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.

本练习是课程的一部分

Designing Forecasting Pipelines for Production

查看课程

练习说明

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.

交互式实操练习

通过完成这段示例代码来试试这个练习。

# 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.____())

编辑并运行代码