# Feature Availability Analytics: Numeric Distribution + Missing Value Fill

> 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.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-085?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-20
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/feature-availability-analytics-numeric-distribution-missing-value-fill-929d6323
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_zBMy17ptROPiKt1hHHdrU

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability feature-availability-analytics-numeric-distribution-missing-value-fill-929d6323 -d '<json body>'
```

Example prompt: Can you analyze the numeric distribution of my populated_features data and fill in any missing values using median imputation? I have about 50 rows across two dataset groups — here's the data with row_id, date, dataset_group, populated_features, and expected_features fields.

## When to prefer this

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).

## Known failure modes

- 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

## 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.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "populated_features",
     "expected_features"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "expected_features": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "populated_features": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "dataset_group": "group-a",
     "expected_features": 1,
     "populated_features": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "dataset_group": "group-b",
     "expected_features": 2,
     "populated_features": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "dataset_group": "group-a",
     "expected_features": 3,
     "populated_features": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "dataset_group": "group-b",
     "expected_features": 4,
     "populated_features": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "populated_features",
    "expected_features"
   ],
   "type": "string",
   "default": "populated_features"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/feature-availability-analytics-numeric-distribution-missing-value-fill-929d6323/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)