In short, the basic idea of TDD is to write tests before writing the actual implementation. Maybe the most significant benefit of the approach is that the developer focuses on writing tests which match with what the program should do. Whereas if the tests are written after the actual implementation, there is a high risk for rushing tests which just show green light for the already written logic.
Tests are first class citizens in modern, agile software development, which is why it's important to start thinking TDD early during your Python learning path.
The workflow of TDD can be summarized as follows:
These are the steps required to run pytest inside Jupyter cells. You can copy the content of this cell to the top of your notebook which contains tests.
# Let's make sure pytest and ipytest packages are installed
# ipytest is required for running pytest inside Jupyter notebooks
import sys
!{sys.executable} -m pip install pytest
!{sys.executable} -m pip install ipytest
# These are needed for running pytest inside Jupyter notebooks
import ipytest
ipytest.autoconfig()
pytest test cases¶Let's consider we have a function called sum_of_three_numbers for which we want to write a test.
def sum_of_three_numbers(num1, num2, num3):
return num1 + num2 + num3
Pytest test cases are actually quite similar as you have already seen in the exercises. Most of the exercises are structured like pytest test cases by dividing each exercise into three cells:
See the example test case below to see the similarities between the exercises and common structure of test cases.
%%ipytest
def test_sum_of_three_numbers():
# 1. Setup the variables used in the test
num1 = 2
num2 = 3
num3 = 5
# 2. Call the functionality you want to test
result = sum_of_three_numbers(num1, num2, num3)
# 3. Verify that the outcome is expected
assert result == 10
Now go ahead and change the line assert result == 10 such that the assertion fails to see the output of a failed test.