Cookiecutter Data Science

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

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.

> ℹ️ 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 3.8+. Since this is a cross-project utility application, we recommend installing it with pipx. Installation command options:

# 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:

ccds

The resulting directory structure

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

├── 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 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
    │
    ├── data           <- Scripts to download or generate data
    │   └── make_dataset.py
    │
    ├── features       <- Scripts to turn raw data into features for modeling
    │   └── build_features.py
    │
    ├── models         <- Scripts to train models and then use trained models to make
    │   │                 predictions
    │   ├── predict_model.py
    │   └── train_model.py
    │
    └── visualization  <- Scripts to create exploratory and results oriented visualizations
        └── visualize.py

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:

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

pip install -r dev-requirements.txt

Running the tests

pytest tests