Test Driven Development and Data Science

• TEST DRIVEN DEVELOPMENT: writing tests before you write the code that’s being tested. Your test would fail at first, and you’ll know you’ve finished implementing a task when this test passes.
• Tests can check for all the different scenarios and edge cases you can think of, before even starting to write your function. This way, when you do start implementing your function, you can run this test to get immediate feedback on whether it works or not in all the ways you can think of, as you tweak your function.
• When refactoring or adding to your code, tests help you rest assured that the rest of your code didn't break while you were making those changes. Tests also helps ensure that your function behavior is repeatable, regardless of external parameters, such as hardware and time.

Test driven development for data science is relatively new and has a lot of experimentation and breakthroughs appearing, which you can learn more about in the resources below.

• Data Science TDD
• TDD for Data Science
• TDD is Essential for Good Data Science Here's Why
• Testing Your Code (general python TDD)