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  3. Unit Testing for Data Science in Python

Exercise

Unit test a ValueError

Sometimes, you want a function to raise an exception when called on bad arguments. This prevents the function from returning nonsense results or hard-to-interpret exceptions. This is an important behavior which should be unit tested.

Remember the function split_into_training_and_testing_sets()? It takes a NumPy array containing housing area and prices as argument. The function randomly splits the array row wise into training and testing arrays in the ratio 3:1, and returns the resulting arrays in a tuple.

If the argument array has only 1 row, the testing array will be empty. To avoid this situation, you want the function to not return anything, but raise a ValueError with the message "Argument data_array must have at least 2 rows, it actually has just 1".

Instructions 1/4

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  • Fill in with the correct context manager that checks if split_into_training_and_testing_sets() raises a ValueError when called on test_argument, which is a NumPy array with a single row.