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Home Guide Unit Testing in Python: Detailed Tutorial

Unit Testing in Python: Detailed Tutorial

By Pawan Kumar, Community Contributor -

Unit testing is a crucial aspect of software development that involves testing individual units or components of code to ensure their proper functioning. It aims to validate that each unit, such as a function or method, works as intended and produces the expected output.

  • Python, being a popular programming language, offers several frameworks and tools to facilitate unit testing. The Python unit testing process typically follows a set of steps. 
  • First, developers write test cases to  validate the behavior of a unit. Test cases often cover both routine and edge cases to ensure robustness. 
  • Next, developers utilize a unit testing framework, such as PyTest, unittest, or doctest, to organize and execute these test cases. 
  • The framework provides essential features like assertion methods to check the expected output against the actual output.

 In this article, we go deep down to unit testing using Python’s different frameworks.

Is Python Good for Unit Testing?

Python is well-suited for unit testing. Python provides several built-in libraries and frameworks, such as unittest, doctest, and PyTest, that make it easy to write and execute unit tests. These tools offer robust features and functionalities to streamline the testing process.

1. The unittest module, which is part of Python’s standard library, provides a comprehensive framework for organizing and running unit tests.

  • It includes useful features like test discovery, test fixtures, test suites, and assertion methods to compare expected and actual results. 
  • Unittest supports test automation and allows developers to write test cases using classes and methods.

2. Another popular choice is PyTest, an external testing framework that offers a more concise and flexible approach to unit testing.

  • PyTest simplifies test writing by leveraging Python’s expressive syntax and provides advanced features like fixture management, parameterized testing, and powerful test discovery. 
  • It also integrates well with other testing tools and libraries, making it a popular choice among Python developers.

Python has a vibrant ecosystem with a wide range of third-party libraries and tools that support testing, such as mocking frameworks like unittest.mock and coverage measurement tools like These resources enhance the effectiveness and efficiency of unit testing in Python.

What is the Python unittest module?

The unittest module is a built-in testing framework in Python, available as part of the standard library. The primary goal of the unittest module is to facilitate the creation of test cases and test suites to verify the behavior and correctness of individual units of code. 

  • These units are typically functions, methods, or classes, and the purpose of unit testing is to ensure that each unit functions as intended and produces the expected output. 
  • The unittest module offers a class-based approach to define test cases. Developers create a test case class that inherits from the unittest.TestCase class. 
  • Within this test case class, individual test methods are defined, where each method represents a specific test scenario.
  • Test methods are named starting with the prefix “test_” to be discovered by the testing framework.

The unittest module in Python offers several benefits for writing and executing unit tests. Some of the key advantages of using unittest for testing your code include:

  • Built-in Testing Framework: unittest is part of Python’s standard library, which means it is readily available without the need for additional installations. This makes it easily accessible and ensures compatibility across different Python environments.
  • Simple and Familiar Syntax: The unittest framework utilizes a class-based approach, where test cases are defined as classes inheriting from unittest.TestCase which intuitive and familiar to developers.
  • Assertion Methods: unittest provides a wide range of assertion methods to compare expected and actual results.
  • Test Discovery: The unittest framework supports automated test discovery, which enables the discovery and execution of all test cases within a directory or module.
  • Test Fixtures: With unittest, you can define setup and teardown methods within test cases. These fixture methods, such as setUp() and tearDown(), allow you to set up the necessary environment before executing each test and clean up any resources afterward. 
  • Test Suites: unittest supports the creation of test suites, allowing you to group related test cases together. 
  • Integration with Test Runners: The unittest module integrates well with test runners, which are responsible for discovering and executing tests.
  • Extensibility: The unittest framework is designed to be extensible. Developers can subclass unittest.TestCase to create custom test case classes with additional functionality or override existing methods.
  • Compatibility and Community Support: Being a part of Python’s standard library, unittest enjoys widespread usage and community support. It is well-documented, and developers can find numerous online resources, tutorials, and examples to guide them in utilizing the framework effectively.

