Enables data-driven testing by generating multiple test invocations from a single test function.

Indicates that the decorated test function should be executed once for each tuple in the provided list, binding the tuple elements to the parameter names.

When writing pytest test functions that need to be run with several different input datasets to avoid duplicating test logic.

Inside pytest test modules, placed directly above a test function definition.

@pytest.mark.parametrize('param_names', [(arg1, expected1), (arg2, expected2), ...])

@pytest.mark.parametrize(string_of_param_names, list_of_tuples)

param_names (string of comma-separated argument names), list_of_tuples (each tuple length matches number of param_names)

• pytest
• test
• fixture
• mark.parametrize
• test_function

• param names string
• list of tuples or lists
• use of ids parameter
• indirect fixture usage

Mismatched number of values per tuple, mismatched argument count, missing commas, incorrect data types, forgetting the * unpacking when needed

@pytest.mark.parametrize with indirect, @pytest.mark.parametrize with ids, @pytest.fixture(params=...)

Confusing the order of values in tuples, mixing up argument names with fixture names, misunderstanding positional vs keyword binding

• pytest parametrize decorator

Enables clear, data-driven test cases; ids improve readability; indirect mode allows fixture-driven setup

Signals that the test is data-driven, reducing boilerplate and making test intent explicit

@pytest.mark.parametrize

Consider adding descriptive ids via the ids argument to make test output more readable

Add ids for clarity, or migrate to indirect fixtures for more complex setup/teardown