cookiecutter https://github.com/nicolasCavalcante/cookiecutter-general.git
Or:
python -m cookiecutter https://github.com/nicolasCavalcante/cookiecutter-general.git
Create Git repository:
git init
git add *
git commit -m 'Inital commit'
Install and launch enviroment:
pipenv install --dev --skip-lock
pipenv shell
Run tests and formating, Doit:
doit
- Pipenv for managing packages and virtualenvs in a modern way.
- Code quality: black, isort, and autoflake already installed.
- Modern CLI with Typer.
- Pytest for testing.
- Doit for automation.
- Mkdocs (optional) for documentation.
This is our your new project will look like:
├── .gitignore <- GitHub's excellent Python .gitignore customized for this project
├── AUTHORS.md <- Project Authors
├── dodo.py <- doit dodo script for linting and tests
├── LICENSE <- Your project's license.
├── Pipfile <- The Pipfile for reproducing the analysis environment
├── README.md <- The top-level README for developers using this project.
├── setup.py <- makes project pip installable (pip install -e .) so <repo_name> can be imported
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `_` delimited description, e.g.
│ `01_cp_exploratory_data_analysis.ipynb`.
│
├───tests <- All project tests
│
├── docs <- MkDocs data files
├── mkdocs.yml <- MkDocs config file
│
└───<repo_name>
└───cli.py <- Entry point, acessible by <repo_name> <args> using typer
https://github.com/drivendata/cookiecutter-data-science
https://github.com/crmne/cookiecutter-modern-datascience
https://github.com/sourcery-ai/python-best-practices-cookiecutter
https://gist.github.com/bradtraversy/c70a93d6536ed63786c434707b898d55