Advanced NumPy Exercises - Find the second-largest value in each row in an array
Write a NumPy program to create a 5x5 array with random values and find the second-largest value in each row.
Sample Solution:
Python Code:
import numpy as np
# create a 5x5 array with random values
nums = np.random.rand(5, 5)
print("Original array elements:")
print(nums)
# find the second-largest value in each row
second_largest = np.partition(nums, -2, axis=1)[:, -2]
print("\nSecond-largest value in each row:")
print(second_largest)
Output:
Original array elements: [[0.82671119 0.28189079 0.37220477 0.89521426 0.92550788] [0.56434694 0.94849553 0.70116302 0.77298376 0.37783512] [0.83622642 0.16947668 0.71134028 0.2114369 0.71333448] [0.74094415 0.02245679 0.10639303 0.40430979 0.01500681] [0.16620753 0.85257244 0.3696228 0.31196318 0.413779 ]] Second-largest value in each row: [0.89521426 0.77298376 0.71333448 0.40430979 0.413779 ]
Explanation:
In the above exercise -
nums = np.random.rand(5, 5): This line creates a 5x5 NumPy array with random values between 0 and 1 using the rand method of the numpy.random module.
second_largest = np.partition(nums, -2, axis=1)[:, -2]: This line finds the second-largest value in each row of the 'nums' 'array'.
The np.partition method performs an in-place partial sort of the array along the given axis (axis=1, i.e. row-wise). The -2 argument specifies that we want to partition the array such that the second largest value is in the second-to-last position (the last position being the largest value).
The [:, -2] indexing syntax extracts the second-to-last element from each row of the partially sorted array. The resulting second_largest array contains the second-largest value in each row of the original ‘nums’ array.
Python-Numpy Code Editor: