Python NumPy transpose() Function

NumPy transpose() Function:

The transpose() function of the NumPy module reverses or permutes the axes of an array and returns the changed array. The function returns matrix transpose for a 2-D array.

One of the most significant functions in matrix multiplication is numpy.transpose().

It converts row items to column elements and column elements back to row elements. This function returns a modified array of the original one.

Syntax:

numpy.transpose(array, axes=None)

Parameters

array: This is required. It is the input array to be transposed.

axes: This is optional. It is a tuple or list containing a permutation of [0,1,…, N-1], where N is the number of axes of a. The ith axis of the returned array will correspond to the input’s axis numbered axes[i]. If not given, range(a.ndim)[::-1] is used, which reverses the order of the axes.

Return Value:

An ndarray is returned. The source array’s axis is permuted in the output array. When possible, a view is returned.

NumPy transpose() Function in Python

Example1

Approach:

Import numpy module using the import keyword.
Pass some random list as an argument to the array() function of the numpy module to create an array.
Store it in a variable.
Print the above-given array.
Pass the given array as an argument to the transpose()function to transpose the given array(interchange rows& columns).
Store it in another variable.
Print the above transposed Array.
The Exit of the Program.

Below is the implementation:

# Import numpy module using the import keyword
import numpy as np
# Pass some random list as an argument to the array() function 
# of the numpy module to create an array. 
# Store it in a variable.
gvn_arry = np.array([[1, 2, 3],
                   [4, 5, 6],
                   [7, 8, 9]])
# Print the above given array.
print("The above given array is:")
print(gvn_arry)
# Pass the given array as an argument to the transpose()function to 
# transpose the given array(interchange rows& columns)
# Store it in another variable.
trnspos_arry = np.transpose(gvn_arry)
# Print the above transposed Array
print("The above transposed Array is:")
print(trnspos_arry)

Output:

The above given array is:
[[1 2 3]
 [4 5 6]
 [7 8 9]]
The above transposed Array is:
[[1 4 7]
 [2 5 8]
 [3 6 9]]

Example2

Approach:

Import numpy module using the import keyword
Create an array using the ones() function by passing the 3-Dimensional
shape as an argument to it.
Store it in a variable.
Pass the given array and tuple of permutations(axis parameter) as
arguments to transpose function and store it in a variable
Print the permuted Array.
Print the shape of the above obtained permuted array using the shape attribute.
The Exit of the Program.

Below is the implementation:

# Import numpy module using the import keyword
import numpy as np
# Create an array using the ones() function by passing the 3-Dimensional 
# shape as an argument to it.
# Store it in a variable.
gvn_arry = np.ones((2, 6, 3))

# Pass the given array and tuple of permutations(axis parameter) as 
# arguments to transpose function and store it in a variable
permutd_arry = np.transpose(gvn_arry, (2, 0, 1))
# Print the permuted Array.
print("The permuted Array is:\n", permutd_arry)
print()
# Print the shape of the above obtained permuted array using the shape attribute
print("The shape of the above obtained permuted array:")
print(permutd_arry.shape)

Output:

The permuted Array is:
[[[1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1.]]

[[1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1.]]

[[1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1.]]]

The shape of the above obtained permuted array:
(3, 2, 6)

NumPy transpose() Function in Python

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