The Best Python Unit Testing Framework
Testing can be the most overwhelming part of the development stages in Python. Unit testing can be an extremely useful stage if utilized smartly. However, inexperience Python developers haven't been aware of the term 'unit testing' and even if they have, they do not understand the term enough to apply it into testing their code.

In this article, we will not just focus on unit testing but also put some light on the best python unit testing framework.

Pytest is considered as one of the best python unit testing framework by many avid Python developers. It has managed to become a favorite in the developer toolset in a short span of time. Pytest is the best unit testing framework for Python because it has simple and easy to use syntax, straightforward and modular fixtures, better assert with clear failure information, handy tests parametrization, abundant of extensible plugins and it allows parallel tests.

What is Unit Testing

Unit testing is a method of software testing where the individual units of code of a software are checked to see if they are work as expected. Unit testing is the lowest level of software testing because it validates the smallest testable part of a program.

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Unit testing is very essential because a good unit test will disclose any defects immediately every time a piece of code is created or modified. Unit testing also promotes code modularity which leads to more reusable codes. It also makes debugging easier because only the latest changes need to be inspected. More importantly, the cost of fixing bugs is cheaper compared to that of fixing bugs found at higher level tests.

What is Pytest?

I often think of pytest as the best, and why-to-anywhere-else when you have this kind of unit testing framework for Python. It is an open-source testing platform written in and for python.

Pytest has a simpler syntax than the Python's standard library for unit testing. It reassures that test suites should be written in a far more compact style, in plain Python functions rather than inside large test classes. Pytest is a real Pythonic way to testing in Python, yet it is powerful enough to carry out complex testing scenarios.

Why is Pytest the Best?

Pytest is not the Python's standard library for unit testing, but more Python programmers think that it is a way better unit testing framework to be adopted. The main reason is that PyTest has a minimum boilerplate code. You don't have to worry making classes, subclassing from a parent class or typing self to each test methods. The test code is much simpler and easier to read.

Nevertheless, pytest is not just a bare minimum library. It has a full set of tools to write a test and support complex functional testing when you need them.

Moreover, in pytest you can use the plain assert statement (read this blog if you haven't known about assert statement). You don't have to worry remembering assert helpers like in other frameworks. The cool part is that pytest's assert presents complete information about what has been asserted and what have failed.

Another pytest's wizard is parametrization. One test scenerio that you want to perform is run a test with multiple test cases. Other frameworks use for loop to acomplish this goal. But, the test will be halted when a test case fail. You won't have a chance to reveal whether or not other test cases bring a problem to your code.

This is where parametrization come in handy. You can make a list of test cases by using @pytest.mark.parametrize decorator. Every single test case inside the list will be evaluated separately, and if a test fails the test will continue executing the other test cases.

In other cases, you want to run many (or all) of your tests with the same objects. In pytest, you can create fixture objects and pass it to the test functions as input arguments. In this manner, you can avoid repetition across your test functions and keeps them independent from each other at the same time.

Fixtures is often called as pytest's killer feature and one of the main reasons why many developer switch to pytest. Pytest fixtures are explicit, modular and scalable which is much better than setUp() and tearDown() methods used by other libraries.

Finally, you can extent the pytest power by adding plugins. You can find hundreds of pytest plugins from this site. One plugin worth mention is pytest-xdist. This plugin allows you to run several tests in a parallel way. To do so, you have to run the tests using py.test -n NUM . Here, NUM is the number of any function you are using the code for.

One more pytest's advantage you need to know is that you can use it not only for unit testing. You can also perform integration testing and system testing by using pytest.  

Since fixtures is the main feature from pytest, here are some points to note on pytest Fixtures:

  • Pytest fixtures give you the ability to set up a helper code. This code can be run before any of the tests are executed at the framework. These fixtures are the most important feature needed to set up the resources needed for tests.
  • The @pytest.fixture is the decorator used to mark the fixture features while creating them. The test functions that are in need of a fixture accept them in the form of arguments, and the length of the boilerplate code can be reduced with the help of Pytest fixtures.
  • After applying fixtures in the code, you need to return the tests to check if everything is working properly. 
  • Fixtures are very beneficial in cases where duplicating or repetitive code is used. The code which appears in a number of tests repetitively is the best case scenario to use fixtures.
  • Each test should be written with a new instance and not the one used in the previous one.
  • You can use the command test -- fixtures to have a look at all the available fixtures.


Some Basic Facts About PyTest:

  • The tests should be located in files with the name starting with or ending with _test.py . 
  • The test functions should always begin with test_.
  • If you write test methods on a class, the name of the class should start with Test. The class should not have an __init__  method.
  • If you don't wish to execute some particular tests, you can just xfail the test or you have the option to skip the test. The difference between skipping a test and xfailing a test is that the xfail test does not appear in the passed or failed tests. It just goes through the execution. 
  • You can type @pytest.mark.xfail to use the xfail feature. 
  • Tests can be skipped by using @pytest.mark.skip.
Pytest is certainly the best testing framework to start with for a beginner. You can dive deep into this testing framework by learning from Python Testing with pytest. This book gets you up to speed quickly on this easy-to-learn and robust library.

PyTest is my personal favorite. Yet, there are many python unit testing frameworks that are equally used and liked. Here are some other top python unit testing frameworks:

Unittest

Unittest is the Python's standard library for unit testing. You don't have to install additional library if you want to use unittest because it's coming out of the box. Unittest has characteristics that are very similar to the xUnit frameworks, makes it easier for developer from other languages to adopt it. The framework can be used in domains like shutdown codes as well as test automation, and sharing of a setup. The framework does not act according to the tests that come from the reporting environment.

Here are some significant features of Unittest:
  • The test fixtures is a prominent feature and enable the collaboration in cleanup actions.
  • Test suites are used as a summation of several tests. These tests are run together with the help of a test suite.
  • A test case is the basic unit of a unit testing framework. It creates a specific response to specific inputs and also creates new test cases.
  • A test runner provides the executed output to the tester.

Zope Testing

The debugger created by Zope is a unique tool because it points out the exact place where the problem lies. It also provides you with a logging option. It gives you an authority to issue warnings.

The information about debugging is fetched by various channels and then given to you.

Here are some aspects of Zope Testing:
  • The feature of product refresh settings gives you the ability to refresh the product modules. You can then notice the updated or uploaded modules.
  • The debug mode is used for displaying tracebacks when an error occurs. Analysis of DTML file objects and external methods is done to check on the update progress. 
  • When Zope is integrated with Python debugger, the server gets shut down. 
  • Infrastructure is created from which you can instantly raise objects and debug them immediately.

Conclusions

The Python community has always been in support of testing. The Python-library is well-equipped with tools that are compatible with debugging and testing. Nowadays, there are a plethora of tools for unit testing in Python. It sometimes becomes a task to choose the best one among them.

The PyTest framework is used widely by many developers and is the most popular framework for python unit testing. I personally recommend using PyTest as your first Python unit testing library. But of course you can advance your skill with several other frameworks that will accomodate other types of usage and style.

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