Pytest vs Unittest for Python Testing – A Detailed Comparison

Software testing is an indispensable part of software development. Python offers a range of testing frameworks for reliable and automated testing, with pytest and unittest as two mainstream options. This in-depth article compares these frameworks to help you decide which one is better suited for your testing needs.

What is Pytest?

Pytest is an open source third-party library that provides a simple, powerful and flexible testing framework for Python. It has gained immense popularity in the Python community due to its ease of use, expressive assertions, strong test discovery and many more useful features that simplify test writing and execution.

Features of Pytest

Intuitive test discovery – Pytest can automatically detect test functions, cases and methods from your project’s directory based on naming conventions like test_*. No need for manual test configuration.
Bare test functions for simple cases – You can write tests simply as normal Python functions with assertions.
Fixtures to handle setup/teardown – Pytest fixtures allow you to define reusable resources that can be set up before and cleaned up after tests using dependency injection.
Parameterized tests – Pytest makes it easy to run the same test logic multiple times with different parameters.
Parallel test execution – Run tests concurrently on multiple CPU cores to reduce overall execution time.
Plugins and customization – Over 350+ existing pytest plugins and ability to customize pytest to your specific needs.
Detailed error reporting – Pytest provides informative failure reports with details on the test scenario, file path, line number, and the expressions that failed.
Built-in support for unittest and doctest – You can run unittest test cases and doctest examples with pytest seamlessly.

How to Write and Run Pytest Tests

Writing pytest tests is straightforward because you can use basic Python assert statements or the pytest built-in assertions directly in Python functions, no need to inherit from any test classes.
Here is an example pytest test for a Python function that validates phone numbers:

# test_phone_validator.py
import pytest

def validate_phone(phone_number):
    if len(phone_number) != 10:
        return False
    if not phone_number.isdigit():
        return False 
    return True

# Pytest test functions 
def test_valid_phone_number():
    assert validate_phone("9982736450") == True  

def test_invalid_phone_number_letters():
    assert validate_phone("abc7894561") == False  

def test_invalid_phone_short():
    assert validate_phone("9875") == False

To run the pytest test cases:

pytest test_phone_validator.py

This automatically discovers and executes all the test functions, providing test run statistics and failure details if any tests fail.

Advantages of Pytest

Easy to start writing tests quickly with bare test functions and asserts.
No need to remember self in test methods. Tests look like standard Python functions.
Powerful automatic test discovery based on file names, classes, and function names.
Fixture functions provide built-in test doubles and object lifetime management.
Supports parameterized testing to test multiple scenarios.
Hooks to customize the behavior of test sessions.
Pytest is modular with over 350+ external plugins for added functionalities.
Pytest can run unittest and nose test suites out of the box.
Highly configurable based on different testing needs via command line options, ini style configuration files, and Python API.

Disadvantages of Pytest

Due to its flexibility, pytest does not impose stringent rules or standards which might be challenging for test structure in large projects.
Customization requires learning Pytest architecture of hooks, plugins etc. which has a learning curve.
Cryptic error and failure stack trace when something unexpected happens during test execution.
Integration with IDEs may require installing pytest plugin on your IDE.

What is Unittest?

Unittest is a unit testing framework that comes bundled as part of the Python standard library. This means you don’t need to install anything extra to start using it for testing Python code.

Some key features of unittest are:

Test case classes that inherit from unittest.TestCase
Methods for setup and teardown (setUp and tearDown)
Many assertion methods to test for conditions
Test suites for organizing test cases
Test runners for running tests and generating reports

Let‘s look at how unittest tests are written:

# test_phone_validator_unittest.py

import unittest

def validate_phone(phone_number):
    # Same phone validator logic
    ...

class TestPhoneValidation(unittest.TestCase):

    def test_valid(self):
        self.assertTrue(validate_phone("9982736450"))

    def test_invalid_letters(self):
        self.assertFalse(validate_phone("abc7894561"))

    def test_invalid_short(self): 
        self.assertFalse(validate_phone("9875"))

if __name__ == "__main__":
    unittest.main()

To run these test cases:

python test_phone_validator_unittest.py

Some key differences from pytest:

Test cases are organized into classes that inherit unittest.TestCase
Setup/teardown possible using setUp and tearDown without fixtures
Special assertion methods instead of standard Python assert

Advantages of Unittest

No extra installation since unittest comes with Python standard library.
Forces modular design for setup, teardown and test organization.
Integration with IDEs and test runners is smooth due to its standardization.
Wide range of assertion methods covering most scenarios.
Can be a common ground for developers used to xUnit style frameworks in other languages.

Disadvantages of Unittest

More verbose and boilerplate code since everything is classes and methods.
Less flexibility due to enforcing OO style over functional testing style.
Harder to do test discovery via standard unittest so requires manual loading tests.
Limited built-in extensibility and customization features compared to pytest plugins.
Cannot run doctest and nose style test code natively with unittest.

