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Time Series Completeness — Exact Row Deduplication + Date by Category Pivot
x402
Time Series Completeness — 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 2026-10-24).
Deduplicates exact rows and generates a date-by-category pivot table from time series completeness records containing observed and expected data points.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Exact row deduplication + Date by category pivot: Exact row deduplication + Date by category pivot for supplied time series completeness records (observed_points, expected_points). 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 analytical reports: (1) an exact row deduplication report identifying and removing duplicate records from the supplied rows, and (2) a date-by-category pivot table showing the selected metric (observed_points or expected_points) broken down by date and dataset_group. Up to 200 rows are processed per call.
Example use cases
Audit pipeline completeness by group
“I have a batch of 120 completeness records across dataset groups like 'sensor-a' and 'sensor-b' with dates and observed vs expected point counts — can you deduplicate the exact rows and give me a pivot of observed_points by date and category so I can see gaps at a glance?”
Clean duplicate ingestion logs
“My ingestion log has 80 rows of completeness data and I think some records got duplicated during the ETL — run the exact row deduplication report and also pivot the expected_points by date and dataset_group so I can verify coverage.”
Cross-dataset completeness comparison
“I've got completeness records from two datasets merged into one table, about 150 rows with observed_points and expected_points per group and date — deduplicate the exact rows and build a date-by-category pivot on observed_points so I can compare completeness across groups over time.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-052?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both exact row deduplication and a date-by-category pivot on time series completeness data in a single low-cost call. It is ideal for data quality workflows where you need to clean duplicates and summarize observed vs expected point counts across groups and dates, without requiring live lookups, predictions, or external data sources. Best suited for datasets of up to 200 rows processed offline.
Side effects
- Input exceeds 200 rows — request rejected with validation error
- Missing required fields (row_id, date, dataset_group, observed_points, expected_points) cause row-level errors
- Invalid date format (not YYYY-MM-DD) triggers schema validation failure
- Null values for observed_points or expected_points may affect pivot aggregation
- Field enum value other than 'observed_points' or 'expected_points' causes validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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