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Null Coverage IQR Outlier Flags and Missing Value Fill
x402
Null Coverage IQR Outlier Flags and Missing Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-14).
Detects IQR-based outliers and fills missing values in null coverage records (missing_values, total_values) for up to 200 rows, returning two analytical reports per call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Null coverage — IQR outlier flags + Explicit missing-value fill: IQR outlier flags + Explicit missing-value fill 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
Two analytical reports per call: (1) an IQR-based outlier flag report identifying which rows have anomalous missing_values or total_values, and (2) a missing-value fill report with imputed values using the chosen method (zero, mean, or median) for the specified field, covering up to 200 input rows.
Example use cases
Outlier detection in pipeline data quality logs
“I have 80 rows of daily data quality records from our ETL pipeline with row_id, group name, date, missing_values, and total_values columns — flag any IQR outliers and fill missing entries using median on the missing_values field.”
Impute missing total_values before dashboard reporting
“Before I publish our monthly data completeness dashboard, can you fill in any null total_values fields across these 120 records using mean imputation and also flag any outlier rows based on IQR?”
Audit dataset group null rates for data governance
“I'm auditing null coverage across five dataset groups — here are 150 rows with date, group, missing_values, and total_values. Run IQR outlier flags and fill missing_values gaps with zero so I can see which groups look anomalous.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-03-180?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need both IQR-based outlier detection and missing value imputation on null coverage metrics (missing_values, total_values) in a single low-cost call. It is ideal for data quality monitoring pipelines, pre-reporting audits, and dataset governance workflows where you have up to 200 rows of structured null coverage records and want two independent analytical reports without external lookups or model inference.
Side effects
- More than 200 rows submitted — request rejected or truncated
- Missing required fields (row_id, date, missing_values, total_values) in any row — validation error
- Date field not in YYYY-MM-DD format — schema validation failure
- Invalid method enum value (not zero/mean/median) — rejected
- Invalid field enum value (not missing_values/total_values) — rejected
- Numeric values outside allowed range (-1T to 1T) — validation error
- Empty rows array — minimum items constraint violation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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