Pytest: What is this framework and how to test Python applications — a guide for beginners / ITech content

Software testing can be performed manually by repeatedly running the application under various conditions and checking its correctness. However, a more effective approach is to automate this process using specialized tools. One such tool is Pytest, which allows you to create tests for your code, simplifying the process of verifying its functionality. Pytest offers a convenient and powerful interface for writing tests, making it an ideal choice for developers looking to improve the quality of their code. Automated testing using Pytest not only speeds up the process of identifying errors but also contributes to a more reliable and stable software product.

In this article, you will gain information on various aspects of the topic that interests you. We'll cover key points and provide helpful tips and tricks to help you better understand the topic. Read on to dig deeper and gain valuable knowledge.

What is Pytest?
What are its advantages and disadvantages?
How to install Pytest?
How to write tests?
What are fixtures in Pytest?
What test labels are there in Pytest?

What is Pytest?

Pytest is a powerful framework for testing Python code that was developed in 2004 and continues to be actively updated. This tool provides developers with the ability to not only write tests, but also create test environments and customize their execution parameters. Pytest supports a simple and understandable test structure, which makes it a popular choice among programmers. Thanks to its flexibility and extensibility, Pytest allows you to integrate various plugins and tailor the testing process to the individual needs of a project. Using Pytest helps improve code quality and simplify the testing process, making it an indispensable tool in the arsenal of Python developers.

According to a study by JetBrains, every second Python developer uses Pytest in their work.

Popularity of testing frameworks (according to statistics) JetBrains) Infographic: Skillbox Media

Advantages and Disadvantages of Pytest

Pytest's advantages over competitors, such as Unittest, make it a successful testing tool. Pytest offers ease of use and a user-friendly syntax, allowing developers to quickly start writing tests. Thanks to its flexibility, Pytest supports both simple and complex test scenarios, making it suitable for projects of any size. In addition, Pytest has a powerful plugin system that extends its functionality and allows you to tailor testing to specific needs. It is also worth noting the ability to run tests in parallel, which significantly speeds up the testing process. These characteristics make Pytest a popular choice among developers and contribute to its success in the testing tools market.

Concise code. Pytest's syntax does not have cumbersome constructs, unlike Unittest. A simple test can consist of just two lines.
Detailed error reporting. If a test fails, Pytest will explain the problem.
Universal assert statement. No need to remember its different types, like in Unittest.
Fixtures. Allow you to create a context for a group of tests at once.
Labels. You can customize test behavior: set launch conditions, pass different input data to the same test, and so on.
Can run tests from other frameworks. Unittest, Doctest, and Nose are compatible with Pytest.
Many plugins. If some out-of-the-box feature is missing, over a thousand plugins have been written for Pytest. Moreover, 180 of them were updated in 2023, and another 360 in 2022.

Despite its advantages, the framework also has certain drawbacks. One of the main drawbacks is its difficulty in learning, especially for beginners. This can lead to a significant learning curve. In addition, frameworks sometimes impose restrictions on development flexibility, which can hinder the implementation of non-standard solutions. Some developers also note that framework performance can be lower compared to using pure code, especially in projects with high speed requirements. It is important to consider these aspects when choosing a development framework to optimally meet the needs of the project.

Implicity and magic. There is a downside to simplicity and conciseness: many processes occur "under the hood." To understand them in detail, you will have to study the documentation.
Not included in the standard library. Pytest must be installed separately. If you have an older version of Python (below 3.7), you will need to connect to the corresponding framework version. You can find a list here.
Other frameworks are incompatible with Pytest. An inevitable consequence of its leading, almost royal, status: Pytest can run tests from other frameworks, but no other framework can run Pytest tests. This is a modern interpretation of the ancient Roman proverb, "What is permitted to Jupiter is not permitted to the bull."

How to install Pytest

Pytest is one of the most popular tools for testing in Python. Its latest version supports Python 3.7 and newer, as well as PyPy 3. To install Pytest in a virtual environment, use the following command:

To install the latest version of pytest, run the command pip install -U pytest. This command updates the pytest package to the latest version, ensuring access to the latest features and fixes. pytest is a powerful Python testing tool that allows developers to easily write and run tests. Regularly updating pytest ensures it uses the latest features and improvements, which leads to higher code quality and fewer bugs. It's important to stay up-to-date with updates and use the pip install -U pytest command to keep your testing toolchain up-to-date.

Alternatively, you can install pytest using your integrated development environment (IDE)'s package manager. In this tool, find the pytest module and download it for use in your projects.

