Optimizing Standard Deviation calculation of large NumPy arrays

NumPy: Performance Optimization Exercise-5 with Solution

Write a NumPy program to create a large NumPy array and write a function to calculate the standard deviation of its elements using a for loop. Optimize it using NumPy's built-in functions.

Sample Solution:

Python Code:

import numpy as np
# Create a large NumPy array with 1 million elements
large_array = np.random.rand(1_000_000)

# Function to calculate the standard deviation using a for loop
def std_dev_using_loop(array):
    mean = np.mean(array)
    variance = 0.0
    for element in array:
        variance += (element - mean) ** 2
    variance /= len(array)
    return np.sqrt(variance)

# Calculate the standard deviation using the for loop
std_loop = std_dev_using_loop(large_array)
print("Standard deviation using for loop:", std_loop)

# Optimize the standard deviation calculation using NumPy's built-in function
std_numpy = np.std(large_array)
print("Standard deviation using NumPy's built-in function:", std_numpy)

Output:

Standard deviation using for loop: 0.28867104907380803
Standard deviation using NumPy's built-in function: 0.288671049073815

Explanation:

• Create a large array: A 1D NumPy array with 1 million elements is created using np.random.rand().
• Function with for loop: A function std_dev_using_loop calculates the standard deviation of the array elements using a for loop.
• Calculate standard deviation with for loop: The standard deviation is calculated using the for loop and printed.
• Optimize with NumPy: The standard deviation calculation is optimized using NumPy's built-in np.std() function and printed.

Python-Numpy Code Editor:

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