# Parametrizing tests

`pytest` allows to easily parametrize test functions. For basic docs, see Parametrizing fixtures and test functions.

In the following we provide some examples using the builtin mechanisms.

## Generating parameters combinations, depending on command line

Let’s say we want to execute a test with different computation parameters and the parameter range shall be determined by a command line argument. Let’s first write a simple (do-nothing) computation test:

    # content of test_compute.py

    def test_compute(param1):
        assert param1 < 4

Now we add a test configuration like this:

    # content of conftest.py

    def pytest_addoption(parser):
        parser.addoption("--all", action="store_true",
            help="run all combinations")

    def pytest_generate_tests(metafunc):
        if 'param1' in metafunc.fixturenames:
            if metafunc.config.option.all:
                end = 5
            else:
                end = 2
            metafunc.parametrize("param1", range(end))

This means that we only run 2 tests if we do not pass `--all`:

    $ py.test -q test_compute.py
    ..
    2 passed in 0.12 seconds

We run only two computations, so we see two dots. let’s run the full monty:

    $ py.test -q --all
    ....F
    ======= FAILURES ========
    _______ test_compute[4] ________

    param1 = 4

        def test_compute(param1):
    >       assert param1 < 4
    E       assert 4 < 4

    test_compute.py:3: AssertionError
    1 failed, 4 passed in 0.12 seconds

As expected when running the full range of `param1` values we’ll get an error on the last one.

## Different options for test IDs

pytest will build a string that is the test ID for each set of values in a parametrized test. These IDs can be used with `-k` to select specific cases to run, and they will also identify the specific case when one is failing. Running pytest with `--collect-only` will show the generated IDs.

Numbers, strings, booleans and None will have their usual string representation used in the test ID. For other objects, pytest will make a string based on the argument name:

    # content of test_time.py

    import pytest

    from datetime import datetime, timedelta

    testdata = [
        (datetime(2001, 12, 12), datetime(2001, 12, 11), timedelta(1)),
        (datetime(2001, 12, 11), datetime(2001, 12, 12), timedelta(-1)),
    ]

    @pytest.mark.parametrize("a,b,expected", testdata)
    def test_timedistance_v0(a, b, expected):
        diff = a - b
        assert diff == expected

    @pytest.mark.parametrize("a,b,expected", testdata, ids=["forward", "backward"])
    def test_timedistance_v1(a, b, expected):
        diff = a - b
        assert diff == expected

    def idfn(val):
        if isinstance(val, (datetime,)):
            # note this wouldn't show any hours/minutes/seconds
            return val.strftime('%Y%m%d')

    @pytest.mark.parametrize("a,b,expected", testdata, ids=idfn)
    def test_timedistance_v2(a, b, expected):
        diff = a - b
        assert diff == expected

In `test_timedistance_v0`, we let pytest generate the test IDs.

In `test_timedistance_v1`, we specified `ids` as a list of strings which were used as the test IDs. These are succinct, but can be a pain to maintain.

In `test_timedistance_v2`, we specified `ids` as a function that can generate a string representation to make part of the test ID. So our `datetime` values use the label generated by `idfn`, but because we didn’t generate a label for `timedelta` objects, they are still using the default pytest representation:

    $ py.test test_time.py --collect-only
    ======= test session starts ========
    platform linux -- Python 3.4.3, pytest-2.8.7, py-1.4.31, pluggy-0.3.1
    rootdir: $REGENDOC_TMPDIR, inifile:
    collected 6 items
    <Module 'test_time.py'>
      <Function 'test_timedistance_v0[a0-b0-expected0]'>
      <Function 'test_timedistance_v0[a1-b1-expected1]'>
      <Function 'test_timedistance_v1[forward]'>
      <Function 'test_timedistance_v1[backward]'>
      <Function 'test_timedistance_v2[20011212-20011211-expected0]'>
      <Function 'test_timedistance_v2[20011211-20011212-expected1]'>