How to Write Tests

First, you need to prepare the code that will be used for testing. Create a file named main.py and implement the sum2 function. This function will accept two arguments and return their sum.

Now let's check how the function works. To do this, create a tests.py file, import the sum2 function, and then write a test_sum2 test. This will help ensure that the function works correctly and identify possible errors.

To run tests, use the pytest command in the console. You can also use the interface of your IDE; for example, PyCharm has the ability to run the entire file or an individual test function. This simplifies the testing process and allows you to quickly check the correctness of the code.

To achieve the desired result, you must follow certain steps. First of all, it is important to define the goals and objectives you want to solve. Then you should gather all the necessary resources and tools. After that, start implementing the plan, remembering to monitor the process and make adjustments if necessary. As a result, you will receive a final product that will meet your expectations and requirements. Proper organization and sequence of actions are key factors for success.

Now let's change our test so that it expects 0 instead of 23. This will allow us to test how the system handles values different from the expected ones. This approach will help identify possible errors and improve the stability of the application. Checking for zero values is important to ensure the correct operation of the software and its adaptability to various scenarios.

The user will receive a notification that the test was not successful.

In order for Pytest to recognize functions as tests, you must follow certain rules in naming files and tests themselves. Files containing tests must have names starting with ‘test_’ or ending with ‘_test’. Also, functions that are intended for testing should be named similarly — starting with ‘test_’. Compliance with these rules ensures the correct operation of Pytest and simplifies the process of running tests.

the file name must start with test or end with test.py;
the function name must be written in lowercase and start with test_.

The «assert» keyword allows you to pass any condition to be checked. If the condition is true (returns True), the test is considered passed. Otherwise, if the condition is false (returns False), the test fails. Using assert helps in testing automation and ensures the reliability of the code, allowing you to quickly identify errors and inconsistencies.

Minimal tests can be created as follows:

When using Pytest, you can add debug messages after the conditions. These messages will be printed if the test fails. This simplifies the error diagnostic process and helps you quickly find the causes of test failures.

A test without an assert statement is considered successful.

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A single test can contain multiple assert statements, but this approach is not optimal. It is recommended to adhere to the principle "One test - one entity, one function - one assert". This improves the readability of tests, simplifies the debugging process, and increases the stability of the test suite. Using one assertion in each test makes the results more understandable and makes it easier to identify the causes of potential failures.

The pytest terminal command allows you to run all tests located in the current directory. For more precise control over the testing process, you can specify the path to a specific file or even a selected function. This allows you to customize test execution for your specific needs and simplifies the debugging process. Using pytest to run tests makes the testing process more flexible and efficient.

The command to run the tests.py file is pytest tests.py.
The command to run the test_sum2 function and only it is pytest tests.py: test_sum2.

For more efficient test running in Pytest, you can use additional flags. The full list of available flags can be found in the Pytest documentation. This will allow you to customize test execution to suit your requirements and improve the testing process.

You can also use the graphical interface of your IDE along with terminal commands. In this article, we demonstrate running tests using PyCharm tools. This approach simplifies the testing process and makes it more visual for developers.

Fixtures in Pytest

Fixtures are functions designed to create a test environment. They are especially useful when you need to provide the same input data for multiple tests. Using fixtures allows you to improve the structure of tests and increase their readability, and reduces code duplication. This makes the testing process more efficient and manageable.

The main.py file can define various functions, each of which performs specific tasks. These functions can include data processing, calculations, or user interaction. Structuring your code into functions promotes readability and makes it easier to maintain and modify. Optimizing and properly organizing functions in main.py also helps other developers understand your code better and improves your project's SEO if it's published online. Make sure each function has a clear purpose and description, which will help not only during development but also in the future promotion of your product.

We'll create tests for each case in the tests.py file. We'll use a list of prime numbers between 1 and 50 as a test array. We'll use a for-else loop to generate this list.

In all test functions, we use the same cumbersome construct to generate a list of prime numbers. To improve code readability and simplify its maintenance, we'll move this construct to a separate fixture. To do this, you need to explicitly import the pytest module. This will not only simplify testing but also increase the efficiency of working with the code.

To define a function as a fixture in pytest, you need to use the @pytest.fixture() decorator. This decorator allows you to create functions that can be used as test helpers, ensuring that necessary data or state is prepared before running tests. Fixtures can be used to set up the test environment, making them an essential tool for more efficient and structured testing in Python. Using fixtures helps avoid code duplication and simplifies the testing process by allowing you to easily manage dependencies between tests.

Now you must use this fixture in all tests that require it. When referencing a fixture, do not use parentheses, as it is treated as a variable, not a function.

