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bundles / numpy 2.5.0.dev0+git20251130.2de293a / numpy / random / RandomState / exponential

cython_function_or_method

numpy.random:RandomState.exponential

Signature

def   exponential ( scale = 1.0 ,  size = None )

Summary

Draw samples from an exponential distribution.

Extended Summary

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].

Parameters

scale : float or array_like of floats

The scale parameter, . Must be non-negative.

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

Returns

out : ndarray or scalar

Drawn samples from the parameterized 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
✗

See also

random.Generator.exponential

which should be used for new code.

Aliases

  • numpy.random.exponential
  • numpy.random.RandomState.exponential