Thorough testing is crucial for writing robust, bug-free Python code. Python Pytest has emerged as the most popular Python testing framework due to its easy syntax and rich features.
This comprehensive hands-on guide will teach you how to leverage pytest to implement effective tests for catching bugs early and shipping quality Python code.
Overview of Python Pytest
Pytest offers an easy yet powerful testing solution for Python. Here are some key capabilities:
- Simple assert-based tests without boilerplate code
- Detailed failure output for quick debugging
- Modular fixtures for managing test state
- Powerful test parameterization
- Automatic test discovery and parallelization
- Granular configuration and customization
- Broad Python version support and 3rd party plugin ecosystem
By mastering pytest, you gain a productive testing framework that helps developers create robust and maintainable code. Let’s jump in!
1. Install Python Pytest
Python Pytest can be installed easily using pip:
pip install pytest
This will fetch the pytest package and all its dependencies.
That’s it! Pytest doesn’t require any other setup or configuration to get started testing Python code.
2. Write Simple Assert-Based Tests
Pytest tests are simple Python functions that use the
assert keyword to verify code behavior and results.
For example, create a
# test_capitalize.py def capital_case(x): return x.capitalize() def test_capitalization(): assert capital_case('hello') == 'Hello'
This tests the
capital_case() function by asserting the capitalized string matches the expected output.
We can run this test with:
test_capitalization(), compares the assert result, and prints out:
=========================== test session starts =========================== platform linux -- Python 3.6.7, pytest-5.4.1, py-1.8.1, pluggy-0.13.1 rootdir: /home/user/tests collected 1 item test_capitalize.py . [100%] ========================== 1 passed in 0.12 seconds ===========================
Our simple test passed! Pytest recursively finds and runs all tests in files and folders. More asserts can be added to thoroughly test a module.
3. Test Failures and Debugging
Pytest provides helpful output when tests fail to pinpoint the issue.
For example, if we introduce a bug in our capitalization function:
def capital_case(x): return x.upper() # Bug!
Running the test now results in:
=========================== test session starts =========================== platform linux -- Python 3.6.7, pytest-5.4.1, py-1.8.1, pluggy-0.13.1 rootdir: /home/user/tests collected 1 item test_capitalize.py F [100%] ================================= FAILURES ================================= _______________________________ test_capitalization _______________________________ def test_capitalization(): > assert capital_case('hello') == 'Hello' E AssertionError: assert 'HELLO' == 'Hello' E - HELLO E + Hello test_capitalize.py:6: AssertionError ========================== 1 failed in 0.12 seconds ===========================
This shows the specific values for the failed assert, allowing us to catch the bug. Pytest failure output is invaluable during test-driven development.
4. Parameterize Tests
Python Pytest allows parameterizing test functions with different inputs using the
import pytest @pytest.mark.parametrize('input, expected', [ ('hello', 'Hello'), ('WORLD', 'World'), ('123', '123') ]) def test_capitalization(input, expected): assert capital_case(input) == expected
This runs the test three times with different
expected values, reducing code duplication.
5. Use Fixtures for Setup/Teardown
Fixtures allow you to execute setup and teardown code before and after tests run.
For example, we can create a temporary file fixture:
import pytest import tempfile import os @pytest.fixture def temp_file(): fp = tempfile.TemporaryFile() yield fp # Provide fixture value # Teardown code fp.close() os.remove(fp.name) def test_with_file(temp_file): temp_file.write(b'Hello World!') assert temp_file.read() == b'Hello World!'
The fixture function is invoked ahead of the test, yielded for the test duration, and teardown code executes after the test finishes.
Fixtures abstract away test setup/cleanup code for more modular, maintainable tests.
6. Improve Test Readability with Pytest marks
Pytest marks allow labeling tests to group them and selectively run subsets.
import pytest @pytest.mark.uppercase def test_capitalization(): assert capital_case('hello') == 'Hello' @pytest.mark.lowercase def test_lowercasing(): assert lower_case('HELLO') == 'hello'
We can run tests with a specific mark:
pytest -m uppercase
Marks can help organize tests and temporarily exclude some during debugging.
7. Test Exceptions
Pytest provides built-in support for testing exceptions via:
pytest.raises(ExpectionType)– Assert block raises a specific exception
with pytest.raises(...):– Assert code inside
withblock raises exception
import pytest def favorite_color(name): if name == '': raise ValueError('Missing name') # ... def test_empty_name(): with pytest.raises(ValueError): favorite_color('')
This validates the right exception is raised for an empty name.
Python Pytest makes it easy and enjoyable to thoroughly test Python code. With its simple assert-based tests, powerful fixtures, parametrization, and exception testing, pytest provides all the tools needed to build robust test suites.
The key benefits are:
- More readable and maintainable tests
- Detailed failure output to enable quick debugging
- Minimal boilerplate code
- Easy test organization and execution
- Extensive customization and 3rd party integration
Pytest adoption continues growing, from open source projects to enterprise teams. Start testing your Python code with pytest to enhance quality and confidence!
Frequently Asked Questions
Here are some common questions about testing Python code with pytest:
What are the main differences between pytest and unittest?
The main difference differences between pytest and unittest is Pytest is more Pythonic and requires less boilerplate code. But unittest is part of the standard library.
How do I run a subset of tests in pytest?
-k EXPRESSION option to filter tests by name, or
-m MARKEXPR to run tests with specific marks.
When should I use parameterized tests vs multiple test functions?
Parameterized tests help reduce redundancy for inputs with predictable outputs. Use multiple tests for varied scenarios.
What IDEs or editors have the best pytest integration?
PyCharm/IntelliJ, VS Code, and Vim all have excellent pytest plugins to run tests and view output.
Can I use pytest with continuous integration (CI) systems like Travis?
Yes, pytest works seamlessly with all major CI systems. Special plugins like pytest-cov generate coverage reports.