NumPy: Create an array of 4,5 shape and swap column1 with column4
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.
For more Practice: Solve these Related Problems:
- Write a NumPy program to swap two specified columns in a 2D array using advanced indexing.
- Create a function that accepts a 2D array and two column indices, then returns a new array with those columns swapped.
- Implement a solution that uses np.copy to perform an in-place swap without altering the original array.
- Test the column swap on arrays with both even and odd numbers of columns to verify consistent behavior.
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