Parametrized fixtures in PyTest

Introduction

Parametrized fixtures let you run the same test with different data easily. This helps check many cases without writing repeated code.

You want to test a function with multiple input values.

You need to check how your code behaves with different settings.

You want to avoid writing many similar tests for different data.

You want to keep your tests clean and organized while testing many cases.

Syntax

PyTest

@pytest.fixture(params=[value1, value2, ...])
def fixture_name(request):
    return request.param

The params list holds all values to test with.

The fixture returns each value one by one to the test.

Examples

This fixture will provide 1, then 2, then 3 to tests using it.

PyTest

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

This fixture gives the test "apple" first, then "banana".

PyTest

@pytest.fixture(params=["apple", "banana"])
def fruit(request):
    return request.param

Sample Program

This test uses the number fixture which runs the test three times with 10, 20, and 30. It checks if the number is even.

PyTest

import pytest

@pytest.fixture(params=[10, 20, 30])
def number(request):
    return request.param


def test_is_even(number):
    assert (number % 2 == 0) == True

Important Notes

Each test using a parametrized fixture runs once per parameter value.

Use request.param to get the current parameter inside the fixture.

Parametrized fixtures help avoid repeating similar test code.

Summary

Parametrized fixtures run tests multiple times with different data.

They keep tests simple and avoid duplication.

Use @pytest.fixture(params=[...]) and request.param to access values.

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