The random.poisson function is part of Python’s NumPy library and is used to model probabilistic experiments with the help of the Poisson distribution.
To use the random.poisson function, you must import numpy at the very start of the program:
from numpy import random
The random.poisson method has the following syntax:
numpy.random.poisson(l,n)
The random.poisson function takes in two parameters:
l: l stands for Lamba and is the rate at which a Poisson process occurs. l must be greater than or equal to zero and of type float.n (optional): the number of samples to be drawn. The default value is 1.The Poisson distribution is used to model a situation or event that occurs at a constant rate, which we denote as Lambda.
The random.poisson method returns an array of length n. This is the bunch of samples drawn from the Poisson distribution modeled with Lambda equal to l.
The code below shows how to use the random.poisson function in Python.
First, we set the values of n and l to 3000 and 2.34, respectively. This is to denote that a particular event occurs at a constant rate of 2.34. The units of l must be consistent with the units of the drawn samples.
Next, we call the random.poisson function and print its return value with the print function. The return value is a list of 3000 values, which corresponds to the 3000 samples drawn from the Poisson process modeled with l as the parameter.
We use the
from numpy import random#declaring and assigning values to n and ln=3000l=2.34#calling random.poisson methodret_val=random.poisson(l, n)#printing return value and its lengthprint("Return value:", ret_val)print("Length of return value:", len(ret_val))
If we graph this Poisson process, our plot would resemble the following sketch:
import seaborn as sns
import matplotlib.pyplot as plt
#ret_val is the return value obtained from the code snippet above
sns.distplot(ret_val, kde=True)
plt.show()As expected, the probability reaches the maximum when the number of occurrences is equal to Lambda.