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.
- 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 cookiecutteror
$ conda config --add channels conda-forge
$ conda install cookiecuttercookiecutter gh:romellfudi/DatascienceNotebooks
.
├── 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
pip install -r requirements.txt
MIT. See the LICENSE file for the copyright notice.
2019, January