Metadata-Version: 2.1
Name: great-expectations
Version: 0.5.0
Summary: Always know what to expect from your data.
Home-page: https://github.com/great-expectations/great_expectations
Author: The Great Expectations Team
Author-email: team@greatexpectations.io
License: Apache-2.0
Keywords: data science testing pipeline
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Other Audience
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Testing
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Requires-Dist: numpy (>=1.9.2)
Requires-Dist: scipy (>=0.19.0)
Requires-Dist: pandas (>=0.20.3)
Requires-Dist: python-dateutil (>=2.4.2)
Requires-Dist: pytz (>=2015.6)
Requires-Dist: six (>=1.9.0)
Requires-Dist: jsonschema (>=2.5.1)
Requires-Dist: sqlalchemy (>=1.2)

|Build Status| |Coverage Status| |Documentation Status|

Great Expectations
==================

*Always know what to expect from your data.*

What is great_expectations?
---------------------------

Great Expectations helps teams save time and promote analytic integrity
by offering a unique approach to automated testing: pipeline tests.
Pipeline tests are applied to data (instead of code) and at batch time
(instead of compile or deploy time). Pipeline tests are like unit tests
for datasets: they help you guard against upstream data changes and
monitor data quality.

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.

Why would I use Great Expectations?
-----------------------------------

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.

How do I get started?
---------------------

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/

What expectations are available?
--------------------------------

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 <http://great-expectations.readthedocs.io/en/latest/glossary.html>`__
for a complete list of expectations that are currently part of the great
expectations vocabulary.

Can I contribute?
-----------------

Absolutely. Yes, please. Start
`here <https://github.com/great-expectations/great_expectations/blob/develop/CONTRIBUTING.md>`__,
and don’t be shy with questions!

How do I learn more?
--------------------

For full documentation, visit `Great Expectations on
readthedocs.io <http://great-expectations.readthedocs.io/en/latest/>`__.

`Down with Pipeline
Debt! <https://medium.com/@expectgreatdata/down-with-pipeline-debt-introducing-great-expectations-862ddc46782a>`__
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 <https://www.youtube.com/watch?v=-_0tG7ACNU4>`__
-  `Using Distributional
   Expectations <https://www.youtube.com/watch?v=l3DYPVZAUmw&t=20s>`__

What’s the best way to get in touch with the Great Expectations team?
---------------------------------------------------------------------

If you have questions, comments, feature requests, etc., `opening an
issue <https://github.com/great-expectations/great_expectations/issues/new>`__
is definitely the best path forward. We also have a slack channel: if
you emal us at team@greatexpectations.io with the subject line “SLACK”
we’ll get you an invite.

Great Expectations doesn’t do X. Is it right for my use case?
-------------------------------------------------------------

It depends. If you have needs that the library doesn’t meet yet, please
`upvote an existing
issue(s) <https://github.com/great-expectations/great_expectations/issues>`__
or `open a new
issue <https://github.com/great-expectations/great_expectations/issues/new>`__
and we’ll see what we can do. Great Expectations is under active
development, so your use case might be supported soon.

.. |Build Status| image:: https://travis-ci.org/great-expectations/great_expectations.svg?branch=develop
   :target: https://travis-ci.org/great-expectations/great_expectations
.. |Coverage Status| image:: https://coveralls.io/repos/github/great-expectations/great_expectations/badge.svg?branch=develop
   :target: https://coveralls.io/github/great-expectations/great_expectations?branch=develop
.. |Documentation Status| image:: https://readthedocs.org/projects/great-expectations/badge/?version=latest
   :target: http://great-expectations.readthedocs.io/en/latest/?badge=latest


