Counting Sort Algorithm in DSA Rust - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main idea behind the Counting Sort algorithm?

Counting Sort counts how many times each number appears, then uses these counts to place numbers in the right order without comparing them directly.

intermediate

Why is Counting Sort efficient for sorting numbers within a small range?

Because it uses a count array sized by the range of numbers, it sorts in linear time when the range is not much bigger than the number of items.

intermediate

What is the time complexity of Counting Sort and what does it depend on?

The time complexity is O(n + k), where n is the number of elements and k is the range of input values.

advanced

In Counting Sort, what is the purpose of the cumulative count array?

It helps to find the correct position of each element in the sorted output by storing how many elements are less than or equal to each value.

advanced

Can Counting Sort be used to sort negative numbers directly? Why or why not?

No, because Counting Sort uses array indices to count occurrences, and array indices must be non-negative. To sort negatives, you must shift values to be non-negative first.

What does Counting Sort use to sort elements?

A Dividing elements into halves

B Comparing elements pairwise

C Swapping elements repeatedly

D Counting the frequency of each element

Counting Sort counts how many times each element appears to sort without direct comparisons.

What is the main limitation of Counting Sort?

A It requires the input numbers to be within a known small range

B It uses too much memory for small ranges

C It cannot sort duplicate elements

D It only works for small input sizes

Counting Sort needs the input values to be within a small range to be efficient.

What is the space complexity of Counting Sort?

A O(n)

B O(n + k)

C O(k)

D O(1)

Counting Sort uses extra space for the count array and output array, totaling O(n + k).

How does Counting Sort place elements into the output array?

A By swapping adjacent elements

B By recursive partitioning

C By using the cumulative counts to find the correct index

D By random placement

Cumulative counts tell where each element should go in the output array.

Which of these is true about Counting Sort?

A It is stable and non-comparison based

B It can sort floating-point numbers directly

C It is a comparison-based sorting algorithm

D It sorts by repeatedly swapping elements

Counting Sort is stable and does not compare elements directly.

Explain how Counting Sort works step-by-step on an array of integers.

Think about counting first, then using counts to place elements.

Describe the advantages and limitations of Counting Sort.

Consider time, space, and input value range.

Practice

1. What is the main idea behind the Counting Sort Algorithm?

easy

A. Compare each element with every other element to sort

B. Count the occurrences of each element and use counts to place elements in order

C. Divide the array into halves and sort recursively

D. Swap adjacent elements if they are in wrong order repeatedly

Solution

1. Step 1: Understand Counting Sort principle

   Counting sort counts how many times each value appears in the input.

2. Step 2: Use counts to place elements

   These counts help place elements directly in the sorted output without comparisons.

3. Final Answer:

   Count the occurrences of each element and use counts to place elements in order -> Option B

4. Quick Check:

   Counting sort = Counting occurrences and placing [OK]

Hint: Counting sort uses counts, not comparisons, to sort [OK]

Common Mistakes:

• Thinking counting sort compares elements
• Confusing counting sort with quicksort or mergesort
• Assuming counting sort works on all data types

2. Which of the following is the correct Rust syntax to create a vector of zeros with length 5 for counting sort?

easy

A. let counts = vec![0; 5];

B. let counts = vec(0, 5);

C. let counts = [0; 5];

D. let counts = vec::new(5, 0);

Solution

1. Step 1: Recall Rust vector initialization

   In Rust, vec![value; size] creates a vector with repeated values.

2. Step 2: Match syntax to create vector of zeros

   vec![0; 5] creates a vector with five zeros, perfect for counting sort.

