﻿ Numpy - Compute Variance of large array using For loop and Optimization

# Numpy - Compute Variance of large array using For loop and Optimization

## NumPy: Performance Optimization Exercise-18 with Solution

Write a NumPy program that generates a large NumPy array and write a function to compute the variance of its elements using a for loop. Optimize it using NumPy's built-in functions.

Sample Solution:

Python Code:

``````import numpy as np

# Generate a large 1D NumPy array with random integers
large_array = np.random.randint(1, 1000, size=1000000)

# Function to compute variance using a for loop
def variance_with_loop(arr):
mean = np.mean(arr)  # Calculate the mean of the array
variance = 0
for i in range(len(arr)):
variance += (arr[i] - mean) ** 2
variance /= len(arr)  # Divide by the number of elements to get the variance
return variance

# Compute variance using the for loop method
variance_with_loop_result = variance_with_loop(large_array)

# Compute variance using NumPy's built-in var() function
variance_with_numpy = np.var(large_array)

# Display the results
print(f"Variance using for loop: {variance_with_loop_result}")
print(f"Variance using NumPy: {variance_with_numpy}")
``````

Output:

```Variance using for loop: 83046.09052346226
Variance using NumPy: 83046.09052346242
```

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 variance_with_loop is defined to compute the variance using a for loop.
• Calculating mean: The mean of the array is calculated.
• Computing with loop: The variance is calculated using the for loop method by summing the squared differences from the mean and dividing by the number of elements.
• Computing with numpy: The variance is calculated using NumPy's built-in var() function.
• Displaying results: The results from both methods are printed out to verify correctness.

Python-Numpy Code Editor:

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