numpy.random.exponential¶

numpy.random.
exponential
(scale=1.0, size=None)¶ Draw samples from an exponential distribution.
Its probability density function is
for
x > 0
and 0 elsewhere. is the scale parameter, which is the inverse of the rate parameter . The rate parameter is an alternative, widely used parameterization of the exponential distribution [3].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 [1], or the time between page requests to Wikipedia [2].
Note
New code should use the
exponential
method of adefault_rng()
instance instead; see randomquickstart. Parameters
 scalefloat or array_like of floats
The scale parameter, . Must be nonnegative.
 sizeint or tuple of ints, optional
Output shape. If the given shape is, e.g.,
(m, n, k)
, thenm * n * k
samples are drawn. If size isNone
(default), a single value is returned ifscale
is a scalar. Otherwise,np.array(scale).size
samples are drawn.
 Returns
 outndarray or scalar
Drawn samples from the parameterized exponential distribution.
See also
Generator.exponential
which should be used for new code.
References
 1
Peyton Z. Peebles Jr., “Probability, Random Variables and Random Signal Principles”, 4th ed, 2001, p. 57.
 2
Wikipedia, “Poisson process”, https://en.wikipedia.org/wiki/Poisson_process
 3
Wikipedia, “Exponential distribution”, https://en.wikipedia.org/wiki/Exponential_distribution