Metadata-Version: 2.0
Name: great-expectations
Version: 0.3.2
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: great_expectations@superconductivehealth.com
License: Apache-2.0
Description-Content-Type: UNKNOWN
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: argh (>=0.26.2)
Requires-Dist: jsonschema (>=2.5.1)

.. _intro:

================================================================================
Introduction
================================================================================


*Always know what to expect from your data.*

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What is Great Expectations?
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Great Expectations is a framework for bringing data pipelines and products under test.

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?
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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.


See :ref:`workflow_advantages` to learn more about how Great Expectations speeds up data teams.

Getting started
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...is easy. Just use pip install:

.. code-block:: bash

    $ pip install great_expecatations

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

.. code-block:: bash

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

Since Great Expectation is under active development, the `develop` branch is often a ahead of the latest production release. If you want to work from the latest commit on `develop`, we recommend you install by branch name or hash.

branch-name:

.. code-block:: bash

	$ pip install git+git://github.com/great-expectations/great_expectations.git@develop


