cookiecutter-data-science

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

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README.md

Cookiecutter Data Science

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

![PyPI - Version](https://pypi.org/project/cookiecutter-data-science/)
![PyPI - Python Version](https://pypi.org/project/cookiecutter-data-science/)
<a target="_blank" href="https://cookiecutter-data-science.drivendata.org/">
<img src="https://img.shields.io/badge/CCDS-Project%20template-328F97?logo=cookiecutter" />
</a>
![tests](https://github.com/drivendataorg/cookiecutter-data-science/actions/workflows/tests.yml)

Cookiecutter Data Science (CCDS) is a tool for setting up a data science project template that incorporates best practices. To learn more about CCDS's philosophy, visit the project homepage.

â„šī¸ Cookiecutter Data Science v2 has changed from v1. It now requires installing the new cookiecutter-data-science Python package, which extends the functionality of the cookiecutter templating utility. Use the provided ccds command-line program instead of cookiecutter.

Installation

Cookiecutter Data Science v2 requires Python 3.9+. Since this is a cross-project utility application, we recommend installing it with pipx. Installation command options:

bash

With pipx from PyPI (recommended)


pipx install cookiecutter-data-science

With pip from PyPI


pip install cookiecutter-data-science

With conda from conda-forge (coming soon)


conda install cookiecutter-data-science -c conda-forge

Starting a new project

To start a new project, run:

bash
ccds

The resulting directory structure

The directory structure of your new project will look something like this (depending on the settings that you choose):

text
├── LICENSE            <- Open-source license if one is chosen
├── Makefile <- Makefile with convenience commands like make data or make train
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default mkdocs project; see www.mkdocs.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short - delimited description, e.g.
│ 1.0-jqp-initial-data-exploration.
│
├── pyproject.toml <- Project configuration file with package metadata for
│ {{ cookiecutter.module_name }} and configuration for tools like black
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with pip freeze > requirements.txt
│
├── setup.cfg <- Configuration file for flake8
│
└── {{ cookiecutter.module_name }} <- Source code for use in this project.
│
├── __init__.py <- Makes {{ cookiecutter.module_name }} a Python module
│
├── config.py <- Store useful variables and configuration
│
├── dataset.py <- Scripts to download or generate data
│
├── features.py <- Code to create features for modeling
│
├── modeling
│ ├── __init__.py
│ ├── predict.py <- Code to run model inference with trained models
│ └── train.py <- Code to train models
│
└── plots.py <- Code to create visualizations

Using unreleased changes

By default, ccds will use the _project template_ version that corresponds to the _installed ccds package_ version (e.g., if you have installed ccds v2.0.1, you'll use the v2.0.1 version of the project template by default). To use a specific version of the project template, use the -c/--checkout flag to provide the branch (or tag or commit hash) of the version you'd like to use. For example to use the project template from the master branch:

bash
ccds -c master

Using v1

If you want to use the old v1 project template, you need to have either the cookiecutter-data-science package or cookiecutter package installed. Then, use either command-line program with the -c v1 option:

bash
ccds https://github.com/drivendataorg/cookiecutter-data-science -c v1

or equivalently


cookiecutter https://github.com/drivendataorg/cookiecutter-data-science -c v1

Contributing

We welcome contributions! See the docs for guidelines.

Installing development requirements

bash
pip install -r dev-requirements.txt

Running the tests

bash
pytest tests