Python Tutorial: Why unit test?

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. --- Hello and welcome to Unit Testing for Data Science in Python! My name is Dibya. I am a Test Automation Engineer and I will be your instructor for this course. Consider this question. Suppose we have just implemented a Python function. How can we test whether our implementation is correct? The easiest way is to open an interpreter, test the function on a few arguments and check whether the return value is correct. If correct, we can accept the implementation and move on. Right? While testing on the interpreter is easy, it is actually very inefficient. This will become clear if we think about the big picture of a function's life cycle in a data science project. The life cycle of a function typically looks like this. We implement the function and then test it. If the tests pass, we accept the implementation. If the tests fail, we fix any bugs that we found and test again. Later, we might get a new feature request or we may be asked to refactor the function. So we implement the new feature or refactor the code, and then test it again. Alternatively, someone might discover a previously unseen bug. In that case, we fix that bug and test again. Notice how many times a function needs to be tested during the life cycle. Every time we modify the function, either to fix bugs or to implement new features, we have to test it. If the project continues for a few years, we might be testing the function about a hundred times, maybe more. Let's look at an example function. It's called row_to_list(). It takes a single argument, which is a Python string. The string is a single row in a data file which contains data on housing area and market price of the house. The string contains the housing area in square feet, followed by a single tab, f