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Great Expectations installed in a Python environment. A Filesystem Data Context for your Expectations. Created a Data Source from which to request a Batch of data for introspection. Import the Great Expectations module and instantiate a Data Context. For this guide we will be working with Python code in a Jupyter Notebook.
Python version support. GX OSS supports Python 3.8 through 3.11 . Experimental support for Python 3.12 and later can be enabled by setting a GX_PYTHON_EXPERIMENTAL environment variable when installing great_expectations. Get started. GX recommends deploying GX OSS within a virtual environment.
6 days ago · Always know what to expect from your data. (See https://github.com/great-expectations/great_expectations for full description).
Use this quickstart to install GX OSS, connect to sample data, build your first Expectation, validate data, and review the validation results. This is a great place to start if you're new to GX OSS and aren't sure if it's the right solution for you or your organization.
Dec 27, 2015 · In this tutorial we'll have a look at Great Expectations, a tool written and configured in Python that aids you in keeping an eye on your data quality. It provides a batteries-included...
Jul 12, 2023 · Great Expectations is an open-source Python library that is specialized in solving three important aspects to manage data: validating data by verifying if it respects some important conditions or expectations; automating data profiling to test your data fastly without the need of starting from scratch
Great Expectations is a framework for defining Expectations and running them against your data. Like assertions in traditional Python unit tests, Expectations provide a flexible, declarative language for describing expected behavior. Unlike traditional unit tests, Great Expectations applies Expectations to data instead of code.
