Python with pytest


Introduction to Unit Testing in Python with pytest

In software development, it is important to ensure that the code being written works as intended. One way to achieve this is through unit testing. Unit testing is the process of testing individual components of a software application to ensure that they are working as expected. This can be done by creating automated tests that check the functionality of each individual part of the code. 

In this article, we will explore the basics of unit testing in Python using pytest.

1. What is Unit Testing?

Unit testing is a software testing technique where individual units or components of a software application are tested to ensure that they function as intended. The idea behind unit testing is to isolate each part of the code and test it separately, so that if any issues arise, they can be easily identified and fixed. This approach can help to prevent bugs from being introduced into the code as it evolves over time.

1.1 Importance of Unit Testing in Software Development

Unit testing plays a critical role in software development. By testing individual parts of the code, developers can ensure that their code is working as intended and that any issues are caught early in the development process. This can save time and resources in the long run, as fixing bugs later on in the development process can be much more time-consuming and expensive.

In addition, unit testing helps to ensure that code is maintainable and easy to modify. By having a set of automated tests that check the functionality of each individual component, developers can be more confident when making changes to the codebase. This can help to prevent new bugs from being introduced when changes are made.

2. Explanation of pytest

pytest is a testing framework for Python that allows developers to write and run unit tests for their code. It provides a simple and intuitive way to write tests, with a variety of powerful features that make it a popular choice for Python developers.

One of the key features of pytest is its ability to automatically discover tests in a project. This means that developers can write tests and have pytest automatically find and run them, without needing to manually specify each test to be run. pytest also provides powerful assertion statements, which can be used to test that a specific condition is met.

Another useful feature of pytest is its support for fixtures. Fixtures are functions that provide a set of pre-defined values or objects that can be used in tests. This can help to simplify the process of writing tests, as developers can create fixtures that provide the necessary data for their tests to run.

3. Setting up pytest

To get started with pytest, you need to install the framework. In this section, we’ll walk through the installation process, writing the first test case, and running pytest.

3.1 Installation of pytest

Before you can start using pytest, you need to install it. The easiest way to install pytest is by using pip, the package installer for Python. To install pytest using pip, open your command prompt or terminal and enter the following command:

pip install pytest

This command will install pytest and its dependencies. After installation, you can verify that pytest is installed by running the following command:


pytest --version

This command should output the version number of pytest.

3.2 Writing the first test case

Once pytest is installed, you can start writing your first test case. In pytest, test cases are defined as functions with names starting with the word “test”. For example, let’s write a test case for a simple function that adds two numbers:


def add(x, y): return x + y def test_add(): assert add(2, 3) == 5

In this example, we define a function called “add” that takes two arguments and returns their sum. We also define a test case called “test_add” that asserts that the add function returns 5 when called with arguments 2 and 3.

3.3 Running pytest

After writing the test case, you can run pytest to execute the test case and see the results. To run pytest, navigate to the directory containing your test file and enter the following command:


This command will discover and run all the test cases in the directory. If the test case passes, pytest will output a dot. If the test case fails, pytest will output an F.

4. Writing tests with pytest

Now that you have pytest set up, it’s time to write some tests. Writing tests with pytest is intuitive and easy, and it can help you catch bugs and ensure that your code is working as intended.

4.1 Structure of a pytest function

The first step in writing tests with pytest is to create a function for each test case. The function must start with the word “test,” and it should include any relevant setup and cleanup code. You can use the assert statement to check if the output of a function matches the expected result.


def test_function_name(): # Setup code goes here result = function_name(argument1, argument2) assert result == expected_result # Cleanup code goes here

4.2 Writing simple test cases

To start, you should write simple tests to check that your code is working as intended. For example, if you have a function that adds two numbers, you might write a test like this:


def test_addition(): result = add_numbers(2, 3) assert result == 5

4.3 Writing complex test cases

Once you’ve written some simple test cases, you can start to write more complex ones. These might involve testing edge cases or ensuring that your code is handling errors properly. For example, if you have a function that sorts a list of numbers, you might write a test like this:


def test_sorting(): result = sort_numbers([3, 1, 4, 1, 5, 9]) assert result == [1, 1, 3, 4, 5, 9]

5. Common pytest assertions

In addition to the assert statement, pytest provides several built-in assertions that you can use in your tests. These include:

  • assert x == y: checks if x is equal to y
  • assert x != y: checks if x is not equal to y
  • assert x in y: checks if x is in y
  • assert x not in y: checks if x is not in y
  • assert x is y: checks if x is the same object as y
  • assert x is not y: checks if x is not the same object as y

6. Test fixtures

Sometimes you need to set up some state before running a test, or you need to clean up after a test has finished. Test fixtures are functions that are run before or after each test to set up or tear down the testing environment. You can use the @pytest.fixture decorator to define a fixture function.

