Defining the schema

Let's start by defining the expected schema for data validation. This is a critical step in ensuring data quality throughout the ETL pipeline.

You'll use the pointblank library to define the schema structure.

The dataset has already been loaded for you as ts.

이 연습은 강의의 일부입니다

Designing Forecasting Pipelines for Production

강의 보기

연습 안내

Start by importing pointblank.
Define the schema using the right method.
Set the respondent column to object type and value column to float64 type.

실습형 인터랙티브 연습

이 예제를 이 샘플 코드를 완성하여 풀어보세요.

# Import the required library
import ____ as ____

# Define the schema and set columns
table_schema =  pb.____(
    columns=[
        ("period", "datetime64[ns]"),   
        ("respondent", "____"),
        ("respondent-name", "object"),
        ("type", "object"),
        ("type-name", "object"),
        ("value", "____"),
        ("value-units", "object")])

print(table_schema)

코드 편집 및 실행