Rotate Image | Leetcode 48 | Explained with Images

LeetCode “Rotate Image” (#48) asks you to rotate an n × n square matrix 90° clockwise in-place (i.e., without returning a new matrix).

Example
Input matrix

1 2 3
4 5 6
7 8 9

Desired output (90° clockwise)

7 4 1
8 5 2
9 6 3

A real-world analogy is rotating a photo on your phone: each pixel’s new position depends on its current row/column.

Here’s the [Problem Link] to begin with.

Brute Force Solution (Extra Matrix)

Intuition & Approach

The simplest mental model is to copy every element into the place where it should appear after rotation:

• Element (row =i, col =j) moves to (row =j, col = n − 1 − i).
• Because every element’s target is unique, we can safely write into a separate result matrix and finally overwrite the original.

This costs extra space but keeps the logic crystal clear.

Code Implementation

class Solution:
    def rotate(self, matrix: List[List[int]]) -> None:        
        # dimensions
        r = len(matrix)
        c = len(matrix[0])

        # create an empty r × c matrix
        result = [[0 for _ in range(c)] for _ in range(r)]

        # place each value in its rotated position
        for i in range(r):
            for j in range(c):
                # new row  = old column
                # new col  = (last row index) - old row
                result[j][r - 1 - i] = matrix[i][j]

        # copy the rotated picture back to the input matrix
        matrix[:] = result

Code Explanation

1. Allocate result filled with zeros.
2. Double loop walks every (i, j) in the original.
3. Compute its new coordinates and write to result.
4. Assign matrix[:] = result to replace contents while keeping the same reference (important for LeetCode’s “in-place” checker).

Dry Run (3 × 3)

Final copy writes the rotated picture back.

Time & Space Complexity

• Time: O(n²) – we visit each of the n² entries once.
• Space: O(n²) extra – the auxiliary matrix.

Conclusion

The brute-force way is straightforward and great for understanding coordinate mapping, but it doubles memory usage—so we can do better!

Optimal Solution (Transpose + Reverse)

Intuition & Approach

A 90° clockwise rotation can be decomposed into two well-known in-place operations:

1. Transpose – swap matrix[i][j] with matrix[j][i] for all i < j.
   Rows become columns.
2. Reverse each row – flips elements left ↔ right.
   Converts the transposed picture into a rotated one.

Because both steps modify cells in-place and never require extra rows/cols, the total extra space is O(1).

Code Implementation

class Solution:
    def rotate(self, matrix: List[List[int]]) -> None:
        """
        Do not return anything, modify matrix in-place instead.
        """
        n = len(matrix)

        # 1️⃣ Transpose the matrix (swap across the main diagonal)
        for i in range(n - 1):
            for j in range(i + 1, n):
                # swap upper-triangle with lower-triangle element
                matrix[i][j], matrix[j][i] = matrix[j][i], matrix[i][j]

        # 2️⃣ Reverse every row to finish the 90° rotation
        for i in range(n):
            matrix[i].reverse()

Code Explanation

• Transpose loop:
  Only the upper triangle (j > i) is swapped to avoid undoing previous swaps.
• Reverse loop:
  Python’s list.reverse() reverses each row in-place.

Together they replicate the mathematical definition of rotation without touching extra memory.

Dry Run (3 × 3)

Start

1 2 3
4 5 6
7 8 9

After transpose

1 4 7
2 5 8
3 6 9

After row reversals

7 4 1
8 5 2
9 6 3

Matches the desired output 👌.

Time & Space Complexity

• Time: O(n²) – same double loop size.
• Space: O(1) – only a few temporary variables for swapping.

Conclusion

By leveraging matrix properties (transpose + reverse), we hit optimal memory usage while maintaining clear, readable code. This is the go-to technique in interviews when asked to “rotate image in-place.”

Final Thoughts

• Start with the brute-force version to internalize coordinate transformations.
• Then graduate to the elegant transpose-then-reverse pattern for production-ready, space-efficient solutions.
• Remember: both run in O(n²) time—but optimal wins on memory.

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