For example, if you have a function that reads a file, you might write a fixture like this:


import pytest @pytest.fixture def file_path(): return 'test.txt' def test_read_file(file_path): result = read_file(file_path) assert result == 'hello, world!'

In this example, the file_path fixture returns the path to a test file. The test_read_file test function takes the file_path fixture as an argument, which ensures that the fixture is run before the test function.

7. Test Coverage

A key aspect of unit testing is ensuring adequate test coverage. This refers to the extent to which your tests cover the various parts of your codebase. Good test coverage ensures that all parts of your code have been tested, and helps to prevent bugs and regressions from sneaking in.

7.1 Importance of Test Coverage

Having good test coverage is essential for ensuring the quality of your code. By ensuring that all parts of your codebase are tested, you can identify and fix bugs early, before they cause bigger problems down the line. Additionally, good test coverage can help you catch regressions when making changes to your code.

7.2 Tools for Measuring Test Coverage

There are several tools available for measuring test coverage in Python. Some of the most popular tools include, pytest-cov, and codecov. These tools provide a range of features for measuring and visualizing test coverage, and can help you identify areas of your codebase that need additional testing.

7.3 Measuring Test Coverage with pytest

pytest provides built-in support for measuring test coverage using the pytest-cov plugin. To use pytest-cov, you’ll first need to install it:

pip install pytest-cov

Once you’ve installed pytest-cov, you can use the –cov command-line option to measure test coverage:


pytest --cov=my_package tests/

This will run all tests in the tests/ directory and generate a coverage report for the my_package package. The coverage report will show you which parts of your code have been tested, and which parts have not.

You can also generate an HTML report using the –cov-report html option:


pytest --cov=my_package --cov-report html tests/

This will generate an HTML report in the htmlcov/ directory, which you can open in your web browser to view the coverage report.

By regularly measuring test coverage and addressing any gaps in your test suite, you can ensure that your code remains robust and maintainable over time.

8. Test Organization and Best Practices

Now that we’ve covered the basics of writing tests with pytest, let’s dive into how to organize and structure your tests effectively. Proper test organization can make it easier to manage and maintain your tests in the long run. Additionally, adhering to best practices can help ensure that your tests are efficient, reliable, and thorough.

8.1 Organizing tests with pytest

Pytest provides several options for organizing your tests, including the use of test modules and packages. Test modules are simply Python modules that contain test functions, while test packages are directories that contain test modules.

To keep your tests organized, it’s important to follow a consistent naming convention for your test modules and functions. By default, pytest looks for test functions that start with “test_” in any module or package with the word “test” in its name. For example, a module containing test functions for a module named “” might be named “”.

8.2 Naming Conventions for test cases

It’s important to follow a consistent naming convention for your test cases to make them easily identifiable and understandable. Use descriptive names that accurately reflect the purpose of the test case. It’s also common practice to use underscores to separate words in the test case name.

8.3 Tips for writing effective test cases

Here are some tips to help you write effective test cases:

  1. Test each function or method in isolation. This helps ensure that your tests are comprehensive and that you’re not testing multiple functions at once.
  2. Test for expected behavior, not implementation details. Your tests should verify that your code behaves as expected, not how it achieves that behavior.
  3. Use fixtures to share common test data and setup/teardown code between tests. Fixtures can help reduce code duplication and make your tests more modular.

8.4 Test-driven development (TDD)

Test-driven development (TDD) is a development methodology that emphasizes writing tests before writing production code. In TDD, you write a failing test case first, then write the minimum amount of production code necessary to make the test pass, and finally refactor the code as necessary.

TDD can help ensure that your code is thoroughly tested and that it meets the requirements set out in your test cases. Additionally, by writing tests first, you can avoid writing unnecessary code and ensure that your code is well-architected and modular.

8.5 Importance of test coverage

Test coverage is a measure of how much of your code is executed by your tests. Higher test coverage generally means that your code is more thoroughly tested and has a lower chance of containing bugs.