The tests we run look like this:

If you want additional script to run after the test completes, you can do this using fixtures. Instead of the return keyword, use yield. The code after yield will be executed when the test completes. This allows you to control the execution sequence and add the necessary logic to complete the test. This way, you can effectively organize the testing process and ensure that all required actions are performed.

In our prime numbers example, we modify the get_prime_nums() fixture by adding a finalizer to it. This change will improve resource management and simplify the cleanup process after running the tests. By adding a finalizer, we can ensure that all necessary actions upon completion of the fixture are performed correctly. As a result, testing prime numbers will become more efficient and reliable.

When running the test, we get the following result:

If the test fails and the assertion returns False, the code in the finalizer block will still be executed. This ensures that cleanup operations, such as resource cleanup or logging, are performed even if a test fails.

Fixtures allow you to customize the scope in which they operate. By default, this scope is function scope. This means that when the test function completes, the fixture is completed and destroyed. The next time the fixture is called, it is recreated. This mechanism is well illustrated by the finalizer example. Setting the fixture scope plays an important role in resource management and keeping the test environment clean.

A fixture's scope is set in its decorator using the scope=’scope’ argument. There are five different levels of fixture scope.

‘function’ — for a function;
‘class’ — for a class;
‘module’ — for a module (i.e. a py file);
‘package’ — for a package;
‘session’ — for the entire testing session.

Let's adjust the scope of the get_prime_nums fixture, changing it to modular. This will limit access to this fixture only within a specific module, which will increase the level of encapsulation and make the code more manageable. Modular scope of the fixture helps to reduce the likelihood of name conflicts and simplifies testing.

Let's study how the testing process will change.

In this example, the fixture is called only once, the first time it is accessed in the function. After that, the result of its execution is cached, which helps avoid repeated calls. The finalizer is executed only once, at the end of file processing. This optimizes performance and saves resources because repetitive calculations are avoided.

You can pass an unlimited number of fixtures to a single test, separating them with commas. Fixtures can also be passed to other fixtures in any quantity, providing flexibility and convenience during testing.

The get_prime_nums fixture could be split into several parts, although this would not be practical in this case. Splitting a function into smaller components may make it easier to test and maintain, but for this specific task, this approach will not provide significant benefits. It is important to keep in mind that code optimizations should be justified and meet the specific requirements of the project.

You should clearly indicate which fixtures are used in your function or other fixtures. This makes it easier to track and manage data dependencies. Proper documentation of fixtures promotes more effective testing and improves code comprehension, which is especially important when working in a team.

Note:

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Fixtures with a broader scope cannot be embedded within fixtures of a smaller level. It is important to keep this limitation in mind when designing and using fixtures to ensure correct system functioning and avoid potential conflicts. Proper adherence to this practice will help maintain data integrity and improve project manageability.

In some cases, it may be necessary for a fixture to execute automatically even if the corresponding function does not call it. This is especially true before running tests, when authorization in the system is required. Automatic fixture initialization helps ensure the consistency of the test environment and eliminate potential errors related to the lack of necessary testing conditions.

In such situations, it is possible to use the autouse parameter in the @pytest.fixture decorator, setting its value to True. This will automatically apply the fixture to all tests without the need for explicit specification. Thus, using this approach, you can significantly simplify the structure of tests and improve their readability.

Caution should be exercised when using the autouse parameter in fixtures. These fixtures can introduce implicit dependencies and modify data in unpredictable ways, which can negatively impact the testing result. This is especially true if the project uses a large number of fixtures in a complex hierarchy. It is recommended to carefully analyze the structure and interrelationships of fixtures to avoid potential problems and ensure more predictable testing.

Test Labels

Pytest provides the ability to customize test execution using labels. These labels can be applied to both individual test functions and entire test classes. To add a label, use the decorator: @pytest.mark.*label name*. This allows for more flexible organization and management of tests, and also makes it easier to select them at run. Using labels in Pytest helps improve the structure of tests and optimize the testing process.

Pytest allows you to run only tests marked with certain labels. To do this, use the command in the terminal with the -m argument, for example: pytest -m «label name». It is also possible to run all tests except those with the specified label. In this case, the command will look like this: pytest -m «not label name». Using labels in Pytest allows you to flexibly manage the testing process, which is especially useful for large projects that require selective test execution.

Each test or class in pytest can have an unlimited number of labels. To see a full list of markers, use the pytest —markers— command, and also check out the documentation. In this article, we'll cover the basic markers that will help you better organize and manage your tests.

