Fixture dependencies (fixture using fixture) in PyTest - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of pytest fixture dependency execution order

Given the following pytest code with fixtures depending on each other, what will be the output when running the test?

PyTest

import pytest

@pytest.fixture
def fixture_a():
    print('Setup A')
    yield 'A'
    print('Teardown A')

@pytest.fixture
def fixture_b(fixture_a):
    print('Setup B')
    yield f'B uses {fixture_a}'
    print('Teardown B')

@pytest.fixture
def fixture_c(fixture_b):
    print('Setup C')
    yield f'C uses {fixture_b}'
    print('Teardown C')

def test_example(fixture_c):
    print(f'Running test with {fixture_c}')

A

Setup B
Setup A
Setup C
Running test with C uses B uses A
Teardown A
Teardown C
Teardown B

B

Setup C
Setup B
Setup A
Running test with C uses B uses A
Teardown A
Teardown B
Teardown C

C

Setup A
Setup C
Setup B
Running test with C uses B uses A
Teardown B
Teardown C
Teardown A

D

Setup A
Setup B
Setup C
Running test with C uses B uses A
Teardown C
Teardown B
Teardown A

💡 Hint

Remember that pytest runs fixtures in dependency order: the deepest dependency first, then upwards.

Fixture 'fixture_c' depends on 'fixture_b', which depends on 'fixture_a'. So pytest runs setup in order: fixture_a, fixture_b, fixture_c. Then the test runs. Then teardown happens in reverse order: fixture_c, fixture_b, fixture_a.

❓ assertion

intermediate

Correct assertion for fixture dependency value

Given these fixtures, which assertion correctly verifies that the test receives the expected combined fixture value?

PyTest

import pytest

@pytest.fixture
def base():
    return 5

@pytest.fixture
def dependent(base):
    return base * 2

def test_value(dependent):
    # Which assertion is correct here?

A assert dependent == 7

B assert dependent == 5

C assert dependent == 10

D assert dependent == 0

💡 Hint

The dependent fixture doubles the base fixture value.

The base fixture returns 5. The dependent fixture returns base * 2, which is 10. So the assertion must check for 10.

🔧 Debug

advanced

Identify the error in fixture dependency usage

What error will occur when running this pytest code?

PyTest

import pytest

@pytest.fixture
def fix1():
    return 1

@pytest.fixture
def fix2():
    return 2

def test_error(fix1, fix3):
    assert fix1 + fix3 == 3

A TypeError: unsupported operand type(s) for +: 'int' and 'NoneType'

B pytest fixture lookup error: fixture 'fix3' not found

C NameError: name 'fix3' is not defined

D Test passes with no error

💡 Hint

Check if all fixtures used in the test are defined.

The test requests a fixture named 'fix3' which is not defined anywhere. Pytest raises a fixture lookup error indicating 'fix3' is missing.

❓ framework

advanced

Best practice for fixture dependency scope

If fixture B depends on fixture A, and fixture A has scope='module', what is the best scope to assign to fixture B to avoid scope mismatch errors?

A scope='module'

B scope='function'

C scope='session'

D scope='class'

💡 Hint

Fixture scope must be the same or broader than its dependencies.

A fixture cannot have a narrower scope than a fixture it depends on. Since fixture A is 'module' scope, fixture B must be 'module' or broader (like 'session'). 'function' or 'class' are narrower and cause errors.

🧠 Conceptual

expert

Why use fixture dependencies in pytest?

Which of the following is the main advantage of using fixture dependencies (fixtures using other fixtures) in pytest?

A To reduce code duplication by reusing setup logic across multiple fixtures

B To make tests run faster by skipping setup steps

C To avoid writing any teardown code

D To force tests to run in a specific order

💡 Hint

Think about how fixtures can share common setup steps.

Fixture dependencies allow you to build complex setups by reusing simpler fixtures. This reduces code duplication and improves maintainability.