    ======= no tests ran in 0.12 seconds ========

## A quick port of “testscenarios”

Here is a quick port to run tests configured with test scenarios, an add-on from Robert Collins for the standard unittest framework. We only have to work a bit to construct the correct arguments for pytest’s `Metafunc.parametrize()`:

    # content of test_scenarios.py

    def pytest_generate_tests(metafunc):
        idlist = []
        argvalues = []
        for scenario in metafunc.cls.scenarios:
            idlist.append(scenario[0])
            items = scenario[1].items()
            argnames = [x[0] for x in items]
            argvalues.append(([x[1] for x in items]))
        metafunc.parametrize(argnames, argvalues, ids=idlist, scope="class")

    scenario1 = ('basic', {'attribute': 'value'})
    scenario2 = ('advanced', {'attribute': 'value2'})

    class TestSampleWithScenarios:
        scenarios = [scenario1, scenario2]

        def test_demo1(self, attribute):
            assert isinstance(attribute, str)

        def test_demo2(self, attribute):
            assert isinstance(attribute, str)

this is a fully self-contained example which you can run with:

    $ py.test test_scenarios.py
    ======= test session starts ========
    platform linux -- Python 3.4.3, pytest-2.8.7, py-1.4.31, pluggy-0.3.1
    rootdir: $REGENDOC_TMPDIR, inifile:
    collected 4 items

    test_scenarios.py ....

    ======= 4 passed in 0.12 seconds ========

If you just collect tests you’ll also nicely see ‘advanced’ and ‘basic’ as variants for the test function:

    $ py.test --collect-only test_scenarios.py
    ======= test session starts ========
    platform linux -- Python 3.4.3, pytest-2.8.7, py-1.4.31, pluggy-0.3.1
    rootdir: $REGENDOC_TMPDIR, inifile:
    collected 4 items
    <Module 'test_scenarios.py'>
      <Class 'TestSampleWithScenarios'>
        <Instance '()'>
          <Function 'test_demo1[basic]'>
          <Function 'test_demo2[basic]'>
          <Function 'test_demo1[advanced]'>
          <Function 'test_demo2[advanced]'>

    ======= no tests ran in 0.12 seconds ========

Note that we told `metafunc.parametrize()` that your scenario values should be considered class-scoped. With pytest-2.3 this leads to a resource-based ordering.

## Deferring the setup of parametrized resources

The parametrization of test functions happens at collection time. It is a good idea to setup expensive resources like DB connections or subprocess only when the actual test is run. Here is a simple example how you can achieve that, first the actual test requiring a `db` object:

    # content of test_backends.py

    import pytest
    def test_db_initialized(db):
        # a dummy test
        if db.__class__.__name__ == "DB2":
            pytest.fail("deliberately failing for demo purposes")

We can now add a test configuration that generates two invocations of the `test_db_initialized` function and also implements a factory that creates a database object for the actual test invocations:

    # content of conftest.py
    import pytest

    def pytest_generate_tests(metafunc):
        if 'db' in metafunc.fixturenames:
            metafunc.parametrize("db", ['d1', 'd2'], indirect=True)

    class DB1:
        "one database object"
    class DB2:
        "alternative database object"

    @pytest.fixture
    def db(request):
        if request.param == "d1":
            return DB1()
        elif request.param == "d2":
            return DB2()
        else:
            raise ValueError("invalid internal test config")

Let’s first see how it looks like at collection time:

    $ py.test test_backends.py --collect-only
    ======= test session starts ========
    platform linux -- Python 3.4.3, pytest-2.8.7, py-1.4.31, pluggy-0.3.1
    rootdir: $REGENDOC_TMPDIR, inifile:
    collected 2 items
    <Module 'test_backends.py'>
      <Function 'test_db_initialized[d1]'>
      <Function 'test_db_initialized[d2]'>

