Python Testing с pytest. What Makes pytest So Useful?

Python Testing с pytest. What Makes pytest So Useful?

If you’ve written unitfor your Python code before, then you may have used Python’s built-in unittest module.unittestprovides a solid base on which to build your test suite, but it has a few shortcomings.

A number of third-party testing frameworks attempt to address some of the issues withunittest, andpytesthas proven to be one of the most popular.pytestis a feature-rich, plugin-based ecosystem for testing your Python code.

If you haven’t had the pleasure of usingpytestyet, then you’re in for a treat! Its philosophy and features will make your testing experience more productive and enjoyable. Withpytest, common tasks require less code and advanced tasks can be achieved through a variety of time-saving commands and plugins. It’ll even run your existing tests out of the box, including those written withunittest.

As with most frameworks, some development patterns that make sense when you first start usingpytestcan start causing pains as your test suite grows. This tutorial will help you understand some of the toolspytestprovides to keep your testing efficient and effective even as it scales.

Less Boilerplate

Most functional tests follow the Arrange-Act-Assert model:

1. Arrange , or set up, the conditions for the test
2. Assert that some end condition is true

Testing frameworks typically hook into your test’s assertions so that they can provide information when an assertion fails.unittest, for example, provides a number of helpful assertion utilities out of the box. However, even a small set of tests requires a fair amount of boilerplate code .

Imagine you’d like to write a test suite just to make sure thatunittestis working properly in your project. You might want to write one test that always passes and one that always fails:

You can then run those tests from the command line using thediscoveroption ofunittest:

(venv) $ python -m unittest discover F. ====================================================================== FAIL: test_always_fails (test_with_unittest.TryTesting) ---------------------------------------------------------------------- Traceback (most recent call last): File "…\effective-python-testing-with-pytest\test_with_unittest.py", line 10, in test_always_fails self.assertTrue(False) AssertionError: False is not true ---------------------------------------------------------------------- Ran 2 tests in 0.006s FAILED (failures=1)

As expected, one test passed and one failed. You’ve proven thatunittestis working, but look at what you had to do:

1. Import theTestCaseclass fromunittest
2. CreateTryTesting, a subclass ofTestCase
3. Write a method inTryTestingfor each test
4. Use one of theself.assert*methods fromunittest.TestCaseto make assertions

That’s a significant amount of code to write, and because it’s the minimum you need for any test, you’d end up writing the same code over and over. assertkeyword directly:

That’s it. You don’t have to deal with any imports or classes. All you need to do is include a function with thetest_prefix. Because you can use theassertkeyword, you don’t need to learn or remember all the differentself.assert*methods inunittest, either. If you can write an expression that you expect to evaluate toTrue, and thenpytestwill test it for you.

Not only doespytesteliminate a lot of boilerplate, but it also provides you with a much more detailed and easy-to-read output.

Python Testing with pytest pdf на русском. Что такое pytest?

Надежный инструмент тестирования Python, pytest может быть использован для всех типов и уровней тестирование программного обеспечения. pytest может быть использован командами разработчиков, QA команд, независимых групп тестирования, лиц практикующих TDD, и проекты с открытым исходным кодом. Фактически, проекты по всему интернету переключились с unittest или nose на pytest , включая Mozilla и Dropbox. Почему? Потому что pytest предлагает мощные функции, такие как перезапись "assert", сторонние модели плагинов и мощную, но простую fixture model, которая не имеет себе равных в любой другой структуре тестирования.

pytest-это платформа для тестирования программного обеспечения, что означает, что pytest-это программа командной строки. Инструмент, который автоматически находит написанные тесты, запускает тесты и пишет отчеты с результатом. Он имеет библиотеку примочек, которые можно использовать в тестах, чтобы помочь вам тестировать более эффективно. Он может быть расширен путем написания собственных плагинов или установки сторонних. Его можно использовать для тестирования дистрибутивов Python. И он легко интегрируется с другими инструментами, такими как непрерывная интеграция и веб автоматизация.

