# numpy.transpose() in Python | np.transpose() in Python

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:

Let's see how we can use `transpose()` function with the help of a couple of examples:

## Python program to transpose 2-D Numpy array

```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.

## Python program to transpose 1-D Numpy 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.