Always know what to expect from your data.

Great Expectations is a framework that helps teams save time and promote analytic integrity with a new twist on automated testing: pipeline tests. Pipeline tests are applied to data (instead of code) and at batch time (instead of compile or deploy time).

Software developers have long known that automated testing is essential for managing complex codebases. Great Expectations brings the same discipline, confidence, and acceleration to data science and engineering teams.

To get more done with data, faster. Teams use great_expectations to

• Save time during data cleaning and munging.
• Accelerate ETL and data normalization.
• Streamline analyst-to-engineer handoffs.
• Monitor data quality in production data pipelines and data products.
• Simplify debugging data pipelines if (when) they break.
• Codify assumptions used to build models when sharing with distributed teams or other analysts.

It's easy! Just use pip install:

$ pip install great_expectations

You can also clone the repository, which includes examples of using great_expectations.

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

Expectations include:

• expect_table_row_count_to_equal
• expect_column_values_to_be_unique
• expect_column_values_to_be_in_set
• expect_column_mean_to_be_between
• ...and many more

Visit the glossary of expectations for a complete list of expectations that are currently part of the great expectations vocabulary.

Absolutely. Yes, please. Start here, and don't be shy with questions!

For full documentation, visit Great Expectations on readthedocs.io.

Down with Pipeline Debt! explains the core philosophy behind Great Expectations. Please give it a read, and clap, follow, and share while you're at it.

For quick, hands-on introductions to Great Expectations' key features, check out our walkthrough videos:

• Introduction to Great Expectations
• Using Distributional Expectations

Issues on GitHub. If you have questions, comments, feature requests, etc., opening an issue is definitely the best path forward.

It depends. If you have needs that the library doesn't meet yet, please upvote an existing issue(s) or open a new issue and we'll see what we can do. Great Expectations is under active development, so your use case might be supported soon.