Вот несколько причин, почему pytest выделяется среди многих других тестов интегрированные системы:

• Простые тесты легко написать в pytest.
• Complex tests еще проще писать.
• Тесты легко читаются.
• Тесты легко читаются. (Так важно, что это указано дважды.)
• Вы можете начать работу в считанные секунды.
• Для провала теста используетсяassert, а неself.assertEqual()илиself.assertLessThan(). Просто assert !
• Можно использовать pytest для запуска тестов, написанных для unittest или nose.

pytest активно развивается и поддерживается страстным и растущим сообществом. Он настолько расширяем и гибок, что легко вписывается в ваш рабочий процесс. И поскольку он установлен отдельно от вашей версии Python, вы можете использовать ту же самую последнюю версию pytest на устаревших Python 2 (2.6 и выше) и Python 3 (3.3 и выше).

Pytest pycharm. Run tests

Generally, PyCharm runs and debugs tests in the same way as other applications, by running the run/debug configurations. When doing so, it passes the specified test classes or methods to the test runner.

In many cases, you can initiate a testing session from a context menu. For this purpose, the Run and Debug commands are provided in certain context menus. For example, these commands are available for a test class, directory, or a package in the. They are also available for a test class or method you are currently working on in the editor.

If you run a test for which there is no permanent run/debug configuration, a temporary configuration is created. You can then save such a configuration using thedialog if you want to reuse it later.

The tests run in the background, so you can execute several tests at the same time.

Each running configuration gets its own tab in the(the). One tab can aggregate several tests.

Note also that the commands shown in the context menu, are context-sensitive, that is the testing command that shows depends on the test runner and the place where this command is invoked.

After PyCharm finishes running your tests, it shows the results in the Run tool window on the Test Runner tab. For more information on how to analyze test results, refer to.

Run tests after commit

After you have set up the test configuration, the specified tests will run every time you make a commit.

Run or debug a test

To start running or debugging a test, you can use the main toolbar or a context menu in the Project tool window or in the editor:

Use the main toolbar:

Select the necessary run/debug configuration from the list on the main toolbar.

PressAlt+Shift+F10to see the list of available run configurations orAlt+Shift+F9for debug configurations.

Click Run or Debug to the right of the list. Alternatively, select Run | Run Shift+F10or Run | Debug Shift+F9from the main menu.

Use a context menu:

Right-click a test file or test class in the Project tool window or open it in the editor, and right-click the background. From the context menu, select Run / Run or Debug… .

For a test method, open the class in the editor and right click anywhere in the method. The context menu suggests the command Run / Debug .

Run all tests in a directory

In the Project tool window, select the directory that contains tests to be executed.

From the context menu, select the corresponding run command.

If the directory contains tests that belong to the different testing frameworks , select the configuration to be used.

For example, select Run pytest in ' .

Explore results in the test runner.

Run tests in parallel

Enable test multiprocessing to optimize execution of your pytest tests.

To explicitly specify the number of CPUs for test execution:

Install the pytest-xdist package as described in Install, uninstall, and upgrade packages .

Specify pytest as the project testing framework. See Testing frameworks for more details.

Select Edit configurations… from the list of the run/debug configurations on the main toolbar. In the Run/Debug Configurations dialog, expand the Pytest tests group, and select pytest in .

PyCharm creates this configuration when you run all tests in a directory for the very first time. If you haven't executed the tests yet, click theicon and specify the.

In the Run/Debug Configurations dialog, in the Additional Arguments field specify the number of the CPUs to run the tests:-n and save the changes.

Now run all the tests in the directory again and inspect the output in the Run tool window. In the shown example, The total execution time is 12s 79ms as compared to 30s 13ms when running the same tests consequentially. The test report provides information about the CPUs used to run the tests and execution time.

