Inverti una Conditional Expectation

È importante ricordare che, come per la probabilità condizionata, le Conditional Expectations non sono necessariamente uguali alla loro inversa. Un'Expectation sulla Colonna A condizionata alla Colonna B può avere esito positivo, ma la sua inversa potrebbe non riuscire. Esploriamolo in questo esercizio.

Un Batch collegato al dataset Shein Footwear è già stato creato e assegnato alla variabile batch. Great Expectations e pandas sono disponibili rispettivamente come gx e pd.

Questo esercizio fa parte del corso

Introduzione alla Data Quality con Great Expectations

Visualizza il corso

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Establish Conditional Expectation
expectation = gx.expectations.ExpectColumnValuesToBeInSet(
    column=____,
    value_set=____,
    condition_parser='pandas',
    row_condition='____.isnull()',
)

# Run Expectation
results = batch.____(____=____)

# Print success status
print(results.success)

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: 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: Aspettative di base sulle colonne Exercise 2: Definisci Expectations a livello di riga Exercise 3: Definisci Expectations a livello aggregato Exercise 4: Aspettative specifiche per tipo Exercise 5: Definisci Expectations numeriche Exercise 6: Definisci Expectations per stringhe Exercise 7: Expectations condizionali Exercise 8: Scrivi una Conditional Expectation Exercise 9: Inverti una Conditional Expectation

Esercizio in corso

Exercise 10: Riepilogo