A Guide to Running Unit Tests in Python

Introduction to unit testing in Python

Python Unit Testing

In software development, it is important to ensure that the code that developers write is functioning as expected. This is where unit testing comes into play. Unit testing is a software testing method where individual units or parts of code are tested in isolation to ensure that each unit is functioning as expected.

Python is a high-level programming language that is well suited for unit testing. Python provides a built-in framework called ‘unittest’ which makes it easy to write unit tests, execute them and analyze the results. In this article, we will dive deeper into the world of unit testing using Python.

To start testing code using Python’s unittest framework, the first step is to write tests that will ensure the functionality of the application. This step is crucial since the effectiveness of the tests depends on the code being tested.

Tests are written as separate entities in Python, with each test checking for a particular part of the code’s functionality. During the testing phase, developers will execute these tests to make sure that all functions work as intended and fix any tests with an unexpected outcome.

Python’s unittest framework provides tools that every developer can use to write test cases. There is no need to write test cases from scratch, and even beginner level programmers can use it.

The ‘assert’ statement is another powerful tool that developers can use to automate testing. It is a statement that tests a piece of code and returns True if it passes the test or False if the test fails. The ‘assert’ statement provides a quick way to check the functionality of the code.

In the context of unit tests, ‘assert’ statements are used to check that the response from each unit is what is expected. For example, if a function should return a result of 5, we can use an ‘assert’ statement to confirm that the function does indeed return 5. If the function returns something else, the test will fail, indicating that there are errors in the code.

Another advantage of using the unittest framework is its ability to generate code coverage reports. This feature shows which parts of the code are executed during the tests and which aren’t. This information is crucial during debugging since it provides insights into the parts of the code that are running as intended and those that need improvement.

Python’s unittest framework also provides another tool that every developer should know about: test fixtures. Test fixtures are functions that provide a baseline for the test environment.

These fixtures ensure that each test has the same starting point, allowing each test to produce consistent results. For example, if we have a class of tests that require the same database connection, we can use a test fixture to set up the database connection. This way, we won’t have to establish a new connection for each test.

In summary, unit testing is an essential part of software development, and Python’s unittest framework provides the tools developers need to automate testing. These tools include assert statements, code coverage reports and test fixtures, making it easy to write effective tests that ensure the functionality of the code.

By writing unit tests, developers can ensure that their code is functioning correctly. Additionally, unit testing can help speed up the development process by allowing developers to catch bugs early before they become bigger problems. The goal of unit testing is to make software development more efficient by providing developers with the tools they need to create high-quality code that meets their user’s needs.

How to Install the UnitTest Library in Python

Install Unittest Library in Python

Unit testing is an important aspect of software development, and the Python programming language has inbuilt support for unit testing through the unittest module. This module provides a framework that makes it easy to write and execute unit tests in Python. However, before you can start writing tests, you need to install the unittest library in your Python environment. Here, we will guide you on how to install this library in Python on different platforms.

Installing unittest on Windows

If you are using Windows, the easiest way to install the unittest library is through the Python package manager – pip. To install pip, follow these steps:

  1. Open your command prompt or PowerShell by pressing the Windows key+X and selecting “Windows PowerShell (Admin).”
  2. Run the following command: python -m ensurepip --default-pip
  3. You should see a message indicating that pip has successfully been installed. You can now install the unittest library by running the following command: pip install unittest
  4. Once the installation completes, you can confirm that you have installed the unittest module by running the following command: python -m unittest. If the module is installed correctly, you will see a message indicating that no tests were found.

Installing unittest on macOS or Linux

If you are on macOS or Linux, you can install the unittest library through the Python package manager – pip. Follow these steps:

  1. Open your terminal application.
  2. Run the following command to ensure that you have pip installed: sudo apt-get install python-pip
  3. You should see a message indicating that pip has successfully been installed. You can now install the unittest library by running the following command: pip install unittest
  4. Once the installation completes, you can confirm that you have installed the unittest module by running the following command: python -m unittest. If the module is installed correctly, you will see a message indicating that no tests were found.

Installing unittest on Anaconda

If you are using Anaconda, unittest is already installed in your environment, and you don’t need to install it separately. You can confirm that unittest is installed by running the following command: python -m unittest. If the module is installed correctly, you will see a message indicating that no tests were found.


Installing the unittest library in Python is essential if you want to start writing unit tests for your Python code effectively. With the above guide, you can quickly install the library on different platforms, and you will be ready to start writing tests in no time. Remember to run the command python -m unittest to confirm that the module has been installed correctly.

