NumPy - Find Indices of Maximum element in large array

NumPy: Performance Optimization Exercise-12 with Solution

Write a NumPy program that creates a large NumPy array and write a function to find the indices of the maximum element using a for loop. Optimize it using NumPy's argmax() function

Sample Solution:

Python Code:

import numpy as np

# Generate a large 1D NumPy array with random integers
large_array = np.random.randint(1, 1000000, size=1000000)

# Function to find the indices of the maximum element using a for loop
def find_max_index_with_loop(arr):
    max_index = 0
    max_value = arr[0]
    for i in range(1, len(arr)):
        if arr[i] > max_value:
            max_value = arr[i]
            max_index = i
    return max_index

# Find the index of the maximum element using the for loop method
max_index_with_loop = find_max_index_with_loop(large_array)

# Find the index of the maximum element using NumPy's argmax() function
max_index_with_numpy = np.argmax(large_array)

# Display the results
print(f"Index of maximum element using for loop: {max_index_with_loop}")
print(f"Index of maximum element using NumPy: {max_index_with_numpy}")

Output:

Index of maximum element using for loop: 546157
Index of maximum element using NumPy: 546157

Explanation:

• Importing numpy: We first import the numpy library for array manipulations.
• Generating a large array: A large 1D NumPy array with random integers is generated.
• Defining the function: A function find_max_index_with_loop is defined to find the index of the maximum element using a for loop.
• Finding index with loop: The index of the maximum element is found using the for loop method.
• Finding index with numpy: The index of the maximum element is found using NumPy's built-in argmax() function.
• Displaying results: The results from both methods are printed out to verify correctness.

Python-Numpy Code Editor:

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