Random number Generation in Python part2(Probability Distributions)

               Part 1 blog post explained the basic random number generations. This blog post includes the methods in Probability distributions.

The methods are listed below…

1. ·       Normal Distribution
2. ·       Binomial distribution
3. ·       Poisson distribution
4. ·       Exponential distribution

1.Normal Distribution

              It is the most widely used distribution. It is symmetric with mean, standard deviation values.

Code:

#create an object for numpy

import numpy as np1

print("Normal distribution values are:")

#generate normal distribution with mean 1 std as 2 with 4 elements

print(np1.random.normal(1,2,4))  

Output:

Normal distribution values are:

[ 1.40837209  2.3509239  -0.15261431 -0.09280015]

2.Binomial Distribution

              Binomial distribution has two parameters. One is number of trails and the outcome of success. Eg: Flipping a coin

Syntax:

 np.random.binomial(n, p, size)

where n=No of trails, p=Probability, size is number of experiments

#create an object for numpy

import numpy as np1

print("Binomial distribution values are:")

#generate Binomial distribution with number of trial as 10 with probability as 0.6 with 10 experiments

print(np1.random.binomial(10,0.6,10))

Output:

Binomial distribution values are:

[4 5 6 6 4 7 4 6 5 7]

3.Poisson Distribution:

              It is a discrete probability distribution with constant mean rate of occurrence (λ).

Syntax: np.random.poisson(lam, size)

Where lam= Expected number of events and size is the number of random samples

Code:

#create an object for numpy

import numpy as np1

print("Poisson distribution values are:")

#generate Poisson distribution with 3.0 expected events and 9 samples

print(np1.random.poisson(3.0,9))

Output:

Poisson distribution values are:

[7 6 1 5 3 0 4 0 3]

4. Exponential distribution:

              It models the time between events in poisson process.

Syntax: np.random.exponential(scale, size)

Code:

#create an object for numpy

import numpy as np1

print("Exponential distribution values are:")

#generate Exponential distribution with 2.0 expected events and 10 samples

print(np1.random.exponential(2.0,6))

Output:

Exponential distribution values are:

[0.85150656 2.32924892 2.69817406 3.22662606 1.18097288 2.00720892]

These are the various Probability Distributions in Python. Hope, this code is useful to you. Keep Coding!!!!

Part1 link https://rajeeva84.blogspot.com/2026/08/random-number-generation-in-python.html