numpy-random

sample_size = 10000

rand=np.random.rand(sample_size)
randn=np.random.randn(sample_size)
randint=np.random.randint(low=10, high=15, size=10)
random_integers=np.random.random_integers(1, 6, 10)

pdf, bins, patches = plt.hist(rand, bins=20, range=(0, 1), density=True)
plt.title('rand')
plt.show()

pdf, bins, patches = plt.hist(randn, bins=20, range=(-4, 4), density=True)
plt.title('randn')
plt.show()

pdf, bins, patches = plt.hist(randint, 10, density=True)
plt.title('randint')
plt.show()

count, bins, patches = plt.hist(random_integers, 10, density=True)
plt.title('random_integers')
plt.show()

 

• rand

Random values in a given shape.

---

np.random.rand(3,5)
>>>
array([[0.28486117, 0.29897638, 0.79203426, 0.3244706 , 0.86471039],
       [0.44751263, 0.54822991, 0.35717199, 0.11231203, 0.14189715],
       [0.44495908, 0.73198023, 0.46010123, 0.59274441, 0.33671386]])

 

• randn

Return a sample from the “standard normal” distribution.

 

• randint

Return random integers from low (inclusive) to high (exclusive).

---

np.random.randint(3,5,(3,))
>>>
array([3, 3, 4])

 

• random_integers

Random integers of type np.int between low and high, inclusive.

 

• choice

Generates a random sample from a given 1-D array.

#np.arange(5) of size 3
np.random.choice(5, 3)
>>>
array([0, 3, 4])