Chapter
3
pytest Fixtures
Now that you’ve seen the basics of pytest, let’s turn our attention to fixtures, which are essential to structuring test code for almost any non-trivial software system. Fixtures are functions that are run by pytest before (and sometimes after) the actual test functions. The code in the fixture can do whatever you want it to. You can use fixtures to get a data set for the tests to work on. You can use fixtures to get a system into a known state before running a test. Fixtures are also used to get data ready for multiple tests.
Here’s a simple fixture that returns a number:
| | import pytest |
| | |
| | |
| | @pytest.fixture() |
| | def some_data(): |
| | """Return answer to ultimate question.""" |
| | return 42 |
| | |
| | |
| | def test_some_data(some_data): |
| | """Use fixture return value in a test.""" |
| | assert some_data == 42 |
The @pytest.fixture() decorator is used to tell pytest that a function is a fixture. When you include the fixture name in the parameter list of a test function, pytest knows to run it before running the test. Fixtures can do work, and can also return data to the test function.
The test test_some_data() has the name of the fixture, some_data, as a parameter. pytest will see this and look for a fixture with this name. Naming is significant in pytest. pytest will look in the module of the test for a fixture of that name. It will also look in conftest.py files if it doesn’t find it in this file.
Before we start our exploration of fixtures (and the conftest.py file), I need to address the fact that the term fixture has many meanings in the programming and test community, and even in the Python community. I use “fixture,” “fixture function,” and “fixture method” interchangeably to refer to the @pytest.fixture() decorated functions discussed in this chapter. Fixture can also be used to refer to the resource that is being set up by the fixture functions. Fixture functions often set up or retrieve some data that the test can work with. Sometimes this data is considered a fixture. For example, the Django community often uses fixture to mean some initial data that gets loaded into a database at the start of an application.
Regardless of other meanings, in pytest and in this book, test fixtures refer to the mechanism pytest provides to allow the separation of “getting ready for” and “cleaning up after” code from your test functions.
pytest fixtures are one of the unique core features that make pytest stand out above other test frameworks, and are the reason why many people switch to and stay with pytest. However, fixtures in pytest are different than fixtures in Django and different than the setup and teardown procedures found in unittest and nose. There are a lot of features and nuances about fixtures. Once you get a good mental model of how they work, they will seem easy to you. However, you have to play with them a while to get there, so let’s get started.