Testing for Data Science

Day 1

This training focuses on several aspects of PyTest. At the end of this training, you should be comfortable with marks, fixtures, parametrization, error handling and temporary_paths.

⚡ Installation

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1. Virtual Environment

Create a virtual environment for this repo, for instance with Anaconda:

conda create --name testing_for_ds -y python=3.10
conda activate testing_for_ds

You can use the same virtual environment for the whole training. Note: if you just installed anaconda, you might have to initialize your terminal with conda init <SHELL_NAME> and restart your terminal afterwards.

2. Install dependencies

Install the project in develop mode, with:

pip install -e ".[develop]"

This will install all the dependencies listed in requirements.txt and requirements.dev.txt. Have a look at them to get an idea with what we're working.

3. setup pre-commit

We will be using pre-commit for this project, install it with:

pre-commit install

If you commit your code, pre-commit will run some checks. Only if you pass the checks, the commit is completed. For example, it checks whether you've used type-hinting. See .pre-commit-config.yaml for the full configuration.

4. create your own branch

git checkout -b <branch-name>
git push

📋 Assignment

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After you completed the installation, work yourself through the exercises in the tests\test_<subject>.py. It's recommended to make at least the exercises marked with an '*', but try to make as many exercises as possible as they will cover even more information than given in the slides.

You've completed the assignment if:

• You answered all exercises.
• All the tests pass.
• The pre-commit steps are successful.
• You pushed your code to your personal branch.

Please let me know if you have any questions!