Parametrization

Q: Can I parametrize fixtures?

Yes: @pytest.fixture(params=[...]).

Q: How do I skip one parameter set?

pytest.param(value, marks=pytest.mark.skip(reason="...")).

Q: How do I name test cases?

ids=["case1", "case2"] or ids=lambda val: f"pct-{val}".

Q: Can I load params from a file?

Yes. Read a CSV/YAML in conftest.py and pass to parametrize programmatically.

Q: How do I parametrize class tests?

Put @pytest.mark.parametrize on the class or each method.

Q: How do I see all generated test names?

pytest --collect-only lists every parametrized variant.

@pytest.mark.parametrize runs one test function against many input/output pairs. One test definition, clear failure output per case.

Recipe

import pytest
 
@pytest.mark.parametrize("input,expected", [
    (2, 4),
    (3, 9),
    (0, 0),
])
def test_square(input, expected):
    assert square(input) == expected

When to reach for this:

Same logic tested with multiple inputs
Boundary value testing (0, -1, max)
Replacing copy-pasted test functions

Working Example

import pytest
from myapp.billing import apply_discount
from decimal import Decimal
 
@pytest.mark.parametrize("price,pct,expected", [
    (Decimal("100"), 10, Decimal("90")),
    (Decimal("100"), 0, Decimal("100")),
    (Decimal("50"), 50, Decimal("25")),
    (Decimal("0"), 10, Decimal("0")),
])
def test_apply_discount(price, pct, expected):
    assert apply_discount(price, pct) == expected
 
@pytest.mark.parametrize("pct", [-1, 101, 150])
def test_invalid_discount_raises(pct):
    with pytest.raises(DiscountError):
        apply_discount(Decimal("100"), pct)

uv run pytest tests/test_billing.py -v
# test_apply_discount[100-10-90] PASSED
# test_apply_discount[100-0-100] PASSED
# test_invalid_discount_raises[-1] PASSED

What this demonstrates:

Multiple (input, expected) tuples in one decorator
Each tuple generates a separate test with a descriptive ID
Separate parametrize for the error cases
Failures show exactly which parameter set failed

Deep Dive

Custom Test IDs

@pytest.mark.parametrize("pct", [10, 20], ids=["ten-percent", "twenty-percent"])
def test_discount(pct): ...

Combining Parametrize

@pytest.mark.parametrize("x", [1, 2])
@pytest.mark.parametrize("y", [10, 20])
def test_combo(x, y): ...  # 4 test runs

Python Notes

# Parametrize with pytest.param for marks
@pytest.mark.parametrize("n", [
    pytest.param(0, marks=pytest.mark.xfail(reason="zero edge case")),
    1, 2, 3,
])

Gotchas

Too many cases in one parametrize - unreadable table. Fix: split by category or use a data file.
No ids on similar inputs - hard to identify failing case. Fix: add ids= or use readable values.
Mutating parametrize data - leaks between runs. Fix: treat parametrize values as immutable.
Parametrize instead of hypothesis - misses edge cases you did not list. Fix: use Hypothesis for complex input domains.
Combining parametrizes accidentally - Cartesian product explosion. Fix: be deliberate; 3x3x3 = 27 tests fast.

Alternatives

Alternative	Use When	Don't Use When
Hypothesis	Large input spaces	3-5 known cases
Loop in test	Never	-
Separate test functions	Unrelated scenarios	Same logic, different inputs

FAQs

How many parametrize cases is too many?

If the table is hard to read (>15 rows), split or use a CSV fixture.

Can I parametrize fixtures?

Yes: @pytest.fixture(params=[...]).

How do I skip one parameter set?

pytest.param(value, marks=pytest.mark.skip(reason="...")).

How do I name test cases?

ids=["case1", "case2"] or ids=lambda val: f"pct-{val}".

Can I load params from a file?

Yes. Read a CSV/YAML in conftest.py and pass to parametrize programmatically.

parametrize vs fixture params?

parametrize on test: explicit inputs. Fixture params: shared setup variations.

How do I parametrize class tests?

Put @pytest.mark.parametrize on the class or each method.

Does order matter?

No for correctness. Order affects test report sequence only.

Can I parametrize async tests?

Yes with pytest-asyncio. Same decorator on async def test_....

How do I see all generated test names?

pytest --collect-only lists every parametrized variant.

