`numpy.random._generator:Generator.geometric`

Signature

def geometric ( p , size = None )

Summary

Draw samples from the geometric distribution.

Extended Summary

Bernoulli trials are experiments with one of two outcomes: success or failure (an example of such an experiment is flipping a coin). The geometric distribution models the number of trials that must be run in order to achieve success. It is therefore supported on the positive integers, k = 1, 2, ....

The probability mass function of the geometric distribution is

f (k) = (1 - p)^{k - 1} p

where p is the probability of success of an individual trial.

Parameters

p : float or array_like of floats: The probability of success of an individual trial.
size : int or tuple of ints, optional: Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. If size is None (default), a single value is returned if p is a scalar. Otherwise, np.array(p).size samples are drawn.

Returns

out : ndarray or scalar: Drawn samples from the parameterized geometric distribution.

Examples

Draw 10,000 values from the geometric distribution, with the probability of an individual success equal to ``p = 0.35``:

p, size = 0.35, 10000
rng = np.random.default_rng()
sample = rng.geometric(p=p, size=size)

✓

What proportion of trials succeeded after a single run?

(sample == 1).sum()/size

✗

The geometric distribution with ``p=0.35`` looks as follows:

import matplotlib.pyplot as plt
count, bins, _ = plt.hist(sample, bins=30, density=True)

✓

plt.plot(bins, (1-p)**(bins-1)*p)
plt.xlim([0, 25])

✗

plt.show()

✓

Aliases

numpy.random.Generator.geometric