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Monte Carlo integration in Python

Last Updated : 01 Aug, 2025

Monte Carlo integration is a statistical technique that uses random sampling to estimate definite integrals, making it ideal for complex or high-dimensional cases where traditional methods fall short. With Python, we can implement and parallelize this technique for fast, flexible numerical integration.

The goal is to estimate a definite integral: I = \int_a^b f(x)\, dx

Instead of evaluating f(x) at every point, we draw N random samples x_i uniformly from [a , b] and calculate:

I \approx (b - a) \frac{1}{N} \sum_{i=1}^N f(x_i)

Due to the law of large numbers, this approximation improves as  N  increases, the more random points sampled, the closer the result will cluster around the true value.

Now lets see its implement it using python:

• NumPy: For fast numeric operations and random number generation.
• Matplotlib: For plotting and visualizations.p.

!pip install numpy matplotlib

We will estimate: \int_0^\pi \sin(x)\, dx

import numpy as np

a, b, N = 0, np.pi, 1000


def f(x):
    return np.sin(x)


x_random = np.random.uniform(a, b, N)
estimate = (b - a) * np.mean(f(x_random))

print(f"Monte Carlo Estimate: {estimate:.4f}")

Output:

Monte Carlo Estimate: 2.0067

Monte Carlo estimates are statistical i.e they fluctuate. We can visualize this variability by repeating the procedure many times and plotting the results:

import matplotlib.pyplot as plt

repeats = 1000
estimates = []

for _ in range(repeats):
    x_samples = np.random.uniform(a, b, N)
    estimates.append((b - a) * np.mean(f(x_samples)))

plt.hist(estimates, bins=30, edgecolor='black')
plt.title("Monte Carlo Estimate Distribution")
plt.xlabel("Estimate")
plt.ylabel("Frequency")
plt.show()

To further speed up calculations, Monte Carlo integration adapts easily to parallel computing. Each batch of random samples can be processed independently.

• Makes full use of all CPU cores.
• Greatly reduces computation time for large N or repeated estimates.

import numpy as np
from multiprocessing import Pool, cpu_count


def monte_carlo_worker(args):
    a, b, N, f = args
    x_rand = np.random.uniform(a, b, N)
    return (b - a) * np.mean(f(x_rand))


a, b, N_per_cpu = 0, np.pi, 10_000
repeats = 100
func = np.sin
num_workers = cpu_count()

with Pool(num_workers) as pool:
    tasks = [(a, b, N_per_cpu, func) for _ in range(repeats)]
    parallel_results = pool.map(monte_carlo_worker, tasks)

print(f"Mean Estimate (parallel): {np.mean(parallel_results):.4f}")

Output:

Mean Estimate (parallel): 2.0016

Here we will Integrate x^2 from 0 to 1

a, b, N = 0, 1, 1000


def f(x):
    return x**2


x_random = np.random.uniform(a, b, N)
estimate = (b - a) * np.mean(f(x_random))

print(f"Monte Carlo Estimate: {estimate:.4f}")

Output:

Monte Carlo Estimate: 0.3137

Monte Carlo integration in Python provides a robust and versatile framework for tackling complex or high-dimensional integrals where traditional analytical or numerical methods may be impractical. Its flexibility allows seamless adaptation to a wide range of problems across science, engineering and data analysis.

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