Pytest vs Unittest – Key Differences

	Pytest	Unittest
Test discovery	Automatic, based on file and function names	Manual, based on importing tests into suites
Test structure	Modular test functions	OOP style test classes/methods
Assertion statements	python built-in assert or pytest library	Special assertion methods
Setup/teardown	Fixtures for dependency injection	Override setUpClass / tearDownClass
Parameterization	pytest.mark.parametrize	SubTest context manager
Plugin ecosystem	350+ plugins	Limited plugins
Handling failures	Inspection via assertion rewriting	Logging mechanisms

Best Practices for Writing pytest and unittest Tests

To leverage pytest and unittest effectively, you should follow these key best practices while writing test code:

General Best Practices

Structure your test project well with separate folders for unit, integration, end-to-end tests
Treat test code with the same standards as application code
Make tests independent and repeatable by resetting the environment after each test
Avoid unnecessary asserts and test logic to keep tests simple
Parametrize data variations instead of duplicating test logic
Clearly name your test functions and methods

pytest Best Practices

Use pytest fixtures for test setup/teardown and pass fixtures as arguments instead of global variables
Reset module-scoped fixtures from conftest fixture to prevent shared state
Avoid fixture interleaving and maintain linear ordering of fixtures
Use marks to select/group tests and fixtures at multiple levels like module, class or method
Add custom pytest plugins for specialized testing needs

unittest Best Practices

Inherit test cases from unittest.TestCase and override setUp/tearDown methods
Use assertion methods instead of assert statements
Group related test cases into separate test case classes
Organize test case classes into test suites
Subclass TestCase to create custom base test classes like an APIBaseTest

Adopting these best practices helps avoid common test pitfalls and builds maintainable test suites using pytest and unittest.

When to Choose Pytest Over Unittest?

Pytest and unittest, both are valuable Python testing frameworks with different strengths. When should you pick one over the other?

Consider Pytest If You Need

Minimal boilerplate code and simpler test syntax
Powerful automatic test discovery
Easier test fixture organization
Customization via Pythonic APIs and plugins
Integration with web frameworks like Django and Flask
Testing JavaScript code with pytest-play for frontends
Detailed and customizable assertions failure reporting

Prefer Unittest If You Require

Leveraging existing unittest test cases
Tighter integration with CI/CD pipelines and testing tools that understand unittest
Standardization and consistency by enforcing OO style tests
Tests that interact smoothly across module/package boundaries
Support for older Python versions

Both frameworks continue to evolve by adopting useful features from each other. Pytest is adding better unittest integration while unittest bolsters its assertion library.

So choose the framework that aligns best with your specific testing needs. Also consider factors like team’s existing skills, integration requirements, and application’s architectural style.

Integrating Pytest and Unittest with CI/CD

Continuous integration and delivery (CI/CD) helps automate building, testing and deploying applications. Pytest and unittest can be easily integrated into these pipelines.

Integrating Pytest with CI/CD

Pytest seamlessly integrates with popular CI/CD platforms via pytest plugins:

pytest-jenkins to trigger pytest test runs from Jenkins and publish results
pytest-azurepipelines for Azure Pipelines integration
pytest-workflow jinja templates for GitHub Actions

Example GitHub Actions workflow:

on: push

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v3
    - uses: actions/setup-python@v4
      with: 
        python-version: 3.7
    - run: pip install pytest
    - run: pytest tests --junitxml=report.xml
    - uses: actions/upload-artifact@v3
      with:
        name: Pytest Report
        path: report.xml

This runs pytest tests on push and uploads an XML report as an artifact.

Integrating Unittest with CI/CD

Unittest integration requires some orchestration code to execute test runners and publish reports:

on: push
jobs:
  test: 
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - uses: actions/setup-python@v4
        with:
          python-version: 3.7 
      - run: python -m unittest discover -v
      - run: |
           python -m xmlrunner --output=test-reports
           echo "::set-output name=report_dir::test-reports"
      - uses: actions/upload-artifact@v3
        with: 
          path: ${{ steps.test.outputs.report_dir }}

This runs unittest test discovery, generates an XML report using xmlrunner plugin and uploads it.

For both frameworks, you can configure triggers to execute tests before merging PRs, after pushes, nightly etc.

Testing Python Web Frameworks with Pytest and Unittest

Pytest and unittest can test Python web frameworks like Django, Flask, FastAPI, etc. Here are some tips:

Django

Pytest

Use pytest-django plugin
Write tests in tests.py or separate file
Access Django app like a pytest fixture
Use django_db fixture for test database

Unittest

Subclass Django’s TestCase
Use assertions focused on model layer
Configure TEST_RUNNER to integrate with CI environments

Flask