np.random

• NumPy

Basic random functions

# Creates a 2x3 array of random numbers between 0 and 1
np.random.rand(2,3) 
 
# Creates a 2x3 array of random numbers from a standard normal distribution
# [0, 1)
np.random.randn(2, 3) 
np.random.randn(3) #creates 1D array with 3 elements
 
# # Creates a 3x4 array of random integers between 0 and 10
np.random.randint(0, 10, size=(3, 4))

• np.random.rand()
  • Generates random numbers from a uniform distribution over [0, 1) for the given shape (dimensions).
• np.random.randn()
  • np.random.randn() generates random numbers drawn from the standard normal distribution, meaning the numbers will be centered around 0, with a standard deviation of 1.
  • The function np.random.randn(100, 2) will generate a 2D array with 100 rows and 2 columns. Each value in this array is a random number drawn from the standard normal distribution.
• np.random.randint(low,high=None,size=None)
  • Generates random integers from the discrete uniform distribution in the specified range [low, high).

Distributions

"""Continuous (generate floats)"""
## Creates a 3x4 array of random numbers between 0 and 10
np.random.uniform(0,10, size=(3,4))
 
# Creates a 2x3 array of random numbers from a normal distribution with mean 0 and std deviation 1
np.random.normal(0, 1, size=(2, 3))  
 
"""Discrete (generate ints)"""
# Simulates 100 trials of 10 coin flips with a probability of 0.5 for heads
np.random.binomial(10, 0.5, size=100)  
 
# Generates 100 samples from a Poisson distribution with an average of 5 events
np.random.poisson(5, size=100)  
 

• np.random.uniform(low=0.0, high=1.0, size=None)
  • Generates random numbers from a uniform distribution over the interval [low, high)
• np.random.normal(loc=0.0, scale=1.0,size=None)
  • Generates random numbers from a normal (Gaussian) distribution with mean loc and standard deviation scale