Supporting Resources

Great Expectations requires a python compute environment and access to data, either locally or through a database or distributed cluster. In addition, developing with great expectations relies heavily on tools in the Python engineering ecosystem: pip, virtual environments, and jupyter notebooks. We also assume some level of familiarity with git and version control.

See the links below for good, practical tutorials for these tools.

Tools Reference

pip

• https://pip.pypa.io/en/stable/

• https://www.datacamp.com/community/tutorials/pip-python-package-manager

virtual environments

• https://virtualenv.pypa.io/en/latest/

• https://python-guide-cn.readthedocs.io/en/latest/dev/virtualenvs.html

• https://www.dabapps.com/blog/introduction-to-pip-and-virtualenv-python/

jupyter notebooks and jupyter lab

• https://jupyter.org/

• https://jupyterlab.readthedocs.io/en/stable/

• https://towardsdatascience.com/jupyter-lab-evolution-of-the-jupyter-notebook-5297cacde6b

git

• https://git-scm.com/

• https://reference.github.com/

• https://www.atlassian.com/git/tutorials

Installing from github

If you plan to make changes to great expectations or live on the bleeding edge, you may want to clone from GitHub and pip install using the –editable flag.

$ git clone https://github.com/great-expectations/great_expectations.git
$ pip install -e great_expectations/

last updated: Aug 13, 2020