The pytest framework makes it easy to write small, readable tests, and can scale to support complex functional testing for applications and libraries

https://docs.pytest.org/

As data scientists & data engineers, we need to build an error-free, well-tested, and reliable data pipeline that does not break in production. That's REALLY hard to do & maintain without automated testing

(these points taken from guru99.com > pytest-tutorial)

• Very easy to start with because of its simple and easy syntax.
• Can run tests in parallel.
• Can run a specific test or a subset of tests
• Automatically detect tests
• Skip tests
• Open source

• Pytest for dbt macros
• Quick & Dirty Pytest Cookbook (by Felipe Zapata)
• pytest-docker-compose: manages containers used in integration tests
• conveyordata/data-product-portal —> backend tests with pytest
• pytest-eucalyptus: BDD plugin for pytest (parses Gherkin statements into test cases)
• PyData talk: Unit Testing for Data Scientists (Hanna Torrence)
  • 01:10 Outline of testing
  • 02:05 Benefits of unit Testing
  • 03:20 Why people do not write unit test
  • 05:25 Data Science Theory workflow and practice
  • 09:05 Pytest Framework with example
  • 12:10 Pytest Fixtures
  • 16:15 Built-in Fixtures
  • 18:10 Flexibility of Fixture
  • 22:15 Mocks
  • 29:15 Mock returns
  • 33:50 Mock asserts
  • 38:00 Running over due to time constraints