Adapted project structure for doing and sharing data science work with anaconda-project. The objectiv of this repo is to provide a lean and flexible project structure to organize your work. Therefore, I removed a whole bunch of artifacts from the original repo (e.g. make files, requirements.txt, etc.) in order to stay lean with dependencies and project structure. This repo is based on
- 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 cookiecutteror
$ conda config --add channels conda-forge
$ conda install cookiecuttercookiecutter https://github.com/leudom/cookiecutter-anaconda-project
The directory structure of your new project looks like this:
├── LICENSE
├── README.md <- The top-level README for developers using this project.
├── anaconda-project.yml <- Anaconda project file
├── bin <- Trained and serialized models, model predictions, or model summaries.
├── data
│ ├── external <- Data from third party
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
├── 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
└── src <- Source code for use in this project.
├── __init__.py <- Makes src a Python module
├── data <- Scripts to download or generate data
│ ├── __init__.py
│ └── make_dataset.py
├── features <- Scripts to turn raw data into features for modeling
│ ├── __init__.py
│ └── build_features.py
├── models <- Scripts to train models and then use trained models to make predictions
│ ├── __init__.py
│ ├── predict_model.py
│ └── train_model.py
└── visualization <- Scripts to create exploratory and results oriented visualizations
├── __init__.py
└── visualize.py
We welcome contributions!
- Fork it (https://github.com/yourname/yourproject/fork)
- Create your feature branch (
git checkout -b feature/fooBar) - Commit your changes (
git commit -am 'Add some fooBar') - Push to the branch (
git push origin feature/fooBar) - Create a new Pull Request
pip install -r requirements.txt
py.test tests