The Ultimate Python Unit Testing Course

Install the libraries you need to run Python unit tests, including unittest and coverage, and learn best practices for organizing test files, naming modules, and directory structure.

Organize your python unit tests by placing all tests in a directory named tests, starting each file with test_<module>, and keeping one test file per module to simplify maintenance.

Learn how to test functions as the foundation of unit testing. Then apply those approaches to testing classes.

Define an average function that takes a list of numbers, sums them into a total, and returns the average by dividing by length, clarifying list versus tuple inputs for tests.

Learn to create your first unit test using Python's unittest, importing the average function and defining an easy test case with two tests that verify expected results.

learn to build medium tests that validate wrong input types, using a test class with methods named test_..., asserting type errors and guiding users with readable messages about numbers only.

Explore how hard tests validate a function with varied user inputs—strings, booleans, or no values—and raise an exception when a non-float is passed, ensuring a float return and security.

Explain the challenge of implementing a counter function that takes a name string and returns the link of that name, and build easy, medium, and hard tests to validate behavior.

Demonstrate solving a Python unit testing coding challenge by building a string character counter for English letters and adding tests that raise errors for non letters, spaces, or empty input.

Learn to test classes with the same rigor used for functions, ensuring programs do what they should, handle wrong inputs, and prepare you to work with objects in big apps.

Set up your python project environment, create a simple counter class with methods add, remove, clear, and get_value, and write tests to verify its behavior.

Implement an easy unit test for a counter class using set up and tear down, instantiate objects, verify that the initial value is zero, and ensure clear leaves it zero.

Implement a medium test by setting up a test class, verifying that the add method increments a counter and the remove method decrements it, and confirming all tests pass.

Learn to implement hard tests by handling remove operations to prevent the counter from dropping below zero, then loop 1000 times to reach 1000, with setup and class vs function.

Implement a car class that starts, stops, adjusts speed, and reports current speed and status. Practice robust testing and learn from mistakes to build reliable software.

Explore implementing and testing a Python car class through coding challenge #2, covering start, speed controls, stop, turn off, and exception handling with unit tests.

Learn how to test console printed output and apply best practices for verifying programs that print to the console.

Set up a new Python project, create a profile class with name, age, and job, and implement methods that print these values; learn to test console output using unit tests.

Learn to test both printed output and return values in Python unit tests by capturing stdout with StringIO, setting up test cases, and asserting exact results.

Test a printer class by verifying its set_value and print_value methods and their console output, and understand how a new value overwrites the previous one.

Implement a solution for a coding challenge by setting up test output capture with sys.stdout and StringIO, using a printer object to set values and verify printed results.

Learn to test for efficiency by comparing two programs to see which computes the correct result faster. Build on your skills testing functions, classes, and outputs to become a tester.

Compare execution times of two functions or programs to identify the faster option or confirm a time-limited task meets its deadline. Ensure better user experience and reliable, timely performance.

Develop two Fibonacci implementations in Python, one recursive and one using a mathematical method, then benchmark their performance with tests to study code efficiency.

Compare the performance of two Fibonacci implementations using time measurements, set up a test that times recursive and math methods, store results in a dictionary, and report which is faster.

Explore a Python unit testing project by comparing two similar methods that subtract two until negative, and write efficiency tests to measure one-line versus two-line implementations, with input validation scenarios.

Implement the section's coding challenge by timing your program with time.time, comparing two methods, and recording results in a dictionary to assess efficiency.