3. Final Answer:

   let counts = vec![0; 5]; -> Option A

4. Quick Check:

   vec![0; 5] creates vector of 5 zeros [OK]

Hint: Use vec![value; size] to create repeated elements vector [OK]

Common Mistakes:

• Using parentheses instead of square brackets for vec!
• Confusing array syntax with vector syntax
• Trying to use vec::new with parameters

3. What will be the output of the following Rust code snippet implementing counting sort on array [4, 2, 2, 8, 3, 3, 1]?

fn counting_sort(arr: &[usize]) -> Vec<usize> {
    let max = *arr.iter().max().unwrap();
    let mut counts = vec![0; max + 1];
    for &num in arr {
        counts[num] += 1;
    }
    let mut sorted = Vec::new();
    for (num, &count) in counts.iter().enumerate() {
        for _ in 0..count {
            sorted.push(num);
        }
    }
    sorted
}

fn main() {
    let arr = [4, 2, 2, 8, 3, 3, 1];
    let sorted = counting_sort(&arr);
    println!("{:?}", sorted);
}

medium

A. [0, 1, 2, 3, 4, 8]

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

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

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

Solution

1. Step 1: Count occurrences of each number

   Input array: [4, 2, 2, 8, 3, 3, 1]. Counts: 1->1, 2->2, 3->2, 4->1, 8->1.

2. Step 2: Build sorted vector using counts

   Push each number count times in order: 1, 2, 2, 3, 3, 4, 8.

3. Final Answer:

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

4. Quick Check:

   Sorted array = [1, 2, 2, 3, 3, 4, 8] [OK]

Hint: Counting sort output is sorted array with repeated elements [OK]

Common Mistakes:

• Forgetting to repeat elements by their count
• Outputting original array instead of sorted
• Miscounting occurrences of elements

4. Identify the bug in this Rust counting sort code snippet and choose the fix:

fn counting_sort(arr: &[usize]) -> Vec<usize> {
    let max = arr.iter().max().unwrap();
    let mut counts = vec![0; *max];
    for &num in arr {
        counts[num] += 1;
    }
    let mut sorted = Vec::new();
    for (num, &count) in counts.iter().enumerate() {
        for _ in 0..count {
            sorted.push(num);
        }
    }
    sorted
}

medium

A. Change let max = arr.iter().max().unwrap(); to let max = *arr.iter().max().unwrap();

B. Change for &num in arr to for num in arr.iter()

C. Change vec![0; *max] to vec![0; *max + 1]

D. Change sorted.push(num) to sorted.push(count)

Solution

1. Step 1: Check vector size for counts

   Vector size is *max, but max element index is *max, so size must be *max + 1 to include max.

2. Step 2: Fix counts vector size

   Changing to vec![0; *max + 1] prevents out-of-bound errors when counting max element.

3. Final Answer:

   Change vec![0; *max] to vec![0; *max + 1] -> Option C

4. Quick Check:

   Counts vector size = max + 1 [OK]

Hint: Counts vector must cover all indices up to max element [OK]

Common Mistakes:

• Not adding +1 to vector size for max element
• Dereferencing max incorrectly
• Pushing count instead of element in sorted vector

5. You want to sort an array of integers that includes negative numbers using counting sort in Rust. Which approach correctly adapts counting sort for this case?

hard

A. Shift all numbers by adding the absolute value of the minimum negative number before counting

B. Use the original counting sort without changes; it works with negatives

C. Ignore negative numbers and sort only non-negative ones

D. Convert all numbers to positive by multiplying by -1 before sorting

Solution

1. Step 1: Understand counting sort limitation

   Counting sort requires non-negative indices for counts, so negatives cause index errors.

2. Step 2: Shift numbers to non-negative range

   Add absolute value of minimum negative number to all elements to shift them to zero or positive before counting.

3. Step 3: Sort and then shift back

   After sorting, subtract the shift to restore original values in sorted order.

4. Final Answer:

   Shift all numbers by adding the absolute value of the minimum negative number before counting -> Option A

5. Quick Check:

   Shift negatives to non-negative before counting sort [OK]

Hint: Shift negatives to zero or positive before counting sort [OK]

Common Mistakes:

• Trying to use counting sort directly on negatives
• Ignoring negative numbers in input
• Multiplying negatives by -1 changes order incorrectly