Eliminare un'Expectation

In questo esercizio metterai in pratica come eliminare un'Expectation da una Expectation Suite. Le seguenti variabili sono già state assegnate:

expectation: l'Expectation da eliminare
suite: la Expectation Suite che contiene expectation
validation_definition: la Validation Definition per la Expectation Suite
dataframe: il DataFrame pandas da eseguire tramite la Validation Definition

Great Expectations e pandas sono disponibili rispettivamente come gx e pd.

Questo esercizio fa parte del corso

Introduzione alla Data Quality con Great Expectations

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Istruzioni dell'esercizio

Elimina l'Expectation expectation dalla Expectation Suite.
Salva le modifiche alla Expectation Suite.
Esegui di nuovo la Validation Definition.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Delete the Expectation
suite.____(
  expectation=expectation
)

# Save changes
suite.____()

# Run the Validation Definition
validation_definition.run(
  batch_parameters={____: ____}
)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Introduzione alla Data Quality con Great Expectations

IntermediárioNível de habilidade

4.8+

Inizia il corso gratis

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 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: Distribuisci le Validation Definitions Exercise 2: Verifica dei concetti Exercise 3: Crea un Checkpoint Exercise 4: Aggiorna le Expectation Suite Exercise 5: Copia un'Expectation Exercise 6: Eliminare un'Expectation

Esercizio in corso

Exercise 7: Aggiorna una Expectation Exercise 8: Gestisci i componenti Exercise 9: Aggiungi e elenca i componenti Exercise 10: Gestisci un 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