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  3. Unit Testing for Data Science in Python

Exercise

Testing with multiple assert statements

You're now going to test the function split_into_training_and_testing_sets() from the models module.

It takes a n x 2 NumPy array containing housing area and prices as argument. To see an example argument, print the variable example_argument in the IPython console.

The function returns a 2-tuple of NumPy arrays (training_set, testing_set). The training set contains int(0.75 * n) (approx. 75%) randomly selected rows of the argument array. The testing set contains the remaining rows.

Print the variable expected_return_value in the IPython console. example_argument had 6 rows. Therefore the training array has int(0.75 * 6) = 4 of its rows and the testing array has the remaining 2 rows.

numpy as np, pytest and split_into_training_and_testing_sets have been imported for you.

Instructions 1/4

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  • Calculate the expected number of rows of the training array using the formula int(0.75*n), where n is the number of rows in example_argument, and assign the variable expected_training_array_num_rows to this number.