9 Best Python Testing Libraries in 2024

Here are the best Python testing libraries for your toolkit to perform Unit testing, acceptance testing, automation testing, functional testing, regression testing, security testing, performance testing, and behavioral development testing using these best Python testing libraries. Here are the best libraries organized by use-cases. Detailed review of each library follows. 

For Automation testing, consider using libraries like Robot framework, behave, pytest, and unittest. For Acceptance testing, consider using Robot framework, etc.. For Functional testing, consider using faker, pytest. For Performance testing, consider using locust, faker. Regression testing: Consider using faker, doctest. For Behavioral Development testing, consider using faker, behave, testproject. For Security testing, consider using PayloadsAllTheThings, sqlmap. For Unit testing, consider using Pytest, unittest, testify, nose2. 

We have identified the best libraries based on popularity, flexibility of use, coverage across the different types of testing, quality, support, reuse factor, etc. Let’s look at each library in detail. You can access package commands, installation notes, code snippets using the links below. 

Robotframework:

• Used for automation and acceptance testing. 
• Supports Keyword driven, data-driven, and behavior-driven approaches used. You do not need programming knowledge to write tests.  
• Offers full web and mobile support. 
• Automatic generation of reports and HTML logs after executing each build. 

Pytest: 

• Used for automation, functional, and unit testing. 
• Provides detailed assert statements. 
• Helps you auto-discover testing scenarios. 
• It has one of the largest collections of plugins covering everything from testing REST API to integrating with external platforms. 

Faker: 

• Used for functional, performance, regression, and behavioral development testing. 
• Uses fake data for mock testing applications. 
• Mock data can be used to demo a product to customer user experience feedback. 
• Using dynamic data is used, can help uncover unanticipated bugs. 

Locust: 

• Used for load and performance testing. 
• Can stimulate complex scenarios for load tests and let us perform multiple load-testing scenarios simultaneously. 
• It uses less memory and takes less time to generate test cases. 
• It has a simple UI and provides a graph of metric timings. 

Testify: 

• Used for automation, system, integration, and Unit testing. 
• Offers test discovery enhancement. 
• Advanced form of Nose and unittest framework. 
• Works on class-level setup and teardown fixture methodology. 

Behave: 

• Used for automation and behavioral development testing.  
• Supports the Gherkin language. 
• Can easily convert a pandas data framework into a behave table which can be parsed by behave-pandas. 
• It has Django and Flask integrations. 

Nose2: 

• Used for Unit and integration testing. 
• Offers multiple plugins for running doctests and unittests. 
• Built-in plugins will help decorators, executors, parameterization, and fixtures. 
• Also, it is called Extend Unittest as it is a Unittest with plugins designed to make it simple and easy.  

Playwright-python: 

• Used for regression testing. 
• Helps configure test retry strategy, and capture execution videos, traces, and screenshots for eliminating flakes. 
• Generates tests by recording actions and saves them to any language we want. 
• Inspects page, generates selections, steps through the execution, views click points, and explores execution logs.

Sqlmap: 

• Used for security testing. 
• Supports six SQL injection techniques like time-based blind, error-based, Boolean-based, stacked queries, UNION query-based, and out-of-band. 
• Automated recognition of password hash formats for supporting cracking passwords using a dictionary-based attack. 
• Supports execution of arbitrary commands and retrieval of their standard outputs.