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Null Coverage String Normalisation and Missing-Value Fill
x402
Null Coverage String Normalisation 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 2026-10-16).
Normalises string fields and fills explicit missing values (zero, mean, or median) in null-coverage records containing missing_values and total_values columns, returning 2 analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Null coverage — String normalisation + Explicit missing-value fill: String normalisation + 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 covering the supplied null coverage rows (up to 200): one covering string normalisation applied to fields like dataset_group and date, and one covering explicit missing-value fill results for the chosen field (missing_values or total_values) using the selected method (zero, mean, or median), including imputed values and coverage statistics.
Example use cases
Median imputation for data quality audit
“I have 150 rows of dataset quality records with row_id, date, dataset_group, missing_values, and total_values — some missing_values entries are null. Can you fill those nulls using median imputation and give me a null coverage report?”
Zero-fill missing coverage for pipeline validation
“My data pipeline produces null coverage records across two groups and some total_values entries are missing. Fill the total_values column with zeros for all 80 rows and return two analytical reports so I can validate completeness.”
Mean imputation across grouped dataset records
“I have daily null coverage records across group-a and group-b for October 2026 — about 40 rows — and some missing_values fields are null. Use mean imputation on the missing_values field and give me the two summary reports.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-03-037?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have a bounded set of up to 200 null coverage records (with missing_values and total_values columns) and need both string normalisation and explicit missing-value imputation (zero, mean, or median) with two structured analytical reports, all processed locally without any external data lookup or live prediction. Prefer it over general-purpose data tools when you specifically need dual-report output on null coverage metrics per dataset group.
Side effects
- Row count exceeds 200 items — request rejected
- Required fields (row_id, date, missing_values, total_values) absent from one or more rows
- Date strings not matching YYYY-MM-DD pattern — validation error
- Numeric values outside allowed range (±1 trillion) — rejected
- Invalid method enum value (not zero/mean/median) — error
- Invalid field enum value (not missing_values/total_values) — error
- Empty rows array — minimum 1 item required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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