NumPy: Create a new array from a given array swapping first and last rows

NumPy: Basic Exercise-50 with Solution

Write a NumPy program to create a 4x4 array with random values. Create an array from the said array swapping first and last rows.

Sample Solution :

Python Code :

# Importing the NumPy library with an alias 'np'
import numpy as np

# Creating a NumPy array 'nums' ranging from 0 to 15 and reshaping it into a 4x4 array
nums = np.arange(16, dtype='int').reshape(-1, 4)

# Printing a message indicating the original array
print("Original array:")
print(nums)

# Swapping the first and last rows of the 'nums' array
# nums[[0,-1],:] accesses the first and last rows, and then swaps them using advanced indexing
nums[[0,-1],:] = nums[[-1,0],:]

# Printing the array after swapping the first and last rows
print("\nNew array after swapping first and last rows of the said array:")
print(nums) 

Sample Output:

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

New array after swapping first and last rows of the said array:
[[12 13 14 15]
 [ 4  5  6  7]
 [ 8  9 10 11]
 [ 0  1  2  3]]

Explanation:

In the above code -

np.arange(16, dtype='int').reshape(-1, 4) creates a 2D array of integers from 0 to 15 with a shape of 4 rows and 4 columns stores in the variable ‘nums’. The -1 in the reshape method means NumPy will automatically infer the number of rows based on the number of columns specified (4 in this case).

nums[[0,-1],:] = nums[[-1,0],:]: This line swaps the first (0-th) and last (-1-th) rows of the nums array. It uses advanced indexing to achieve this:

• nums[[0,-1],:]: Selects the first and last rows of the array.
• nums[[-1,0],:]: Selects the last and first rows of the array, in that order.

Then, the selected rows are assigned to each other, effectively swapping their positions in the array.

Python-Numpy Code Editor:

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