Performing data validation

Now that you've defined the schema, it's time to perform data validation. In this exercise, you'll create validation rules to ensure data quality and check for common issues like duplicates and null values.

The table_schema from the previous exercise is preloaded for you, along with the ts DataFrame and pointblank library.

This exercise is part of the course

Designing Forecasting Pipelines for Production

View Course

Exercise instructions

Define validation using the right method and passing the ts DataFrame.
Set up validation rules with the table_schema and check for duplicates.
Print the validation report.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Define the validation
validation = (____.____(data=____,
tbl_name="US48 Data Validation",
label="Data Refresh",
thresholds=pb.Thresholds(warning=0.2, error=0, critical=0.1))
             
    # Set up the validation rules
    .col_schema_match(schema=____)
    .col_vals_gt(columns="value", value=0)
    .col_vals_in_set(columns="respondent", set = ["US48"])
    .col_vals_in_set(columns="type", set = ["D"])
    .col_vals_not_null(columns=["period", "value"])
    .____()
    .interrogate())

# Print the validation report
print(validation.____())

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