PyTest vs Unittest: Core Differences

Test DiscoveryAutomatic test discovery, finds and runs tests without boilerplateRequires manual test discovery by explicitly defining test cases
Fixture SupportPowerful and flexible fixture supportLimited fixture support, mainly through the setup and teardown methods
Test ExecutionSupports parallel test execution, faster runtimeSequential test execution, one test at a time
Test Execution OptionsProvides various options for test execution customizationOffers fewer options for customizing the test execution process
Assertion MethodsRich set of built-in assertion methodsStandard assertion methods provided by the unittest module
Test OrganizationTest functions can be organized in a flexible mannerTest cases are organized as classes, providing a more structured approach
Skipping TestsBuilt-in mechanism for skipping testsAbility to skip tests using decorators or conditional statements
Test ParameterizationBuilt-in support for parameterized testsParameterization can be achieved using decorators or conditional logic
Plugin EcosystemLarge and active plugin ecosystem with many useful pluginsLimited plugin support, fewer third-party extensions available
Output ReadabilityDetailed and readable output for failed testsBasic output with less detailed information
Integration with IDEsGood integration with various IDEs, plugins, and reporting toolsStandard integration with IDEs, some IDEs may have limited support
Python Version SupportCompatible with Python 2.7 and aboveCompatible with Python 2.1 and above

Why is PyUnit (Unittest) preferred for Unit testing?

The inclusion in the standard library, familiarity, community support, compatibility, and industry acceptance make PyUnit (unittest) a preferred choice for unit testing in Python. 

  1. Standard Library Inclusion: PyUnit (unittest) is included in Python’s standard library, making it readily available without the need for additional installations.
  2. Familiarity and Consistency: PyUnit follows the design principles and patterns of the xUnit family of testing frameworks. Developers who are familiar with other xUnit frameworks, such as JUnit in Java or NUnit in .NET, will find PyUnit easy to understand and use. 
  3. Community Support and Resources: PyUnit has a large and active community of Python developers who have been using and contributing to the framework for years. This translates into a wealth of resources, tutorials, documentation, and online forums where developers can seek guidance and share knowledge.
  4. Compatibility and Portability: PyUnit is compatible with different versions of Python, including both Python 2 and Python 3. This compatibility is especially valuable for projects that have specific version requirements or need to maintain compatibility with older versions of Python.
  5. Integration with Tools and Ecosystem: It works seamlessly with popular Python IDEs, providing features like test discovery, test execution, and result reporting within the IDE environment. PyUnit is also supported by test runners, continuous integration (CI) systems, and code coverage tools, allowing for seamless integration of unit tests into the development workflow.
  6. Stability and Reliability: Being a core component of Python, PyUnit benefits from the collective experience and expertise of the Python core development team, further bolstering its stability and trustworthiness.
  7. Industry Acceptance: Many popular Python libraries and frameworks, such as Django and Flask, utilize PyUnit for their testing needs. The widespread acceptance and usage of PyUnit in the industry contribute to its credibility and reliability as a unit testing framework.

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Unit Testing in Python: Detailed Tutorial

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Prerequisites for Setting up Python Unit Testing

To set up Python unit testing, there are a few prerequisites you should consider:

  1. Python Installation: Ensure that Python is installed on your system. You can download the latest version of Python from the official Python website ( and follow the installation instructions for your operating system.
  2. Testing Framework: Choose a testing framework that suits your needs. Python offers several options, including unittest, PyTest, and doctest.
  3. Project Setup: Set up your project structure. Create a dedicated directory for your project, and organize your source code files, modules, and tests accordingly. A well-structured project makes it easier to manage and run unit tests.
  4. Testing Dependencies: You can typically install dependencies using Python’s package manager, pip, by executing commands like pip install <package-name>.
  5. Virtual Environments: Virtual environments allow you to have separate Python environments for different projects, avoiding conflicts between packages and ensuring project-specific dependencies are installed.
  6. Editor or IDE: Choose a code editor or IDE for writing your tests. Popular options include Visual Studio Code, PyCharm, Atom, and Sublime Text. An editor or IDE with built-in support for Python and unit testing can provide helpful features like syntax highlighting, code completion, and test execution within the development environment.
  7. Test Runner: Familiarize yourself with the concept of test runners. A test runner is a tool or command-line utility that discovers and executes unit tests. Many testing frameworks, including unittest and PyTest, come with built-in test runners. Understand how to use the test runner of your chosen framework to run your unit tests effectively.
  8. Test Coverage: Consider using a code coverage tool to measure the extent to which your unit tests cover your codebase. Tools like can help you identify areas of your code that lack test coverage, ensuring that your tests are comprehensive and thorough.

