Comparison Based vs Non Comparison Based Sorting in DSA Python - Interactive Comparison Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to perform a comparison-based sort using Python's built-in method.

DSA Python

numbers = [5, 3, 8, 1]
numbers.[1]()

Drag options to blanks, or click blank then click option'

Aappend

Bpop

Csort

Dreverse

Attempts:

3 left

💡 Hint

Common Mistakes

Using methods like append or pop which modify the list differently.

The sort() method sorts the list in place using comparison-based sorting.

2fill in blank

medium

Complete the code to perform a non-comparison based sort using counting sort logic for small integers.

DSA Python

def counting_sort(arr):
    count = [0] * (max(arr) + 1)
    for num in arr:
        count[num] += 1
    sorted_arr = []
    for i in range(len(count)):
        sorted_arr.extend([i] * [1])
    return sorted_arr

Drag options to blanks, or click blank then click option'

Alen(arr)

Barr[i]

Ci

Dcount[i]

Attempts:

3 left

💡 Hint

Common Mistakes

Using the original array or index length instead of count values.

We extend the sorted array by repeating the number i as many times as it appears, which is stored in count[i].

3fill in blank

hard

Fix the error in the code that attempts to sort a list using a comparison-based method but incorrectly uses a function.

DSA Python

def sort_list(lst):
    return sorted(lst, [1]=True)

Drag options to blanks, or click blank then click option'

Areverse

Breversed

Cdescending

Dreverse_order

Attempts:

3 left

💡 Hint

Common Mistakes

Using non-existent keywords like reversed or descending.

The correct keyword argument is reverse which takes a boolean to sort descending if True.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps numbers to their frequency, a step in non-comparison sorting.

DSA Python

freq = {num: [1] for num in arr if arr.count(num) [2] 0}

Drag options to blanks, or click blank then click option'

Aarr.count(num)

Bnum

C>

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using the number itself as value or wrong comparison operators.

The dictionary maps each number to how many times it appears using arr.count(num). The condition checks if the count is greater than zero.

5fill in blank

hard

Fill all three blanks to create a list comprehension that extracts keys from a frequency dictionary where frequency is at least 2.

DSA Python

result = [[1] for [2], [3] in freq.items() if [3] >= 2]

Drag options to blanks, or click blank then click option'

Akey

Bk

Cv

Dvalue

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing variable names or using incorrect unpacking.

The comprehension iterates over freq.items() unpacking keys and values as k and v. It collects keys k where value v is at least 2.

Practice

1. Which of the following sorting algorithms is not based on comparing elements?

easy

A. Quick Sort

B. Merge Sort

C. Counting Sort

D. Heap Sort

Solution

1. Step 1: Understand comparison based sorting

   Comparison based sorting algorithms decide order by comparing elements pairwise, 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: Counting Sort uses counts, no element comparisons [OK]

Common Mistakes:

• Confusing Counting Sort with Quick Sort
• Thinking Merge Sort is non-comparison based
• Assuming Heap Sort doesn't compare elements

2. Which of the following is the correct syntax to call a non-comparison based sorting function counting_sort(arr) in Python?

easy

A. counting_sort(arr)

B. counting_sort arr

C. counting_sort{arr}

D. counting_sort[arr]

Solution

1. Step 1: Recall Python function call syntax

   Functions are called with parentheses enclosing arguments, like func(args).

2. Step 2: Match syntax with options

   Only counting_sort(arr) uses correct parentheses syntax.

3. Final Answer:

   counting_sort(arr) -> Option A

4. Quick Check:

   Function call uses parentheses [OK]

Hint: Use parentheses () to call functions in Python [OK]

Common Mistakes:

• Using square brackets instead of parentheses
• Omitting parentheses when calling functions
• Using curly braces which are invalid for calls

3. What will be the output of the following Python code using Counting Sort on the array [4, 2, 2, 8, 3, 3, 1]?

def counting_sort(arr):
    max_val = max(arr)
    count = [0] * (max_val + 1)
    for num in arr:
        count[num] += 1
    sorted_arr = []
    for i in range(len(count)):
        sorted_arr.extend([i] * count[i])
    return sorted_arr

print(counting_sort([4, 2, 2, 8, 3, 3, 1]))

medium

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

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

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

D. [4, 2, 2, 8, 3, 3, 1]

Solution

1. Step 1: Count frequencies of each number

   Count array will have counts: 1->1, 2->2, 3->2, 4->1, 8->1, others 0.

2. Step 2: Build sorted array by repeating each number count times

   Sorted array is [1, 2, 2, 3, 3, 4, 8].

3. Final Answer:

   [1, 2, 2, 3, 3, 4, 8] -> Option A

4. Quick Check:

   Counting Sort output = sorted array [OK]

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

Common Mistakes:

• Confusing original array with sorted output
• Reversing the sorted order
• Missing duplicates in output

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

def counting_sort(arr):
    max_val = max(arr)
    count = [0] * max_val
    for num in arr:
        count[num] += 1
    sorted_arr = []
    for i in range(len(count)):
        sorted_arr.extend([i] * count[i])
    return sorted_arr

print(counting_sort([1, 4, 1, 2, 7, 5]))

medium

A. Using extend instead of append causes error

B. count array size is too small, missing last element count

C. max_val should be min_val

D. Loop range should be over arr, not count

Solution

1. Step 1: Check count array size

   Count array size is max_val, but should be max_val + 1 to include max element index.

2. Step 2: Identify impact of size error

   Missing last index causes IndexError or missing counts for max element.

3. Final Answer:

   count array size is too small, missing last element count -> Option B

4. Quick Check:

   Count array size = max_val + 1 needed [OK]

Hint: Count array must cover all values including max [OK]

Common Mistakes:

• Ignoring off-by-one error in count array size
• Thinking extend causes error
• Confusing max_val with min_val

5. You have a large list of integers ranging from 0 to 1000. Which sorting algorithm is best to sort this list efficiently and why? A) Quick Sort because it has average O(n log n) time B) Counting Sort because it sorts in O(n + k) time where k is max value C) Merge Sort because it is stable and always O(n log n) D) Bubble Sort because it is simple to implement

hard

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

B. Bubble Sort because it is simple to implement

C. Merge Sort because it is stable and always O(n log n)

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

Solution

1. Step 1: Analyze input range and size

   Input values are integers from 0 to 1000, so k = 1000 is small compared to n (large list).

2. Step 2: Choose sorting algorithm based on time complexity

   Counting Sort runs in O(n + k), which is efficient for small k and large n, better than O(n log n).

3. Final Answer:

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

4. Quick Check:

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

Hint: Use Counting Sort for small range large list [OK]

Common Mistakes:

• Choosing Quick Sort ignoring input range
• Preferring Bubble Sort despite poor performance
• Ignoring Counting Sort's linear time advantage