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Feature Availability Analytics: Numeric Distribution + Missing Value Fill
x402
Feature Availability Analytics: Numeric Distribution + 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 2026-10-20).
Computes numeric distribution statistics and fills missing values (zero, mean, or median) for feature availability records across up to 200 rows, producing two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Numeric distribution + Explicit missing-value fill: Numeric distribution + Explicit missing-value fill for supplied feature availability records (populated_features, expected_features). 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 numeric distribution summary (e.g. min, max, mean, median, percentiles) for the selected field across submitted rows, and (2) a missing-value fill report showing the original and imputed values using the chosen method (zero, mean, or median) — all computed locally without external lookups.
Example use cases
Feature coverage audit across dataset groups
“I have 120 rows of feature availability records with row_id, date, dataset_group, populated_features, and expected_features columns — can you give me the numeric distribution of populated_features and fill any nulls using the mean?”
Missing value imputation for ML pipeline prep
“Before I train my model, I need to fill in the missing expected_features values in my dataset using zero-fill. I have up to 200 rows with row_id, date, dataset_group, and feature counts — can you run the missing value fill and distribution report on expected_features?”
Daily feature availability trend QA
“I'm doing a data quality check on 80 rows of daily feature availability logs — can you compute the numeric distribution of populated_features and replace any null entries with the median value? Each row has a date, row_id, dataset_group, and both populated and expected feature counts.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-085?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a numeric distribution summary and missing-value imputation in a single call for feature availability data (populated_features / expected_features). It is best suited for small-to-medium datasets (up to 200 rows) where you want offline, deterministic statistical analysis without live data lookups or external ML inference. Prefer it over general-purpose analytics APIs when cost predictability matters ($0.01 USDC flat for both reports).
Side effects
- Input array is empty or exceeds 200 rows — returns validation error
- Invalid date format (not YYYY-MM-DD) — rejected by schema pattern validation
- Null or missing row_id — rejected as required field
- Invalid method enum value (not zero/mean/median) — schema rejection
- Invalid field enum (not populated_features/expected_features) — schema rejection
- Payment failure via x402 protocol — no report returned
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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