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.

Este exercício faz parte do curso

Designing Forecasting Pipelines for Production

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Instruções do exercício

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

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

# 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)

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