Writing and running your first unit test in Python

automated testing python

As a developer, it’s important to ensure that your code is functional and reliable. One way of achieving this is through automated testing, specifically unit testing. Unit testing is the process of testing individual units or components of your code to verify that they function as intended.

Python provides several tools and frameworks for unit testing, such as Pytest and Unittest. In this article, we’ll cover the basics of writing and running your first unit test in Python using Pytest.

Installing Pytest


Before we can start writing our tests, we need to install Pytest. We can do this by running the following command in our terminal:

pip install pytest

This will install Pytest and all of its dependencies. Once we have Pytest installed, we can start writing our tests.

Writing a simple test

writing test cases in python

Let’s start by writing a simple test for a function that adds two numbers. First, we create a new file called test_addition.py in our project directory. Then, we define our function and test case in the file:

# test_addition.py

def add_numbers(x, y):
return x + y

def test_addition():
assert add_numbers(2, 5) == 7

Let’s break down what’s happening in this code. First, we define a function called add_numbers that takes two arguments and returns their sum. Then, we define a test case called test_addition that uses the assert statement to verify that the result of calling add_numbers with arguments 2 and 5 is equal to 7.

The assert statement is a key component of a test case. It takes an expression that should evaluate to True if the code being tested is working correctly. If the expression evaluates to False, the test fails.

Running our test

testing python

Now that we’ve written our test, let’s run it using Pytest. In our terminal, we navigate to the directory where our test file is located and run the following command:


We should see an output similar to the following:

collected 1 item

test_addition.py . [100%]

=============================== 1 passed in 0.01s ===============================

Our test passed! Pytest collected our one test case, ran it, and reported that it passed. If the test had failed, we’d see an output indicating which assertion failed and why.

Expanding our test suite

unit testing python

Now that we’ve written and run our first test, let’s expand our test suite to cover more cases. We’ll create a new file called test_math.py and write several test cases for different math functions:

# test_math.py

def add_numbers(x, y):
return x + y

def subtract_numbers(x, y):
return x – y

def multiply_numbers(x, y):
return x * y

def test_addition():
assert add_numbers(2, 3) == 5
assert add_numbers(0, 0) == 0
assert add_numbers(-1, 2) == 1

def test_subtraction():
assert subtract_numbers(5, 3) == 2
assert subtract_numbers(0, 0) == 0
assert subtract_numbers(-1, -2) == 1

def test_multiplication():
assert multiply_numbers(2, 3) == 6
assert multiply_numbers(0, 0) == 0
assert multiply_numbers(-1, 2) == -2

In this file, we’ve defined three math functions and written test cases for each of them. We’ve included several assertions for each test case to cover different inputs and edge cases. Now, when we run Pytest, it will discover and run all of our test cases:

collected 9 items

test_addition.py . [ 33%]
test_math.py …FFF… [100%]

==================================== FAILURES ====================================
______________________________ test_subtraction _______________________________

def test_subtraction():
> assert subtract_numbers(2, 3) == -1
E assert 2 == -1
E + where 2 = subtract_numbers(2, 3)

test_math.py:18: AssertionError
_____________________________ test_multiplication ______________________________

def test_multiplication():
> assert multiply_numbers(2, 3) == -6
E assert 6 == -6
E + where 6 = multiply_numbers(2, 3)

test_math.py:24: AssertionError
============================== 2 failed, 7 passed in 0.02s ==============================

As we can see, two of our test cases failed. Pytest reports the test case names, the assertion that failed, and the values that were expected and received. We can use this information to debug our code and fix the failing tests.

By writing unit tests for our code, we can ensure that our functions work correctly under various conditions. This can save time and prevent bugs down the line. With Pytest, writing and running unit tests in Python is easy and straightforward.

Best practices for organizing and running unit tests in Python

Python unit test

In this section, we will discuss some best practices for organizing and running unit tests in Python. We will cover various aspects of unit tests, such as naming conventions, test organization, test fixtures, and more.

1. Follow the naming conventions

Python unit test name

It is essential to follow the naming convention for unit tests to make them easy to understand and maintain. For instance, test functions should start with the prefix “test_,” and the name should describe the test’s functionality. Following clear naming conventions not only makes the test more readable but also helps to locate and identify problems more quickly.

2. Organize your test cases

Python unit test organization

It is beneficial to organize test cases appropriately. Separate files should be created, each containing test cases for a specific feature. Another way to organize tests is to use test suites – a collection of test cases that are related to each other. Test suites can be used to run specific types of tests, such as smoke tests, regression tests, or integration tests. Organizing test cases enhances their readability and maintainability, making them easier to manage in the long run.

