xorbits.numpy.random.weibull(a, size=None)[source]#

Draw samples from a Weibull distribution.

Draw samples from a 1-parameter Weibull distribution with the given shape parameter a.

\[X = (-ln(U))^{1/a}\]

Here, U is drawn from the uniform distribution over (0,1].

The more common 2-parameter Weibull, including a scale parameter \(\lambda\) is just \(X = \lambda(-ln(U))^{1/a}\).


New code should use the ~numpy.random.Generator.weibull method of a ~numpy.random.Generator instance instead; please see the random-quick-start.

  • a (float or array_like of floats) – Shape parameter of the distribution. Must be nonnegative.

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


out – Drawn samples from the parameterized Weibull distribution.

Return type

ndarray or scalar

See also

scipy.stats.weibull_max, scipy.stats.weibull_min, scipy.stats.genextreme, gumbel


which should be used for new code.


The Weibull (or Type III asymptotic extreme value distribution for smallest values, SEV Type III, or Rosin-Rammler distribution) is one of a class of Generalized Extreme Value (GEV) distributions used in modeling extreme value problems. This class includes the Gumbel and Frechet distributions.

The probability density for the Weibull distribution is

\[p(x) = \frac{a} {\lambda}(\frac{x}{\lambda})^{a-1}e^{-(x/\lambda)^a},\]

where \(a\) is the shape and \(\lambda\) the scale.

The function has its peak (the mode) at \(\lambda(\frac{a-1}{a})^{1/a}\).

When a = 1, the Weibull distribution reduces to the exponential distribution.



Waloddi Weibull, Royal Technical University, Stockholm, 1939 “A Statistical Theory Of The Strength Of Materials”, Ingeniorsvetenskapsakademiens Handlingar Nr 151, 1939, Generalstabens Litografiska Anstalts Forlag, Stockholm.


Waloddi Weibull, “A Statistical Distribution Function of Wide Applicability”, Journal Of Applied Mechanics ASME Paper 1951.


Wikipedia, “Weibull distribution”, https://en.wikipedia.org/wiki/Weibull_distribution


Draw samples from the distribution:

>>> a = 5. # shape  
>>> s = np.random.weibull(a, 1000)  

Display the histogram of the samples, along with the probability density function:

>>> import matplotlib.pyplot as plt  
>>> x = np.arange(1,100.)/50.  
>>> def weib(x,n,a):  
...     return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a)
>>> count, bins, ignored = plt.hist(np.random.weibull(5.,1000))  
>>> x = np.arange(1,100.)/50.  
>>> scale = count.max()/weib(x, 1., 5.).max()  
>>> plt.plot(x, weib(x, 1., 5.)*scale)  
>>> plt.show()  

This docstring was copied from numpy.random.