These prerequisites will help you set up a solid foundation for Python unit testing. It is important to understand the tools and frameworks you are using and to follow best practices in organizing and structuring your tests and project files.

Setting Up the Testing Environment

To set up the testing environment for PyUnit (unittest), you can follow these steps:

Step 1 –  Create a Project Directory: Start by creating a dedicated directory for your project. This directory will serve as the root directory for your code and tests.

Step 2 –  Create a Virtual Environment: It is recommended to create a virtual environment to isolate your project’s dependencies. 

Open a command-line interface, navigate to your project directory, and execute the following command:

python3 -m venv myenv

This command creates a virtual environment named myenv in your project directory

Step 3 – Activate the Virtual Environment: Activate the virtual environment using the appropriate command for your operating system:

For Windows:


For macOS/Linux:

source myenv/bin/activate

Once activated, your command prompt should show the name of the virtual environment.

Step 4 – Create Test Files: Create a separate directory within your project for your test files. For example, you can create a directory named tests. Inside the tests directory, create Python files with names starting with test_ (e.g., These files will contain your unit tests.

Step 5 – Import the  Required Packages: As unittest is a pre-installed python package . So we can easily import it in your Python code by using the import unittest command.After importing the unittest module, we can  write and execute unit tests for our Python code using the module’s classes and methods.

How to Define Test Cases for Python Functions?

Defining test cases for Python functions involves creating test methods that verify the behavior of the function under various scenarios. Each test method represents a specific test scenario, such as testing different input values, edge cases, or expected error conditions. Assertions are used within the test methods to compare the expected output of the function with the actual output.

To define test cases for Python functions, You can follow the below steps

1. Import the unittest Module: Start by importing the unittest module at the beginning of your test file.

import unittest

2. Create a Test Case Class: Define a test case class that inherits from unittest.TestCase. This class will contain your test methods.

class MyTestCase(unittest.TestCase):


3. Define Test Methods: Within the test case class, define individual test methods. Each test method should start with the prefix test_ to be discovered by the testing framework.

class MyTestCase(unittest.TestCase):

    def test_function_name(self):

        # Test code goes here


4. Write Assertions: Inside each test method, write assertions to check the expected behavior of the function being tested. Assertions compare the actual output of the function to the expected output.

class MyTestCase(unittest.TestCase):

   def test_function_name(self):

       result = my_function()  # Call the function being tested

       self.assertEqual(result, expected_result)

You can use various assertion methods provided by unittest like assertEqual, assertTrue,     assertFalse, etc., depending on the specific conditions you want to check.

5. Run the Tests: To execute the test cases, you need to run a test runner. You can use the unittest test runner provided by Python. You can either run the tests from the command line or use a testing framework or IDE that supports test execution.

From the command line, execute the following command in the same directory as your test file:

python -m unittest <>

Alternatively, you can use a testing framework or IDE that provides a graphical interface or test runner integration for executing the tests.

Here’s an example that demonstrates how to define test cases for a simple Python function

def multiply(a, b):

   return a b

Let’s define test cases for the multiply function using the unittest module:

import unittest

class MultiplyTestCase(unittest.TestCase):

   def test_multiply_positive_numbers(self):

       result = multiply(3, 4)

       self.assertEqual(result, 12)

   def test_multiply_negative_numbers(self):

       result = multiply(-2, -5)

       self.assertEqual(result, 10)

   def test_multiply_zero(self):

       result = multiply(10, 0)

       self.assertEqual(result, 0)

   def test_multiply_with_one(self):

       result = multiply(7, 1)

       self.assertEqual(result, 7)
  • In this example, we define a test case class MultiplyTestCase that inherits from unittest.TestCase. Inside this class, we define individual test methods, each starting with the prefix test_.
  • In the test_multiply_positive_numbers method, we test the multiply function with positive numbers 3 and 4. We expect the result to be 12, so we use self.assertEqual(result, 12) to check if the actual result matches the expected result.
  • In the test_multiply_negative_numbers method, we test the multiply function with negative numbers -2 and -5. We expect the result to be 10, so we use self.assertEqual(result, 10) for assertion.
  • In the test_multiply_zero method, we test the multiply function with one operand as 0. We expect the result to be 0, so we use self.assertEqual(result, 0) for assertion.
  • In the test_multiply_with_one method, we test the multiply function with one operand as 1.
  • We expect the result to be the other operand itself, so we use self.assertEqual(result, 7) for assertion.

