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

Starting a new project:

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To start a new project, cd into a directory where you want the new project and run

cookiecutter https://github.com/Ilkka-LBL/cookiecutter-data-science

This version of the cookiecutter data science template provides a few modifications to the original one. For one, the directory structure is simplified. Second, this template provides an option to choose from various different projects types (e.g. mapping, torch_ml, tensorflow_ml, general, and minimal), which will add packages to the requirements.txt file accordingly. This template also adds the option of including boilerplate code in the clean_dataset.py file in the form of paths to the different data and models folders. Finally, as Windows Command Prompt cannot handle UNC paths (although PowerShell can), the final_destination option allows the user to define a final path for the project manually. This requires a full path to be provided, after which the post_gen_project.py hook will run to move the project folder to the specified location. Leave the final_destination option empty if you do not wish to move the project.

The below video is not an accurate representation of the options shown when using this fork.

New version of Cookiecutter Data Science

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Cookiecutter data science is moving to v2 soon, which will entail using the command ccds ... rather than cookiecutter .... The cookiecutter command will continue to work, and this version of the template will still be available. To use the legacy template, you will need to explicitly use -c v1 to select it. Please update any scripts/automation you have to append the -c v1 option (as above), which is available now.

The resulting directory structure

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The directory structure of your new project looks like this:

├── README.md          <- The top-level README for developers using this project.
├── .gitignore         <- NEVER DELETE THIS FILE.
├── data
│   ├── downloaded     <- Data from downloads.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt
├── .env               <- Environment variables                          
│
├── clean_dataset.py   <- Compilation and reformatting of data to create data files in /data/processed/
│                          
└── analysis.py        <- Analyse data and create final data product based on files in /data/processed/

Contributing

We welcome contributions! See the docs for guidelines.

Installing development requirements

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pip install -r requirements.txt