pytest_project

Prerequisites

Install Docker

These tests have been packaged to run with all dependencies installed within a Docker container. Due to the use of f-strings, this must be run with python 3.6+. The Docker image is based on python 3.7

To run open a shell

$ docker-compose build
$ docker-compose run test sh

This will open the docker shell and you can run one of the following commands:

Run the entire test suite

$ pytest

Run the tests for a certain file matching a keyword

$ pytest -k <test_file_name>

Run tests while printing all variables and verbose output

$ pytest -vvl

To exit the shell

$ exit

If you page up (ctrl + fn) within the debug output when running pytest -vvl or when encountering test errors, your cursor may stick and be unable to continue writing in the docker shell. You can get past this by typing q to return to entry mode in the docker container.
If you'd like to debug a piece of code, you can add either of the following built-in functions to a section of the code to enter into the pdb debugger while running pytest.
- breakpoint() (python 3)
- import pdb; pdb.set_trace() (python 2)

Try to create a Map class that assembles the points you've created into a data structure. Create one test to make sure the map has the appropriate points and create another test to raise an exception if you assert the Map has a point that it has not stored.
Write a simple xml or json data processor to our data_processor.py file. After doing so, update the factory fixture to also allow a user to pass in the data for testing. There's a json_processor.py file in the scripts file if you get stuck and want to compare what you've done to a sample json reader.
Create a User class. Each User should have a height and favorite color. Based on their height, create a function that awards them different types of candy. Try working from the test first as you add this functionality and think of ways to parametrize your test.
Find open source Python projects that use pytest and read their tests. Then go to the source code to validate your assumptions. Try doing this repeatedly over a few projects that use the pytest framework. Here are a few to get you started: - Pytest retains a list of projects. - sentry, cross-platform application monitoring, with a focus on error reporting - pdb++, a drop-in replacement for pdb - Astropy,a single core package for Astronomy in Python