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Time Series Completeness — Exact Row Deduplication + Equal-Width Histogram
x402
Time Series Completeness — Exact Row Deduplication + Equal-Width Histogram is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).
Deduplicates rows and computes an equal-width histogram over observed_points or expected_points fields in time series completeness data (up to 200 rows, two analytical reports per call).
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Exact row deduplication + Equal-width histogram: Exact row deduplication + Equal-width histogram 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) a deduplicated version of the submitted rows with exact duplicates removed, and (2) an equal-width histogram showing the distribution of the selected field (observed_points or expected_points) across the specified number of bins, with counts per bin. All analysis is performed locally on the submitted data with no external lookups.
Example use cases
Sensor feed completeness audit
“I have 120 rows of daily sensor completeness data with observed_points and expected_points — can you deduplicate them and show me an equal-width histogram of observed_points across 8 bins so I can see where data gaps cluster?”
ETL pipeline completeness distribution
“We ran our ETL pipeline for 90 days and logged observed vs expected data points per dataset group each day. Can you deduplicate any repeated rows and give me a 5-bin histogram of expected_points to understand how evenly the pipeline loaded data?”
Multi-group time series quality check
“I've got completeness records across two dataset groups — group-a and group-b — for the past 60 days. Please deduplicate the rows and generate an equal-width histogram on observed_points using 15 bins, so I can spot which completeness ranges are most common.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-048?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both exact duplicate row removal and a histogram distribution report on time series completeness data in a single low-cost call. It is purpose-built for completeness records (observed vs expected points per date/group), handles up to 200 rows, and returns two structured analytical outputs simultaneously. Prefer this over generic statistics tools when your data is already structured with row_id, dataset_group, date, observed_points, and expected_points fields and you need reproducible, offline analysis without external data lookups.
Side effects
- Rows exceeding 200 items will be rejected — the endpoint enforces a maxItems:200 limit
- Invalid date format (not YYYY-MM-DD) will fail schema validation
- Missing required fields (row_id, date, observed_points, expected_points) will cause rejection
- bins value outside 1–30 range will be rejected
- Null values in observed_points or expected_points may affect histogram computation
- Submitting an empty rows array will fail minItems:1 validation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| bins | integer | |
| rows | array | |
| field | string |
Tags
x402
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