## Use Nested List Comprehensions:

Nested list comprehensions are utilized to repeat over every component in the matrix. Nested List Comprehension is quite similar to a nested loop.

We must install the software “Spyder” version 5 to run the Python program. We begin by creating a new project. We did this by selecting “new file” from the menu bar of the Spyder software. After that, we start coding:

In this case, we take a matrix. The variable used to represent that matrix is “matrix”. These matrices have two columns and three rows. We utilize nested loop comprehension here. We loop over every item of the matrix in row main mode and allocate the outcome to the “t” variable, which shows the transpose of matrices.

Now, we have to run the code. So, for running the code we have to tap the “run” option on the menu bar:

The transpose of the provided matrices is printed using the print command. We get the transpose of the matrix by changing the elements of rows into columns and elements of a column into rows. After transpose, the matrix contains two rows and two columns.

## Use the Zip() Method:

In Python, the zip is a container that contains data. The zip() method creates a repeatable object that combines items from any two iterators. And then, it returns a Zip object that is a tuple iterator, matches the main object for every passed iterator, and joins the second one for every iterator. The ith tuple has the ith item from every argument order or repeatable object.

We utilize this technique to get the transpose of a matrix. The following instance illustrates this:

The variable “m” represents the defined matrix. There is a matrix. This matrix represents three columns and four rows. The first print statement prints the real matrix. We utilize the zip() function to find the transpose of these three matrices:

In this case, the array is unzipped by *, then zipped and transposed. The resultant matrix has four columns and three rows.

## Use the NumPy() Method:

NumPy is the basic package for all technical calculations in Python. This package is considered for efficient manipulation of different multidimensional arrays. This is an extremely enhanced library for arithmetical operations. It simplifies different tasks. It offers a transpose() function for returning a transpose of a definite multidimensional matrix:

In this program, we need to install NumPy to import it. We have a matrix. This is a one-dimensional matrix. There are four columns and four rows in the matrix. First, the print statement prints the original matrix. Now, for finding the transpose of the matrix, we apply the NumPy.transpose() method on the variable “x”. This variable shows the defined matrix:

After running the above code, we get a subsequent matrix with four rows and four columns.

## Use Nested Loops:

We use nested loops for finding the transpose of different matrices. In this example, we utilize a nested for loop that repeats over every row and column. At every iteration, x [j][i] element is placed by the element x [i][j]:

Here, we have a matrix. The variable “m” is used to indicate this matrix. The matrix contains three columns and three rows. We want to take the transpose of these matrices. First, we need to iterate the matrix through rows and then iterate through columns. We use nested for loop. This loop iterates every row and column. The resultant matrix is stored in a variable “r”:

In the output, the elements of the rows of the defined matrix are changed into columns, and the elements of columns are changed to rows. By this, we get the transpose of the defined matrix. The resultant matrix contains three rows and three columns.

## Conclusion:

In this article, we have learned the different techniques with their examples to transpose a matrix in the Python language. We utilize the nested list comprehensions, utilize the zip() method, utilize the NumPy() method, and utilize the nested loops for finding the transpose. We execute a matrix, such as a nested list. Every element is served in place of a row in a matrix. We hope you found this article helpful. Check out other Linux Hint articles for more tips and information.