Cookiecutter for Data Science with python

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

Requirements

• Conda
• Cookiecutter Python package: This can be installed with pip by or conda depending on how you manage your Python packages:

pip install cookiecutter

or

conda install -c conda-forge cookiecutter

Create a new project

In a folder where you want your project generated:

cookiecutter https://github.com/storreglosa/cookiecutter-data-science-python

Resulting directory structure

├── LICENSE
├── tasks.py           <- Invoke with commands like `notebook`.
├── README.md          <- The top-level README for developers using this project.
├── install.md         <- Detailed instructions to set up 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.
│
├── 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.
│
├── environment.yml    <- The requirements file for reproducing the analysis environment.
│
├── .here              <- File that will stop the search if none of the other criteria
│                         apply when searching head of project.
│
├── setup.py           <- Makes project pip installable (pip install -e .)
│                         so {{ cookiecutter.project_module_name }} can be imported.
│
└── {{ cookiecutter.project_module_name }}               <- Source code for use in this project.
    ├── __init__.py    <- Makes {{ cookiecutter.project_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
    │
    ├── utils          <- Scripts to help with common tasks.
        └── paths.py   <- Helper functions to relative file referencing across project.
    │
    └── visualization  <- Scripts to create exploratory and results oriented visualizations.
        └── visualize.py

Contributing guide

All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome.

Credits

This project is an adaptation of the jvelezmagic cookiecutter-conda-data-science which is, in turn, heavily influenced by drivendata's Cookiecutter Data Science, andfanilo's Cookiecutter for Kaggle Conda projects, and julia's package DrWatson.

Other links that helped shape this cookiecutter :

• Write less terrible code with Jupyter Notebook
• Cookiecutter DataScience Opinions