Comparison Based vs Non Comparison Based Sorting in DSA C# - Build Both Approaches

Comparison Based vs Non Comparison Based Sorting

📖 Scenario: You work in a small shop that needs to organize its product prices quickly. Sometimes you sort prices by comparing them, and sometimes you use special tricks that don't compare each price directly.

🎯 Goal: You will create two lists of product prices. Then you will set a limit price. Next, you will find all prices less than the limit using a comparison method. Finally, you will print those prices.

📋 What You'll Learn

Create a list of integers called prices with these exact values: 50, 20, 70, 10, 40

Create an integer variable called limitPrice and set it to 40

Use a List<int> called filteredPrices to store prices less than limitPrice using a foreach loop and comparison

Print the filteredPrices list elements separated by spaces

💡 Why This Matters

🌍 Real World

Filtering product prices quickly helps shops show affordable options to customers.

💼 Career

Understanding comparison-based filtering and sorting is key for software developers working with data.

1

Create the list of product prices

Create a list of integers called prices with these exact values: 50, 20, 70, 10, 40

DSA C#

# Create the list of prices
// Your code here

Hint

Use List<int> and initialize it with the exact values inside curly braces.

2

Set the limit price

Create an integer variable called limitPrice and set it to 40

DSA C#

using System;
using System.Collections.Generic;

class Program
{
    static void Main()
    {
        List prices = new List { 50, 20, 70, 10, 40 };
        // Set the limit price
        // Your code here
    }
}

Hint

Use int limitPrice = 40; to create the variable.

3

Filter prices less than the limit using comparison

Create a list called filteredPrices and use a foreach loop with variables price to add prices less than limitPrice to filteredPrices

DSA C#

using System;
using System.Collections.Generic;

class Program
{
    static void Main()
    {
        List prices = new List { 50, 20, 70, 10, 40 };
        int limitPrice = 40;
        // Create filteredPrices list
        // Use foreach loop to add prices less than limitPrice
        // Your code here
    }
}

Hint

Use List<int> filteredPrices = new List<int>(); and a foreach loop with if to add prices less than limitPrice.

4

Print the filtered prices

Use a foreach loop with variable price to print each element of filteredPrices separated by spaces

DSA C#

using System;
using System.Collections.Generic;

class Program
{
    static void Main()
    {
        List prices = new List { 50, 20, 70, 10, 40 };
        int limitPrice = 40;
        List filteredPrices = new List();
        foreach (int price in prices)
        {
            if (price < limitPrice)
            {
                filteredPrices.Add(price);
            }
        }
        // Print filteredPrices separated by spaces
        // Your code here
    }
}

Hint

Use a foreach loop to print each price followed by a space.

Practice

1. Which of the following sorting algorithms is non-comparison based?

easy

A. Merge Sort

B. Quick Sort

C. Counting Sort

D. Heap Sort

Solution

1. Step 1: Understand comparison based sorting

   Comparison based sorting algorithms decide order by comparing elements, like Merge Sort, Quick Sort, and Heap Sort.

2. Step 2: Identify non-comparison based sorting

   Counting Sort uses counting frequencies and does not compare elements directly.

3. Final Answer:

   Counting Sort -> Option C

4. Quick Check:

   Non-comparison based = Counting Sort [OK]

Hint: Non-comparison sorts use counting or buckets, not element comparisons [OK]

Common Mistakes:

• Confusing Quick Sort as non-comparison
• Thinking Heap Sort is non-comparison
• Assuming all sorts compare elements

2. Which of the following is the correct syntax to call Radix Sort method named RadixSort on an integer array arr in C#?

easy

A. RadixSort(arr);

B. arr.RadixSort();

C. RadixSort<int>(arr);

D. RadixSort arr;

Solution

1. Step 1: Understand method call syntax in C#

   Calling a static method with an array argument uses method name and parentheses with the array inside.

2. Step 2: Analyze options

   RadixSort(arr); uses correct syntax: RadixSort(arr); arr.RadixSort(); tries to call as instance method which is incorrect unless defined so. RadixSort<int>(arr); uses generic syntax unnecessarily. RadixSort arr; is invalid syntax.