3. Use test fixtures

Python unit test Fixtures

Test fixtures refer to the objects and data configurations needed to run a specific test. In Python, test fixtures are defined using the setUp method that prepares the test environment and the tearDown method that cleans up the environment after the test runs. Using test fixtures can significantly reduce the time to recreate the environment each time a test is run, and it ensures that tests are consistent and reliable. Test fixtures should be defined to create data for each test and tear down the data after the test is finished to keep the environment clean.

4. Write Readable Tests

Python unit test readability

Readable tests are essential for making the most of unit testing. Writing readable tests means that they are precise, descriptive, and easy to understand. Start with setting up the objects and data that are necessary for the test, and also, use clear naming conventions to explain what the test is doing. Also, ensure that the code is readable by breaking up lengthy and complex tests into smaller, easily understandable sections. Ideally, each section of code should contain one assertion. Moreover, it is always a good practice to include comments that describe the purpose of the test and any relevant information.

5. Keep Your Tests Independent

Python unit test independence

The tests should be independent of each other to ensure that the test works correctly. The tests must not rely on the previous test output or environment for the test. If this is the case, maintain order in which the tests are performed. When tests are independent, it’s easier to identify and isolate issues if any. When tests are independent, it speeds up the testing process. It takes less time to run tests that rely on each other.

6. Running Your Tests

Python unit test run

There are various frameworks available to run Python unit tests such as unittest, pytest, and nose. unittest is a standard python library that provides a framework for building and running tests. It is lightweight, and it comes with great features such as automated discovery of tests and control over test execution order. Pytest is known for its simple syntax and robust fixtures that make writing tests and running them more comfortable. Pytest supports both unit and functional testing. It emphasizes testing code as a black box due to its powerful assertion introspection mechanism. Nose is another test framework for Python that simplifies test discovery and test execution and is compatible with plugins from the unittest and pytest frameworks. By selecting the right tool(s) to use, you can run tests easily and conveniently.

In conclusion, effective and efficient management of your tests in Python is essential to ensure that they continue to provide value to the development process. Utilizing test fixtures, clear naming conventions, independence, and organization are some of the most effective ways to ensure that tests remain relevant and useful over time. Make sure that you follow best practices for running unit tests and use the appropriate tools for testing, you can save time and detect issues early on in the development cycle.

Tips for Optimizing and Analyzing Unit Test Results in Python

Notebooks with code written on them

Unit testing is an essential skill for every programmer as it helps identify errors and bugs within your code before it goes to production. However, it is not sufficient to write any type of test. The test should be reliable, consistent, and able to detect all bugs. In this article, we will discuss tips on optimizing and analyzing unit test results in Python.

1. Minimize Dependencies

Code with minimized dependencies on the left and multiple dependencies on the right

Tests that are dependent on other elements are less reliable as the bugs may be from the dependent element. As much as possible, minimize the dependencies of your tests on other components within your system. This way, the tests can be written in isolation, and bugs can be isolated. Thus, ensuring that you have a reliable test suite.

2. Focus on one area at a time

Person looking at a computer screen with highlighted area in red

The best way to create a reliable and consistent test is to focus on one area of the system at a time. This approach is known as unit testing, whereby each area of the system is tested in isolation. It enables you to have a better understanding of the different parts of your system, identify bugs, and fix them without disrupting the entire system.

3. Automate Your Tests

Robotic arm working on a computer

Automating your tests is an essential step in optimizing your test suite. Automation tools such as pytest, unittest, and Nose are used to automate test execution, and it helps in running tests quickly, eliminates manual errors, and saves development time. Once automated, it will be easier to run tests, identify errors, and fix them timely.

4. Measure Your Test Coverage

Graph showing test coverage blocks in green and non test covered blocks in red

Test coverage is an important metric in analyzing the effectiveness of your tests. It measures the percentage of code that is executed while running a particular test suite. It helps to identify areas that have not been tested. The higher the code coverage, the greater the confidence you have in the tests. Tools such as coverage.py can be used to measure test coverage.

5. Continuous Integration

Multiple servers showing continuous integration process

Continuous Integration is a process whereby codes are integrated into a shared repository. This process helps in identifying bugs early and solves integration problems faster. Once integrated, an automated build, test, and deployment can be run, highlighting any errors or bugs in the software. By incorporating continuous integration practices, it becomes easier to write quality tests that can consistently and reliably detect any possible bugs in your codebase.

Optimizing and analyzing unit test results in Python will enable you to write quality tests faster and more consistently. Adopting the tips discussed in this article, like minimizing dependencies, focusing on one area at a time, automating your tests, measuring your test coverage, and incorporating continuous integration, will help create a robust test suite.

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