What Is a Python Testing Framework?

Python is a popular and versatile general-purpose programming language that you can utilize to develop software, design applications for the web, or build machine learning (ML) models. Several factors contribute to the widespread use of Python. Not only is it a beginner-friendly language, it’s one of the highest-demand programming languages, and its popularity continues to grow. One area Python is making an impact is in testing frameworks, a technology that can help you, as a developer, automate your work. With no shortage of Python testing frameworks available, you can find a framework to automate processes and help produce reliable, high-quality applications.

In this article, you’ll learn more about Python testing frameworks while discovering the benefits of this technology and which testing frameworks will best support your project.

What is a Python testing framework?

A Python testing framework is a set of libraries and tools that allow you to automate tests of a software application while building it. With these tests, you can determine whether or not the outcomes of your applications are lining up with the intended results and produce test reports automatically. This ultimately serves the purpose of catching any errors early in the development process. Implementing Python testing frameworks to test your developing applications can save your business both money and time. Compared to manual testing, the automatic testing enabled by Python frameworks allows you to spot errors more consistently and more efficiently while also providing you with insights manual testing simply doesn’t offer. This includes file contents, data tables, and other features that help you get straight to the cause of errors.

8 top Python testing frameworks

With numerous Python testing frameworks available, here’s a closer look at some of the top choices.

1. PyUnit

PyUnit is a testing framework specifically for unit testing. It’s built directly into Python, so you don’t have to install it separately. With PyUnit, you can test specific parts of code and organize your different tests into collections. You can also test automation frameworks and prepare different test cases. PyTorch effectively finds bugs during the beginning stages of the testing cycle, allowing you to make the necessary modifications.

2. Testify

The Testify framework is a relatively simple framework used for system testing and integration. Like PyUnit, you can also employ this framework for unit testing. Additionally, you can expand the capabilities of Testify with the help of various plugins it offers. It also aims to expand on some of the features provided by PyUnit. These extra features include assertion methods, test discovery, and test report generation. However, one area where Testify struggles is in parallel testing, which means it has difficulty running tests on two or more applications simultaneously.

3. lettuce

Cucumber, another behavior-driven development framework, inspires lettuce. Even if you’re less technically advanced, you can still use and benefit from lettuce as a framework for automating tests in Python since it uses basic text explanations that anyone can read. With the reports generated by lettuce, you can implement them into decision-making processes. To install lettuce, first ensure you have at least Python version 2.7.14 or higher. 

4. pytest

Pytest is a customizable and scalable Python testing framework that uses plugins and other features to make it less challenging to write tests while also automating the debugging process. Pytest allows you to use less code, is great for completing small tests, and can efficiently take care of complicated tasks. However, pytest doesn’t always integrate well with other Python frameworks. You can further extend the capabilities of pytest by integrating with one of the many plugins it supports.

5. doctest

With doctest, you can test your code while writing it with embedded test cases directly in the functions' docstrings. Doctest also comes preinstalled with Python; however, you'll need a text editor to write and edit code for doctest. You can perform several tasks with doctest, including regression testing and testing interactive features to ensure they function correctly.

6. behave

The behave Python testing framework is specifically for behavior-driven development. Behave is convenient to use, with the foundational components to create test cases provided for you. Other beneficial features of behave include simple integration with PyCharm, user-friendly testing for those without advanced technical skills, and you can direct the focus of testing on certain areas of your programs.

7. Robot

Robot is a framework for test automation, acceptance testing, and robotic process automation that you can use in Python and several other programming languages. Not only that, but Robot works with many application types and operating systems. Thanks to its simple syntax and keyword-driven testing, you don’t need previous programming knowledge to use Robot. 

8. nose2

Nose2 is a Python automation testing framework that features several plugins to assist in areas such as organizing the features of your tests and building reports. The test reports provided by nose2 help you address problem areas by showing where your code went both right and wrong. Another advantage of nose2 is the ability to run tests parallel to one another without one impacting the other.

Who uses Python testing frameworks?

As one of the most widely used programming languages, here are some of the job titles you can pursue with these skills as part of your repertoire, including careers working with artificial intelligence, machine learning, data science, and web development. We've also included the average annual salaries for each position, according to Glassdoor:

• Artificial intelligence developer: $116,460

• Machine learning engineer: $126,116

• Big data analyst: $96,093

• Data scientist: $129,519

• Web developer: $84,496

• Data analyst: $76,933

• Software developer: $105,866

• Deep learning engineer: $121,406

*All annual base salary data is sourced from Glassdoor as of January 2024 and does not include additional pay, such as commission and benefits.

Benefits and limitations of Python testing frameworks

From being user-friendly to boasting a substantial online community, Python testing frameworks provide you with numerous benefits. In addition to Python being a user-friendly language requiring fewer lines of code for more efficient programming, one of Python's strengths is supporting data structures. This allows you to automate many different tasks, such as testing.

Python also integrates well with other tools, such as those for continuous integration and continuous development. This helps to increase the speed of testing cycles so that you can bring products to market sooner. Since Python is such a popular language, it boasts a large online community that you can use to your advantage when encountering any challenges.

Although Python frameworks come with plenty of advantages, some limitations also exist. 

For example, Python doesn’t execute code at as high of a rate as alternative languages, such as Java. Additionally, if you are working on applications for mobile devices, Python isn’t ideal due to the lower speed, as well as limited memory.

Getting started on Coursera

On Coursera, you can find highly-rated courses to learn more about Python and develop Python-related skills. Python for Everybody Specialization, from the University of Michigan, is a great opportunity to learn the basics of programming with Python, including data structures, databases, and visualizing data.

You can also earn a Google IT Automation with Python Professional Certificate to develop debugging, automation, configuration management skills, and more. At the end of this six-course series, you will earn an employer-recognized Certificate to boost your resume and help demonstrate your technical proficiency to employers.