Python Testing Explained: pytest vs unittest and When to Use Each
Learn Python testing the right way with a clear, practical breakdown of pytest vs unittest, when to use each, and how to build a testing workflow that actually works.
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You stare at the diff and think there is no way that tiny change caused this. But it did.
That exact moment is why testing exists. Testing is not about proving your code is perfect. It is about confidence.
Confidence that a change did not quietly break something else. Confidence that you can refactor without fear. Confidence that your code still does what you think it does, even when you come back to it months later.
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In Python, there are two testing tools you will see over and over. unittest, which comes built into Python, and pytest, which has become the go to choice for most developers.
They both solve the same problem, but they feel very different in how you write tests and how you think about testing.
In this article, we will walk through both. Not just what they are, but how they fit into your day to day workflow, when each one makes sense, and how you can start using them today in real code.
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What Testing Really Means in Real Life
Before getting into tools, it helps to clear up what testing actually is.
A test is just code that runs your code and checks that the result is what you expect. That is all it is. There is no magic happening behind the scenes. You call a function, it gives you a result, and you check that the result is correct.
You can think of tests like guardrails on a mountain road. They do not control how you drive. They are there to keep you from going over the edge when you mess up.
A good testing setup makes those guardrails easy to put in place and hard to ignore once they are there.
That is where unittest and pytest start to feel different. Both give you guardrails, but they go about it in very different ways.
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The Built In Option: unittest
unittest comes with Python out of the box. You do not need to install anything. You do not need to set anything up. You just import it and start writing tests.
That matters more than it sounds. unittest exists because Python wanted a testing tool that works everywhere, all the time, with no extra dependencies. That goal shaped how it works and how it feels to use.
unittest is built around classes. Every test lives inside a class that inherits from unittest.TestCase. Each test is a method on that class. Setup and cleanup logic live in special methods. Even assertions are written as methods.
If you have ever written tests in Java or C#, this will feel familiar. The structure is formal. Everything has a clear place. Tests are found by their names. Assertions are called on self.
That structure has real advantages. It is clear. It is predictable. It is hard to accidentally do something unexpected. But it also comes with some friction.
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The Friction of unittest
The first thing most people notice is how wordy it feels. You end up writing more code to say the same thing. Instead of writing assert result == 5, you write self.assertEqual(result, 5). Instead of plain functions, everything lives inside a class.
That is not a mistake. It was a deliberate choice. unittest was designed at a time when Python favored more formality, especially for tools meant to support large teams and long running projects.
Another common pain point is fixtures. Fixtures are the setup and cleanup code your tests rely on. In unittest, these live in methods like setUp, tearDown, setUpClass, and tearDownClass.
Even with these downsides, unittest is still very common. You see it a lot in older codebases, corporate environments, and places where installing third party packages is not allowed.
The Modern Favorite: pytest
pytest was built to fix a very common frustration. Writing tests should feel like writing normal Python, not like filling out paperwork.
It keeps the core idea of testing simple. You write regular functions. You use plain assert statements. pytest takes care of everything else.
Here is the same test written using pytest.
That is all there is to it. No classes. No special assertion methods. No main block at the bottom of the file.
pytest finds tests by name. If the file starts with test_ or ends with _test.py, and the function starts with test, pytest will pick it up automatically.
That simplicity changes how people approach testing.
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Why pytest Feels Different
pytest strips away a lot of ceremony. Testing feels lighter and more natural. When tests are easy to write, people tend to write more of them.
Another big difference is how pytest handles failures. When something breaks, pytest does more than say a test failed. It shows you why it failed.
If you compare two complex objects, pytest will point out exactly where they differ. With unittest, you usually just get a message saying they are not equal.
That faster and clearer feedback matters. When you understand failures quickly, you fix them faster, and your tests get better over time.
A fixture is just a function that provides something a test needs. pytest handles setting it up and cleaning it up, and it passes it into your test when needed.
There is no class involved. No inheritance to worry about. That fixture can be reused in any test file. You can also control how long it lives, whether it runs once per test, once per file, or once for the whole test run.
This leads to a very clean way of thinking about tests. A test simply states what it needs, and pytest provides it.
It is similar to dependency injection, but in a much simpler and more practical form.
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When unittest Is the Right Choice
unittest makes sense when you are working in an environment that limits what you can install. If third party packages are not allowed, unittest is already available and ready to use.
You will also see it a lot in older projects. If a codebase already uses unittest everywhere, switching to pytest partway through often adds more confusion than value.
At the end of the day, unittest is steady and predictable. It does not try to be fancy. In many environments, that reliability is a good thing.
When pytest Is the Better Tool
For new projects, pytest is usually the better option.
It pushes you toward small, focused tests and cuts down on extra code. Test failures are easier to read and understand. As your test suite grows, fixtures scale cleanly without getting in the way.
If you are building libraries, APIs, data pipelines, or anything that you expect to change and grow over time, pytest tends to make the whole process smoother.
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Wrapping it up
If you want the quickest way to get started with testing, install pytest and write a single test for a single function. That alone is enough to get moving.
If you are working in a locked down environment or on an existing project, start with unittest and be consistent with how you use it.
What matters most is not which tool you choose. What matters is choosing one and actually using it.
Once testing becomes part of how you work, the way you think about code changes. Refactoring feels safer. Bugs are easier to track down. Your code makes more sense when you come back to it later.
Testing does not slow you down. It saves time by taking uncertainty out of the process. That is the real value of testing in Python.
Hope you all have an amazing week nerds ~ Josh (Chief Nerd Officer 🤓)
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