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  3. Introduction to Data Quality with Great Expectations
  • 1

    Connecting to Data

    Free

    Understand why Great Expectations (GX) is such a powerful tool for monitoring data quality. Get familiar with the basics of GX, including how to start a session using a Data Context, and how to load in a pandas dataframe using a Data Source, Data Asset, and Batch Definition.

  • 2

    Establishing Expectations

    Create and evaluate basic shape and schema Expectations. Validate your Expectations either individually, as part of an Expectation Suite with a Batch Definition, or using a Validation Definition.

  • 3

    GX in Practice

    Learn practical skills that will help you dominate the dynamic nature of Expectations in the real world. Deploy Validation Definitions using Checkpoints; update your Expectation Suites; and learn how to add, retrieve, list, and delete key GX components.

  • 4

    All About Expectations

    Dive head-first into the world of Expectations. Practice creating basic column Expectations, row- and aggregate-level numeric Expectations, string and string parseability Expectations, and more. Learn how to apply Expectations to only some rows of a dataframe.

Initializing

Exercise

Copy an Expectation

In this exercise, you'll practice copying an Expectation from one Expectation Suite to another, without producing an error. Two Expectation Suites have already been created for you: one called suite and one called new_suite. The Expectation you will copy has been assigned to the variable expectation. Great Expectations and pandas are available as gx and pd, respectively.

Instructions 1/2

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  • Make a copy of the Expectation expectation
  • Set the ID of the copy to None.