FastAPI Backend Development Bootcamp · 课时

使用 Pytest 进行单元测试

使用 Pytest 框架为 FastAPI 函数和模块编写有效的单元测试。

使用 Pytest 进行单元测试 是 CoddyKit 上的免费 FastAPI Backend Development Bootcamp 课时。这是第 1 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 FastAPI Backend Development Bootcamp 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 FastAPI Backend Development Bootcamp 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

What is Unit Testing?

Welcome to unit testing! This is a fundamental practice in software development that helps ensure your code works correctly.

Unit testing focuses on testing the smallest testable parts of an application, called 'units'.
A 'unit' can be a function, a method, or a class. The goal is to isolate it and verify it behaves as expected.
This helps catch bugs early, makes code easier to refactor, and builds confidence in your application's reliability.

Introducing Pytest

For Python, one of the most popular and powerful testing frameworks is Pytest. It's known for its simplicity and flexibility.

Pytest makes it easy to write simple, yet scalable tests.
It automatically discovers tests, provides detailed failure reports, and supports advanced features like fixtures.
You'll find Pytest widely used in the Python community, including for FastAPI projects.

Installing Pytest

Before we write our first test, we need to install Pytest. It's a standard Python package.

Open your terminal or command prompt and run the following command:

pip install pytest

Your First Function to Test

Let's create a simple Python function that we want to test. We'll put this in a file named utils.py.

This function simply multiplies two numbers. We'll write tests to ensure it always returns the correct product.

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

if __name__ == "__main__":
    # This part shows how to use the function directly
    print(f"5 * 3 = {multiply(5, 3)}")
    print(f"10 * 0 = {multiply(10, 0)}")

Crafting Your First Test

Now, let's write tests for our multiply function. Pytest automatically discovers test files that start with test_ (e.g., test_utils.py) and test functions within them that also start with test_.

Create a new file named test_utils.py in the same directory as utils.py:

from utils import multiply # Import the function to test

def test_multiply_positive_numbers():
    # Check if 2 * 3 equals 6
    assert multiply(2, 3) == 6

def test_multiply_by_zero():
    # Check if any number multiplied by zero is zero
    assert multiply(5, 0) == 0

def test_multiply_negative_numbers():
    # Check multiplication with a negative number
    assert multiply(-2, 4) == -8

Executing Your Tests

With both utils.py and test_utils.py saved in the same folder, you can now run your tests!

Open your terminal in that directory and simply type pytest. Pytest will find and execute all tests.

# In your terminal or command prompt:
pytest

Understanding Assertions

The core of any test is the assert statement. It's how you verify that a condition is true. If an assert statement fails, Pytest marks the test as failed.

Here are some common ways to use assert:

assert actual == expected: Checks if two values are equal.
assert actual != unexpected: Checks if two values are not equal.
assert item in collection: Checks if an item exists in a list, set, or string.
assert not condition: Checks if a condition is false.
assert value > 0: Checks for numerical comparisons.

Reusable Test Setup with Fixtures

Sometimes, multiple tests need the same setup (e.g., a temporary file, a configured object). Pytest fixtures provide a way to define reusable setup and teardown logic.

A fixture is a function decorated with @pytest.fixture. Tests can then request the fixture by naming it as an argument.

import pytest

@pytest.fixture
def sample_data():
    # This fixture provides a list of numbers for tests
    print("\nSetting up sample_data") # Runs before tests
    yield [10, 20, 30]
    print("\nTeardown sample_data") # Runs after tests

def test_data_length(sample_data):
    assert len(sample_data) == 3

def test_data_contains_value(sample_data):
    assert 20 in sample_data

Handling Expected Errors

What if your function is supposed to raise an error under certain conditions? Pytest can test for that too, using pytest.raises.

This ensures your error handling logic works correctly by asserting that a specific exception is raised.

# my_processor.py
def divide(a, b):
    if b == 0:
        raise ValueError("Cannot divide by zero!")
    return a / b

# test_my_processor.py
import pytest
from my_processor import divide

def test_divide_by_zero_raises_error():
    with pytest.raises(ValueError, match="Cannot divide by zero!"):
        divide(10, 0)

def test_divide_positive_numbers():
    assert divide(10, 2) == 5.0

Pytest Fundamentals Check

Consider the Python function and the Pytest test below:

# calculator.py
def add(a, b):
    return a + b

# test_calculator.py
from calculator import add

def test_add_positive():
    assert add(5, 3) == 8

def test_add_negative_numbers():
    assert add(-2, -4) == -6

def test_add_mixed_numbers():
    assert add(10, -5) == 5

If you run pytest in the terminal, what will be the outcome?

Unit Testing Recap

Great job! You've learned the basics of unit testing with Pytest. This is a crucial skill for building robust applications.

Unit tests verify small, isolated parts of your code.
Pytest is a powerful and easy-to-use framework for writing and running these tests.
You use assert statements to check for expected outcomes.
Fixtures help set up reusable data or resources for your tests.
pytest.raises is used to test that your code correctly raises specific exceptions.

Next, we'll explore how to perform integration testing specifically for your FastAPI endpoints!

常见问题解答

「使用 Pytest 进行单元测试」课时是免费的吗？

是的 — 「使用 Pytest 进行单元测试」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 FastAPI Backend Development Bootcamp 课程的其余内容，请升级到 CoddyKit PRO。 FastAPI Backend Development Bootcamp 课程共包含 4 节课。

「使用 Pytest 进行单元测试」这节课中我会学到什么？

使用 Pytest 框架为 FastAPI 函数和模块编写有效的单元测试。你通过在浏览器中直接运行的动手代码来练习 FastAPI Backend Development Bootcamp，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 FastAPI Backend Development Bootcamp 需要有经验吗？

无需任何先前经验。CoddyKit 上的 FastAPI Backend Development Bootcamp 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。这是第 1 节课，共 4 节。

「使用 Pytest 进行单元测试」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 FastAPI Backend Development Bootcamp 课中编写并运行代码吗？

能。每节 FastAPI Backend Development Bootcamp 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

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