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.

이 연습은 강의의 일부입니다

Designing Forecasting Pipelines for Production

강의 보기

연습 안내

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.

실습형 인터랙티브 연습

이 예제를 이 샘플 코드를 완성하여 풀어보세요.

# Define the validation
validation = (pb.____(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.____())

코드 편집 및 실행