Alternatively, you can specify the number of CPUs to run your tests in the pytest.ini file. For example,

addopts = -n3

Pytest Vs Unittest. Python Unittest Vs Pytest: Choose the Best

Like any programming language, testing in python is checking individual units of source code. After the software is developed, it is important to test the source code by undergoing various forms of tests. With testing, we ensure that the code is working as expected and is free frombugs. The main idea is to test it against several inputs. In this article, we shall be comparing two such testing methods – python unittest vs pytest.

Automated vs Manual Testing

There are two ways of performing testing in python – and . In manual testing, the code is tested manually by a human. The tester needs to manually make lists of all possible inputs and the expected output. It is a tedious job and also time-consuming.

This is where automated testing comes into play. Python consists of built-in frameworks that make testing code easier. Since automated testing is script-based, it is accurate. Python has tools and libraries – such as and

Testing using Unittest framework

The unit testing framework in python is known as unittest. We can have a single function, a class, or an entire module as the unit for testing. The idea behind unit testing is to validate the smallest unit first and then gradually move to other units. The unittest in python is actually inspired by Java’s JUnit.

Outputs possible

Using the unittest framework, there are three possible outputs – and .

• : This is the output when all the tests are passed.
• FAILED : This is the output shown when all the tests did not pass successfully. When an AssertionError exception is raised, the output shows FAIL.
• ERROR: This was the output when all the test cases did not pass, and it raises an exception other than the AssertionError .

Test Code example

Let us consider a python code for running the tests. First, we will have to import the unittest module.

Now, we shall define a function named square() which takes a number ‘n’ as a parameter and returns the square of the number as the output.

def square(n): return n*n

Now, we will define a class named ‘Test’ . The class test will inherit the unittest module by ‘unittest.TestCase’ . We define a function inside the class named test_square. Here, the naming convention should be used such that the name ‘test’ precedes the function name.

We will pass ‘self’ as a parameter to the function. Inside the function, we will call self.assertEquals() .There are several assert methods present for testing, and assertEquals() is one of them. In assertEquals(), the first and the second argument should be equal for the test to be successful.

So here, we will pass the square(2) function as the first argument and 4 as the second argument.

class Test(unittest.TestCase): def test_square(self): self.assertEquals(square(2), 4)

To run the file ‘test_Calculation.py’, we shall use the following piece of code:

python -m unittest test_Calculation

The output shall be:

. --------------------------------------------------------------- Ran 1 test in 0.001s OK

Because the output of the function square(2) was equal to 4, the test ran successfuly and OK was printed.

Now, let us try to run two functions as two different test cases.

We shall define a second function named ‘cube’ which takes a single number as an argument and returns its cube as the output.

def cube(n): return n*n*n

Now, inside the class Test, we shall define another function named test_cube(). We shall use self.assertEquals() and pass cube(2) and 8 as first and second arguments.

def test_cube(self): self.assertEquals(cube(2), 8)

Now, we shall again run the same code in the terminal:

python -m unittest test_Calculation

Since, there were two tests here, the output will be OK signifying that the two tests were run successfully.

Python Testing with pytest на русском. Python Testing с pytest. Глава 2, Написание тестовых функций +8

Вы узнаете, как организовать тесты в классы, модули и каталоги. Затем я покажу вам, как использовать маркеры, чтобы отметить, какие тесты вы хотите запустить, и обсудить, как встроенные маркеры могут помочь вам пропустить тесты и отметить тесты, ожидая неудачи. Наконец, я расскажу о параметризации тестов, которая позволяет тестам вызываться с разными данными.

Примеры в этой книге написаны с использованием Python 3.6 и pytest 3.2. pytest 3.2 поддерживает Python 2.6, 2.7 и Python 3.3+.

Исходный код для проекта Tasks, а также для всех тестов, показанных в этой книге, доступен пона веб-странице книги в. Вам не нужно загружать исходный код, чтобы понять тестовый код; тестовый код представлен в удобной форме в примерах. Но что бы следовать вместе с задачами проекта, или адаптировать примеры тестирования для проверки своего собственного проекта (руки у вас развязаны!), вы должны перейти на веб-страницу книги и скачать работу. Там же, на веб-странице книги есть ссылка для сообщенийи.

