10 Pytest Best Practices

Pytest is a Python testing tool that allows you to write tests that are easy to read and understand. It also has a rich set of features that make it a powerful tool for testing Python applications.

In this article, we will discuss 10 best practices that you should follow when using pytest. By following these best practices, you can write tests that are more reliable and easier to maintain.

1. Use assert instead of the assertEquals built-in

The assert statement is used to check if the condition is true or not. If the condition is true, then the program proceeds to the next line of code. However, if the condition is false, then the program throws an AssertionError.

On the other hand, the assertEquals built-in is used to check if two values are equal. If they are equal, then the program proceeds to the next line of code. However, if they are not equal, then the program throws an AssertionError.

Now, let’s see how we can use these two statements in a pytest test case.

Suppose we have a function that calculates the sum of two numbers. We want to test this function using pytest.

def test_sum():
assert sum(2, 3) == 5

As you can see, we have used the assert statement to check if the sum of two numbers is equal to 5. If it is, then the program will proceed to the next line. Otherwise, it will throw an AssertionError.

Similarly, we can use the assertEquals built-in to check if the sum of two numbers is equal to 5.

def test_sum():
assertEquals(sum(2, 3), 5)

Both of these approaches are valid. However, the assert statement is more concise and easier to read. Therefore, it is considered as one of the best practices in pytest.

2. Test one thing in each test

If you have a test that’s testing multiple things, and one of those things fails, it can be difficult to figure out which part of the test failed. By testing only one thing in each test, it’s much easier to pinpoint which test is failing and why.

It can also be helpful to think of each test as its own little mini-program. By testing only one thing in each test, you make it easier to understand what the purpose of each test is. And if you need to change something in a test, it’s less likely that you’ll break other tests.

3. Make tests easy to read and maintain

As your test suite grows, it becomes more and more difficult to keep track of what each test is doing. This is especially true if tests are not well organized. Furthermore, if tests are not easy to read, it becomes harder to understand what they are trying to achieve. As a result, it takes longer to make changes to the tests, and it is more likely that errors will be introduced.

To avoid these problems, it is important to follow some basic pytest best practices. For example, you should use clear and concise names for your tests, and you should organize them into logical groups. Additionally, you should make sure that each test only performs a single assertion. By following these pytest best practices, you can make your tests easier to read and maintain, and you can avoid introducing errors into your test suite.

4. Write short, isolated unit tests

Isolated unit tests are easier to write, understand, and maintain. They’re also less likely to break when code changes, making them more robust.

When writing unit tests, it’s important to focus on testing one thing at a time. This means avoiding “integration tests” that test multiple things at once. Integration tests are important, but they’re best saved for a separate suite of tests.

Unit tests should be short because they’re only testing one thing. If a unit test starts getting too long or complex, it’s probably doing too much. In that case, it’s better to break it up into multiple unit tests.

Finally, unit tests should be isolated from each other. This means each test should set up its own data and not depend on any side effects from other tests. Isolation makes tests more reliable and easier to debug.

5. Avoid setup and teardown code

When you have setup and teardown code, it’s hard to reuse fixtures across tests. This leads to duplication and more code to maintain.

It’s also hard to know what order your setup and teardown code is run in. This can lead to subtle bugs that are hard to reproduce and debug.

Finally, setup and teardown code can mask problems with your tests. If a test fails because of an error in setup or teardown code, it can be hard to figure out what the problem is.

The best way to avoid these problems is to use fixtures. Fixtures are functions that are called before and after each test. They are a great way to share state between tests, and they make it easy to know what order your code is run in.

6. Use pytest fixtures for complex set up

When you’re writing tests, there’s always going to be some set up involved. For example, if you’re testing a function that interacts with a database, you’ll need to set up a connection to the database before you can run your test.

If your set up is simple, you can just write it into your test code. But if it’s complex, it’s better to use a pytest fixture. That way, your set up code is separate from your test code, and it’s easy to reuse fixtures in multiple tests.

Plus, using fixtures makes your tests more readable and easier to maintain.

7. Use parametrize for multiple inputs

If you have a test that needs to be run with different input values, using parametrize allows you to write the test once and then specify the input values as parameters. This is much more efficient than writing separate tests for each input value.

Not only does this save time, but it also makes your tests more DRY (Don’t Repeat Yourself). DRY tests are easier to maintain because if you need to change something, you only have to make the change in one place.

Parametrize is also useful for testing edge cases. For example, if you’re testing a function that takes an integer as an input, you can use parametrize to test what happens when the input is 0, 1, -1, and so on.

To use parametrize, simply add @pytest.mark.parametrize as a decorator above your test function, and then specify the input values as arguments.

8. Use @pytest.mark decorators to run subsets of tests

Suppose you have a large test suite with hundreds of tests. Not all of these tests need to be run every time you execute your test suite. For example, you might have some tests that are only relevant when testing a specific feature, and others that are only relevant when testing on a specific platform.

By using @pytest.mark decorators, you can specify which tests should be run in which situations. This way, you can easily control which tests are executed, without having to manually comment out or delete tests every time you want to run a subset of your tests.

For example, you could use the @pytest.mark.windows marker to indicate which tests should only be run on Windows, and the @pytest.mark.linux marker to indicate which tests should only be run on Linux. Then, when you execute your test suite, you can simply specify which platforms you want to test on, and pytest will only run the relevant tests.

9. Run only failed tests with –lf or –ff

Suppose you have a test suite with 100 tests and one of them fails. When you run the entire suite, it takes a long time to complete. But if you use –lf or –ff, pytest will only run the failed test (or the first failing test), which saves you a lot of time.

This is especially useful when you’re developing new features and you want to quickly check if the changes you made broke any existing functionality.

10. Keep your test suite fast

If your test suite is slow, it will be a hindrance to your development process. When you’re trying to iterate quickly on features or bug fixes, waiting for a long test suite to run can be frustrating. A slow test suite can also lead to developers becoming less likely to run the tests at all, which can eventually result in a lower quality codebase.

There are a few things you can do to keep your test suite fast:

– Run tests in parallel: This can be done using pytest-xdist or pytest-parallel.
– Use fixture caching: This can speed up tests that rely on expensive setup operations, such as connecting to a database.
– Filter out tests that don’t need to be run: You can use pytest’s -k option to specify which tests to run.

By following these pytest best practices, you can keep your test suite running quickly and efficiently.