Generates multiple test invocations from a single test function by supplying argument tuples.

Indicates that the decorated test function should be executed repeatedly with different input values.

When you need to test a function with several input-output pairs, especially for table‑driven or property‑based testing scenarios.

Inside a Python test module, placed directly above a test function definition that uses pytest.

@pytest.mark.parametrize('arg1,arg2,...', [(val1,val2,...), ...])

@pytest.mark.parametrize(arg_names, arg_values) where arg_names is a comma‑separated string of parameter names and arg_values is an iterable of tuples matching those names.

arg_names: str of comma‑separated parameter names, arg_values: iterable of tuples with values matching the arg_names count

• def test_...
• assert statements
• pytest.raises
• fixtures
• ids parameter

• Using zip to combine separate lists
• using the ids parameter for custom test IDs
• using indirect to pass fixtures as arguments

1. Mismatched number of values per tuple vs. arg names → ValueError during test collection.\n2. Using mutable default objects in param lists → shared state across tests.\n3. Forgetting the decorator → test runs once with missing arguments.\n4. Passing a list of lists instead of tuples (still works but less idiomatic).\n5. Using non‑hashable items in the ids parameter → errors.

pytest.mark.parametrize vs unittest.subTest: subTest runs iterations within a single test method, giving less isolated reporting; pytest.mark.parametrize with indirect: passes arguments as fixtures for dynamic setup; pytest.mark.parametrize with ids: customizes test IDs for clearer output.

Coming from unittest: may write loops inside a test method instead of using parametrize → less isolated test reporting and harder failure pinning; use parametrize for separate test invocations.

• pytest.mark.parametrize
• pytest.fixture
• pytest.mark.parametrize with indirect
• pytest.mark.parametrize with ids

1. Avoid parametrizing tests that require heavy shared setup; use fixtures to avoid redundant work.\n2. Many parametrized cases increase test runtime linearly; consider sampling or property‑based testing for large domains.\n3. When using indirect parameters, the fixture must accept the argument as a parameter; otherwise the fixture receives the raw value, causing fixture misuse.

Think of @pytest.mark.parametrize as a stamp that stamps out multiple copies of a test, each with its own set of arguments, like a cookie cutter shaping dough.

The ids parameter can accept a callable to generate IDs dynamically, improving readability of parametrized test outputs.

Using parametrize with indirect fixtures to dynamically generate test data based on parameters.