Do more with structured and named data than putting everything into a Dictionary. Learn about Python type hints for arguments and variables to tell users what to expect. Structure data into Dataclasses for functional named data handlers. Leverage validation and the powerful third party tool pydantic to handle complex schema and automatic validation.
If you'd like to develop on and build the Scientific Visualization using Python book, you should:
- Clone this repository and run
- Run
pip install -r requirements.txt(it is recommended you do this within a virtual environment) - (Recommended) Remove the existing
python-type-hints-and-pydantic/_build/directory - Run
jupyter-book build python-type-hints-and-pydantic/
A fully-rendered HTML version of the book will be built in python-type-hints-and-pydantic/_build/html/.
The html version of the book is hosted on the gh-pages branch of this repo. A GitHub actions workflow has been created that automatically builds and pushes the book to this branch on a push or pull request to main.
If you wish to disable this automation, you may remove the GitHub actions workflow and build the book manually by:
- Navigating to your local build; and running,
ghp-import -n -p -f python-type-hints-and-pydantic/_build/html
This will automatically push your build to the gh-pages branch. More information on this hosting process can be found here.
We welcome and recognize all contributions. You can see a list of current contributors in the contributors tab.
This project is created using the excellent open source Jupyter Book project and the executablebooks/cookiecutter-jupyter-book template.