Cookiecutter Data Science

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

Project homepage

Requirements to use the cookiecutter template:

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• 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 cookiecutter

or

$ conda config --add channels conda-forge
$ conda install cookiecutter

To start a new project, run:

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cookiecutter https://github.com/CityOfLosAngeles/cookiecutter-data-science

The resulting directory structure

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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                     <- A directory for local data.
    ├── models                   <- Trained and serialized models, model predictions, or model summaries
    │
    ├── notebooks                <- Jupyter notebooks.
    │
    ├── 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
    │
    │
    ├── conda-requirements.txt   <- The requirements file for conda installs.
    ├── requirements.txt         <- The requirements file for reproducing the analysis environment, e.g.
    │                               generated with `pip freeze > requirements.txt`
    │
    ├── setup.py                 <- makes project pip installable (pip install -e .) so src can be imported
    ├── src                      <- Source code for use in this project.
    │   ├── __init__.py          <- Makes src a Python module
    │   │
    │   ├── data                 <- Scripts to download or generate data
    │   ├── features             <- Scripts to turn raw data into features for modeling
    │   ├── models               <- Scripts to train models and then use trained models to make
    │   └── visualization        <- Scripts to create exploratory and results oriented visualizations

Contributing

We welcome contributions! See the docs for guidelines.

Installing development requirements

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pip install -r requirements.txt

Running the tests

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py.test tests