To skip a test, set the skip marker. You can optionally add the reason=’skip reason’ parameter to specify a specific reason. For example:

The skipif marker takes two arguments. The first argument is a condition: if it is true (True), the test will be skipped; otherwise (False), the test will be executed normally. The second argument, similar to the skip marker, allows you to specify a string with the reason for skipping the test. Using this marker helps control test execution based on certain conditions, which can improve testing efficiency and avoid unnecessary failures during the process.

The result remains unchanged, as in the previous case.

Tests labeled xfail in Pytest can have two outcomes. If the test passes, it will be marked as XPASS. If the test fails, which matches the expected result, it will be marked as XFAIL. It's important to note that neither of these outcomes affects the overall status of the test suite.

Xfail offers several capabilities similar to skipif. You can specify a condition under which the test is expected to fail and specify a reason parameter to explain the reason. In addition, xfail provides the following functions:

add an exception to raises=*exception name*;
do not run the test at all with run=False (then it will automatically be counted as XFAIL);
make a test failure cause the entire test suite to fail with strict=True.

Learn more about xfail's capabilities in the official documentation.

The parametrize label is designed to run the same test with different input data. It is an indispensable tool for checking many different scenarios, which is especially important in software testing. Using this label can significantly reduce the amount of code and improve its readability, since one test can be checked on multiple data sets. This improves the quality of testing and helps identify potential errors and flaws in the application.

In this article, we will consider a function that determines whether a number is positive or negative. This function helps you quickly and efficiently classify numeric values, which can be useful in various applications and programming. Determining the sign of a number is a fundamental element when working with mathematical calculations and data analysis. We will analyze a simple implementation of this function and explain how to use it in your projects.

First, we will check how the system handles positive numbers, including integers, fractions, and very small values. Without parameterization, we would have to create three separate tests for each category of numbers.

To optimize testing of the function, we need to develop three tests for negative numbers and one for zero, which will total seven tests for one small function. This may seem like an inefficient approach. In such cases, parameterization comes to the rescue, reducing the amount of code and making testing more manageable and structured. Parameterization helps testers easily add new scenarios without having to duplicate code, which significantly improves the readability and maintainability of tests.

The parametrize label accepts two arguments: the name of a variable and a list of values for this variable. The variable name is passed to the test in the same way as with fixtures. Thus, using this label allows you to dynamically pass different sets of data to tests, which significantly simplifies the testing process and makes it more flexible.

Multiple variables can be passed in a single line using commas. Each list element is represented as a tuple in which the values of the corresponding variables are sequentially specified. This allows for convenient and compact organization of data, which simplifies its processing and use in further operations.

When passing multiple labels to the parameterization of a single test, it will execute all possible combinations of these labels. This allows for more efficient testing of various scenarios. For example, consider the following case:

You can create your own labels in addition to the built-in ones. This is especially useful for organizing tests, allowing you to split them into groups and run them separately. Using custom labels simplifies test management and increases their efficiency, providing flexibility in the testing process.

To create your own label, use only its name. This will allow you to effectively organize your content and improve its visibility in search engines. Proper use of labels will help structure information and facilitate site navigation.

To run only those tests marked with the my_mark label, use the pytest command —no-summary -m my_mark tests.py. This will allow you to isolate and test specific parts of the code, which will significantly simplify the debugging process and increase the efficiency of testing. The command will return output containing only the results of tests that match the specified label.

Of the five files, only three were activated, which had the required label set.

We received three warnings from Pytest. One of them indicates that the my_mark label is not registered in the pytest.ini file. Pytest clarifies whether we actually used our own label and did not make a typo when writing a built-in label. This warning is important for the correct operation of tests and allows you to avoid potential errors during testing. Registering labels in the pytest.ini configuration file will help ensure the reliability and accuracy of test execution.

The documentation provides information on the process of registering your own label. You will find all the necessary steps and recommendations for successful completion of this procedure.

Summary

Pytest is the most popular testing framework in the Python development community. It offers the ability to write more concise and readable tests compared to the built-in Unittest module and does not require the use of test classes, which simplifies the testing process. The main tools of Pytest include support for test parameterization, fixtures for preparing the test environment, and a convenient mechanism for organizing and running tests. Thanks to these features, Pytest significantly improves the efficiency of testing and promotes higher-quality software development.

The assert keyword is responsible for the test result. If the condition specified after it is true, the test passes; if it is false, it fails.
Fixtures are additional functions that allow you to specify the test environment. They can use other fixtures, creating entire hierarchies.
Labels are decorators that allow you to adjust test behavior: skip them, wait for specific results, pass different input data, and so on. You can create your own custom labels.

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