Python Tutorial: More benefits and test types

Want to learn more? Take the full course at https://learn.datacamp.com/courses/unit-testing-for-data-science-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- In a previous lesson, we learned that unit tests save a lot of time. But the benefits of unit testing goes beyond time savings. The unit tests that we wrote for row_to_list() also serve as documentation. If a collaborator didn't know this function's purpose, they could recreate the argument and return value table on the right by looking at the boolean expressions used in the assert statements. The table would give them a good hint about what row_to_list() does, helping them understand the function's code faster. To mimic this real life situation, some exercises may ask you to guess a function's job by looking at a test module. In this case, just type exclamation cat followed by the test module name in the IPython console to see the unit tests. Unit tests also increase trust in a package, as users can run the unit tests and verify that the functions work. In the picture, we see NumPy's Github page. This highlighted badge shows how much of the code base is unit tested and this other badge shows whether the tests are passing. Users trust NumPy more because of these badges. We should implement these badges for our projects too, because user trust is important, and we will learn how to do that later in the course. Unit tests can also reduce downtime for a productive system. Suppose we make a mistake and push bad code to a productive system. This will bring the system down and annoy users. We can cure this by setting up Continuous Integration or CI. CI runs all unit tests when any code is pushed, and if any unit test fails, it rejects the change, preventing downtime. It also informs us that the code needs to be fixed. If we run productive systems that many people depend upon, we must write unit tests and setup CI. We will see an examp