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The great_expectations module is the root of the gx core library and contains shortcuts and convenience methods for starting a gx project in a python session This workflow is generally used when engaging in exploration of new data, or when building out a set of expectations to comprehensively describe the state that your data should conform to. The pandas library is used to ingest sample data for this example.

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Great expectations documentation learn everything you need to know about gx cloud and gx core The following are the available data context types: Great expectations (gx) is a framework for describing data using expressive tests and then validating that the data meets test criteria

Gx core is a python library that provides a programmatic interface to building and running data validation workflows using gx.

Create expectations with a python interpreter or a script and then use interactive feedback to validate them with batch data. This workflow is solely intended for interactively creating expectations and engaging in data exploration For further information on using an individual batch to test expectations see test an expectation. Expectations make implicit assumptions about your data explicit, and they provide a flexible, declarative language for describing expected behavior

They can help you better understand your data and help you improve data quality Prerequisites python version 3.9 to 3.13 An installation of gx core Learn about key great expectations (gx) core components and workflows

Use the gx core python library and provided sample data to create a data validation workflow.

To use great expectations (gx) you need to install python and the gx core python library Gx also recommends you set up a virtual environment for your gx python projects. It also contains your validation results and the metrics associated with them, and it provides access to those objects in python, along with other helper functions All scripts that utilize gx core should start with the creation of a data context