% My Cookiecutter

Forked from Cookiecutter Data Science (GitHub):

A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.

New version of Cookiecutter Data Science

Cookiecutter data science is moving to v2 soon, which will entail using the command ccds ... rather than cookiecutter .... The cookiecutter command will continue to work, and this version of the template will still be available. To use the legacy template, you will need to explicitly use -c v1 to select it. Please update any scripts/automation you have to append the -c v1 option (as above), which is available now.

Requires

• Python 2.7 or 3.5+
• Cookiecutter Python package >= 1.4.0

Create a new project directory using this template

Install Cookiecutter with pip:

$ pip install cookiecutter

or conda:

$ conda config --add channels conda-forge
$ conda install cookiecutter

Initialize a new project:

$ cookiecutter https://github.com/izaakm/cookiecutter

Follow the prompts, and Cookiecutter will automatically produce a project like this:

├── LICENSE
├── README.md       <- The top-level README for developers using this project.
├── data            <- Data directory (data is NOT tracked by git!!!)
├── docs            <- A default Sphinx project; see sphinx-doc.org for details
├── models          <- Trained and serialized models, model predictions, or model summaries
├── references      <- Data dictionaries, manuals, and all other explanatory materials.
├── reports         <- Generated analysis as HTML, PDF, LaTeX, etc.
├── environment.yml <- Dependencies for your project's conda environment.
├── setup.py        <- Install the code for your project with `pip install -e`.
├── src             <- Source code for your project.
└── tox.ini         <- tox file with settings for running tox; see tox.readthedocs.io

Development

Installing development requirements

$ pip install -r requirements.txt

Running the tests

$ py.test tests