The pro coookiecutter template for data science projects. The objectives of the project are to:
- leverage .devcontainers in VS Code to isolate dev environment
- use modern VS Code extentions, like ruff
- use fast package manager: poetry, with automated project configuration
- reuse safely .env file from a local host
- automatically create repo on Github
- pre-configure standard Python tools: pytest, tox, mypy, etc.
Required to be installed and pre-configured:
- VS Code
- Docker
- Python package cookiecutter, i.e.: pip install cookiecutter
- [Optional] Github's official CLI tool
- [Optional] Github Copilot extension in VS Code
- [Optional] .env file located in ~/.ssh/.env with environmental variables/secrets
Simply:
cookiecutter https://github.com/gox6/datascience-pro-cookiecutter.git
├── {{ cookiecutter.project_slug }}
│ └── ...
├── hooks
│ └── ...
├── cookiecutter.json
├── LICENCE.txt
└── README.md
2 directories, 3 files at the top level
To make the regular use even faster I created two aliases in my .zshrc file (please be mindful what shell you are using …):
alias pro="cd /Users/mg/inbox/projects"
alias new="pro && cookiecutter https://github.com/gox6/datascience-pro-cookiecutter.git"
Another speedup of project creation can be achieved by providing the default values for some project variables in ~/.cookiecutterrc file, i.e.:
default_context:
author: "Jarek Grygolec"
email: "jgrygolec@gmail.com"
github_username: "gox6"
Consult component's docs.
- [0.2] Incorporate docs creation
Contributors names and contact info:
- Jarek Grygolec: jgrygolec@gmail.com
- 0.1
- Initial Release
This project is licensed under the MIT License - see the LICENSE.md file for details
Inspiration, code snippets, etc.