Python:NumPy .binomial()

In NumPy’s random module, the .binomial() method generates random samples from a binomial distribution, representing the number of successes in n independent trials, each with a fixed probability p of success.

Syntax

numpy.random.binomial(n, p, size=None)

Parameters:

• n (int or array-like of ints): Number of trials (must be non-negative).
• p (float or array-like of floats): Probability of success on each trial (must be between 0 and 1 inclusive).
• size (Optional): The shape of the output array. If None, a single value is returned. If given as a tuple, it specifies the shape of the output array.

Return value:

The .binomial() function returns one or more random integers representing the number of successes across n trials with success probability p.

• If size is None, a single integer is returned.
• If size is specified, an array of integers is returned with the given shape.

Example: Generating Random Binomial Samples in NumPy

This example simulates 10 independent trials, repeated 5 times. Each value in the result represents the number of successes in one set of 10 trials:

import numpy as np

# Generate 5 random samples from a binomial distribution
result = np.random.binomial(n=10, p=0.5, size=5)

print(result)

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A possible output of this code could be:

[3 5 4 5 4]

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Note: The output will vary with each execution since it is randomly sampled.

Codebyte Example

In this codebyte example, a single random value is generated from a binomial distribution with 15 trials and a 60% chance of success:

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