While working on math problems, we do run into loads of matrix calculations, in which calculating the transpose of a matrix can get quite complicated as the number of rows and columns increases. Here are two simple methods in NumPy using which you can easily transpose a matrix:

#### 1. Using the "T attribute":

NumPy provides a T attribute that can transpose a 2D array.

- Imports the NumPy library and creates a 2D array using NumPy
- Then transposes the array by swapping the rows and columns and stores it in a new variable.
- Finally, it prints the transposed array.
- Note that this approach
**works with any 2D array**.

This code uses NumPy to transpose a 2D array, providing easy manipulation but it may be challenging for beginners without a prior understanding of NumPy’s array methods.

#### 2. Using the "axes parameter of np.transpose()" method:

Works similarly to `np.transpose()`

, though the difference is this method is called with two arguments, which specify the order of the axes after transposing.

- Imports the NumPy library and creates a 2D array using NumPy.
- Then transposes the array by switching the rows and columns using the
`np.transpose()`

method with the arguments “(1,0)” which means the second axis (columns) becomes the first axis (rows) and vice versa. - Finally, it prints the transposed array.

This code transposes a 2D array efficiently using NumPy’s transpose method, but it may not be suitable for more complex array operations that require additional NumPy functions and methods.