Project structure for doing and sharing data science work.
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README.md

DataScience Notebooks Structure using cookiecutter

Workspace Language License

By Romell Domínguez

Project structure for doing and sharing data science work.

Allows to automatically generate Jupyter content (repo info, navigation bar, index) from a configuration file (config.yml) and Jupyter notebooks (notebooks/*.ipynb).

Inspired by cookiecutter-data-science and PythonDataScienceHandbook.

Requirements to use the cookiecutter template:


  • Python 2.7 or 3.6
  • 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  gh:romellfudi/DatascienceNotebooks

Project Organization

.
├── data
│   ├── 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
├── reports                  <- Generated analysis as HTML, PDF, LaTeX, etc
│   └── figures              <- Generated graphics and figures to be used in reporting
├── src                      <- Source code for use in this project
│   ├── data                 <- Scripts to download or generate data
│   │   ├── __init__.py
│   │   └── make_dataset.py
│   ├── features             <- Scripts to turn raw data into features for modeling
│   │   ├── build_features.py
│   │   └── __init__.py
│   ├── models               <- Scripts to train models
│   │   ├── __init__.py
│   │   ├── predict_model.py
│   │   └── train_model.py
│   ├── visualization        <- Scripts to create exploratory and results oriented visualizations
│   │   ├── __init__.py
│   │   └── visualize.py
│   └── __init__.py
├── config.yml
├── environment.yml          <- The requirements file for reproducing the analysis environment
├── LICENSE
├── Makefile                 <- Makefile with commands like `make create_env` or `make data`
├── README.md                <- The top-level README for developers using this project
└── setup.py                 <- makes project pip installable (pip install -e .) so src can be imported

Installing development requirements


pip install -r requirements.txt

License

MIT. See the LICENSE file for the copyright notice.

2019, January