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Quickstart:

cookiecutter https://github.com/nicolasCavalcante/cookiecutter-general.git

Or:

python -m cookiecutter https://github.com/nicolasCavalcante/cookiecutter-general.git

If you said "No" to "setup_project":

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

Features

Directory structure

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

References:

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

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