8.6 Tools for measuring test coverage

There are several tools available for measuring test coverage in Python, including, pytest-cov, and more. These tools provide information about which lines of code are executed by your tests and can help you identify areas of your code that are not being adequately tested.

8.7 How to measure test coverage with pytest

To measure test coverage with pytest, you can use the pytest-cov plugin. First, install pytest-cov using pip:

pip install pytest-cov

Next, run your tests with the –cov option:


pytest --cov=my_package tests/

This will generate a coverage report showing the percentage of your code that is covered by your tests.

9. Debugging pytest Tests

Even with careful planning and execution, sometimes tests may still fail. That’s where debugging comes in. In this section, we’ll explore some debugging tips for pytest and learn how to use pdb with pytest.

Here are some tips for debugging pytest tests:

  1. Print statements: One of the easiest and most common ways to debug tests is to use print statements. Add print statements at strategic points in your test code to see what values are being assigned to variables or if certain conditions are met.
  2. pytest.set_trace(): Another way to debug tests is to use the set_trace() function from the built-in pdb module. This function stops the execution of the code at the point where it’s called and allows you to inspect the state of the program.
  3. Isolate the problem: If you’re not sure where the problem is coming from, try to isolate it. Comment out sections of your code until you find the section that’s causing the problem. Once you’ve isolated the problem, you can focus your debugging efforts on that specific section

9.1 Using pdb with pytest

pdb is a powerful built-in Python debugger that allows you to inspect the state of a program at runtime. You can use pdb with pytest by adding the –pdb option when running your tests. This will automatically drop you into the pdb debugger when a test fails.

Here’s an example of how to use pdb with pytest:


$ pytest --pdb

This will run the tests in the file and drop you into the pdb debugger if any tests fail.

Once you’re in the pdb debugger, you can use the usual pdb commands to inspect the state of the program. Here are some of the most useful pdb commands:

  • l: Lists the current line of code and the surrounding code.
  • n: Executes the current line of code and moves to the next line.
  • s: Steps into a function call.
  • c: Continues the execution of the program until the next breakpoint or until the program finishes.
  • p: Prints the value of a variable.

9.2 Inspecting test failures

When a test fails, pytest provides detailed information about the failure, including the file name, the line number, and a traceback. This information can be used to identify the cause of the failure.

Here’s an example of what a test failure message looks like:

makefile AssertionError

This message tells us that there was an AssertionError on line 5 of the file.

If you want more detailed information about the failure, you can use the -vv option when running pytest. This will give you a verbose output that includes the actual and expected values for failed assertions.


$ pytest -vv

This will run the tests in the file and provide verbose output for any failed assertions.

Overall, effective debugging is crucial for writing and maintaining reliable and robust tests. With the right tools and techniques, debugging pytest tests can be a straightforward and efficient process.

10. Mocking with pytest

When writing unit tests, you may find yourself needing to test the behavior of a function or method that depends on other parts of your code or external libraries. This can lead to complex and brittle tests that are difficult to maintain. One way to simplify your tests and make them more reliable is to use mocking.

Mocking is a technique for replacing part of your code with a “mock” object that simulates the behavior of the original code. This can be useful for isolating the code you are testing and making your tests more focused. Mocking can also be used to simulate external dependencies, such as a database or a web service, so that your tests can run without the need for these dependencies to be present.

10.1 Writing tests with mock objects

To write tests with mock objects in pytest, you can use the built-in unittest.mock library that comes with Python. The library provides a Mock class that you can use to create mock objects. You can then use these mock objects in your tests to simulate the behavior of the code you are testing.

For example, let’s say you have a function that depends on an external web service:


import requests def get_data(): response = requests.get("") return response.json()

To test this function, you would normally need to make a real HTTP request to the web service, which can be slow and unreliable. Instead, you can use a mock object to simulate the response from the web service:


from unittest.mock import MagicMock import my_module def test_get_data(): # Create a mock response object mock_response = MagicMock() mock_response.json.return_value = {"foo": "bar"} # Patch the requests module to return the mock response with patch("my_module.requests.get", return_value=mock_response): data = my_module.get_data() # Check that the function returns the expected data assert data == {"foo": "bar"}

In this example, we create a mock response object using the MagicMock class. We then set the return value of the json() method to a dictionary that simulates the response from the web service. We use the patch() context manager to temporarily replace the requests.get() function with our mock object. Finally, we call the get_data() function and check that it returns the expected data.

