Numpy - Sort elements of a large array using For loop and Optimization

NumPy: Performance Optimization Exercise-15 with Solution

Write a NumPy program that creates a large NumPy array and write a function to sort its elements using a for loop. Optimize it using NumPy's sort() function.

Sample Solution:

Python Code:

import numpy as np

# Generate a large 1D NumPy array with random integers
large_array = np.random.randint(1, 10000, size=10000)

# Function to sort the array using a for loop (bubble sort for simplicity)
def sort_with_loop(arr):
    arr_copy = arr.copy()
    n = len(arr_copy)
    for i in range(n):
        for j in range(0, n-i-1):
            if arr_copy[j] > arr_copy[j+1]:
                arr_copy[j], arr_copy[j+1] = arr_copy[j+1], arr_copy[j]
    return arr_copy

# Sort the array using the for loop method
sorted_with_loop = sort_with_loop(large_array)

# Sort the array using NumPy's built-in sort() function
sorted_with_numpy = np.sort(large_array)

# Display first 10 sorted elements to verify
print("First 10 sorted elements using for loop:")
print(sorted_with_loop[:10])

print("First 10 sorted elements using NumPy:")
print(sorted_with_numpy[:10])

Output:

First 10 sorted elements using for loop:
[ 1  1  1  2  2  3  6 10 10 10]
First 10 sorted elements using NumPy:
[ 1  1  1  2  2  3  6 10 10 10]

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 sort_with_loop is defined to sort the array using a for loop (bubble sort is used for simplicity).
• Sorting with loop: The array is sorted using the for loop method.
• Sorting with numpy: The array is sorted using NumPy's built-in sort() function.
• Displaying results: The first 10 sorted elements from both methods are printed out to verify correctness.

Python-Numpy Code Editor:

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