Testing

pytest.mark lets you label test functions for conditional or selective execution based on specific needs.

For instance, you can mark slow tests or tests involving integration with external services to run them separately or exclude them from regular test runs. This helps you organize and execute your tests more effectively.
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Use the tmp_path fixture in pytest to create a temporary directory for testing the function that interacts with files. This will prevent any changes to the actual filesystem or production files.
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Test-driven development (TDD) is a technique that helps you write better code, faster and with more confidence. It emphasizes writing automated tests before writing the code. Here’s the process:

Create a test that fails because the code doesn’t exist yet.

Write the minimum amount of code necessary to make the test pass.

Refactor the code to make it more maintainable and efficient.

Repeat the process until your code meets the desired behaviors.

My articles on the topic of testing.
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To test for a specific exception in unit testing, use pytest.raises.

For example, you can use it to test if a ValueError is thrown when there are NaN values in the group column.

To quickly create fake data for testing, use Faker.

AB testing is crucial for assessing the effectiveness of changes in a controlled environment. With the leAB library, you can determine the optimal sample size prior to the test launch and subsequently compare the successes of Group A and Group B.

To ensure that your application logs an error under a specific condition, use the built-in fixture called caplog in pytest. 

This fixture allows you to capture log messages generated during the execution of your test.

When testing a Python class, it can be repetitive and time-consuming to create multiple instances to test a large number of instance methods. 

A better approach is to use the setUp method to instantiate a class object before running each test.