    ======= no tests ran in 0.12 seconds ========

And then when we run the test:

    $ py.test -q test_backends.py
    .F
    ======= FAILURES ========
    _______ test_db_initialized[d2] ________

    db = <conftest.DB2 object at 0xdeadbeef>

        def test_db_initialized(db):
            # a dummy test
            if db.__class__.__name__ == "DB2":
    >           pytest.fail("deliberately failing for demo purposes")
    E           Failed: deliberately failing for demo purposes

    test_backends.py:6: Failed
    1 failed, 1 passed in 0.12 seconds

The first invocation with `db == "DB1"` passed while the second with `db == "DB2"` failed. Our `db` fixture function has instantiated each of the DB values during the setup phase while the `pytest_generate_tests` generated two according calls to the `test_db_initialized` during the collection phase.

## Apply indirect on particular arguments

Very often parametrization uses more than one argument name. There is opportunity to apply `indirect` parameter on particular arguments. It can be done by passing list or tuple of arguments’ names to `indirect`. In the example below there is a function `test_indirect` which uses two fixtures: `x` and `y`. Here we give to indirect the list, which contains the name of the fixture `x`. The indirect parameter will be applied to this argument only, and the value `a` will be passed to respective fixture function:

    # content of test_indirect_list.py

    import pytest
    @pytest.fixture(scope='function')
    def x(request):
        return request.param * 3

    @pytest.fixture(scope='function')
    def y(request):
        return request.param * 2

    @pytest.mark.parametrize('x, y', [('a', 'b')], indirect=['x'])
    def test_indirect(x,y):
        assert x == 'aaa'
        assert y == 'b'

The result of this test will be successful:

    $ py.test test_indirect_list.py --collect-only
    ======= test session starts ========
    platform linux -- Python 3.4.3, pytest-2.8.7, py-1.4.31, pluggy-0.3.1
    rootdir: $REGENDOC_TMPDIR, inifile:
    collected 1 items
    <Module 'test_indirect_list.py'>
      <Function 'test_indirect[a-b]'>

    ======= no tests ran in 0.12 seconds ========

## Parametrizing test methods through per-class configuration

Here is an example `pytest_generate_function` function implementing a parametrization scheme similar to Michael Foord’s unittest parametrizer but in a lot less code:

    # content of ./test_parametrize.py
    import pytest

    def pytest_generate_tests(metafunc):
        # called once per each test function
        funcarglist = metafunc.cls.params[metafunc.function.__name__]
        argnames = list(funcarglist[0])
        metafunc.parametrize(argnames, [[funcargs[name] for name in argnames]
                for funcargs in funcarglist])

    class TestClass:
        # a map specifying multiple argument sets for a test method
        params = {
            'test_equals': [dict(a=1, b=2), dict(a=3, b=3), ],
            'test_zerodivision': [dict(a=1, b=0), ],
        }

        def test_equals(self, a, b):
            assert a == b

        def test_zerodivision(self, a, b):
            pytest.raises(ZeroDivisionError, "a/b")

Our test generator looks up a class-level definition which specifies which argument sets to use for each test function. Let’s run it:

    $ py.test -q
    F..
    ======= FAILURES ========
    _______ TestClass.test_equals[1-2] ________

    self = <test_parametrize.TestClass object at 0xdeadbeef>, a = 1, b = 2

        def test_equals(self, a, b):
    >       assert a == b
    E       assert 1 == 2

    test_parametrize.py:18: AssertionError
    1 failed, 2 passed in 0.12 seconds

## Indirect parametrization with multiple fixtures

Here is a stripped down real-life example of using parametrized testing for testing serialization of objects between different python interpreters. We define a `test_basic_objects` function which is to be run with different sets of arguments for its three arguments:

- `python1`: first python interpreter, run to pickle-dump an object to a file
- `python2`: second interpreter, run to pickle-load an object from a file
- `obj`: object to be dumped/loaded

