.. image:: https://img.shields.io/pypi/v/datakit-project.svg :target: https://pypi.python.org/pypi/datakit-project

.. image:: https://img.shields.io/pypi/pyversions/datakit-project.svg :target: https://pypi.python.org/pypi/datakit-project

.. image:: https://img.shields.io/travis/associatedpress/datakit-project.svg :target: https://travis-ci.org/associatedpress/datakit-project :alt: Linux build status on Travis CI

.. image:: https://readthedocs.org/projects/datakit-project/badge/ :target: http://datakit-project.readthedocs.io/en/latest/ :alt: Documentation Status

Datakit

Datakit is a pluggable command-line tool for managing the life cycle of data projects.

The Associated Press Data Team uses Datakit to auto-generate project skeletons, archive and share data on Amazon S3, and other routine tasks.

Datakit is a thin wrapper around the Cliff_ command-line framework and is intended for use with a growing ecosystem of plugins.

Overview

datakit-project provides command-line tools to help generate project skeletons from Cookiecutter_ templates. The plugin is part of the datakit command-line framework_.

Some highlights include the ability to:

• Install templates from local Git repos or from Github
• Interactively select a template when creating new projects
• List locally installed templates
• Check whether templates are out-of-date
• Update templates

The Associated Press Data Team relies on this plugin because it fits nicely into a broader data science workflow built around datakit.

It's worth noting that Cookiecutter's command-line tool offers some of these features, and its developers are working to add others. Additionally, we primarily support Git-based templates, whereas Cookiecutter also works with mercurial repositories.

Please be sure to check out the latest release of Cookiecutter to see if it might be a better fit.

.. _Cookiecutter: https://cookiecutter.readthedocs.io/en/latest/ .. _datakit command-line framework: https://datakit.ap.org/

https://datakit-core.readthedocs.io/en/latest/readme.html