Establish a Column Range Expectation

In this exercise, you'll be writing an Expectation regarding the column count of the Renewable Power Generation dataset. Suppose the number of columns can change, but it should always stay in an interval around 16.

The Renewable Power Generation dataset has already been loaded into a Batch, assigned to the variable batch. Great Expectations and pandas are available as gx and pd, respectively.

This exercise is part of the course

Introduction to Data Quality with Great Expectations

Exercise instructions

Write an Expectation that the column count of the dataset should be around 16, plus or minus 2.
Validate your Expectation.
Print out the success status of the Expectation, as well as the actual column count.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Create the Expectation
col_count_expectation = gx.expectations.____(
  min_value=____, ____=____
)

# Validate the Expectation
validation_results = batch.____(
  expect=____
)

# Print out the success status
print(____.____)

# Print out the actual column count
print(____.____)

Edit and Run Code

This exercise is part of the course

Introduction to Data Quality with Great Expectations

IntermediateSkill Level

4.8+

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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.

Exercise 1: Create a Data Context Exercise 2: Create your Data Context Exercise 3: Sneak Peek into GX Exercise 4: Keyword Definitions Exercise 5: Connect to Data Exercise 6: Concept Review Exercise 7: Create a Data Source and Data Asset Exercise 8: Read Data in Batches Exercise 9: Create a Batch Definition and Batch Exercise 10: Inspect your Batch

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.

Exercise 1: Create Expectations Exercise 2: Establish a Column Count Expectation Exercise 3: Evaluate Validation Results Exercise 4: Schema Expectations Exercise 5: Establish a Column Name Expectation Exercise 6: Establish a Column Range Expectation

Current Exercise

Exercise 7: Create a Suite of Expectations Exercise 8: Create an Expectation Suite Exercise 9: Validate your Expectation Suite Exercise 10: Validate Expectation Suites Exercise 11: Create and Run a Validation Definition Exercise 12: Validation Definition vs. Batch Definition

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.

Exercise 1: Deploy Validation Definitions Exercise 2: Concept Check Exercise 3: Create a Checkpoint Exercise 4: Update Expectation Suites Exercise 5: Copy an Expectation Exercise 6: Delete an Expectation Exercise 7: Update an Expectation Exercise 8: Manage Components Exercise 9: Add and list components Exercise 10: Manage a Checkpoint

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.

Exercise 1: Basic Column Expectations Exercise 2: Establish row-level Expectations Exercise 3: Establish aggregate-level Expectations Exercise 4: Type-Specific Expectations Exercise 5: Establish numeric Expectations Exercise 6: Establish string Expectations Exercise 7: Conditional Expectations Exercise 8: Write up a Conditional Expectation Exercise 9: Invert a Conditional Expectation Exercise 10: Wrap-Up