Membuat Expectation Jumlah Kolom

Pada video sebelumnya, kita mempelajari ExpectTableRowCountToEqual(value: int) untuk memeriksa jumlah baris tabel. Seperti yang ditunjukkan di akhir video, Great Expectations juga memiliki Expectation bernama ExpectTableColumnCountToEqual(value: int). Kelas ini sama persis, tetapi diterapkan pada jumlah kolom, bukan jumlah baris. Dalam latihan ini, Anda akan mengimplementasikan kelas tersebut untuk membuat dan memvalidasi Expectation jumlah kolom.

Anda akan menggunakan himpunan data Renewable Power Generation yang juga dibahas dalam video. Anda dapat mempelajari lebih lanjut tentang himpunan data tersebut di sini.

Himpunan data telah dimuat ke dalam sebuah Batch dan disimpan pada variabel batch. Great Expectations dan pandas tersedia masing-masing sebagai gx dan pd.

Latihan ini adalah bagian dari kursus

Pengantar Data Quality dengan Great Expectations

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.


# Create table column count Expectation
expectation = gx.expectations.____(
	value=____
)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Pengantar Data Quality dengan Great Expectations

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus 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: Membuat Expectation Exercise 2: Membuat Expectation Jumlah Kolom

Latihan Saat Ini

Exercise 3: Mengevaluasi Hasil Validasi Exercise 4: Ekspektasi Skema Exercise 5: Menyusun Expectation Nama Kolom Exercise 6: Menetapkan Expectation Rentang Jumlah Kolom Exercise 7: Membuat Satu Set Expectation Exercise 8: Membuat Expectation Suite Exercise 9: Validasikan Expectation Suite Anda Exercise 10: Validasi Expectation Suite Exercise 11: Membuat dan Menjalankan 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