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Arrays and matrices form the core of this Python library. The transpose of these arrays and matrices plays a vital role in certain topics such as machine learning. In NumPy, it is easy to calculate the transpose of an array or a matrix.

Transposing a 1D array in NumPy

To transpose an array or matrix in NumPy, we have to use Tthe transposed array or matrix property, which stores the transposed array or matrix.

TThe attribute is exclusive to NumPy arrays, that is, only ndarray. This attribute has no effect on Python lists.

In theory, it is possible to transpose a 1D array, but technically, or more precisely, in terms of the programming language, it is not possible to transpose a 1D array.

Don’t get me wrong. It’s just that transposing a 1D array in Python or any other programming language is a little different. It all comes down to how arrays are represented in the programming language.

A 1D array is just a row of a matrix. If you have to transpose this array (technically a matrix), you have to convert this 1D matrix into a 2D matrix. Then use the specified function to transpose the 2D matrix.

Please refer to the following code for better explanation.

import numpy as np

a = [1, 2, 3, 4, 5]
b = np.array(a)
c = np.array([a])
print(b)
print(c)
print(b.shape)
print(c.shape)
print(b.T)
print(c.T)

Output:

[1 2 3 4 5]
[[1 2 3 4 5]]
(5,)
(1, 5)
[1 2 3 4 5]
[[1]
 [2]
 [3]
 [4]
 [5]]

First, we use np.array()the method and a Python list to form two NumPy arrays, b1D and c2D. To convert the lists to 2D matrices, we []enclose them in . Then, we print the NumPy arrays and their respective shapes.

But the most important thing to note is that the transpose of a 1D array is the same as the array itself, but the transpose of a 2D array has completely changed. The results are very obvious.

The non-transposed 2D array has one array inside it with five elements representing one row of the matrix. After the transposition, there are five arrays inside the 2D array representing the five rows of the transposed matrix with one element per row. That's how transposition works!

For a 1D array, the same array is returned because [1 2 3 4 5]the transposed array in Python looks like this [1 2 3 4 5]. This result requires that our original array is 2D instead of 1D.