Cookiecutter Data Exploration

Project structure for Data Exploration.

Requirements to use the cookiecutter template:

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• 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 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/drivendata/cookiecutter-data-science

The resulting directory structure

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The directory structure of your new project looks like this:

├── LICENSE
├── 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
│
├── models             <- Trained models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a date (for ordering),
│                         the creator's initials, and a short `_` delimited description, e.g.
│                         `2020_06_01-initial-data-exploration`.
│
├── 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
│   │
│   ├── version.py     <- Source code version
│
└── setup.cfg          <- Setup configuration

Installing development requirements

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pip install -e .

Running the tests

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python setup.py test

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