elaynelemos / cookiecutter-data-science

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

Cookiecutter Data Science

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

Project homepage

Requirements to use the cookiecutter template:

• Python 2.7 or 3.5
• Cookiecutter Python package >= 1.4.0: This can be installed with pip by or conda depending on how you manage your Python packages:

pip install cookiecutter

or

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

To start a new project, run:

cookiecutter https://github.com/Sysvale/cookiecutter-data-science.git

The resulting directory structure

The directory structure of your new project looks like this:

├── 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.
│
├── docker-compose.yml      <- The docker-compose file to manage environments as services.
├── docker
│   ├── dev.Dockerfile      <- Dockerfile to the project development environment container.
│   ├── jupyter.Dockerfile  <- Dockerfile to the project jupyter environment container.
│   ├── prod.Dockerfile     <- Dockerfile to the project production environment container.
│
├── docs                    <- A default Sphinx project; see sphinx-doc.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`.
│
├── 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_dev.txt    <- The requirements file for reproducing the analysis environment
│                              and the development routines such as tests.
├── requirements.txt        <- The requirements file for reproducing the analysis environment,
│                              e.g. generated with `pip freeze > requirements.txt`
│
├── {{cookiecutter.repo_name}} <- Source code for use in this project.
│   ├── __init__.py         <- Makes src 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
│
└── tox.ini                <- tox file with settings for running tox; see
                              tox.readthedocs.io

Contributing

We welcome contributions! See the docs for guidelines.

Installing development requirements

pip install -r requirements.txt

Running the tests

py.test tests