    """
    module containing a parametrized tests testing cross-python
    serialization via the pickle module.
    """
    import py
    import pytest

    pythonlist = ['python2.6', 'python2.7', 'python3.3']
    @pytest.fixture(params=pythonlist)
    def python1(request, tmpdir):
        picklefile = tmpdir.join("data.pickle")
        return Python(request.param, picklefile)

    @pytest.fixture(params=pythonlist)
    def python2(request, python1):
        return Python(request.param, python1.picklefile)

    class Python:
        def __init__(self, version, picklefile):
            self.pythonpath = py.path.local.sysfind(version)
            if not self.pythonpath:
                pytest.skip("%r not found" %(version,))
            self.picklefile = picklefile
        def dumps(self, obj):
            dumpfile = self.picklefile.dirpath("dump.py")
            dumpfile.write(py.code.Source("""
                import pickle
                f = open(%r, 'wb')
                s = pickle.dump(%r, f, protocol=2)
                f.close()
            """ % (str(self.picklefile), obj)))
            py.process.cmdexec("%s %s" %(self.pythonpath, dumpfile))

        def load_and_is_true(self, expression):
            loadfile = self.picklefile.dirpath("load.py")
            loadfile.write(py.code.Source("""
                import pickle
                f = open(%r, 'rb')
                obj = pickle.load(f)
                f.close()
                res = eval(%r)
                if not res:
                    raise SystemExit(1)
            """ % (str(self.picklefile), expression)))
            print (loadfile)
            py.process.cmdexec("%s %s" %(self.pythonpath, loadfile))

    @pytest.mark.parametrize("obj", [42, {}, {1:3},])
    def test_basic_objects(python1, python2, obj):
        python1.dumps(obj)
        python2.load_and_is_true("obj == %s" % obj)

Running it results in some skips if we don’t have all the python interpreters installed and otherwise runs all combinations (5 interpreters times 5 interpreters times 3 objects to serialize/deserialize):

    . $ py.test -rs -q multipython.py
    ...........................
    27 passed in 0.12 seconds

## Indirect parametrization of optional implementations/imports

If you want to compare the outcomes of several implementations of a given API, you can write test functions that receive the already imported implementations and get skipped in case the implementation is not importable/available. Let’s say we have a “base” implementation and the other (possibly optimized ones) need to provide similar results:

    # content of conftest.py

    import pytest

    @pytest.fixture(scope="session")
    def basemod(request):
        return pytest.importorskip("base")

    @pytest.fixture(scope="session", params=["opt1", "opt2"])
    def optmod(request):
        return pytest.importorskip(request.param)

And then a base implementation of a simple function:

    # content of base.py
    def func1():
        return 1

And an optimized version:

    # content of opt1.py
    def func1():
        return 1.0001

And finally a little test module:

    # content of test_module.py

    def test_func1(basemod, optmod):
        assert round(basemod.func1(), 3) == round(optmod.func1(), 3)

If you run this with reporting for skips enabled:

    $ py.test -rs test_module.py
    ======= test session starts ========
    platform linux -- Python 3.4.3, pytest-2.8.7, py-1.4.31, pluggy-0.3.1
    rootdir: $REGENDOC_TMPDIR, inifile:
    collected 2 items

    test_module.py .s
    ======= short test summary info ========
    SKIP [1] $REGENDOC_TMPDIR/conftest.py:10: could not import 'opt2'

    ======= 1 passed, 1 skipped in 0.12 seconds ========

You’ll see that we don’t have a `opt2` module and thus the second test run of our `test_func1` was skipped. A few notes:

- the fixture functions in the `conftest.py` file are “session-scoped” because we don’t need to import more than once
- if you have multiple test functions and a skipped import, you will see the `[1]` count increasing in the report
- you can put @pytest.mark.parametrize style parametrization on the test functions to parametrize input/output values as well.