Parametrized fixtures in PyTest - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of a parametrized fixture with multiple values

What will be the output when running this pytest test with the given parametrized fixture?

PyTest

import pytest

@pytest.fixture(params=[1, 2, 3])
def number(request):
    return request.param

def test_is_positive(number):
    assert number > 0
    print(f"Testing with number: {number}")

A Test runs once printing: Testing with number: [1, 2, 3]

B Test runs 3 times printing: Testing with number: 1, Testing with number: 2, Testing with number: 3

C Test runs 3 times but fails on number 1

D Test runs 1 time and fails because number is not defined

💡 Hint

Remember that pytest runs the test once for each parameter in the fixture.

The fixture 'number' is parametrized with three values: 1, 2, and 3. The test runs once per value, printing the message each time. Since all numbers are positive, the assertion passes each time.

❓ assertion

intermediate

Correct assertion for a parametrized fixture test

Given this parametrized fixture, which assertion correctly verifies the fixture value is even?

PyTest

import pytest

@pytest.fixture(params=[2, 4, 5])
def even_number(request):
    return request.param

def test_even(even_number):
    # Which assertion is correct here?

A assert even_number % 2 == 0

B assert even_number % 2 != 0

C assert even_number == 5

D assert even_number > 5

💡 Hint

An even number is divisible by 2 with no remainder.

The assertion 'assert even_number % 2 == 0' checks if the number is even. Since the fixture includes 5, which is odd, this test will fail for that parameter, which is expected behavior.

🔧 Debug

advanced

Identify the error in this parametrized fixture usage

What error will this pytest code raise when executed?

PyTest

import pytest

@pytest.fixture(params=["a", "b", "c"])
def letter(request):
    return letter

def test_letter(letter):
    assert letter in ['a', 'b', 'c']

A AssertionError because letter is not in the list

B TypeError because 'letter' is not iterable

C RecursionError due to fixture returning itself

D No error, test passes

💡 Hint

Check what the fixture returns inside its function body.

The fixture function 'letter' returns itself instead of the parameter value. This causes infinite recursion when pytest tries to resolve the fixture, leading to a RecursionError.

❓ framework

advanced

Using indirect parametrization with fixtures

Which option correctly uses indirect parametrization to pass parameters to a fixture?

PyTest

import pytest

@pytest.fixture
def data(request):
    return request.param * 2

def test_data(data):
    assert data in [2, 4, 6]

A

@pytest.mark.parametrize('data', [1, 2, 3])
def test_data(data):
    assert data in [2, 4, 6]

B

@pytest.mark.parametrize('data', [2, 4, 6], indirect=False)
def test_data(data):
    assert data in [2, 4, 6]

C

@pytest.mark.parametrize('request', [1, 2, 3], indirect=True)
def test_data(data):
    assert data in [2, 4, 6]

D

@pytest.mark.parametrize('data', [1, 2, 3], indirect=True)
def test_data(data):
    assert data in [2, 4, 6]

💡 Hint

Indirect parametrization tells pytest to pass parameters to the fixture, not directly to the test.

Option D uses indirect=True to pass the parameters to the fixture 'data'. The fixture doubles the parameter, so the test checks for doubled values 2, 4, 6. Other options either miss indirect=True or use wrong parameter names.

🧠 Conceptual

expert

Why use parametrized fixtures instead of simple parametrized tests?

Which is the best explanation for using parametrized fixtures over parametrized tests in pytest?

A Parametrized fixtures allow sharing setup code and parameters across multiple tests, improving code reuse and clarity.

B Parametrized fixtures run tests faster by skipping setup steps.

C Parametrized fixtures prevent tests from running multiple times.

D Parametrized fixtures automatically generate test reports without assertions.

💡 Hint

Think about how fixtures help organize setup and data for tests.

Parametrized fixtures let you define setup logic once and reuse it with different data in many tests. This reduces duplication and makes tests easier to maintain. Other options are incorrect because fixtures don't skip setup, don't remove assertions, and don't prevent multiple runs.

Practice

1. What is the main purpose of using @pytest.fixture(params=[...]) in pytest?

easy

A. To run the same test multiple times with different input data

B. To skip a test conditionally

C. To mark a test as expected to fail

D. To group multiple tests into a single test case

Solution

1. Step 1: Understand the role of parametrized fixtures

   Parametrized fixtures allow running a test function multiple times with different data inputs automatically.

2. Step 2: Match the purpose with the options

   To run the same test multiple times with different input data correctly describes this behavior, while others describe unrelated pytest features.

