Pytests

Pytest⚑

Test layouts⚑

• Directory layout starts with tests
• From tests you can add anything like unit, functional or other meaningful names like database
• Files need to be pre-fixed with test_
• Test functions need to be prefixed with test_
• Test classes need to be prefixed with Test

How syntax works⚑

Tests can be functions or classes

Functions⚑

def test_my_function():
  assert 1 == 1

Classes⚑

Classes do not need inheritance:

# This function is here for convenience only, in a real-world scenario this function
# would be elsewhere in a package

def str_to_int(string):
    '''
    Parses a string number into an integer, optionally converting to a float
    and rounding down.
    You can pass '1.1' which returns 1
    ['1'] -> raises RuntimeError
    '''
    error_msg = 'Unable to convert to integer: '%s'' % str(string)
    try:
        integer = float(string.replace(',', '.'))
    except AttributeError:
        # this might be a integer already, so try to use it, otherwise raise
        # the original exception
        if isinstance(string, (int, float)):
            integer = string
        else:
            raise RuntimeError(error_msg)
    except (TypeError, ValueError):
        raise RuntimeError(error_msg)

    return int(integer)

# When you create yout class test you have special methods
class TestStrToInt:

    def setup_method(self):
        print('\nthis is setup')

    def teardown_method(self):
        print('\nthis is teardown')

    def setup_class(cls):
        print('\nthis is setup class')

    def teardown_class(cls):
        print('\nthis is teardown class')

    def test_rounds_down(self):
        result = str_to_int('1.99')
        assert result == 2

    def test_round_down_lesser_half(self):
        result = str_to_int('1.2')
        assert result == 2

That setup_class is executed before a test in a class and happens just once, and setup_method is executed before every test in the class.

You can use these special methods to run code before all tests in a class or before each one.

You can see the ouput here:

======================================= test session starts =======================================
platform linux -- Python 3.8.10, pytest-7.2.0, pluggy-1.0.0
rootdir: /home/coder/python-testing/notebooks/lesson2
collected 2 items

test-classes/test_classes.py FF                                                             [100%]

============================================ FAILURES =============================================
__________________________________ TestStrToInt.test_rounds_down __________________________________

self = <test_classes.TestStrToInt object at 0x7f9e8a8c8220>

    def test_rounds_down(self):
        result = str_to_int('1.99')
>       assert result == 2
E       assert 1 == 2

test-classes/test_classes.py:44: AssertionError
-------------------------------------- Captured stdout setup --------------------------------------

this is setup class

this is setup
------------------------------------ Captured stdout teardown -------------------------------------

this is teardown
____________________________ TestStrToInt.test_round_down_lesser_half _____________________________

self = <test_classes.TestStrToInt object at 0x7f9e8a8c8340>

    def test_round_down_lesser_half(self):
        result = str_to_int('1.2')
>       assert result == 2
E       assert 1 == 2

test-classes/test_classes.py:48: AssertionError
-------------------------------------- Captured stdout setup --------------------------------------

this is setup
------------------------------------ Captured stdout teardown -------------------------------------

this is teardown

this is teardown class
===================================== short test summary info =====================================
FAILED test-classes/test_classes.py::TestStrToInt::test_rounds_down - assert 1 == 2
FAILED test-classes/test_classes.py::TestStrToInt::test_round_down_lesser_half - assert 1 == 2
======================================== 2 failed in 0.02s ========================================

Run tests⚑

In the test directory

pytest -vvvv tests/

============================= test session starts ==============================
platform linux2 -- Python 2.7.17, pytest-3.6.4, py-1.8.0, pluggy-0.7.1 -- /usr/bin/python2
cachedir: .pytest_cache
rootdir: /content, inifile:

collecting 0 items
collecting 2 items
collecting 2 items
collecting 2 items
collected 2 items

test_util.py::TestFloats::test_rounds_down FAILED                        [ 50%]
test_util.py::TestFloats::test_round_down_lesser_half FAILED             [100%]

=================================== FAILURES ===================================
_________________________ TestFloats.test_rounds_down __________________________

self = <test_util.TestFloats instance at 0x7fbf26d90870>

    def test_rounds_down(self):
        result = str_to_int('1.99')
>       assert result == 2
E       assert 1 == 2

test_util.py:42: AssertionError
show more (open the raw output data in a text editor) ...


this is teardown

this is teardown class
=========================== 2 failed in 0.04 seconds ===========================

Testing failures⚑

Enter to the python debugger where your code is failing:

pytest --pdb test_failure_output.py

Once entered in the debugger you can type h to see the commands that you can use.

Another commands for pytest⚑

• --collect-only -> Only collect tests, don't execute them
• -x -> Stop at the first failure

To see all type:

pytest --help

Plugins⚑

pytest-xdist⚑

Gives you the ability to run instance for running your test using the -n cli parameter.

pytest -n 4 test/

Going to set 4 differents runner instances and run them at the same time.

nbval⚑

The plugin adds functionality to py.test to recognise and collect Jupyter notebooks. The intended purpose of the tests is to determine whether execution of the stored inputs match the stored outputs of the .ipynb file. Whilst also ensuring that the notebooks are running without errors.

