Transpose a matrix in Python?

Transpose a matrix means we're turning its columns into its rows. Let's understand it by an example what it looks like after the transpose.

Let's say you have original matrix something like ?

matrix = [[1,2],[3,4],[5,6]]
print(matrix)

[[1, 2], [3, 4], [5, 6]]

In above matrix we have two columns, containing 1, 3, 5 and 2, 4, 6.

So when we transpose above matrix, the columns becomes the rows. So the transposed version would look something like ?

[[1, 3, 5], [2, 4, 6]]

Below are several ways to accomplish matrix transpose in Python ?

Method 1 ? Using Nested Loops

The traditional approach using loops to iterate through rows and columns ?

# Original Matrix
matrix = [[1,2],[3,4],[5,6]]

# Create empty result matrix with swapped dimensions
result = [[0, 0, 0], [0, 0, 0]]

# Iterate through rows
for i in range(len(matrix)):
    # Iterate through columns
    for j in range(len(matrix[0])):
        result[j][i] = matrix[i][j]

for row in result:
    print(row)

[1, 3, 5]
[2, 4, 6]

Method 2 ? Using List Comprehension

A more Pythonic approach using nested list comprehension ?

# Original Matrix
matrix = [[1,2],[3,4],[5,6]]

result = [[matrix[j][i] for j in range(len(matrix))] for i in range(len(matrix[0]))]

for row in result:
    print(row)

[1, 3, 5]
[2, 4, 6]

Method 3 ? Using zip() Function

The most elegant solution using zip() with unpacking operator ?

# Original Matrix
matrix = [[1,2],[3,4],[5,6]]

result = list(map(list, zip(*matrix)))

for row in result:
    print(row)

[1, 3, 5]
[2, 4, 6]

Method 4 ? Using NumPy Library

NumPy provides the most efficient way for matrix operations ?

import numpy as np

# Original Matrix
matrix = [[1,2],[3,4],[5,6]]

result = np.transpose(matrix)
print(result)

[[1 3 5]
 [2 4 6]]

Comparison

Conclusion

Use zip(*matrix) for simple Python matrix transpose. For large matrices or mathematical operations, NumPy's transpose() provides the best performance and functionality.