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python testing

Python testing best practices using pytest including fixtures, parametrization, mocking, coverage analysis, async testing, and test organization. Use when writing or improving Python tests.

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Skill Introduction

Overview
Python testing best practices using pytest including fixtures, parametrization, mocking, coverage analysis, async testing, and test organization. Use when writing or improving Python tests.

Core value

Turns reusable Testing know-how into an installable skill, helping users complete github, community work faster.

Target users

  • Developers, testers, and maintainers who handle Testing tasks in Focus Code.
  • Teams that already trust workflows or content from affaan-m.
  • Users who want standardized prompts, steps, or conventions instead of repeating setup work.

Best practices

  • Read the skill content first to confirm required inputs, expected outputs, and dependencies.
  • Try it on a small task before relying on it for critical work.
  • Add project-specific constraints such as coding style, target platform, test expectations, and delivery format.
  • For external sources, verify the source link, version, and recent maintenance activity.

Best use cases

  • Tasks related to github, community that need a reusable execution flow.
  • Converting a community repo, team convention, or personal workflow into day-to-day assistance.
  • Starting from a proven skill instead of writing prompts or procedures from scratch.

Limits and boundaries

  • Results depend on the quality of the original skill content and may need human correction.
  • It does not replace code review, tests, security review, or professional judgment.
  • External tools, APIs, account permissions, and local dependencies still need separate setup.

Differentiation

  • Structured around Testing, making it easier to discover and reuse than loose prompt snippets.
  • Marked as GitHub, which helps users judge trust and maintenance expectations.
  • Keeps the original source link available for repository, documentation, or discussion follow-up.
  • Tagged with github, community, so it can be filtered by concrete task intent.

Install and use

Install
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focus install python-testing-eb40f8
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name: python-testing description: > Python testing best practices using pytest including fixtures, parametrization, mocking, coverage analysis, async testing, and test organization. Use when writing or improving Python tests. metadata: origin: ECC globs: ["/*.py", "/*.pyi"]

Python Testing

This skill provides comprehensive Python testing patterns using pytest as the primary testing framework.

Testing Framework

Use pytest as the testing framework for its powerful features and clean syntax.

Basic Test Structure

def test_user_creation():
    """Test that a user can be created with valid data"""
    user = User(name="Alice", email="alice@example.com")

    assert user.name == "Alice"
    assert user.email == "alice@example.com"
    assert user.is_active is True

Test Discovery

pytest automatically discovers tests following these conventions:

  • Files: test_*.py or *_test.py
  • Functions: test_*
  • Classes: Test* (without __init__)
  • Methods: test_*

Fixtures

Fixtures provide reusable test setup and teardown:

import pytest
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker

@pytest.fixture
def db_session():
    """Provide a database session for tests"""
    engine = create_engine("sqlite:///:memory:")
    Session = sessionmaker(bind=engine)
    session = Session()

    # Setup
    Base.metadata.create_all(engine)

    yield session

    # Teardown
    session.close()

def test_user_repository(db_session):
    """Test using the db_session fixture"""
    repo = UserRepository(db_session)
    user = repo.create(name="Alice", email="alice@example.com")

    assert user.id is not None

Fixture Scopes

@pytest.fixture(scope="function")  # Default: per test
def user():
    return User(name="Alice")

@pytest.fixture(scope="class")  # Per test class
def database():
    db = Database()
    db.connect()
    yield db
    db.disconnect()

@pytest.fixture(scope="module")  # Per module
def app():
    return create_app()

@pytest.fixture(scope="session")  # Once per test session
def config():
    return load_config()

Fixture Dependencies

@pytest.fixture
def database():
    db = Database()
    db.connect()
    yield db
    db.disconnect()

@pytest.fixture
def user_repository(database):
    """Fixture that depends on database fixture"""
    return UserRepository(database)

def test_create_user(user_repository):
    user = user_repository.create(name="Alice")
    assert user.id is not None

Parametrization

Test multiple inputs with @pytest.mark.parametrize:

import pytest

@pytest.mark.parametrize("email,expected", [
    ("user@example.com", True),
    ("invalid-email", False),
    ("", False),
    ("user@", False),
    ("@example.com", False),
])
def test_email_validation(email, expected):
    result = validate_email(email)
    assert result == expected

Multiple Parameters

@pytest.mark.parametrize("name,age,valid", [
    ("Alice", 25, True),
    ("Bob", 17, False),
    ("", 25, False),
    ("Charlie", -1, False),
])
def test_user_validation(name, age, valid):
    result = validate_user(name, age)
    assert result == valid

Parametrize with IDs

@pytest.mark.parametrize("input,expected", [
    ("hello", "HELLO"),
    ("world", "WORLD"),
], ids=["lowercase", "another_lowercase"])
def test_uppercase(input, expected):
    assert input.upper() == expected

Test Markers

Use markers for test categorization and selective execution:

import pytest

@pytest.mark.unit
def test_calculate_total():
    """Fast unit test"""
    assert calculate_total([1, 2, 3]) == 6

@pytest.mark.integration
def test_database_connection():
    """Slower integration test"""
    db = Database()
    assert db.connect() is True

@pytest.mark.slow
def test_large_dataset():
    """Very slow test"""
    process_million_records()

@pytest.mark.skip(reason="Not implemented yet")
def test_future_feature():
    pass

@pytest.mark.skipif(sys.version_info < (3, 10), reason="Requires Python 3.10+")
def test_new_syntax():
    pass

Run specific markers:

pytest -m unit              # Run only unit tests
pytest -m "not slow"        # Skip slow tests
pytest -m "unit or integration"  # Run unit OR integration

