NumPy - random numbers

https://docs.scipy.org/doc/numpy-1.16.0/reference/routines.random.html
Random sampling (numpy.random)

https://numpy.org/doc/stable/reference/random/index.html
Random sampling (numpy.random) for numpy versions > 1.17

RANDOM

# Simple random data.

np.random.rand()   # return a random float from [0,1) (a uniform distribution)
np.random.rand(d0,d1,...,dn)   # return an array, shape=(d0,d1,...,dn)

np.random.random_sample()
np.random.random_sample((d0,d1,...,dn))

np.random.random()   # alias for np.random.random_sample()

np.random.randn()   # return a sample from the 'standard normal' distribution
np.random.randn(d0,d1,...,dn)   # return an array, shape=(d0,d1,...,dn)

np.random.standard_normal(size=1000)   # mu=0, sigma=1
np.random.standard_normal(size=(d0,d1,...,dn))   # shape=(d0,d1,...,dn)

# np.random.randint(low, high=None, size=None, dtype=int)
#     size=10, size=(2,4), return size-shaped array

np.random.randint(high)   # random integers from [0, high)
np.random.randint(low, high)   # random integers from [low, high)

np.random.randint(1,7,10)
# array([6, 3, 3, 3, 6, 3, 1, 1, 2, 2])
np.random.randint(5, size=(2, 4))
# array([[4, 0, 2, 1],
#        [3, 2, 2, 0]])

# np.random.choice(a, size=None, replace=True, p=None)
# a : 1-D array-like or int
# size : output shape (int or tuple of ints)
# replace : boolean, whether the sample is with or without replacement
# p : the probabilities associated with each entry in a

np.random.choice(arr)   # return one item from arr
np.random.choice(8)   # like np.random.choice(np.arange(8))
np.random.choice(list_four_strings, size=6, p=[0.5, 0.1, 0.1, 0.3])
np.random.choice(arr, replace=False)   # every item at most once
np.random.choice(range(1,7), size=(2,3))
# array([[1, 5, 4],
#        [5, 6, 2]])

# Permutations

np.random.shuffle(arr)
np.random.permutation(arr)
np.random.permutation(n)   # np.random.permutation(np.arange(n))

# Distributions

mu, sigma = 0, 0.1   # mean and standard deviation
s = np.random.normal(mu, sigma, 1000)

n, p = 10, .5   # number of trials, probability of each trial
s = np.random.binomial(n, p, 1000)
# result of flipping a coin 10 times, tested 1000 times.

loc, scale = 10, 1
s = np.random.logistic(loc, scale, 1000)

low, high = -1, 1
s = np.random.uniform(low, high, 1000)   # interval [low, high)

left, mode, right = -3, 0, 8
s = np.random.triangular(left, mode, right, 1000)   # interval [left, right]

s = np.random.poisson(lam=5, size=1000)

s = np.random.exponential(scale=1.0, size=1000)

loc, scale = 0., 1.
s = np.random.laplace(loc, scale, 1000)

# Random generator

# seed must be convertible to 32 bit unsigned integers
np.random.seed(42)