# numpy.random.Generator.exponential#

method

random.Generator.exponential(scale=1.0, size=None)#

Draw samples from an exponential distribution.

Its probability density function is

$f(x; \frac{1}{\beta}) = \frac{1}{\beta} \exp(-\frac{x}{\beta}),$

for x > 0 and 0 elsewhere. $$\beta$$ is the scale parameter, which is the inverse of the rate parameter $$\lambda = 1/\beta$$. The rate parameter is an alternative, widely used parameterization of the exponential distribution .

The exponential distribution is a continuous analogue of the geometric distribution. It describes many common situations, such as the size of raindrops measured over many rainstorms , or the time between page requests to Wikipedia .

Parameters:
scalefloat or array_like of floats

The scale parameter, $$\beta = 1/\lambda$$. Must be non-negative.

sizeint 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 scale is a scalar. Otherwise, np.array(scale).size samples are drawn.

Returns:
outndarray or scalar

Drawn samples from the parameterized exponential distribution.

References



Peyton Z. Peebles Jr., “Probability, Random Variables and Random Signal Principles”, 4th ed, 2001, p. 57.



Wikipedia, “Poisson process”, https://en.wikipedia.org/wiki/Poisson_process



Wikipedia, “Exponential distribution”, https://en.wikipedia.org/wiki/Exponential_distribution

Examples

A real world example: Assume a company has 10000 customer support agents and the average time between customer calls is 4 minutes.

>>> n = 10000
>>> time_between_calls = np.random.default_rng().exponential(scale=4, size=n)


What is the probability that a customer will call in the next 4 to 5 minutes?

>>> x = ((time_between_calls < 5).sum())/n
>>> y = ((time_between_calls < 4).sum())/n
>>> x-y
0.08 # may vary