Transpose is a way of obtaining a new matrix whose columns are the rows of the original matrix and rows are the columns of the original matrix.

Numpy provides `transpose()`

function to achieve this operation. There are two different syntax of `transpose()`

function:

np.transpose(array) OR np.ndarray.transpose()

Here, `array`

parameter is a Numpy array.

It is just a matter of preference which syntax you want to use.

**Note:** Following tutorials are essential to improve your understanding of performing various matrix operations in NumPy:

- If you want to learn how to create a matrix in NumPy, then visit How to use NumPy matrix() in Python?
- Also, if you are interested in learning how to do matrix multiplication in NumPy, then visit How to use NumPy dot() in Python?

Let's see how we can use `transpose()`

function with the help of a couple of examples:

import numpy as np a = np.array([[14, 84, 13, 24, 45], [75, 32, 99, 21, 46]]) print('Original array:\n', a) print('Transpose of array using ndarray.transpose():\n', a.transpose()) print('Transpose of array using np.transpose():\n', np.transpose(a))

Output

Original array: [[14 84 13 24 45] [75 32 99 21 46]] Transpose of array using ndarray.transpose(): [[14 75] [84 32] [13 99] [24 21] [45 46]] Transpose of array using np.transpose(): [[14 75] [84 32] [13 99] [24 21] [45 46]]

**Note**: `transpose()`

function works on 2-D array and does not work on 1-D array.

import numpy as np a = np.array([1, 2, 3, 4, 5]) print('Original array:', a) print('Transpose of array: ', a.transpose())

Output

Original array: [1 2 3 4 5] Transpose of array: [1 2 3 4 5]

As you can see above `transpose()`

function has no effect on one-dimensional array.

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