Bir Expectation'ı Sil

Bu egzersizde, bir Expectation'ı bir Expectation Suite'inden silmeyi pratik yapacaksın. Aşağıdaki değişkenler zaten atanmış durumda:

expectation: silinecek Expectation
suite: expectationı içeren Expectation Suite
validation_definition: Expectation Suite için Validation Definition
dataframe: Validation Definition üzerinden çalıştırılacak pandas DataFrame'i

Great Expectations ve pandas sırasıyla gx ve pd olarak kullanılabilir.

Bu egzersiz, kursun bir parçasıdır

Great Expectations ile Veri Kalitesine Giriş

Kursa Göz Atın

Egzersiz talimatları

expectation adlı Expectation'ı Expectation Suite'inden sil.
Değişikliklerini Expectation Suite'e kaydet.
Validation Definition'ı yeniden çalıştır.

Uygulamalı etkileşimli egzersiz

Bu egzersizi bu örnek kodu tamamlayarak deneyin.

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

# Save changes
suite.____()

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

Kodu Düzenle ve Çalıştır

Bu egzersiz, kursun bir parçasıdır

Great Expectations ile Veri Kalitesine Giriş

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başla

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: Doğrulama Tanımlarını Dağıt Exercise 2: Kavram Kontrolü Exercise 3: Bir Checkpoint Oluştur Exercise 4: Expectation Suite'lerini Güncelle Exercise 5: Bir Expectation'ı Kopyala Exercise 6: Bir Expectation'ı Sil

Geçerli egzersiz

Exercise 7: Bir Expectation'ı Güncelle Exercise 8: Bileşenleri Yönet Exercise 9: Bileşen ekle ve listele Exercise 10: Bir Checkpoint'i Yönet

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