10.2 pytest-mock plugin

pytest also has a built-in plugin called pytest-mock that provides some useful helpers for working with mock objects. To use the plugin, you need to install it:

pip install pytest-mock

Once you have installed the plugin, you can use the mocker fixture in your tests to create mock objects:


def test_my_function(mocker): # Create a mock object mock_obj = mocker.Mock() # Set a return value for the mock object mock_obj.some_method.return_value = "foo" # Use the mock object in your test assert my_function(mock_obj) == "foo"

In this example, we use the mocker fixture to create a mock object using the Mock class. We then set a return value for the some_method() method of the mock object. Finally, we call the my_function() function with the mock object and check that it returns the expected value.

11. Integrating pytest with Continuous Integration (CI)

In today’s software development practices, it’s common to use a Continuous Integration (CI) system that automatically builds and tests code changes in a repository to catch errors and conflicts early. Integrating pytest with a CI system can provide many benefits, including faster feedback loops and better quality assurance. In this section, we will explore the benefits of integrating pytest with CI, some popular CI tools that support pytest, and how to configure pytest with CI.

11.1 Benefits of integrating pytest with CI

Integrating pytest with a CI system can provide many benefits. Firstly, it can speed up the feedback loop by automating the testing process, so that developers don’t have to wait for the test results to come back manually. Secondly, it can help catch errors and conflicts early in the development process, before they become bigger problems. This can help improve the quality of the codebase and reduce the likelihood of bugs and issues in production. Lastly, it can help ensure that the codebase is always in a stable state, which is essential for maintaining a reliable and scalable software system.

11.2 Examples of CI tools that support pytes

There are many CI tools available that support pytest. Some popular ones include Travis CI, CircleCI, Jenkins, and GitLab CI/CD. These tools allow developers to automate the build, test, and deployment process, which can save time and effort in the development cycle. They also provide a centralized location for viewing test results, which can be useful for tracking progress and identifying issues.

11.3 Configuring pytest with a CI

Configuring pytest with a CI system is relatively straightforward. The first step is to ensure that pytest is installed and configured correctly on the development machine. Next, the developer needs to configure the CI tool to execute the pytest command and report the test results. This involves specifying the test command, the location of the test files, and the output format for the test results.

For example, with Travis CI, the developer can specify the following in the .travis.yml file:


language: python python: - "3.6" script: - pytest

This tells Travis CI to use Python 3.6 and run the pytest command. The output of the pytest command will be displayed in the Travis CI console, and a summary of the test results will be displayed at the end of the build process.

Similarly, with CircleCI, the developer can specify the following in the .circleci/config.yml file:


version: 2.1 jobs: build: docker: - image: circleci/python:3.6 steps: - checkout - run: pip install -r requirements.txt - run: pytest

This tells CircleCI to use the Python 3.6 Docker image, install the dependencies specified in the requirements.txt file, and run the pytest command.

In summary, integrating pytest with a CI system can provide many benefits for software development, including faster feedback loops, better quality assurance, and a more stable codebase. There are many CI tools available that support pytest, and configuring pytest with CI is relatively straightforward. With the right setup, developers can ensure that their code is always in a stable and reliable state, which is essential for building scalable and robust software systems.

In conclusion, unit testing is an essential aspect of software development that helps to ensure the quality and reliability of code. Pytest is a popular testing framework for Python that simplifies the process of writing and executing unit tests.

In this guide, we have covered various aspects of unit testing with pytest, including its introduction, installation, writing test cases, measuring test coverage, test organization and best practices, debugging, mocking, and integrating with Continuous Integration. We have also explored different tools and techniques that can help developers in their testing efforts.

As a recap, some of the key takeaways from this guide are:

  • Unit testing is an important part of software development that helps ensure code quality and reliability.
  • Pytest is a popular testing framework for Python that simplifies the process of writing and executing unit tests.
  • Pytest provides a rich set of features for writing simple and complex test cases, test fixtures, and assertions.
  • Test coverage is a useful metric for measuring the effectiveness of unit tests.
  • Test organization and naming conventions can make it easier to manage and maintain test suites.
  • Debugging tools and techniques can help developers diagnose and fix issues in their tests.
  • Mocking can help to isolate code under test and make it easier to write test cases.
  • Continuous Integration can help to automate the testing process and ensure that code changes do not introduce regressions.

In conclusion, by following best practices and using the right tools, developers can create reliable and maintainable code through unit testing. Pytest is an excellent choice for Python developers looking to streamline their testing process and improve the quality of their code.

Hire top vetted developers today!