Под спойлером приведен список статей этой серии.

В предыдущей главе вы запустили pytest. Вы видели, как запустить его с файлами и каталогами и сколько из опций работали. В этой главе вы узнаете, как писать тестовые функции в контексте тестирования пакета Python. Если вы используете pytest для тестирования чего-либо, кроме пакета Python, большая часть этой главы будет полезна.

Мы напишем тесты для пакета Tasks. Прежде чем мы это сделаем, я расскажу о структуре распространяемого пакета Python и тестах для него, а также о том, как заставить тесты видеть тестируемый пакет. Затем я покажу вам, как использовать assert в тестах, как тесты обрабатывают непредвиденные исключения и тестируют ожидаемые исключения.

В конце концов, у нас будет много тестов. Таким образом, вы узнаете, как организовать тесты в классы, модули и каталоги. Затем я покажу вам, как использовать маркеры, чтобы отметить, какие тесты вы хотите запустить, и обсудить, как встроенные маркеры могут помочь вам пропустить тесты и отметить тесты, ожидая неудачи. Наконец, я расскажу о параметризации тестов, которая позволяет тестам вызываться с разными данными.

Pytest python примеры. Basic patterns and examples

How to change command line options defaults

It can be tedious to type the same series of command line options every time you use pytest . For example, if you always want to see detailed info on skipped and xfailed tests, as well as have terser “dot” progress output, you can write it into a configuration file:

Alternatively, you can set a PYTEST_ADDOPTS environment variable to add command line options while the environment is in use:

Here’s how the command-line is built in the presence of addopts or the environment variable:

$PYTEST_ADDOPTS

So if the user executes in the command-line:

The actual command line executed is:

Note that as usual for other command-line applications, in case of conflicting options the last one wins, so the example above will show verbose output because -v overwrites -q .

Pass different values to a test function, depending on command line options

Suppose we want to write a test that depends on a command line option. Here is a basic pattern to achieve this:

For this to work we need to add a command line option and provide the cmdopt through a:

# content of conftest.py import pytest def pytest_addoption ( parser ): parser . addoption ( "--cmdopt" , action = "store" , default = "type1" , help = "my option: type1 or type2" ) @pytest . fixture def cmdopt ( request ): return request . config . getoption ( "--cmdopt"

Let’s run this without supplying our new option:

$ pytest -q test_sample.py F ================================= FAILURES ================================= _______________________________ test_answer ________________________________ cmdopt = 'type1' def test_answer(cmdopt): if cmdopt == "type1": print("first") elif cmdopt == "type2": print("second") > assert 0 # to see what was printed E assert 0 test_sample.py :6: AssertionError --------------------------- Captured stdout call --------------------------- first ========================= short test summary info ========================== FAILED test_sample.py:: test_answer - assert 0 1 failed in 0.12s

And now with supplying a command line option:

$ pytest -q --cmdopt=type2 F ================================= FAILURES ================================= _______________________________ test_answer ________________________________ cmdopt = 'type2' def test_answer(cmdopt): if cmdopt == "type1": print("first") elif cmdopt == "type2": print("second") > assert 0 # to see what was printed E assert 0 test_sample.py :6: AssertionError --------------------------- Captured stdout call --------------------------- second ========================= short test summary info ========================== FAILED test_sample.py:: test_answer - assert 0 1 failed in 0.12s

You can see that the command line option arrived in our test.

Now we’ll get feedback on a bad argument:

$ pytest -q --cmdopt=type3 ERROR: usage: pytest pytest: error: argument --cmdopt: invalid choice: 'type3' (choose from 'type1', 'type2')…>

If you need to provide more detailed error messages, you can use the type parameter and raise pytest.UsageError :

This completes the basic pattern. However, one often rather wants to process command line options outside of the test and rather pass in different or more complex objects.