How to parametrize fixtures and test functions

pytest enables test parametrization at several levels:

  • :pypytest.fixture allows one to parametrize fixture functions <fixture-parametrize>.
  • @pytest.mark.parametrize allows one to define multiple sets of arguments and fixtures at the test function or class.
  • pytest_generate_tests allows one to define custom parametrization schemes or extensions.

@pytest.mark.parametrize: parametrizing test functions

Several improvements.

The builtin pytest.mark.parametrize ref decorator enables parametrization of arguments for a test function. Here is a typical example of a test function that implements checking that a certain input leads to an expected output:

Python
# content of test_expectation.py
import pytest


@pytest.mark.parametrize("test_input,expected", [("3+5", 8), ("2+4", 6), ("6*9", 42)])
def test_eval(test_input, expected):
    assert eval(test_input) == expected

Here, the @parametrize decorator defines three different (test_input,expected) tuples so that the test_eval function will run three times using them in turn:

$ pytest
=========================== test session starts ============================
platform linux -- Python 3.x.y, pytest-7.x.y, pluggy-1.x.y
rootdir: /home/sweet/project
collected 3 items

test_expectation.py ..F                                              [100%]

================================= FAILURES =================================
____________________________ test_eval[6*9-42] _____________________________

test_input = '6*9', expected = 42

    @pytest.mark.parametrize("test_input,expected", [("3+5", 8), ("2+4", 6), ("6*9", 42)])
    def test_eval(test_input, expected):
>       assert eval(test_input) == expected
E       AssertionError: assert 54 == 42
E        +  where 54 = eval('6*9')

test_expectation.py:6: AssertionError
========================= short test summary info ==========================
FAILED test_expectation.py::test_eval[6*9-42] - AssertionError: assert 54...
======================= 1 failed, 2 passed in 0.12s ========================

Parameter values are passed as-is to tests (no copy whatsoever).

For example, if you pass a list or a dict as a parameter value, and the test case code mutates it, the mutations will be reflected in subsequent test case calls.

pytest by default escapes any non-ascii characters used in unicode strings for the parametrization because it has several downsides. If however you would like to use unicode strings in parametrization and see them in the terminal as is (non-escaped), use this option in your pytest.ini:

[pytest]
disable_test_id_escaping_and_forfeit_all_rights_to_community_support = True

Keep in mind however that this might cause unwanted side effects and even bugs depending on the OS used and plugins currently installed, so use it at your own risk.

As designed in this example, only one pair of input/output values fails the simple test function. And as usual with test function arguments, you can see the input and output values in the traceback.

Note that you could also use the parametrize marker on a class or a module (see mark) which would invoke several functions with the argument sets, for instance:

Python
import pytest


@pytest.mark.parametrize("n,expected", [(1, 2), (3, 4)])
class TestClass:
    def test_simple_case(self, n, expected):
        assert n + 1 == expected

    def test_weird_simple_case(self, n, expected):
        assert (n * 1) + 1 == expected

To parametrize all tests in a module, you can assign to the pytestmark global variable:

Python
import pytest

pytestmark = pytest.mark.parametrize("n,expected", [(1, 2), (3, 4)])


class TestClass:
    def test_simple_case(self, n, expected):
        assert n + 1 == expected

    def test_weird_simple_case(self, n, expected):
        assert (n * 1) + 1 == expected

It is also possible to mark individual test instances within parametrize, for example with the builtin mark.xfail:

Python
# content of test_expectation.py
import pytest


@pytest.mark.parametrize(
    "test_input,expected",
    [("3+5", 8), ("2+4", 6), pytest.param("6*9", 42, marks=pytest.mark.xfail)],
)
def test_eval(test_input, expected):
    assert eval(test_input) == expected

Let's run this:

$ pytest
=========================== test session starts ============================
platform linux -- Python 3.x.y, pytest-7.x.y, pluggy-1.x.y
rootdir: /home/sweet/project
collected 3 items

test_expectation.py ..x                                              [100%]

======================= 2 passed, 1 xfailed in 0.12s =======================

The one parameter set which caused a failure previously now shows up as an "xfailed" (expected to fail) test.

In case the values provided to parametrize result in an empty list - for example, if they're dynamically generated by some function - the behaviour of pytest is defined by the empty_parameter_set_mark option.

To get all combinations of multiple parametrized arguments you can stack parametrize decorators:

Python
import pytest


@pytest.mark.parametrize("x", [0, 1])
@pytest.mark.parametrize("y", [2, 3])
def test_foo(x, y):
    pass

This will run the test with the arguments set to x=0/y=2, x=1/y=2, x=0/y=3, and x=1/y=3 exhausting parameters in the order of the decorators.

Basic pytest_generate_tests example

Sometimes you may want to implement your own parametrization scheme or implement some dynamism for determining the parameters or scope of a fixture. For this, you can use the pytest_generate_tests hook which is called when collecting a test function. Through the passed in metafunc object you can inspect the requesting test context and, most importantly, you can call metafunc.parametrize() to cause parametrization.

For example, let's say we want to run a test taking string inputs which we want to set via a new pytest command line option. Let's first write a simple test accepting a stringinput fixture function argument:

Python
# content of test_strings.py


def test_valid_string(stringinput):
    assert stringinput.isalpha()

Now we add a conftest.py file containing the addition of a command line option and the parametrization of our test function:

Python
# content of conftest.py


def pytest_addoption(parser):
    parser.addoption(
        "--stringinput",
        action="append",
        default=[],
        help="list of stringinputs to pass to test functions",
    )


def pytest_generate_tests(metafunc):
    if "stringinput" in metafunc.fixturenames:
        metafunc.parametrize("stringinput", metafunc.config.getoption("stringinput"))

If we now pass two stringinput values, our test will run twice:

$ pytest -q --stringinput="hello" --stringinput="world" test_strings.py
..                                                                   [100%]
2 passed in 0.12s

Let's also run with a stringinput that will lead to a failing test:

$ pytest -q --stringinput="!" test_strings.py
F                                                                    [100%]
================================= FAILURES =================================
___________________________ test_valid_string[!] ___________________________

stringinput = '!'

    def test_valid_string(stringinput):
>       assert stringinput.isalpha()
E       AssertionError: assert False
E        +  where False = <built-in method isalpha of str object at 0xdeadbeef0001>()
E        +    where <built-in method isalpha of str object at 0xdeadbeef0001> = '!'.isalpha

test_strings.py:4: AssertionError
========================= short test summary info ==========================
FAILED test_strings.py::test_valid_string[!] - AssertionError: assert False
1 failed in 0.12s

As expected our test function fails.

If you don't specify a stringinput it will be skipped because metafunc.parametrize() will be called with an empty parameter list:

$ pytest -q -rs test_strings.py
s                                                                    [100%]
========================= short test summary info ==========================
SKIPPED [1] test_strings.py: got empty parameter set ['stringinput'], function test_valid_string at /home/sweet/project/test_strings.py:2
1 skipped in 0.12s

Note that when calling metafunc.parametrize multiple times with different parameter sets, all parameter names across those sets cannot be duplicated, otherwise an error will be raised.

More examples

For further examples, you might want to look at more parametrization examples <paramexamples>.