使用pytest編寫(xiě)測(cè)試的步驟包括：1.遵循命名規(guī)範(fàn)，如文件名以test_開(kāi)頭、函數(shù)名以test_開(kāi)頭；2.用assert編寫(xiě)清晰的斷言；3.運(yùn)行測(cè)試並查看輸出；4.使用fixture重用設(shè)置代碼。具體來(lái)說(shuō)，pytest通過(guò)文件和函數(shù)命名規(guī)則自動(dòng)發(fā)現(xiàn)測(cè)試，支持簡(jiǎn)單的assert語(yǔ)句提供詳細(xì)錯(cuò)誤信息，並可通過(guò)命令行選項(xiàng)控制執(zhí)行方式，同時(shí)利用fixture實(shí)現(xiàn)高效復(fù)用與資源管理。

To get started with using pytest to write tests in Python, the key idea is that you don't need any special boilerplate or classes — just define simple functions and let pytest find and run them. It's straightforward, but there are a few conventions and tools you should know to use it effectively.

Naming Conventions Matter

Pytest discovers test files and functions based on naming rules. By default, it looks for:

• Files named test_*.py or *test.py
• Functions inside those files that start with test_

For example:

 # test_math.py
def test_addition():
    assert 1 1 == 2

This function will be picked up automatically when you run pytest .

If your function doesn't follow this naming convention, pytest won't treat it as a test. That's helpful when you want to reuse helper functions across tests without running them directly.

Also, if you're organizing tests into classes (which is optional), the class name should start with Test , like TestClass , and shouldn't have an __init__ method.

Writing Simple Assertions

One of the best parts of pytest is how it handles assertions. You can use regular Python assert statements, and pytest will give you detailed error messages showing what went wrong.

For example:

 def test_list_length():
    data = [1, 2, 3]
    assert len(data) == 4 # This will fail

When this runs, pytest will show you exactly why the assertion failed — not just that it failed, but what the actual values were.

You can also do more complex checks:

 def test_string_contains():
    result = "hello world"
    assert "hello" in result

The key here is to keep your assertions clear and focused on one thing per test function.

Running Tests and Reading Output

Once you've written your tests, you can run them by simply typing pytest in the terminal from the directory where your test files live.

Some common options you might find useful:

• pytest -v : Verbose output — shows each test name and whether it passed or failed.
• pytest -x : Stop after the first failure.
• pytest --pdb : Drops into the Python debugger on failures.
• pytest test_file.py::test_function_name : Run a specific test function.

If you have many tests, you may want to install plugins like pytest-xdist to run tests in parallel or pytest-cov to check code coverage.

Fixtures: Reuse Setup Code

Fixtures are a powerful feature in pytest that let you define reusable setup logic. You use the @pytest.fixture decorator to mark a function as a fixture.

For example:

 import pytest

@pytest.fixture
def sample_data():
    return [1, 2, 3, 4, 5]

def test_sum(sample_data):
    assert sum(sample_data) == 15

Here, sample_data is passed into the test function as an argument. Pytest knows to inject the return value of the fixture automatically.

Fixtures can also handle setup and teardown, like starting and stopping a database connection or preparing temporary files.

And if you need fixtures to run once per module or session, you can specify that with the scope parameter:

 @pytest.fixture(scope="module")
def setup_database():
    # connect to DB
    yield
    # disconnect from DB

Using fixtures keeps your test code clean and avoids repetition.

That's basically how you start writing tests with pytest. It's designed to be simple for small cases and flexible enough for bigger ones. Once you're comfortable with the basics, exploring plugins and advanced fixture usage opens up even more possibilities.

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