Parametrized fixtures in PyTest - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a parametrized fixture in pytest?

A parametrized fixture is a fixture that runs multiple times with different input values, allowing tests to reuse setup code with various data.

beginner

How do you define a parametrized fixture in pytest?

Use the @pytest.fixture decorator with the 'params' argument listing values. pytest runs the fixture once for each value.

beginner

Example: What does this fixture do?

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

It creates a fixture named 'number' that will provide values 1, 2, and 3 to tests, running each test three times with these values.

intermediate

Why use parametrized fixtures instead of multiple test functions?

Parametrized fixtures reduce code duplication and keep tests clean by reusing setup logic with different inputs automatically.

beginner

How can you access the current parameter value inside a parametrized fixture?

Use 'request.param' inside the fixture function to get the current parameter value.

What does the 'params' argument in @pytest.fixture do?

A Runs the fixture multiple times with each value in the list

B Skips the fixture

C Runs the fixture only once

D Marks the fixture as private

The 'params' argument tells pytest to run the fixture once for each value provided.

How do you get the current parameter value inside a parametrized fixture?

A Using 'param.value'

B Using 'self.param'

C Using 'fixture.param'

D Using 'request.param'

Inside the fixture, 'request.param' holds the current parameter value.

If a fixture is parametrized with 3 values, how many times will a test using it run?

A 1

B Depends on test count

C 3

D 0

The test runs once for each parameter value, so 3 times.

Which decorator is used to create a parametrized fixture?

A @pytest.run

B @pytest.fixture(params=[...])

C @pytest.test

D @pytest.parametrize

Parametrized fixtures use @pytest.fixture with the 'params' argument.

What is a benefit of parametrized fixtures?

A They reduce code duplication

B They slow down tests

C They hide test failures

D They disable tests

Parametrized fixtures help reuse setup code and avoid repeating similar tests.

Explain how to create and use a parametrized fixture in pytest.

Think about how pytest runs tests multiple times with different data.

Describe the advantages of using parametrized fixtures over writing multiple test functions.

Consider how setup code can be reused.

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