Introducing Cookiecutter Data Science
Cookiecutter Data Science (CCDS) is an innovative tool designed to help data scientists kick-start their projects with a structured and standardized template. This tool offers a flexible project structure, enabling professionals to share their work more effectively while adhering to best practices in the field.
What is Cookiecutter Data Science?
Cookiecutter Data Science is built on top of the cookiecutter templating utility, which is a project template generator commonly used in software development. With the latest version, CCDS v2, users are encouraged to install a new Python package specifically tailored for data science initiatives. The tool simplifies the setup of projects with its own command-line utility known as ccds, distinguishing it from the general-purpose cookiecutter.
Installation
Installing Cookiecutter Data Science is straightforward, requiring Python version 3.8 or higher. Since it’s designed for cross-project utility, the recommended installation method is via pipx.
Here's how you can install it:
# Recommended installation using pipx from PyPI
pipx install cookiecutter-data-science
# Alternatively, install using pip
pip install cookiecutter-data-science
# Soon to be available on conda-forge
# conda install cookiecutter-data-science -c conda-forge
Starting a New Project
To start a new project, users simply execute the ccds command in their terminal.
Upon initiating a project, CCDS generates a well-organized directory structure that resembles the following, with directories tailored to the specific needs of your data project:
├── LICENSE <- Your chosen open-source license
├── Makefile <- Commands to automate tasks like data processing or model training
├── README.md <- A README file providing instructions and context for developers
├── data <- Data storage with sub-directories for raw, interim, processed, and external data
├── docs <- Documentation for the project
├── models <- Contains trained models and outputs
├── notebooks <- Jupyter notebooks for exploratory data analysis and experimentation
├── pyproject.toml <- Project configuration file with metadata and tool configuration
├── references <- Supplementary materials like data dictionaries and manuals
├── reports <- Analysis outputs in various formats
├── requirements.txt <- Dependency list generated from `pip freeze` for environment replication
├── setup.cfg <- Configuration settings for code style checks
└── {{ cookiecutter.module_name }} <- Python source code directory
Using Cookiecutter Data Science v1
For users preferring the older version of the template, it remains accessible with a simple command. This requires having the original cookiecutter package installed and can be initiated using:
ccds https://github.com/drivendataorg/cookiecutter-data-science -c v1
# Alternatively
cookiecutter https://github.com/drivendataorg/cookiecutter-data-science -c v1
Contributing to CCDS
The Cookiecutter Data Science community is open to contributions, encouraging developers to enhance and refine the tool. Prospective contributors can find guidelines on the project’s documentation page.
Development and Testing
To work on CCDS development, install the development requirements:
pip install -r dev-requirements.txt
Running tests is straightforward using:
pytest tests
In summary, Cookiecutter Data Science offers an efficient, conventional, and adaptable approach for data scientists to organize and share their work, making it easier to adhere to best practices and ensure high-quality, reproducible results.