An important part of any simulation is the ability to generate random numbers. For this purpose, NumPy provides various routines in the submodule random
. It uses a particular algorithm, called the Mersenne Twister, to generate pseudorandom numbers.
First, we need to define a seed that makes the random numbers predictable. When the value is reset, the same numbers will appear every time. If we do not assign the seed, NumPy automatically selects a random seed value based on the system's random number generator device or on the clock:
>>> np.random.seed(20)
An array of random numbers in the [0.0, 1.0]
interval can be generated as follows:
>>> np.random.rand(5) array([0.5881308, 0.89771373, 0.89153073, 0.81583748, 0.03588959]) >>> np.random.rand(5) array([0.69175758, 0.37868094, 0.51851095, 0.65795147, 0.19385022]) >>> np.random.seed(20) # reset seed number >>> np.random.rand(5) array([0.5881308, 0.89771373, 0.89153073, 0.81583748, 0.03588959])
If we want to generate random integers in the half-open interval [min, max]
, we can user the randint
(min
, max
, length
) function:
>>> np.random.randint(10, 20, 5) array([17, 12, 10, 16, 18])
NumPy also provides for many other distributions, including the Beta
, bionomial
, chi
-square
, Dirichlet
, exponential
, F
, Gamma
, geometric
, or Gumbel
.
The following table will list some distribution functions and give examples for generating random numbers:
Function |
Description |
Example |
---|---|---|
|
Draw samples from a binomial distribution (n: number of trials, p: probability) |
>>> n, p = 100, 0.2 >>> np.random.binomial(n, p, 3) array([17, 14, 23])
|
|
Draw samples using a Dirichlet distribution |
>>> np.random.dirichlet(alpha=(2,3), size=3) array([[0.519, 0.480], [0.639, 0.36], [0.838, 0.161]])
|
|
Draw samples from a Poisson distribution |
>>> np.random.poisson(lam=2, size= 2) array([4,1])
|
|
Draw samples using a normal Gaussian distribution |
>>> np.random.normal (loc=2.5, scale=0.3, size=3) array([2.4436, 2.849, 2.741)
|
|
Draw samples using a uniform distribution |
>>> np.random.uniform( low=0.5, high=2.5, size=3) array([1.38, 1.04, 2.19[)
|
We can also use the random number generation to shuffle items in a list. Sometimes this is useful when we want to sort a list in a random order:
>>> a = np.arange(10) >>> np.random.shuffle(a) >>> a array([7, 6, 3, 1, 4, 2, 5, 0, 9, 8])
The following figure shows two distributions, binomial
and poisson
, side by side with various parameters (the visualization was created with matplotlib
, which will be covered in Chapter 4, Data Visualization):