**A matrix is a collection of numbers arranged in rows and columns. The matrix transpose can be computed by interchanging its rows into columns or columns into rows**.

Here are three different ways of doing Python matrix transpose.

## Using Numpy array

```
import numpy as np
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
transpose = np.transpose(matrix)
print(transpose)
```

### Output

```
[[1 4 7]
[2 5 8]
[3 6 9]]
```

Here in the above program, we use the NumPy library, which is a powerful tool for working with arrays and matrices in Python. The Numpy module has a transpose function, which is used to transpose a matrix.

## Using Python For loop

```
matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
transpose = []
for i in range(len(matrix[0])):
row = []
for j in range(len(matrix)):
row.append(matrix[j][i])
transpose.append(row)
print(transpose)
```

In the above program we create a matrix with three rows and three columns, then transposes it using a loop. The for loop iterates through each element in the matrix and creates a new row for each column in the original matrix.

### Output

`[[1, 4, 7], [2, 5, 8], [3, 6, 9]]`

## Using python List comprehension

```
matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
transpose = [[row[i] for row in matrix] for i in range(len(matrix[0]))]
print(transpose)
```

### Output

`[[1, 4, 7], [2, 5, 8], [3, 6, 9]]`

In the above program, we create a matrix with three rows and three columns, then transposes it using a nested list comprehension.