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.

Den här övningen är en del av kursen

Designing Forecasting Pipelines for Production

Visa kurs

Övningsinstruktioner

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

Interaktiv övning med praktiskt arbete

Testa den här övningen genom att slutföra den här exempelkoden.

# 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)

Redigera och kör kod