Feature and performance tests

In this exercise, you will continue to test the pandas data pipeline. Here, you will create two types of tests. A feature test with pytest to validate that the feature "finding a median salary for a 2022 year" actually works. And a performance test with pytest-benchmark to find out how fast the process is with pytest-benchmark. Note: The function name testreadingspeed() in the solution is used to benchmark the performance. This is consistent with the naming convention and functionality.

This exercise is part of the course

Introduction to Testing in Python

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Exercise instructions

Check that the resulting median has the float type.
Check that the median salary is greater than 0.
Define the test_reading_speed() function with the benchmark argument.
Run pytest from CLI to see the results.

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This exercise is part of the course

Introduction to Testing in Python

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Learn what a test is and how to run the first one of your own with the pytest library! You will get used to the pytest testing framework and the command-line interface. You will also learn how to process specific contexts, like "failed tests" and "skipping the test" with pytest markers.

Exercise 1: Introduction to Testing in Python Exercise 2: The first test suite Exercise 3: pytest.raises Exercise 4: Invoking pytest from CLI Exercise 5: Run the test!Exercise 6: Run with the keyword Exercise 7: Applying test markers Exercise 8: Markers use cases Exercise 9: Failed tests with xfail Exercise 10: Conditional skipping

Learn what a fixture is and how to simplify your code by using it in tests. You will get familiar with the fixture @pytest.fixture decorator and the fixture tools. You will analyze your code to see the "fixture part" in it. Finally, learn how to use teardowns to prevent software failures.

Exercise 1: Introduction to fixtures Exercise 2: Getting familiar with fixtures Exercise 3: Data preparation Exercise 4: Run with a fixture Exercise 5: Chain Fixtures Requests Exercise 6: Chain this out Exercise 7: List with a custom length Exercise 8: Fixtures autouse Exercise 9: autouse statements Exercise 10: Auto add numbers Exercise 11: Fixtures Teardowns Exercise 12: Data with teardown Exercise 13: Read data with teardown

Learn what the basic testing types are and their features. Learn about test cases and how they help to implement tests. You will get more skilled with creating test functions and running pytest from CLI in IDE exercises. Finally, you will be able to differentiate the different testing types and create tests for each of them.

Exercise 1: Unit testing with pytest Exercise 2: Unit testing terms Exercise 3: Cover more test cases Exercise 4: Factorial of number Exercise 5: Run factorial Exercise 6: Feature testing with pytest Exercise 7: Feature or unit testing Exercise 8: Aggregate with sum Exercise 9: Integration testing with pytest Exercise 10: Integration test or not Exercise 11: Read the file Exercise 12: Performance testing with pytest Exercise 13: What is performance testing?Exercise 14: Finding an element Exercise 15: Speed of loops

In this final chapter, you will meet the unittest framework. First, you will learn basic assertion methods, then its CLI interface, and how to use fixtures. Finally, you will put everything together in the practical examples of data pipelines.

Exercise 1: Meeting the Unittest Exercise 2: Factorial with unittest Exercise 3: Is prime or not Exercise 4: CLI Interface Exercise 5: Run factorial with unittest Exercise 6: Erroneouos factorial Exercise 7: Unittest options Exercise 8: Fixtures in unittest Exercise 9: Test the string variable Exercise 10: Palindrome check Exercise 11: Practical examples Exercise 12: Integration and unit tests Exercise 13: Feature and performance tests

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Exercise 14: Energy pipeline Exercise 15: Congratulations!