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Python style guide | GitLab Docs

Testing at GitLab, including in Python codebases is a core priority rather than an afterthought. It is therefore important to consider test design quality alongside feature design from the start.

Use Pytest for Python testing.

• Five Factor Testing: Why do we need tests?
• Principles of Automated Testing: Levels of testing. Prioritize tests. Cost of tests.

Before writing tests, understand the different testing levels and determine the appropriate level for your changes.

Learn more about the different testing levels, and how to decide at what level your changes should be tested.

For unit tests, naming the test file with test_{file_being_tested}.py and placing it in the same directory structure helps with later discoverability of tests. This also avoids confusion between files that have the same name, but different modules.

File: /foo/bar/cool_feature.py

# Bad

Test file: /tests/my_cool_feature.py

# Good

Test file: /tests/foo/bar/test_cool_feature.py

Pytest parametrized tests effectively reduce code repetition, but they rely on tuples for test case definition, unlike Ruby’s more readable syntax. As your parameters or test cases increase, these tuple-based tests become harder to understand and maintain.

By using Python NamedTuples instead, you can:

• Enforce clearer organization with named fields.
• Make tests more self-documenting.
• Easily define default values for parameters.
• Improve readability in complex test scenarios.

# Good: Short examples, with small numbers of arguments. Easy to map what each value maps to each argument

@pytest.mark.parametrize(
    (
        "argument1",
        "argument2",
        "expected_result",
    ),
    [
        # description of case 1,
        ("value1", "value2", 200),
        # description of case 2,
        ...,
    ],
)
def test_get_product_price(argument1, argument2, expected_result):
    assert get_product_price(value1, value2) == expected_cost

# Bad: difficult to map a value to an argument, and to add or remove arguments when updating test cases

@pytest.mark.parametrize(
    (
        "argument1",
        "argument2",
        "argument3",
        "expected_response",
    ),
    [
      # Test case 1:
      (
        "value1",
        {
          ...
        },
        ...
      ),
      # Test case 2:
      ...
    ]
)

def test_my_function(argument1, argument2, argument3, expected_response):
   ...

# Good: NamedTuples improve readability for larger test scenarios.

from typing import NamedTuple

class TestMyFunction:
  class Case(NamedTuple):
      argument1: str
      argument2: int = 3
      argument3: dict
      expected_response: int

  TEST_CASE_1 = Case(
      argument1="my argument",
      argument3={
          "key": "value"
      },
      expected_response=2
  )

  TEST_CASE_2 = Case(
      ...
  )
  @pytest.mark.parametrize(
      "test_case", [TEST_CASE_1, TEST_CASE_2]
  )
  def test_my_function(test_case):
      assert my_function(case.argument1, case.argument2, case.argument3) == case.expected_response

• Use unittest.mock library.
• Mock at the right level, for example, at method call boundaries.
• Mock external services and APIs.

It’s recommended to use automated tools to ensure code quality and security. Consider running the following tools in your CI pipeline as well as locally:

• Black: Code formatter that enforces a consistent style
• isort: Sorts and organizes import statements

• flake8: Checks for PEP-8 compliance and common errors
• pylint: More comprehensive linter for code quality
• mypy: Static type checker for Python

• pytest: Testing framework with coverage reporting

• Dependency scanning: Checks for vulnerabilities in dependencies
• Secret detection: Ensures no secrets are committed to the repository
• SAST (semgrep): Static Application Security Testing

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