Mocking

Using unittest.mock

from unittest.mock import Mock, patch, MagicMock

def test_user_service_with_mock():
    """Test with mock repository"""
    mock_repo = Mock()
    mock_repo.find_by_id.return_value = User(id="1", name="Alice")

    service = UserService(mock_repo)
    user = service.get_user("1")

    assert user.name == "Alice"
    mock_repo.find_by_id.assert_called_once_with("1")

@patch('myapp.services.EmailService')
def test_send_notification(mock_email_service):
    """Test with patched dependency"""
    service = NotificationService()
    service.send("user@example.com", "Hello")

    mock_email_service.send.assert_called_once()

pytest-mock Plugin

def test_with_mocker(mocker):
    """Using pytest-mock plugin"""
    mock_repo = mocker.Mock()
    mock_repo.find_by_id.return_value = User(id="1", name="Alice")

    service = UserService(mock_repo)
    user = service.get_user("1")

    assert user.name == "Alice"

Coverage Analysis

Basic Coverage

pytest --cov=src --cov-report=term-missing

HTML Coverage Report

pytest --cov=src --cov-report=html
open htmlcov/index.html

Coverage Configuration

# pytest.ini or pyproject.toml
[tool.pytest.ini_options]
addopts = """
    --cov=src
    --cov-report=term-missing
    --cov-report=html
    --cov-fail-under=80
"""

Branch Coverage

pytest --cov=src --cov-branch

Async Testing

Testing Async Functions

import pytest

@pytest.mark.asyncio
async def test_async_fetch_user():
    """Test async function"""
    user = await fetch_user("1")
    assert user.name == "Alice"

@pytest.fixture
async def async_client():
    """Async fixture"""
    client = AsyncClient()
    await client.connect()
    yield client
    await client.disconnect()

@pytest.mark.asyncio
async def test_with_async_fixture(async_client):
    result = await async_client.get("/users/1")
    assert result.status == 200

Test Organization

Directory Structure

tests/
├── unit/
│   ├── test_models.py
│   ├── test_services.py
│   └── test_utils.py
├── integration/
│   ├── test_database.py
│   └── test_api.py
├── conftest.py          # Shared fixtures
└── pytest.ini           # Configuration

conftest.py

# tests/conftest.py
import pytest

@pytest.fixture(scope="session")
def app():
    """Application fixture available to all tests"""
    return create_app()

@pytest.fixture
def client(app):
    """Test client fixture"""
    return app.test_client()

def pytest_configure(config):
    """Register custom markers"""
    config.addinivalue_line("markers", "unit: Unit tests")
    config.addinivalue_line("markers", "integration: Integration tests")
    config.addinivalue_line("markers", "slow: Slow tests")

Assertions

Basic Assertions

def test_assertions():
    assert value == expected
    assert value != other
    assert value > 0
    assert value in collection
    assert isinstance(value, str)

pytest Assertions with Better Error Messages

def test_with_context():
    """pytest provides detailed assertion introspection"""
    result = calculate_total([1, 2, 3])
    expected = 6

    # pytest shows: assert 5 == 6
    assert result == expected

Custom Assertion Messages

def test_with_message():
    result = process_data(input_data)
    assert result.is_valid, f"Expected valid result, got errors: {result.errors}"

Approximate Comparisons

import pytest

def test_float_comparison():
    result = 0.1 + 0.2
    assert result == pytest.approx(0.3)

    # With tolerance
    assert result == pytest.approx(0.3, abs=1e-9)

Exception Testing

import pytest

def test_raises_exception():
    """Test that function raises expected exception"""
    with pytest.raises(ValueError):
        validate_age(-1)

def test_exception_message():
    """Test exception message"""
    with pytest.raises(ValueError, match="Age must be positive"):
        validate_age(-1)

def test_exception_details():
    """Capture and inspect exception"""
    with pytest.raises(ValidationError) as exc_info:
        validate_user(name="", age=-1)

    assert "name" in exc_info.value.errors
    assert "age" in exc_info.value.errors

Test Helpers

# tests/helpers.py
def assert_user_equal(actual, expected):
    """Custom assertion helper"""
    assert actual.id == expected.id
    assert actual.name == expected.name
    assert actual.email == expected.email

def create_test_user(**kwargs):
    """Test data factory"""
    defaults = {
        "name": "Test User",
        "email": "test@example.com",
        "age": 25,
    }
    defaults.update(kwargs)
    return User(**defaults)

Property-Based Testing

Using hypothesis for property-based testing:

from hypothesis import given, strategies as st

@given(st.integers(), st.integers())
def test_addition_commutative(a, b):
    """Test that addition is commutative"""
    assert a + b == b + a

@given(st.lists(st.integers()))
def test_sort_idempotent(lst):
    """Test that sorting twice gives same result"""
    sorted_once = sorted(lst)
    sorted_twice = sorted(sorted_once)
    assert sorted_once == sorted_twice

Best Practices

  1. One assertion per test (when possible)
  2. Use descriptive test names - describe what's being tested
  3. Arrange-Act-Assert pattern - clear test structure
  4. Use fixtures for setup - avoid duplication
  5. Mock external dependencies - keep tests fast and isolated
  6. Test edge cases - empty inputs, None, boundaries
  7. Use parametrize - test multiple scenarios efficiently
  8. Keep tests independent - no shared state between tests

Running Tests

# Run all tests
pytest

# Run specific file
pytest tests/test_user.py

# Run specific test
pytest tests/test_user.py::test_create_user

# Run with verbose output
pytest -v

# Run with output capture disabled
pytest -s

# Run in parallel (requires pytest-xdist)
pytest -n auto

# Run only failed tests from last run
pytest --lf

# Run failed tests first
pytest --ff

When to Use This Skill

  • Writing new Python tests
  • Improving test coverage
  • Setting up pytest infrastructure
  • Debugging flaky tests
  • Implementing integration tests
  • Testing async Python code

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