How to Use the Binomial Distribution in Python

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The binomial distribution is a fundamental concept in statistics, used to model the number of successes in a fixed number of independent Bernoulli trials. It is defined by two parameters: the number of trials (n) and the probability of success in a single trial (p). With its diverse applications in fields such as finance, health sciences, and data science, understanding how to work with a binomial distribution is a key skill.

Python, a leading programming language in data science, offers libraries like NumPy, SciPy, and Matplotlib, that make working with statistical distributions, including the binomial distribution, straightforward. In this article, we will learn how to use the binomial distribution in Python.

Understanding the Binomial Distribution

In a binomial experiment, we perform ‘n’ identical trials where each trial results in one of two outcomes: success (with probability ‘p’) or failure (with probability ‘1-p’). The binomial distribution models the total number of successes out of ‘n’ trials.

A classic example of a binomial distribution is flipping a coin. If we flip a fair coin (where p = 0.5) 10 times, the binomial distribution can tell us the probability of getting exactly 5 heads.

Creating a Binomial Distribution with NumPy

NumPy offers the numpy.random.binomial function to simulate a binomial distribution. This function takes three parameters: the number of trials ‘n’, the probability of success ‘p’, and the number of experiments to perform.

Let’s simulate flipping a fair coin 10 times, repeated 1000 times:

import numpy as np

n = 10  # number of trials
p = 0.5  # probability of success
size = 1000  # number of experiments

# Generate binomial distribution
distribution = np.random.binomial(n, p, size)


The distribution array now contains 1000 numbers, each representing the number of successes in 10 coin flips.

Visualizing the Binomial Distribution with Matplotlib

To better understand our binomial distribution, we can visualize it using a histogram with the help of the Matplotlib library:

import matplotlib.pyplot as plt

plt.hist(distribution, bins=range(n+2), align='left', rwidth=0.8)
plt.xlabel('Number of Successes')
plt.title('Binomial Distribution (n=10, p=0.5)')

This histogram shows the frequency of each possible outcome, giving us a visual representation of our binomial distribution.

Calculating Probabilities with SciPy

SciPy’s scipy.stats.binom object allows us to compute theoretical probabilities associated with the binomial distribution. For example, we can calculate the probability of getting exactly 5 successes in 10 trials with a success probability of 0.5:

from scipy.stats import binom

n = 10  # number of trials
p = 0.5  # probability of success
k = 5  # number of successes

# Calculate probability
probability = binom.pmf(k, n, p)


Here, binom.pmf(k, n, p) calculates the Probability Mass Function (PMF) at ‘k’, which is the probability of getting exactly ‘k’ successes.

Calculating Cumulative Probabilities

In addition to individual probabilities, we can also calculate cumulative probabilities using the Cumulative Distribution Function (CDF). For example, we can calculate the probability of getting 5 or fewer successes:

# Calculate cumulative probability
cumulative_probability = binom.cdf(k, n, p)


Here, binom.cdf(k, n, p) gives the probability of getting ‘k’ successes or fewer.

Expectation and Variance

The binomial distribution has an expected value (mean) of np and a variance of np(1-p). You can calculate these in Python as follows:

# Calculate mean and variance
mean, var = binom.stats(n, p)

print("Mean:", mean)
print("Variance:", var)


The binomial distribution is an essential concept in statistics and probability theory. It allows us to model and understand experiments with binary outcomes, and Python’s rich ecosystem of libraries makes it easy to work with binomial distributions. By mastering the binomial distribution and Python’s statistical libraries, you can perform robust data analysis and build sophisticated machine learning models.

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