Learn Python by Building Data Science Applications

Writing tests with hypothesis

Finally, we're going to go back to a topic we've already covered—unit tests. Unit tests are very important; they will give you peace of mind during development—you really don't want to play a whack-a-mole game with your bugs.

Now, testing a data-heavy application is hard. Depending on complex datasets, data structures expose us to dozens of rare, but possible, quirks and edge cases. Often, we don't even need to think of those possibilities, instead focusing on the datasets we have at hand. For example, any function that operates on a dataframe should deal (one way or another) with an empty dataframe, the dataframe of a wrong datatype, a NumPy array, a dataframe of null values, and so on.

One approach to mitigate ...