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Null Coverage Analytics: Exact Row Deduplication + Date-by-Category Pivot
x402
Null Coverage Analytics: Exact Row Deduplication + Date-by-Category Pivot is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-18).
Analyzes up to 200 null-coverage records by performing exact row deduplication and a date-by-category pivot on missing_values or total_values fields, returning two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Null coverage — Exact row deduplication + Date by category pivot: Exact row deduplication + Date by category pivot for supplied null coverage records (missing_values, total_values). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Returns two independent analytical reports: (1) an exact row deduplication report identifying and removing duplicate records from the submitted null coverage rows, and (2) a date-by-category pivot table summarizing the chosen field (missing_values or total_values) broken down by date and dataset_group — all computed locally with no external data lookups.
Example use cases
Data quality audit across pipeline stages
“I have 150 rows of null coverage records from my data pipeline, each tagged with a dataset_group and date. Can you deduplicate any exact duplicates and then pivot the missing_values counts by date and group so I can see where nulls are clustering?”
Monthly null rate trend by team
“Here are our team's daily null coverage metrics for October — each row has a team name as dataset_group, a date, and missing_values plus total_values. Please deduplicate the rows and give me a pivot table showing missing_values by date and team.”
Validating data completeness before ML training
“Before I train my model, I need to check null coverage in my feature dataset. I have 80 records with feature_group, date, missing_values, and total_values. Can you run exact row deduplication and then pivot total_values by date and feature_group to see where coverage is thin?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-03-052?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both exact row deduplication and a date-by-category pivot on null coverage data (missing_values and total_values) in a single low-cost call. Ideal for data quality workflows where you want to clean duplicate records and immediately summarize null rates across groups and dates without any external API calls, predictions, or live data lookups. Best suited for datasets up to 200 rows.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, dataset_group, date, missing_values, total_values) cause schema rejection
- Invalid date format (not YYYY-MM-DD) triggers pattern validation failure
- Numeric values outside ±1,000,000,000,000 are rejected
- Empty rows array is rejected (minItems: 1)
- Invalid field enum value (not 'missing_values' or 'total_values') causes an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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