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.

この演習はコースの一部です

Designing Forecasting Pipelines for Production

コースを見る

演習の手順

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

実践的なインタラクティブ演習

このサンプルコードを完成させて、この演習に挑戦してみましょう。

# 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)

コードを編集して実行