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.

Questo esercizio fa parte del corso

Designing Forecasting Pipelines for Production

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Istruzioni dell'esercizio

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

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# 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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