To run these test cases, you can use a test runner. For example, executing python -m unittest in the command line will discover and run all the tests in the current directory. And there is another command if we need verbose of our outputs then we can use  python -m unittest -v.

And if required to run with a specific file then give the file name  in command like this  python -m unittest -v <filename>.py .

Unit Testing Python

Common Assertion Methods for Python Functions

When writing unit tests for Python functions using the unittest module, you can use various assertion methods provided by the module to compare expected and actual values.

Here are 10 commonly used assertion methods for Python functions using the unittest module:

1. assertEqual(a, b): Checks if a and b are equal.

self.assertEqual(10, add(5, 5))  # Passes if add(5, 5) equals 10

2. assertTrue(x): Checks if x evaluates to True.

self.assertTrue(result) # Passes if result is True

3. assertFalse(x): Checks if x evaluates to False.

self.assertFalse(error)  # Passes if error is False

4. assertIs(a, b): Checks if a is the same object as b.

self.assertIs(result, expected_result)  # Passes if result is expected_result (same object)

5. assertIsNone(x): Checks if x is None.

self.assertIsNone(result)  # Passes if result is None

6. assertIsNotNone(x): Checks if x is not None.

self.assertIsNotNone(result)  # Passes if result is not None

7. assertIn(a, b): Checks if a is present in b.

self.assertIn(item, my_list)  # Passes if item is present in my_list

8. assertNotIn(a, b): Checks if a is not present in b.

self.assertNotIn(item, my_list)  # Passes if item is not present in my_list

9. assertRaises(exception, callable, *args, **kwargs): Checks if calling callable raises exception.

self.assertRaises(ValueError, divide, 10, 0# Passes if calling divide(10, 0) raises ValueError

10. assertAlmostEqual(a, b, places): Checks if a and b are approximately equal up to a specified number of decimal places.

self.assertAlmostEqual(result, expected_result, places=2# Passes if result and expected_result are approximately equal up to 2 decimal places

These are just a few examples of commonly used assertion methods provided by unittest. There are more assertion methods available, including those for checking exception messages, container equality, string containment, and more. Choose the appropriate assertion method based on the specific conditions you want to test and the expected behavior of your function.

Best Practices for Python Unit Testing

When writing unit tests in Python, it’s important to follow best practices to ensure effective and maintainable tests. Here are some best practices for Python unit testing:

  • Test Readability and Maintainability: Write clear, concise, and readable test cases. Use descriptive names for test methods and provide meaningful comments when necessary.
  • One Assertion per Test: Aim to have only one assertion per test method. This helps in isolating failures and makes it easier to identify the specific condition that caused the failure.
  • Test Independence: Ensure that each test is independent and does not rely on the state or results of other tests. Avoid sharing state between tests, as it can lead to unexpected dependencies and make test failures harder to diagnose.
  • Test Coverage: Strive for comprehensive test coverage by testing different scenarios, including typical cases, edge cases, and error conditions. Aim to cover all branches and possible execution paths of your code.
  • Test Isolation: Ensure that tests are isolated from external dependencies, such as databases, web services, or file systems. Use techniques like mocking or dependency injection.
  • Test Documentation: Good documentation helps other developers understand the tests quickly and facilitates test maintenance and future enhancements.
  • Continuous Integration (CI): Integrate your unit tests into a continuous integration (CI) system that automatically runs the tests whenever changes are made to the codebase. This helps catch regressions early and ensures that the tests are executed consistently and regularly.
  • Regular Test Maintenance: Update and maintain your tests as your code evolves. Review and revise tests periodically to ensure their effectiveness and relevance.

You may write strong, maintainable, and dependable unit tests that improve the overall stability and quality of your Python codebase by adhering to these recommended practices.


Python offers multiple testing frameworks, including unittest and PyTest, each with its own strengths and features.

  • While unittest is part of the standard library and provides a consistent and familiar testing approach, PyTest offers a more concise syntax and advanced features, making the choice dependent on project requirements and developer preferences.
  • Overall, adopting best practices in unit testing, such as writing readable and maintainable tests, ensuring test independence and coverage, following the AAA pattern, and integrating tests into CI systems, helps maintain code quality, catch bugs early, and foster robust software development. 
  • By considering these factors and utilizing the appropriate testing frameworks, Python developers can create effective and reliable unit tests for their codebases.
Python Unit Testing

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