, i, j);
            }
        }
 
        swap(arr, i + 1, high);
        return i + 1;
    }
 
    static void quicksort(ele[] arr, int low, int high) {
        if (low < high) {
            int pi = partition(arr, low, high);
            quicksort(arr, low, pi - 1);
            quicksort(arr, pi + 1, high);
        }
    }
 
    static void sortByFrequency(int[] arr, int n) {
        ele[] element = new ele[n];
        for (int i = 0; i < n; i++) {
            element[i] = new ele();
            element[i].val = arr[i];
            element[i].count = 0;
        }
 
        for (int i = 0; i < n; i++) {
            int j;
            for (j = 0; j < i; j++)
                if (arr[i] == arr[j])
                    break;
 
            if (i == j)
                element[i].count++;
            else
                element[j].count++;
        }
 
        quicksort(element, 0, n - 1);
 
        for (int i = n - 1, index = 0; i >= 0; i--) {
            for (int j = 0; j < element[i].count; j++)
                arr[index++] = element[i].val;
        }
    }
 
    public static void main(String[] args) {
        int[] arr = { 2, 5, 2, 8, 5, 6, 8, 8 };
        int n = arr.length;
 
        sortByFrequency(arr, n);
 
        for (int i = 0; i < n; i++)
            System.out.print(arr[i] + " ");
    }
}
 
// This code is contributed by Pushpesh Raj

                    

Python

def sortByFrequency(arr):
    element = []
    for i in range(len(arr)):
        element.append({'val': arr[i], 'count': 0})
 
    for i in range(len(arr)):
        j = 0
        while j < i:
            if arr[i] == arr[j]:
                break
            j += 1
 
        if i == j:
            element[i]['count'] += 1
        else:
            element[j]['count'] += 1
 
     
    def partition(arr, low, high):
        pivot = arr[high]
        i = low - 1
 
        for j in range(low, high):
            if arr[j]['count'] < pivot['count'] or (arr[j]['count'] == pivot['count'] and arr[j]['val'] > pivot['val']):
                i += 1
                arr[i], arr[j] = arr[j], arr[i]
                 
        arr[i + 1], arr[high] = arr[high], arr[i + 1]
        return i + 1
 
    def quicksort(arr, low, high):
        if low < high:
            pi = partition(arr, low, high)
            quicksort(arr, low, pi - 1)
            quicksort(arr, pi + 1, high)
 
    quicksort(element, 0, len(arr) - 1)
 
    index = 0
    for i in range(len(arr) - 1, -1, -1):
        for j in range(element[i]['count']):
            arr[index] = element[i]['val']
            index += 1
 
    return arr
 
 
arr = [2, 5, 2, 8, 5, 6, 8, 8]
sortedArr = sortByFrequency(arr)
print(sortedArr)
 
 
# by phasing17

                    

C#

using System;
 
public class Program
{
    struct ele
    {
        public int val;
        public int count;
    }
 
    static void swap(ref ele a, ref ele b)
    {
        ele temp = a;
        a = b;
        b = temp;
    }
 
    static int partition(ele[] arr, int low, int high)
    {
        ele pivot = arr[high];
        int i = (low - 1);
 
        for (int j = low; j <= high - 1; j++)
        {
            if (arr[j].count < pivot.count || (arr[j].count == pivot.count && arr[j].val > pivot.val))
            {
                i++;
                swap(ref arr[i], ref arr[j]);
            }
        }
 
        swap(ref arr[i + 1], ref arr[high]);
        return (i + 1);
    }
 
    static void quicksort(ele[] arr, int low, int high)
    {
        if (low < high)
        {
            int pi = partition(arr, low, high);
            quicksort(arr, low, pi - 1);
            quicksort(arr, pi + 1, high);
        }
    }
 
    static void sortByFrequency(int[] arr, int n)
    {
        ele[] element = new ele[n];
        for (int i = 0; i < n; i++)
        {
            element[i].val = arr[i];
            element[i].count = 0;
        }
 
        for (int i = 0; i < n; i++)
        {
            int j;
            for (j = 0; j < i; j++)
                if (arr[i] == arr[j])
                    break;
 
            if (i == j)
                element[i].count++;
            else
                element[j].count++;
        }
 
        quicksort(element, 0, n - 1);
 
        for (int i = n - 1, index = 0; i >= 0; i--)
        {
            for (int j = 0; j < element[i].count; j++)
                arr[index++] = element[i].val;
        }
    }
 
    static void Main()
    {
        int[] arr = { 2, 5, 2, 8, 5, 6, 8, 8 };
        int n = arr.Length;
 
        sortByFrequency(arr, n);
 
        for (int i = 0; i < n; i++)
            Console.Write(arr[i] + " ");
    }
}
 
 
// This code is contributed by Vaibhav Saroj

                    

Javascript

function sortByFrequency(arr) {
  const element = [];
  for (let i = 0; i < arr.length; i++) {
    element[i] = { val: arr[i], count: 0 };
  }
 
  for (let i = 0; i < arr.length; i++) {
    let j;
    for (j = 0; j < i; j++) {
      if (arr[i] == arr[j]) {
        break;
      }
    }
 
    if (i == j) {
      element[i].count++;
    } else {
      element[j].count++;
    }
  }
 
  function swap(a, b) {
    const temp = a;
    a = b;
    b = temp;
  }
 
  function partition(arr, low, high) {
    const pivot = arr[high];
    let i = low - 1;
 
    for (let j = low; j <= high - 1; j++) {
      if (
        arr[j].count < pivot.count ||
        (arr[j].count == pivot.count && arr[j].val > pivot.val)
      ) {
        i++;
        swap(arr[i], arr[j]);
      }
    }
 
    swap(arr[i + 1], arr[high]);
    return i + 1;
  }
 
  function quicksort(arr, low, high) {
    if (low < high) {
      const pi = partition(arr, low, high);
      quicksort(arr, low, pi - 1);
      quicksort(arr, pi + 1, high);
    }
  }
 
  quicksort(element, 0, arr.length - 1);
 
  for (let i = arr.length - 1, index = 0; i >= 0; i--) {
    for (let j = 0; j < element[i].count; j++) {
      arr[index++] = element[i].val;
    }
  }
 
  return arr;
}
 
const arr = [2, 5, 2, 8, 5, 6, 8, 8];
const sortedArr = sortByFrequency(arr);
console.log(sortedArr);
 
 
// This code is contributed by Vaibhav Saroj

                    

Output
8 8 8 2 2 5 5 6 




This Quicksort approach and code is contributed by Vaibhav Saroj.

Time complexity:  O(n log n).
Space complexity: O(n).

Related Article: Sort elements by frequency | Set 2

 



Last Updated : 12 Jul, 2023
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