NumPy: Create an array of 4,5 shape and swap column1 with column4

NumPy: Array Object Exercise-176 with Solution

Swap specific columns in a 2D array.

Write a NumPy program to create an array of 4,5 shape and swap column1 with column4.

Pictorial Presentation:

Sample Solution:

Python Code:

# Importing NumPy library
import numpy as np

# Creating a NumPy array using arange from 0 to 19 and reshaping it to a 4x5 array
array_nums = np.arange(20).reshape(4, 5)

# Printing the original array
print("Original array:")
print(array_nums)

# Swapping column 1 with column 4 in the array
print("\nAfter swapping column1 with column4:")
array_nums[:, [0, 3]] = array_nums[:, [3, 0]]
print(array_nums) 

Sample Output:

Original array:
[[ 0  1  2  3  4]
 [ 5  6  7  8  9]
 [10 11 12 13 14]
 [15 16 17 18 19]]

After swapping column1 with column4:
[[ 3  1  2  0  4]
 [ 8  6  7  5  9]
 [13 11 12 10 14]
 [18 16 17 15 19]]

Explanation:

In the above code -

array_nums = np.arange(20).reshape(4,5): This line creates a 1-dimensional NumPy array containing numbers from 0 to 19 and then reshapes it into a 2-dimensional array with 4 rows and 5 columns.

array_nums[:,[0,3]] = array_nums[:,[3,0]]: This line swaps the first (0-th) and fourth (3-rd) columns within the array. The syntax array_nums[:,[0,3]] selects all rows of the first and fourth columns, while array_nums[:,[3,0]] selects all rows of the fourth and first columns, respectively. By setting array_nums[:,[0,3]] equal to array_nums[:,[3,0]], we effectively swap the positions of the first and fourth columns.

Python-Numpy Code Editor:

Previous: Write a NumPy program to create an 1-D array of 20 elements. Now create a new array of shape (5, 4) from the said array, then restores the reshaped array into a 1-D array.
Next: Write a NumPy program to create an array of 4,5 shape and to reverse the rows of the said array. After reversing 1st row will be 4th and 4th will be 1st, 2nd row will be 3rd row and 3rd row will be 2nd row.