Question

City, Country: Write a function that accepts two parameters: a city name and a country name. The function should return a single string of the form city, country, such as santiago, Chile. Store the function in a module called city functions.py. Create a file called test_cities.py that tests the function you just wrote (remember that you need to import unittest and the function you want to test). Write a method called test_city_country () to verify that calling your function with values such as 'santiago' and 'chile' results in the correct string. Run test_cities.py, and make sure test_city_country() passes.

Short answer

Define `city_country` in `city_functions.py` and test it in `test_cities.py` using unittest.

Step-by-step solution

Define the Function

Create a Python function in a module named `city_functions.py`. The function `city_country` should accept two parameters: a city name and a country name, and return a string formatted as 'city, country'. ```python # city_functions.py def city_country(city, country): return f"{city}, {country}" ```

Create a Test File

Write a test script named `test_cities.py` to verify the functionality of the `city_country` function. This script will contain a test case using the unittest framework. ```python # test_cities.py import unittest from city_functions import city_country class CityCountryTestCase(unittest.TestCase): def test_city_country(self): formatted_name = city_country('santiago', 'chile') self.assertEqual(formatted_name, 'santiago, chile') if __name__ == '__main__': unittest.main() ```

Running the Test

To verify that your function works as expected, run the `test_cities.py` script. This will execute the test using unittest framework and should pass if the function is correctly implemented. Enter the terminal command: ```shell python -m unittest test_cities.py ```

Key concepts

Unit Testing in Python

Unit testing in Python is an essential practice to ensure that individual parts of your code (often called "units") work as expected. It allows developers to write tests for specific pieces of functionality, such as functions or methods in a codebase. This helps catch bugs early and reduces the risk of errors as the project grows.

Python offers a built-in library for unit testing called `unittest`. This module supports creating simple and modular test cases. These tests can automatically verify whether the function outputs are as expected. To implement unit tests, you'd need to import the `unittest` module in your test file, then define a class that extends `unittest.TestCase`. Within this class, you can write methods to test different scenarios or functionalities.

Benefits of unit testing include:

• Identifying and fixing bugs early in the development process.
• Providing documentation and clarity on the expected behavior of code.
• Facilitating code refactoring by ensuring existing functionality is preserved.

Function Definition

In Python, defining a function is a fundamental skill, allowing programmers to create reusable code segments. A function definition typically involves naming the function, specifying its parameters, and implementing the function logic.

The syntax for defining a function in Python is straightforward: begin with the `def` keyword, followed by the function name and parentheses encapsulating any parameters. After defining a function, include the logic inside a block of code indented under the definition line.

The example function `city_country` from the exercise accepts two parameters: `city` and `country`. It returns a formatted string consisting of these parameters, structured as "city, country". This format makes it easy to understand and use, demonstrating how function parameters can be used to create flexible and dynamic outputs.

Important considerations when defining functions:

• Choose descriptive names for your functions and parameters for better readability.
• Consider default values for parameters to increase function versatility.
• Document the function with comments or docstrings to explain its purpose.

Test Case Development

Developing test cases is a critical step in verifying the correctness of your functions and ensuring they perform as intended under various conditions. When creating test cases, you must identify input and expected output combinations that reflect real-world scenarios and edge cases.

In the context of the `city_country` function, test case development involves writing a specific test to check if the function correctly formats the input strings. The test file `test_cities.py` implements this by importing `unittest` and defining a test case class. Within it, the method `test_city_country` compares the function's output to the expected result using the `assertEqual` method from the `unittest` library.

When developing test cases, keep these tips in mind:

• Address a range of input cases, including typical values and potential edge cases.
• Clearly document what each test case checks for, to facilitate understanding and maintenance.
• Regularly run tests during the development cycle to ensure code changes don't introduce new errors.

By diligently developing test cases, you can achieve more robust and reliable code behavior over time.