Assertions & Test Structure - test layout
Property-Based Testing - generative inputs
Fixtures - shared setup
Testing Best Practices - when to parametrize

Stack versions: This page was written for Python 3.14.0, FastAPI 0.115+, Django 5.2, Flask 3.1, Pydantic 2, PyTorch 2.6+, pandas 2.2+, Polars 1.x, ruff 0.9+, and uv 0.6+.

Recipe

import pytest
 
@pytest.mark.parametrize("input,expected", [
    (2, 4),
    (3, 9),
    (0, 0),
])
def test_square(input, expected):
    assert square(input) == expected

When to reach for this:

Same logic tested with multiple inputs
Boundary value testing (0, -1, max)
Replacing copy-pasted test functions

Working Example

import pytest
from myapp.billing import apply_discount
from decimal import Decimal
 
@pytest.mark.parametrize("price,pct,expected", [
    (Decimal("100"), 10, Decimal("90")),
    (Decimal("100"), 0, Decimal("100")),
    (Decimal("50"), 50, Decimal("25")),
    (Decimal("0"), 10, Decimal("0")),
])
def test_apply_discount(price, pct, expected):
    assert apply_discount(price, pct) == expected
 
@pytest.mark.parametrize("pct", [-1, 101, 150])
def test_invalid_discount_raises(pct):
    with pytest.raises(DiscountError):
        apply_discount(Decimal("100"), pct)

uv run pytest tests/test_billing.py -v
# test_apply_discount[100-10-90] PASSED
# test_apply_discount[100-0-100] PASSED
# test_invalid_discount_raises[-1] PASSED

What this demonstrates:

Multiple (input, expected) tuples in one decorator
Each tuple generates a separate test with a descriptive ID
Separate parametrize for the error cases
Failures show exactly which parameter set failed

Deep Dive

Custom Test IDs

@pytest.mark.parametrize("pct", [10, 20], ids=["ten-percent", "twenty-percent"])
def test_discount(pct): ...

Combining Parametrize

@pytest.mark.parametrize("x", [1, 2])
@pytest.mark.parametrize("y", [10, 20])
def test_combo(x, y): ...  # 4 test runs

Python Notes

# Parametrize with pytest.param for marks
@pytest.mark.parametrize("n", [
    pytest.param(0, marks=pytest.mark.xfail(reason="zero edge case")),
    1, 2, 3,
])

Gotchas

Too many cases in one parametrize - unreadable table. Fix: split by category or use a data file.
No ids on similar inputs - hard to identify failing case. Fix: add ids= or use readable values.
Mutating parametrize data - leaks between runs. Fix: treat parametrize values as immutable.
Parametrize instead of hypothesis - misses edge cases you did not list. Fix: use Hypothesis for complex input domains.
Combining parametrizes accidentally - Cartesian product explosion. Fix: be deliberate; 3x3x3 = 27 tests fast.

Alternatives

Alternative	Use When	Don't Use When
Hypothesis	Large input spaces	3-5 known cases
Loop in test	Never	-
Separate test functions	Unrelated scenarios	Same logic, different inputs

FAQs

How many parametrize cases is too many?

If the table is hard to read (>15 rows), split or use a CSV fixture.

Can I parametrize fixtures?

Yes: @pytest.fixture(params=[...]).

How do I skip one parameter set?

pytest.param(value, marks=pytest.mark.skip(reason="...")).

How do I name test cases?

ids=["case1", "case2"] or ids=lambda val: f"pct-{val}".

Can I load params from a file?

Yes. Read a CSV/YAML in conftest.py and pass to parametrize programmatically.

parametrize vs fixture params?

parametrize on test: explicit inputs. Fixture params: shared setup variations.

How do I parametrize class tests?

Put @pytest.mark.parametrize on the class or each method.

Does order matter?

No for correctness. Order affects test report sequence only.

Can I parametrize async tests?

Yes with pytest-asyncio. Same decorator on async def test_....

How do I see all generated test names?

pytest --collect-only lists every parametrized variant.

Assertions & Test Structure - test layout
Property-Based Testing - generative inputs
Fixtures - shared setup
Testing Best Practices - when to parametrize

Stack versions: This page was written for Python 3.14.0, FastAPI 0.115+, Django 5.2, Flask 3.1, Pydantic 2, PyTorch 2.6+, pandas 2.2+, Polars 1.x, ruff 0.9+, and uv 0.6+.

Parametrization

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Parametrization