3. Final Answer:

   RadixSort(arr); -> Option A

4. Quick Check:

   Static method call = RadixSort(arr); [OK]

Hint: Static method calls use MethodName(args) syntax [OK]

Common Mistakes:

• Using instance method call on array
• Adding unnecessary generic syntax
• Writing invalid declaration instead of call

3. What will be the output of the following C# code snippet?

int[] arr = {4, 2, 2, 8, 3, 3, 1};
CountingSort(arr);
Console.WriteLine(string.Join(", ", arr));

void CountingSort(int[] array) {
    int max = array.Max();
    int[] count = new int[max + 1];
    foreach (int num in array) count[num]++;
    int index = 0;
    for (int i = 0; i <= max; i++) {
        while (count[i] > 0) {
            array[index++] = i;
            count[i]--;
        }
    }
}

medium

A. 8, 4, 3, 3, 2, 2, 1

B. 1, 2, 2, 3, 3, 4, 8

C. 4, 2, 2, 8, 3, 3, 1

D. 1, 1, 2, 2, 3, 3, 4

Solution

1. Step 1: Understand Counting Sort logic

   Counting Sort counts occurrences of each number and then writes them back in sorted order.

2. Step 2: Trace the code with input array

   Input: {4, 2, 2, 8, 3, 3, 1} Count array: counts of 1=1, 2=2, 3=2, 4=1, 8=1 Output array after sorting: 1, 2, 2, 3, 3, 4, 8

3. Final Answer:

   1, 2, 2, 3, 3, 4, 8 -> Option B

4. Quick Check:

   Counting Sort output = sorted array [OK]

Hint: Counting Sort outputs sorted array by frequency count [OK]

Common Mistakes:

• Assuming original array remains unchanged
• Confusing Counting Sort with reverse order
• Miscounting frequencies

4. The following code attempts to implement Radix Sort but has a bug. What is the error?

void RadixSort(int[] arr) {
    int max = arr.Max();
    for (int exp = 1; max / exp > 0; exp *= 10) {
        CountingSortByDigit(arr, exp);
    }
}

void CountingSortByDigit(int[] arr, int exp) {
    int n = arr.Length;
    int[] output = new int[n];
    int[] count = new int[10];

    for (int i = 0; i < n; i++)
        count[(arr[i] / exp) % 10]++;

    for (int i = 1; i < 10; i++)
        count[i] += count[i - 1];

    for (int i = n - 1; i >= 0; i--) {
        output[count[(arr[i] / exp) % 10] - 1] = arr[i];
        count[(arr[i] / exp) % 10]--;
    }

    // Missing step here
}

medium

A. The loop for counting prefix sums should start from 0

B. The count array size should be 100 instead of 10

C. The exponent variable exp should be incremented by 1 each iteration

D. The sorted output array is not copied back to the original array

Solution

1. Step 1: Analyze CountingSortByDigit function

   The function builds output array sorted by digit but does not copy output back to arr.

2. Step 2: Identify missing step

   After building output, we must copy output elements back to arr to update it for next digit sorting.

3. Final Answer:

   The sorted output array is not copied back to the original array -> Option D

4. Quick Check:

   Missing copy back step = bug [OK]

Hint: Radix Sort needs copying output back each digit pass [OK]

Common Mistakes:

• Thinking count array size should be larger
• Starting prefix sum loop incorrectly
• Incrementing exp by 1 instead of multiplying by 10

5. You have an array of 1 million integers ranging from 0 to 1000. Which sorting algorithm is the most efficient choice and why?

hard

A. Counting Sort, because it sorts in O(n + k) time where k is max value

B. Heap Sort, because it uses constant extra space

C. Merge Sort, because it is stable and works well on large data

D. Quick Sort, because it has average O(n log n) time

Solution

1. Step 1: Analyze input size and range

   Array size n = 1,000,000; max value k = 1000 (small compared to n).

2. Step 2: Compare algorithm complexities

   Counting Sort runs in O(n + k) which is O(1,001,000) here, faster than O(n log n) for comparison sorts.

3. Step 3: Choose best algorithm

   Counting Sort is efficient for small range large data, so best choice.

4. Final Answer:

   Counting Sort, because it sorts in O(n + k) time where k is max value -> Option A

5. Quick Check:

   Small range + large n = Counting Sort best [OK]

Hint: Use Counting Sort for large arrays with small value range [OK]

Common Mistakes:

• Choosing Quick Sort ignoring small range advantage
• Preferring Merge Sort for speed over Counting Sort
• Ignoring Counting Sort's linear time benefit