A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
- Python 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 cookiecutteror
$ conda config --add channels conda-forge
$ conda install cookiecutter
or using pipx
pipx install cookiecutterPYTHONPATH=<this directory> ccds https://github.com/akail/cookiecutter-data-science
The directory structure of your new project looks like this:
├── LICENSE
├── Makefile <- Makefile with 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 Sphinx project; see sphinx-doc.org for details
│
├── .gitignore <- Avoids uploading data, credentials, outputs, system files, etc
│
├── 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`.
│ │
│ ├── README.md <- README with specific information related to using notebooks
│ ├── eda <- Exploratory Data Analysis. For short term usage
│ ├── poc <- Proof of concept
│ ├── modeling <- Model building and training
│ └── evaluation <- How good is the model
│
├── 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`
│
├── test_requirements.txt <- The requirements file for automated test running
│
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
│
├── scripts <- Reusable but independent python and bash scripts
│
├── {{ 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
│
├── tests <- Unit and functional tests using pytest
│
└── tox.ini <- tox file with settings for running tox; see tox.testrun.org
We welcome contributions! See the docs for guidelines.
pip install -r requirements.txt
pytest tests