Great expectations integration is no longer supported. You can recreate the integration with the following packages versions:
Great Expectations is a Python-based
open-source library for validating, documenting, and profiling your
data. It helps you to maintain data quality and improve communication
about data between teams. With Great Expectations, you can assert what
you expect from the data you load and transform, and catch data issues
quickly -- Expectations are basically unit tests for your data.
ydata-profiling features a method to create a suite of Expectations
based on the results of your
About Great Expectations
Expectations are assertions about your data. In Great Expectations,
those assertions are expressed in a declarative language in the form of
simple, human-readable Python methods. For example, in order to assert
that you want values in a column
passenger_count in your dataset to be
integers between 1 and 6, you can say:
expect_column_values_to_be_between(column="passenger_count", min_value=1, max_value=6)
Great Expectations then uses this statement to validate whether the
passenger_count in a given table is indeed between 1 and 6, and
returns a success or failure result. The library currently provides
several dozen highly expressive built-in
and allows you to write custom Expectations.
Great Expectations renders Expectations to clean, human-readable documentation called Data Docs. These HTML docs contain both your Expectation Suites as well as your data validation results each time validation is run -- think of it as a continuously updated data quality report.
Creating Expectation Suites with ydata-profiling
An Expectation Suite is simply a set of Expectations. You can create Expectation Suites by writing out individual statements, such as the one above, or by automatically generating them based on profiler results.
ydata-profiling provides a simple
to_expectation_suite() method that
returns a Great Expectations
ExpectationSuite object which contains a
set of Expectations.
Pre-requisites: In order to run the
you will need to install Great Expectations with
pip install great_expectations
If you would like to use the additional features such as saving the
Suite and building Data Docs, you will also need to configure a Great
Expectations Data Context by running
great_expectations init in your
This assumes that the
great_expectations Data Context directory is in
the same path where you run the script. In order to specify the
location of your Data Context, pass it in as an argument:
|Generate a suite of expectations
You can also configure each feature individually in the function call:
See the Great Expectations Examples for complete examples.