The tests were designed to ensure that Jupyter notebooks (especially those for reference and documentation), are executing consistently.

Each cell is taken as a test, a cell that doesn't reproduce the expected output will fail.

Installation⚑

pip install nbval

Usage⚑

python -m pytest --nbval notebooks/

Other functionalities⚑

Parametrize tests⚑

Parametrize tests it's like put a for loop over the tests that you want to expect the same result using the same function. The problem with plain for loops it's that the output it's a little bit confused. You don't know really where the error is located and if the rest of the loop it's going to be cover. So a good option it's parametrize tests.

  def str_to_bool(val):
    '''
    Convert a string representation of truth to True or False
    True values are 'y', 'yes', or ''; case-insensitive
    False values are 'n', or 'no'; case-insensitive
    Raises ValueError if 'val' is anything else.
    '''
    true_vals = ['yes', 'y', '']
    false_vals = ['no', 'n']
    try:
        val = val.lower()
    except AttributeError:
        val = str(val).lower()
    if val in true_vals:
        return True
    elif val in false_vals:
        return False
    else:
        raise ValueError('Invalid input value: %s' % val)

 import pytest
from src impport str_to_bool # function to convert string to bool


@pytest.mark.parametrize('value', ['y', 'yes', ''])
def test_is_true(value):
    result = str_to_bool(value)
    assert result is True

======================================= test session starts =======================================
platform linux -- Python 3.8.10, pytest-7.2.0, pluggy-1.0.0
rootdir: /home/coder/python-testing/notebooks/lesson2
collected 3 items

parametrize/test_utils.py ...                                                               [100%]

======================================== 3 passed in 0.01s ========================================

Fixtures⚑

In pytest, fixtures are a way to provide data or test-doubles (mocks, stubs, etc) to your tests. They are created using the @pytest.fixture decorator and then injected into your tests as arguments. Fixtures are meant to simplify test setup and teardown code, and they help to make your tests more modular and scalable.

Here's a basic example of how to use a fixture in pytest:

import pytest

# Define a fixture
@pytest.fixture
def my_fixture():
    return 'Hello, World!'

# Use the fixture in a test
def test_hello(my_fixture):
    assert my_fixture == 'Hello, World!'

In this example, the my_fixture fixture is defined to return the string 'Hello, World!'. Then, in the test_hello test, my_fixture is injected as an argument. When pytest runs this test, it first calls the my_fixture fixture function and then passes its return value to test_hello.

Here's a more complex example where a fixture is used for setup and teardown:

import pytest

# Define a fixture
@pytest.fixture
def database():
    db = setup_database()  # Setup code
    yield db  # This is what will be injected into your tests
    teardown_database(db)  # Teardown code

# Use the fixture in a test
def test_db(database):
    assert database.is_connected()

In this example, the database fixture is used to manage a database connection. The setup_database function is called to establish the connection, and then the connection object is yielded to the test. After the test runs, the teardown_database function is called to clean up the connection.

fixture scopes⚑

Fixture scope determines when a fixture is set up and torn down. The possible scopes are function, class, module, package or session:

• function: The default scope, the fixture is set up and torn down for each test function.
• class: The fixture is set up and torn down for each test class.
• module: The fixture is set up and torn down once per test module.
• package: The fixture is set up and torn down once per test package.
• session: The fixture is set up once when the test session starts, and is torn down once at the end of the test session.

import pytest

@pytest.fixture(scope='module')
def module_fixture():
    # Setup code here
    yield 'Hello, Module!'
    # Teardown code here

Fixture dependencies⚑

Fixtures can use other fixtures. This is often useful when you want to modularize your fixtures for reuse and better organization.

import pytest

@pytest.fixture
def order():
    return {'name': 'Burger', 'price': 7.99}

@pytest.fixture
def cart(order):
    return [order]

def test_cart(cart):
    assert len(cart) == 1
````

#### conftest

The conftest.py file serves as a means of providing fixtures for an entire directory
of tests. Any fixture defined in conftest.py will be automatically available to all
test files in the same directory and subdirectories.

```python
# conftest.py
import pytest

@pytest.fixture
def my_fixture():
    return 'Available Everywhere'

temporal directories⚑

The tmpdir fixture is a built-in pytest fixture that creates a temporary directory unique to the test invocation, which is automatically cleaned up after the test.

class TestMyClass:

    def test_write_Yes(self, tmpdir):
        path = str(tmpdir.join('test_value'))
        write_integer('Yes', path)
        with open(path, 'r') as _f:
            value = _f.read()

        assert value == 'True'

Monkeypatch⚑

The monkeypatch fixture helps to safely set/delete an attribute, dictionary item or environment variable or to modify sys.path for importing.

def test_monkeypatch(monkeypatch):
    result = {'HELLO': 'world'}
    monkeypatch.setenv('HELLO', 'monkeypatched')
    assert result['HELLO'] == 'monkeypatched'