In pytest, patching globally means applying a patch to a specific function or object throughout the entire test session. This can be useful when you need to simulate a specific behavior or override a certain functionality for multiple tests.
To patch globally in pytest, you can use the pytest.fixture decorator combined with the mocker.patch method from the pytest-mock library. First, you define a fixture that applies the patch to the desired function or object using the mocker.patch method. Then, you specify the scope of the fixture as session, which ensures that the patch is applied globally for all tests in the session.
For example, if you want to patch a function called get_data globally in your test suite, you can create a fixture like this:
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import pytest @pytest.fixture(scope='session') def patch_get_data(mocker): mocker.patch('module_name.get_data', return_value='mocked_data') |
In this example, the mocker.patch method is used to patch the get_data function from a module called module_name and return a predefined value 'mocked_data'. By setting the scope of the fixture as session, the patch will be applied globally for all tests in the session.
You can then use this fixture in your tests by including it as a parameter:
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def test_get_data(patch_get_data): assert get_data() == 'mocked_data' |
This way, the get_data function will be patched globally with the mocked data for all tests that use the patch_get_data fixture.
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What is the benefit of using markers with global patching in pytest?
Using markers with global patching in pytest allows for more flexibility and control over the behavior of tests. By applying markers at a global level, you can define certain attributes or behaviors that should be applied to multiple tests without having to repeat the same code in each test function.
This can help to streamline test writing and make it more efficient, as well as encourage consistency and maintainability across tests. Additionally, global patching with markers can help to organize and categorize tests based on certain attributes or criteria, making it easier to manage and analyze test results.
Overall, using markers with global patching in pytest can improve the structure, efficiency, and overall quality of test suites.
What is the syntax for global patching in pytest?
To perform global patching in pytest, you can use the monkeypatch fixture provided by the pytest library. Here is an example of the syntax for global patching in pytest:
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# test_example.py import pytest def test_example(monkeypatch): # Patching a global variable monkeypatch.setattr('module_name.global_var', 'new_value') # Patching a function monkeypatch.setattr('module_name.function_name', lambda x: x + 1) # Perform tests using the patched global variable or function assert module_name.global_var == 'new_value' assert module_name.function_name(2) == 3 |
In this example, the monkeypatch fixture is used to patch a global variable and a function in the module_name module. The setattr method of the monkeypatch fixture is used to set the new value for the global variable and to replace the function with a lambda function that increments the input by one.
You can then write your test cases using the patched global variable and function to ensure that your code behaves as expected with the patched values.
What is the difference between patching globally and locally in pytest?
In pytest, patching globally and locally refer to two different ways of applying patches to functions or objects during testing.
- Patching globally: When patching globally, a patch is applied to a function or object for the entire test session. This means that the patch will be applied to all test functions that use the patched function or object within the same test session. This can be useful for cases where the same patch needs to be applied to multiple tests.
- Patching locally: When patching locally, a patch is applied only within the scope of a specific test function. This means that the patch is only active for that particular test function and does not affect any other test functions. This can be useful for cases where a specific test requires a unique patch that should not affect other tests.
Overall, patching globally is more convenient for applying the same patch to multiple tests, while patching locally allows for more fine-grained control over which tests use a specific patch.
What is the difference between fixtures and global patching in pytest?
Fixtures in pytest are functions that are set up before and torn down after the execution of test functions. They provide a way to set up the necessary environment for the tests to run, such as initializing databases, creating temporary files, or mocking external services.
Global patching, on the other hand, is a way to patch objects or functions globally throughout the entire test suite. This allows you to mock certain behaviors or values in your code without needing to explicitly do so in each individual test function.
The main difference between fixtures and global patching is that fixtures are specific to individual test functions and provide setup and teardown functionality for each test, while global patching affects the behavior of the code globally across all test functions. Fixtures are more specific and controlled, while global patching is more general and can have wider-ranging effects.
What is the best practice for global patching in pytest?
The best practice for global patching in Pytest is to use the pytest-mock plugin, which provides a simple way to patch objects during test execution without the need for explicit patch statements in each test function. This plugin allows you to use the pytest.fixture decorator to create a fixture that automatically patches the specified objects before each test function is run.
Here is an example of how to use the pytest-mock plugin for global patching in Pytest:
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import pytest @pytest.fixture def my_patch(mocker): return mocker.patch('my_module.my_function') def test_my_function_patch(my_patch): assert my_patch.return_value == 'mocked_value' |
In this example, the my_patch fixture uses the mocker fixture provided by the pytest-mock plugin to patch the my_module.my_function object. This allows you to easily patch the specified object in all test functions that use the my_patch fixture.
Using the pytest-mock plugin for global patching in Pytest helps to keep your test code clean and readable by reducing the amount of boilerplate code needed to set up patches in each test function. It also makes it easier to maintain and update patches when changes are made to the code under test.
