Use @pytest.fixture(params=[1, 2]) or @pytest.mark.parametrize('a, b', [(1, 2), (3, 4)]) to create multiple tests
Imagine you want to write a test for a particular function, but for multiple input values.
Writing a for-loop is a bad idea as the test will fail as soon as it hits the first AssertionError.
Subsequent input values will not be tested and you have no idea which part of your code is actually broken.
At the same time you want to stick to DRY and not implement the same unittest.Testcase
method over and over again with slightly different input values.
Keep in mind why we write unit tests:
We want to know when we break stuff, but also at the same time get as many hints as possible on why the error occurs!
Pytest provides various ways of creating individual test items. There are parametrized fixtures and mark.parametrize (and hooks).
Using this built-in marker you do not need to implement any fixtures. Instead you define your scenarios in a decorator and the only thing you really need to look out for is to match the number of positional test arguments with your iterable.
import pytest
@pytest.mark.parametrize(
'number, word', [
(1, '1'),
(3, 'Fizz'),
(5, 'Buzz'),
(10, 'Buzz'),
(15, 'FizzBuzz'),
(16, '16')
]
)
def test_fizzbuzz(number, word):
assert fizzbuzz(number) == word
To parametrize a fixture you need pass an interable to the params keyword argument.
The built-in fixture request knows about the current parameter and if you don't want to do anything fancy,
you can pass it right to the test via the return statement.
import pytest
@pytest.fixture(params=['apple', 'banana', 'plum'])
def fruit(request):
return request.param
def test_is_healthy(fruit):
assert is_healthy(fruit)
Please note that the examples are written in Python3
Sometimes you may find yourself struggling to chose which is the best way to parametrize your tests. At the end of the day it really depends on what you want to test. But... Good news! Pytest lets you combine both methods to get the most out of both worlds.
Imagine this Python module (foobar.py) which contains a few class definitions with a bit of logic:
# -*- coding: utf-8 -*-
FOSS_LICENSES = ['Apache 2.0', 'MIT', 'GPL', 'BSD']
PYTHON_PKGS = ['pytest', 'requests', 'django', 'cookiecutter']
class Package:
def __init__(self, name, license):
self.name = name
self.license = license
@property
def is_open_source(self):
return self.license in FOSS_LICENSES
class Person:
def __init__(self, name, gender):
self.name = name
self.gender = gender
self._skills = ['eating', 'sleeping']
def learn(self, skill):
self._skills.append(skill)
@property
def looks_like_a_programmer(self):
return any(
package in self._skills
for package in PYTHON_PKGS
)
class Woman(Person):
def __init__(self, name):
super().__init__(name, 'female')
class Man(Person):
def __init__(self, name):
super().__init__(name, 'male')
With only two few lines of pytest code, we can create loads of different scenarios that we would like to test.
By re-using parametrized fixtures and applying the aforementioned markers to your tests, you can focus on the actual test implementation, as opposed to
writing the same boilerplate code for each of the methods that you would have to write with unittest.TestCase.
# -*- coding: utf-8 -*-
import operator
import pytest
from foobar import Package, Woman, Man
PACKAGES = [
Package('requests', 'Apache 2.0'),
Package('django', 'BSD'),
Package('pytest', 'MIT'),
]
@pytest.fixture(params=PACKAGES, ids=operator.attrgetter('name'))
def python_package(request):
return request.param
@pytest.mark.parametrize('person', [
Woman('Audrey'), Woman('Brianna'),
Man('Daniel'), Woman('Ola'), Man('Kenneth')
])
def test_become_a_programmer(person, python_package):
person.learn(python_package.name)
assert person.looks_like_a_programmer
def test_is_open_source(python_package):
assert python_package.is_open_source
Going the extra mile and setting up ids for your test scenarios greatly increases
the comprehensibilty of your test report. In this case we would like to display the name
of each Package rather than the fixture name with a numbered suffix such as python_package2.
If you run the tests now, you will see that pytest created 18 individual tests for us (Yes, yes indeed. 18 = 3 * 5 + 3).
$ py.test -v
================================== test session starts ==================================
platform darwin -- Python 3.5.0, pytest-2.8.7, py-1.4.31, pluggy-0.3.1
collected 18 items
test_foobar.py::test_become_a_programmer[requests-person0] PASSED
test_foobar.py::test_become_a_programmer[requests-person1] PASSED
test_foobar.py::test_become_a_programmer[requests-person2] PASSED
test_foobar.py::test_become_a_programmer[requests-person3] PASSED
test_foobar.py::test_become_a_programmer[requests-person4] PASSED
test_foobar.py::test_become_a_programmer[django-person0] PASSED
test_foobar.py::test_become_a_programmer[django-person1] PASSED
test_foobar.py::test_become_a_programmer[django-person2] PASSED
test_foobar.py::test_become_a_programmer[django-person3] PASSED
test_foobar.py::test_become_a_programmer[django-person4] PASSED
test_foobar.py::test_become_a_programmer[pytest-person0] PASSED
test_foobar.py::test_become_a_programmer[pytest-person1] PASSED
test_foobar.py::test_become_a_programmer[pytest-person2] PASSED
test_foobar.py::test_become_a_programmer[pytest-person3] PASSED
test_foobar.py::test_become_a_programmer[pytest-person4] PASSED
test_foobar.py::test_is_open_source[requests] PASSED
test_foobar.py::test_is_open_source[django] PASSED
test_foobar.py::test_is_open_source[pytest] PASSED
=============================== 18 passed in 0.02 seconds ===============================
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