Matrix Transpose in C, C++, Java & Python – Code with Explanation & Examples in Short and Simple

   

C Program

void transpose(int a[10][10], int r, int c) {
    for (int i = 0; i < c; i++) {
        for (int j = 0; j < r; j++)
            printf("%d ", a[j][i]);
        printf("\n");
    }
}

C Output

Input:  
1 2 3  
4 5 6  
 

Output:  
1 4  
2 5  
3 6

C++ Program

void transpose(vector<vector<int>> &a) {
    for (int i = 0; i < a[0].size(); i++) {
        for (int j = 0; j < a.size(); j++)
            cout << a[j][i] << " ";
        cout << "\n";
    }
}

C++ Output

Input:  
{1, 2}  
{3, 4}  
{5, 6}
  

Output:  
1 3 5  
2 4 6

JAVA Program

void transpose(int[][] a) {
    for (int i = 0; i < a[0].length; i++) {
        for (int j = 0; j < a.length; j++)
            System.out.print(a[j][i] + " ");
        System.out.println();
    }
}

JAVA Output

Input:  
{{7, 8}, {9, 10}, {11, 12}}  

Output:  
7 9 11  
8 10 12

Python Program

def transpose(a):
    for i in zip(*a):
        print(*i)

Python Output

Input:  
[[1, 2, 3], [4, 5, 6]]9  

Output:  
1 4  
2 5  
3 6

Explanation in Detail

Example

Let's say that we have the following matrix:

1 2 3  

4 5 6

To transpose it, we reverse the rows and columns. Thus, the first column turns into the first row:

1 4  

2 5  

3 6

If the given matrix is r x c (rows × columns), then the transposed matrix will be c x r.

Analogy with Real-Life Situation

Think of a table with rows of students and columns of subjects. Reversing the table will make subjects rows and students columns. It's useful when changing point of view — from student-wise scores to subject-wise results.

You can even consider turning a notebook from horizontal to vertical orientation — what was earlier written horizontally is now read vertically.

Why It Matters

Matrix transposition is basic in:

Linear algebra

Image processing (image rotation)

Data science (reshaping data)

Machine learning (sizing matrices)

It assists in matrix multiplication, where matrix A needs to be compatible with the transpose of matrix B for multiplication.

It also instructs on:

Appropriate usage of nested loops

Matrix boundaries (row/column switches)

Preventing index-out-of-bound errors

What You Learn from This

Working with 2D arrays or nested lists

Row-column indexing

Appropriate usage of zip() in Python for clean transposition

Usage of dimensions wisely for cross-iteration

This is a central idea for higher-order mathematical computations and applications of matrices to finance, simulation, and deep learning.

Interview Relevance and Real Projects

This is frequently tested in interviews to verify:

Loop and index manipulation

Matrix comprehension

Good code reasoning for 2D data structures

In real-world usage:

Data scientists apply it when restructuring data

Web developers may apply it when rotating grids

Engineers apply it when using mathematical modeling

AI systems frequently have large matrix operations which involve transposition

SEO-Optimized Explanation

Matrix transpose is a fundamental concept in data science and programming that entails reversing the rows and columns of a matrix. Matrix transpose is applied extensively in mathematics, machine learning, picture processing, and data transformation operations. When working with C, C++, Java, and Python, you can achieve matrix transposition with nested loops or built-in procedures such as zip() when using Python. Understanding how to transpose a matrix helps developers work with algorithms involving matrix multiplication, reshaping datasets, and linear transformations. Whether in technical interviews or real-world projects, mastering matrix transposition is essential for handling multidimensional data efficiently.