Did you ever think that most of your test functions were actually the same test
code, but with different data inputs and expected results/exceptions?
- pytest-cases leverages pytest and its great @pytest.mark.parametrize
  decorator, so that you can separate your test cases from your test functions.
- In addition, pytest-cases provides several useful goodies to empower pytest.
  In particular it improves the fixture mechanism to support "fixture unions".
  This is a major change in the internal pytest engine, unlocking many
  possibilities such as using fixture references as parameter values in a test
  function. See here.

pytest-cases is fully compliant with pytest-harvest so you can easily monitor
the execution times and created artifacts. With it, it becomes very easy to
create a complete data science benchmark, for example comparing various models
on various datasets.