3. Final Answer:

   To run the same test multiple times with different input data -> Option A

4. Quick Check:

   Parametrized fixtures = run test multiple times [OK]

Hint: Parametrized fixtures repeat tests with different data [OK]

Common Mistakes:

• Confusing parametrized fixtures with test skipping
• Thinking it groups tests instead of repeating them
• Mixing up with expected failure markers

2. Which of the following is the correct syntax to define a parametrized fixture in pytest?

easy

A. @pytest.fixture(param=[1, 2, 3])\ndef data(request):\n return request.param

B. @pytest.param(params=[1, 2, 3])\ndef data(request):\n return request.param

C. @pytest.fixture(params=[1, 2, 3])\ndef data(request):\n return request.param

D. @pytest.fixture(params=(1, 2, 3))\ndef data():\n return param

Solution

1. Step 1: Identify correct decorator and parameter name

   The correct decorator is @pytest.fixture with the keyword params as a list.

2. Step 2: Check function signature and usage

   The fixture function must accept request and return request.param to access each parameter value.

3. Final Answer:

   @pytest.fixture(params=[1, 2, 3])\ndef data(request):\n return request.param -> Option C

4. Quick Check:

   Use @pytest.fixture(params=[...]) and request.param [OK]

Hint: Use @pytest.fixture(params=[...]) and request.param inside function [OK]

Common Mistakes:

• Using @pytest.param instead of @pytest.fixture
• Misspelling 'params' as 'param'
• Not including 'request' parameter in fixture function
• Returning undefined variable instead of request.param

3. Given the following pytest code, what will be the output when running the test?

import pytest

@pytest.fixture(params=[10, 20])
def number(request):
    return request.param

def test_double(number):
    assert number * 2 in [20, 40]

medium

A. First test fails, second test passes

B. Both tests fail

C. First test passes, second test fails

D. Both tests pass

Solution

1. Step 1: Understand parametrized fixture values

   The fixture 'number' will run the test twice: once with 10, once with 20.

2. Step 2: Evaluate the assertion for each value

   For 10: 10*2=20, which is in [20,40]. For 20: 20*2=40, also in [20,40]. Both assertions pass.

3. Final Answer:

   Both tests pass -> Option D

4. Quick Check:

   10*2=20 and 20*2=40 both in list [OK]

Hint: Check each param value and test assertion separately [OK]

Common Mistakes:

• Assuming only one test runs
• Misreading the assertion condition
• Forgetting fixture runs test multiple times

4. Identify the error in this parametrized fixture code snippet:

@pytest.fixture(params=["a", "b"])
def letter():
    return param

def test_letter(letter):
    assert letter in ["a", "b"]

medium

A. Wrong assertion in test function

B. Missing 'request' parameter in fixture function

C. Incorrect decorator name

D. Fixture should not return any value

Solution

1. Step 1: Check fixture function parameters

   The fixture uses params, so it must accept request to access request.param.

2. Step 2: Identify incorrect return statement

   The function returns param, which is undefined. It should return request.param.

3. Final Answer:

   Missing 'request' parameter in fixture function -> Option B

4. Quick Check:

   Parametrized fixture needs 'request' param [OK]

Hint: Parametrized fixtures must accept 'request' to use request.param [OK]

Common Mistakes:

• Not including 'request' parameter in fixture
• Returning undefined variable instead of request.param
• Confusing fixture decorator names

5. You want to test a function with multiple sets of user data, but some data sets are invalid and should be skipped. How can you modify a parametrized fixture to skip tests for invalid data?

hard

A. Use pytest.skip() inside the fixture when data is invalid

B. Remove invalid data from the params list before parametrizing

C. Raise an exception inside the test function for invalid data

D. Use pytest.mark.skipif on the test function with a condition

Solution

1. Step 1: Understand skipping inside parametrized fixtures

   Inside the fixture, you can call pytest.skip() when the current parameter is invalid to skip that test run.

2. Step 2: Compare with other options

   Removing invalid data beforehand avoids testing them but doesn't show skipped tests. Raising exceptions fails tests, not skip. Using skipif on test function is less flexible for per-parameter skipping.

3. Final Answer:

   Use pytest.skip() inside the fixture when data is invalid -> Option A

4. Quick Check:

   Call pytest.skip() in fixture to skip invalid params [OK]

Hint: Call pytest.skip() inside fixture for invalid params [OK]

Common Mistakes:

• Removing invalid data silently instead of skipping
• Raising exceptions instead of skipping
• Using skipif without parameter-specific logic