# Transposing NumPy array

Transposing numpy array is extremely simple using `np.transpose` function. Fundamentally, transposing numpy array only make sense when you have array of 2 or more than 2 dimensions.

## Import numpy package

Before moving ahead, you have a look at my posts on Creating NumPy Array for Beginners and Reshaping NumPy Array.

## Transposing a Vector

array([0, 1, 2, 3, 4, 5, 6, 7, 8])
array([0, 1, 2, 3, 4, 5, 6, 7, 8])

Here you can see that transposed vector is the same array. Mainly because transpose of a 1 dimensional array is still a 1 dimensional array.

You can always convert vector to 2D arrays using `np.newaxis` and than transpose it.

(1, 3)
array([[1, 2, 3]])

You can notice that above vector is converted in 2D array with shape of (1, 3).

(3, 1)
array([,
,
])

[0 1 2]

array([,
,
])

array([,
,
])

## Transposing a Matrix

array([[1, 1, 1],
[2, 2, 2],
[3, 3, 3]])
array([[1, 2, 3],
[1, 2, 3],
[1, 2, 3]])

## Transposing numpy array with 3 dimensions (tensors)

(2, 2, 2)
array([[[1, 5],
[3, 7]],

[[2, 6],
[4, 8]]])