Count Inversions in Array in DSA Python - Time & Space Complexity

Time Complexity: Count Inversions in Array

O(n log n)

Understanding Time Complexity

We want to understand how long it takes to count inversions in an array as the array grows.

Specifically, how does the work increase when the array size gets bigger?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

def merge_sort_count(arr):
    if len(arr) <= 1:
        return arr, 0
    mid = len(arr) // 2
    left, left_inv = merge_sort_count(arr[:mid])
    right, right_inv = merge_sort_count(arr[mid:])
    merged, split_inv = merge_count(left, right)
    return merged, left_inv + right_inv + split_inv

# merge_count merges two sorted arrays and counts split inversions

This code counts how many pairs of elements are out of order by using a merge sort approach.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Recursive splitting and merging of the array.
• How many times: The array is split into halves repeatedly until size 1, then merged back.

How Execution Grows With Input

Each split divides the array in half, and merging compares elements to count inversions.

Input Size (n)	Approx. Operations
10	About 50 comparisons and merges
100	About 700 comparisons and merges
1000	About 10,000 comparisons and merges

Pattern observation: The work grows roughly by n times log n.

Final Time Complexity

Time Complexity: O(n log n)

This means the time to count inversions grows a little more than linearly, because we split and merge repeatedly.

Common Mistake

[X] Wrong: "Counting inversions must be O(n²) because we compare every pair."

[OK] Correct: The merge sort method cleverly counts inversions during merging, avoiding checking all pairs directly.

Interview Connect

Understanding this method shows you can improve naive solutions by using smart divide-and-conquer techniques.

Self-Check

"What if we used a simple nested loop to count inversions? How would the time complexity change?"

Practice

1. What does the term inversion in an array mean?

easy

A. A pair of elements where the first is smaller than the second and appears before it

B. A pair of elements where the first is greater than the second and appears before it

C. The total number of elements in the array

D. The sum of all elements in the array

Solution

1. Step 1: Understand the definition of inversion

   An inversion is when a larger number appears before a smaller number in the array.

2. Step 2: Compare options with definition

   A pair of elements where the first is greater than the second and appears before it correctly describes this pair condition, others do not.

3. Final Answer:

   A pair of elements where the first is greater than the second and appears before it -> Option B

4. Quick Check:

   Inversion = larger before smaller [OK]

Hint: Inversion means bigger number comes before smaller one [OK]

Common Mistakes:

• Confusing inversion with sorted order
• Thinking inversion counts equal elements
• Mixing up element positions

2. Which of the following Python code snippets correctly counts inversions using a simple nested loop?

easy

A. count = 0
for i in range(len(arr)):
for j in range(i+1, len(arr)):
if arr[i] > arr[j]:
count += 1

B. count = 0
for i in range(len(arr)):
for j in range(i):
if arr[i] > arr[j]:
count += 1

C. count = 0
for i in range(len(arr)):
for j in range(i+1, len(arr)):
if arr[i] < arr[j]:
count += 1

D. count = 0
for i in range(len(arr)):
for j in range(i+1, len(arr)):
if arr[i] == arr[j]:
count += 1

Solution

1. Step 1: Check the loop ranges and condition

   count = 0 for i in range(len(arr)): for j in range(i+1, len(arr)): if arr[i] > arr[j]: count += 1 loops i from 0 to end and j from i+1 to end, comparing arr[i] > arr[j]. This matches inversion definition.

2. Step 2: Verify other options

   count = 0 for i in range(len(arr)): for j in range(i): if arr[i] > arr[j]: count += 1 checks arr[i] > arr[j] but loops j before i, counting later > earlier which is incorrect. count = 0 for i in range(len(arr)): for j in range(i+1, len(arr)): if arr[i] < arr[j]: count += 1 counts when arr[i] < arr[j], which is not inversion. count = 0 for i in range(len(arr)): for j in range(i+1, len(arr)): if arr[i] == arr[j]: count += 1 counts equal elements, not inversions.

3. Final Answer:

   count = 0 for i in range(len(arr)): for j in range(i+1, len(arr)): if arr[i] > arr[j]: count += 1 -> Option A

4. Quick Check:

   Nested loops with arr[i] > arr[j] count inversions [OK]

Hint: Use nested loops with j > i and check arr[i] > arr[j] [OK]

Common Mistakes:

• Checking arr[i] < arr[j] instead of >
• Looping j before i
• Counting equal elements as inversions

3. What is the output of the following code?

def count_inversions(arr):
    count = 0
    for i in range(len(arr)):
        for j in range(i+1, len(arr)):
            if arr[i] > arr[j]:
                count += 1
    return count

print(count_inversions([2, 4, 1, 3, 5]))

medium

A. 5

B. 4

C. 2

D. 3

Solution

1. Step 1: Identify all inversion pairs in the array

   Array: [2, 4, 1, 3, 5]. Inversions are (2,1), (4,1), (4,3). Total 3 inversions.

2. Step 2: Confirm code counts these correctly

   Code loops through pairs and increments count for each inversion found, so returns 3.

3. Final Answer:

   3 -> Option D

4. Quick Check:

   Count inversions in [2,4,1,3,5] = 3 [OK]

Hint: Count pairs where earlier number is bigger [OK]

Common Mistakes:

• Missing some inversion pairs
• Counting pairs with equal elements
• Confusing order of pairs

4. Find the error in this code snippet for counting inversions:

def count_inversions(arr):
    count = 0
    for i in range(len(arr)):
        for j in range(i):
            if arr[i] > arr[j]:
                count += 1
    return count

print(count_inversions([3, 1, 2]))

medium

A. The inner loop should start from i+1, not 0 to i

B. The count variable is not initialized

C. The comparison should be arr[i] < arr[j] instead of arr[i] > arr[j]

D. The function does not return any value

Solution

1. Step 1: Analyze loop ranges and comparison

   Inner loop runs j from 0 to i-1, so j < i. The code checks if arr[i] > arr[j], counting pairs where later element is bigger.

2. Step 2: Identify correct inversion condition

   Inversion means earlier element is bigger than later one, so condition should be arr[j] > arr[i]. Current condition is reversed, causing wrong count.

3. Final Answer:

   The comparison should be arr[i] < arr[j] instead of arr[i] > arr[j] -> Option C

4. Quick Check:

   Inversion needs earlier > later, fix comparison [OK]

Hint: Check if earlier element is greater, not later [OK]

Common Mistakes:

• Reversing comparison operator
• Wrong loop ranges causing missed pairs
• Not returning count

5. Given the array [8, 4, 2, 1], what is the number of inversions? Also, explain why this count represents the worst case for inversion counting algorithms.

hard

A. 6; because the array is sorted in descending order, all pairs are inversions

B. 4; because only adjacent pairs are inversions

C. 0; because the array is sorted ascending

D. 3; because only some pairs are inversions

Solution

1. Step 1: Count all inversion pairs in the array

   Array: [8, 4, 2, 1]. Inversions: (8,4), (8,2), (8,1), (4,2), (4,1), (2,1) = 6 total.

2. Step 2: Explain why this is worst case

   Array sorted descending means every pair is inverted. Algorithms checking pairs must count all, causing maximum work.

3. Final Answer:

   6; because the array is sorted in descending order, all pairs are inversions -> Option A

4. Quick Check:

   Descending array has max inversions = n(n-1)/2 [OK]

Hint: Descending order array has max inversions n(n-1)/2 [OK]

Common Mistakes:

• Counting only adjacent pairs
• Confusing ascending with descending
• Not recognizing worst case scenario