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Null Coverage Grouped Numeric Summary with IQR Outlier Flags
x402
Null Coverage Grouped Numeric Summary with IQR Outlier Flags is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-16).
Computes grouped numeric summary statistics and IQR-based outlier flags for null coverage data (missing_values, total_values) across up to 200 rows, producing 2 independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Null coverage — Grouped numeric summary + IQR outlier flags: Grouped numeric summary + IQR outlier flags 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) a grouped numeric summary with statistics (mean, median, min, max, std dev, etc.) segmented by dataset_group for the selected field, and (2) IQR-based outlier flags identifying which rows fall beyond 1.5× the interquartile range. Covers up to 200 input rows with no external data lookups.
Example use cases
Data quality audit by dataset group
“I have up to 150 rows of null coverage data with dataset groups like 'sales', 'inventory', and 'orders', each with a missing_values and total_values count per date. Can you group them and give me summary statistics plus flag any IQR outliers in the missing_values field?”
Detecting anomalous completeness spikes
“My pipeline tracks total_values and missing_values daily across several dataset groups. Can you analyze these records and flag which rows are outliers based on IQR for total_values, so I can see where completeness drops unexpectedly?”
Comparing null rates across two dataset groups
“I've got 80 rows of null coverage records split between 'group-a' and 'group-b' with dates from January 2026 and missing_values counts for each. Please give me a grouped numeric summary and highlight any IQR outliers in the missing_values column.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-03-094?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have pre-collected null coverage or data quality records (missing_values and total_values) that need grouped summary statistics and IQR-based outlier detection in a single call. It is ideal for offline or batch data quality analysis where you supply your own data rather than fetching live datasets. Prefer this over generic statistics endpoints when you specifically need group-aware summaries alongside outlier flagging for completeness metrics.
Side effects
- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, dataset_group, date, missing_values, total_values) — validation error
- Date field does not match YYYY-MM-DD pattern — schema validation failure
- Numeric values outside allowed range (-1,000,000,000,000 to 1,000,000,000,000) — rejected
- Empty rows array — no data to analyze
- Invalid field enum value (not 'missing_values' or 'total_values') — error response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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