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pytest plugin for comparing call arguments.

Project description


PyPI Latest Release License Documentation Downloads Code style: blackx

What is it?

The pytest-compare helps validate method call arguments when testing python code.

pytest-compare is designed to work with assert methods. While python native variables can be easily compared, a more complicated structures sometimes do not. For example validating a pd.DataFrame will raise an exception. This is where pytest-compare comes in. It allows this kind of structures to be easily compared.

How to install

To install pytest-compare from PyPi, run the command:

pip install pytest-compare

Optional dependencies

Some compare modules may require additional packages to be installed.


To compare pandas module, add the pandas option to the installation:

pip install pytest-compare[pandas]

How to use

The comparation modules are design to be used with assert methods. When validating patched method call arguments

df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})

with patch.object(ProductionClass, 'method', return_value=None) as mock_method:
    thing = ProductionClass()
# will raise an exception

# the correct way

Multiple arguments in a call

When a method is called using multiple arguments, all of them must be addressed in the test. while python native variables can be easily compared, pytest-compare is designed to compare a more complicated structures and do custom compares.

with patch.object(ProductionClass, 'method', return_value=None) as mock_method:
    thing = ProductionClass()
    thing.method(1, "str", df1, df2)
# the correct way
mock_method.assert_called_once_with(1, "str", CompareDataFrame(df1), CompareDataFrame(df2))

Args and Kwargs

When validating the call, the expected values must be passed in the exact same mix of args and kwargs as when they were called.

with patch.object(ProductionClass, 'method', return_value=None) as mock_method:
    thing = ProductionClass()
    thing.method(df1, dataframe=df2)
# the correct way
mock_method.assert_called_once_with(CompareDataFrame(df1), dataframe=CompareDataFrame(df2))

Actual and Expected convention

  • actual: The values that the method was originally called with.
  • expected: Test values to see if the method was called with.

For example here, arg_actual is the actual value while arg_expected is the expected value.

mock_method = Mock()


If arg_actual is not equal to arg_expected, an exception will be raised.



Virtual Environment

Create a virtual environment and install the dependencies:

$ python3 -m venv venv
$ source venv/bin/activate
$ pip install -e ".[pandas]" -r requirements-dev.txt -r requirements-quality.txt

Pre-commit Hooks

Start by installing the pre-commit hooks which will run black, mypy, flake8, and codespell on every commit.:

$ hooks/ install

Creating tests

Running tests

To test the code, run the following command:

$ pytest

Writing tests

Tests are written using the pytest framework. To create a test for a new Compare module, create two files in the tests directory: and the test file witch must start with test_. The file must implement the following fixtures:

def actual_call_args() -> Tuple[Any]:
    raise NotImplementedError("`actual_call_args` must be implemented")

def actual_call_kwargs() -> Dict[str, Any]:
    raise NotImplementedError("`actual_call_kwargs` must be implemented")

def expected_call_args() -> Tuple[Any]:
    raise NotImplementedError("`expected_call_args` must be implemented")

def expected_call_kwargs() -> Dict[str, CompareBase]:
    raise NotImplementedError("`expected_call_kwargs` must be implemented")

The test file must implement a test class that inherits from BaseTest.

That way two base tests will be run for each Compare module that wil test the args and kwargs of the method call.

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