pytest is one of the most popular testing frameworks in Python. It makes writing and running tests simple and efficient.

See the official documentation for more details: https://docs.pytest.org/en/stable/index.html

Installing pytest:

To install pytest for the current user:
$ python3 -m pip install --user pytest
Alternatively, to install it globally on Ubuntu:
$ sudo apt install python3-pytest
To verify the installation:
$ pytest --version Output:
pytest 7.4.4

pytest.ini:

The pytest.ini file is used to configure pytest behavior. It takes precedence, even when empty, over other config files. You may also use a hidden version named .pytest.ini.

Create a pytest.ini file in your project's root directory:
$ vi pytest.ini [pytest] minversion = 6.0 addopts = -ra -q testpaths = tests integration

Assertions:

Assertions are used to validate conditions in unit tests.

• Basic assertions:

  assert output # Assert that 'output' is truthy (i.e., evaluates to True) assert not output # Assert that 'output' is falsy (i.e., evaluates to False) assert output == value # Assert that 'output' is equal to 'value' (assert equality) assert output != value # Assert that 'output' is not equal to 'value' (assert inequality) assert output is None # Assert that 'output' is None assert output is not None # Assert that 'output' is not None
  

• Collection assertions:

  assert item in collection # Assert that 'item' is in the 'collection' (assert membership) assert item not in collection # Assert that 'item' is not in the 'collection' (assert non membership) assert len(collection) == value # Assert that the length of the 'collection' is equal to 'value' (assert collection length) assert all(collection) # Assert that all items in the 'collection' are truthy assert any(collection) # Assert that at least one item in the 'collection' is truthy
  

• Numeric assertions:

  assert output > value # Assert that 'output' is greater than 'value' assert output >= value # Assert that 'output' is greater than or equal to 'value' assert output < value # Assert that 'output' is less than 'value' assert output <= value # Assert that 'output' is less than or equal to 'value' assert abs(output - value) < 0.001 # Assert that 'output' is approximately equal to 'value' (floating-point comparison)
  

• Type assertions:

  assert isinstance(output, specific_type) # Assert that 'output' is of type 'specific_type' (i.e., str) assert hasattr(obj, 'method_name') # Assert that 'obj' has the attribute 'method_name'
  

• Exception assertions:

  with pytest.raises(ValueError): function_that_should_raise_value_error() with pytest.raises(ValueError, match="invalid input"): function_with_specific_error_message()

Simple unit test:

• Create a file hello.py that contains a simple function 'hello':
  $ vi hello.py def hello(name): if not isinstance(name, str): raise TypeError("Arguments must be strings") elif name == '': raise ValueError("Arguments cannot be empty") else: return name
  
• Create a unit test file test_hello.py:
  $ vi test_hello.py import pytest from hello import hello def test_hello(): """hello unit test""" with pytest.raises(TypeError, match="Arguments must be strings"): hello(123) with pytest.raises(ValueError): hello('') output = hello('abc') assert output == 'abc'
  
• To execute the unit test, open a terminal and run 'pytest' command from the project folder:
  $ pytest Output:
  ================================================================================ test session starts ================================================================================ platform linux -- Python 3.12.3, pytest-7.4.4, pluggy-1.4.0 rootdir: ~/dev/python/my_project collected 1 item test_hello.py . [100%] ================================================================================= 1 passed in 0.01s =================================================================================
  
• If the test encounters a failed assertion or an unexpected exception, pytest will report the test as failed or errored.
  
  Here's an example of a unit test that will fail:
  import pytest from hello import hello def test_hello(): """hello unit test""" with pytest.raises(ValueError, match="Arguments must be strings"): hello(123) with pytest.raises(TypeError, match="Arguments should be strings"): hello(123) with pytest.raises(TypeError): hello('') output = hello('abc') assert output == 'xyz'
  Running pytest on this test will produce the following errors:
  > raise TypeError("Arguments must be strings") E TypeError: Arguments must be strings
  > with pytest.raises(TypeError, match="Arguments should be strings"): E AssertionError: Regex pattern did not match. E Regex: 'Arguments should be strings' E Input: 'Arguments must be strings'
  > raise ValueError("Arguments cannot be empty") E ValueError: Arguments cannot be empty
  > assert output == 'xyz' E AssertionError: assert 'abc' == 'xyz' E - xyz E + abc

Using Fixtures:

Fixtures (@pytest.fixture) in pytest provide a flexible way to set up test data, create and inject mock objects into unit tests, and establish reusable test environments across multiple test functions.

• Create a file addition.py that contains a simple class 'Addition':
  $ vi addition.py class Addition: def __init__(self, i, j): self.i = i self.j = j def add(self): return self.i + self.j
  
• Create a unit test file test_addition.py:
  $ vi test_addition.py import pytest from addition import Addition @pytest.fixture def addition(): addition = Addition(4, 6) return addition def test_add(addition): """add unit test""" output = addition.add() assert output == 10
  
• To execute the unit test, open a terminal and run 'pytest' command from the project folder:
  $ pytest ================================================================================ test session starts ================================================================================ platform linux -- Python 3.12.3, pytest-7.4.4, pluggy-1.4.0 rootdir: ~/dev/python/my_project collected 1 item test_addition.py . [100%] ================================================================================= 1 passed in 0.01s =================================================================================

Fixture Scopes:

Fixtures can have different scopes to optimize test execution.

• Fixtures recreated for each test (default):

  @pytest.fixture(scope="function") def addition(): addition = Addition(4, 6) return addition
  

• Fixtures shared across all tests in a class:

  @pytest.fixture(scope="class") def addition(): addition = Addition(4, 6) return addition
  

• Fixtures shared across all tests in a module:

  @pytest.fixture(scope="module") def addition(): addition = Addition(4, 6) return addition
  

• Fixtures shared across entire test session:

  @pytest.fixture(scope="session") def addition(): addition = Addition(4, 6) return addition