Poisson Distribution in NumPy Last Updated : 15 Jul, 2025 Comments Improve Suggest changes Like Article Like Report The Poisson Distribution model the number of times an event happens within a fixed time or space when we know the average number of occurrences. It is used for events that occur independently such as customer arrivals at a store, Website clicks where events happen independently.numpy.random.poisson() MethodIn Python'sNumPylibrary we can generate random numbers following a Poisson Distribution using the numpy.random.poisson() method. It has two key parameters:lam : The average number of events (λ) expected to occur in the interval.size : The shape of the returned array.Syntax:numpy.random.poisson(lam=1.0, size=None)Example 1: Generate a Single Random NumberTo generate a single random number from a Poisson Distribution with an average rate of λ = 5: Python import numpy as np random_number = np.random.poisson(lam=5) print(random_number) Output :5Example 2: Generate an Array of Random NumbersTo generate multiple random numbers: Python random_numbers = np.random.poisson(lam=5, size=5) print(random_numbers) Output :[13 6 4 4 10]Visualizing the Poisson DistributionTo understand the distribution better we can visualize the generated numbers. Here is an example of plotting a histogram of random numbers generated using numpy.random.poisson. Python import numpy as np from numpy import random import matplotlib.pyplot as plt import seaborn as sns lam = 2 size = 1000 data = random.poisson(lam=lam, size=size) sns.displot(data, kde=False, bins=np.arange(-0.5, max(data)+1.5, 1), color='skyblue', edgecolor='black') plt.title(f"Poisson Distribution (λ={lam})") plt.xlabel("Number of Events") plt.ylabel("Frequency") plt.grid(True) plt.show() Output:Poisson DistributionThe image shows a Poisson Distribution with λ=2 displaying the frequency of events. The histogram represents simulated data highlighting the peak at 0 and 1 events, with frequencies decreasing as the number of events increases. Comment More info J jitender_1998 Follow Improve Article Tags : Python Python-numpy Python numpy-Random Explore Python FundamentalsPython Introduction 3 min read Input and Output in Python 4 min read Python Variables 5 min read Python Operators 5 min read Python Keywords 2 min read Python Data Types 7 min read Conditional Statements in Python 3 min read Loops in Python - For, While and Nested Loops 6 min read Python Functions 5 min read Recursion in Python 4 min read Python Lambda Functions 5 min read Python Data StructuresPython String 5 min read Python Lists 4 min read Python Tuples 4 min read Python Dictionary 3 min read Python Sets 6 min read Python Arrays 7 min read List Comprehension in Python 4 min read Advanced PythonPython OOP Concepts 11 min read Python Exception Handling 5 min read File Handling in Python 4 min read Python Database Tutorial 4 min read Python MongoDB Tutorial 2 min read Python MySQL 9 min read Python Packages 12 min read Python Modules 7 min read Python DSA Libraries 15 min read List of Python GUI Library and Packages 3 min read Data Science with PythonNumPy Tutorial - Python Library 3 min read Pandas Tutorial 6 min read Matplotlib Tutorial 5 min read Python Seaborn Tutorial 15+ min read StatsModel Library- Tutorial 4 min read Learning Model Building in Scikit-learn 8 min read TensorFlow Tutorial 2 min read PyTorch Tutorial 6 min read Web Development with PythonFlask Tutorial 8 min read Django Tutorial | Learn Django Framework 7 min read Django ORM - Inserting, Updating & Deleting Data 4 min read Templating With Jinja2 in Flask 6 min read Django Templates 7 min read Python | Build a REST API using Flask 3 min read How to Create a basic API using Django Rest Framework ? 4 min read Python PracticePython Quiz 3 min read Python Coding Practice 1 min read Python Interview Questions and Answers 15+ min read Like