# Sampling Observations Numeric Distribution and Missing-Value Fill

> Sampling Observations Numeric Distribution 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 2026-10-15).

Computes numeric distribution statistics and fills missing values (zero, mean, or median) for up to 200 weighted sampling observation rows, returning two analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-15-085?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/sampling-observations-numeric-distribution-and-missing-value-fill-08bb1dc4
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_0MFdUrv-4E2KJtZD9KvFl

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 sampling-observations-numeric-distribution-and-missing-value-fill-08bb1dc4 -d '<json body>'
```

Example prompt: I have 50 sampling observation rows with sample_value and sample_weight — can you compute the numeric distribution of sample_value and fill any missing values using the median?

## When to prefer this

Choose this endpoint when you have up to 200 rows of weighted sampling observations and need both a numeric distribution summary and explicit missing-value imputation in a single low-cost call ($0.01 USDC). It is ideal for lightweight, offline statistical analysis of sample data without requiring external data sources, predictions, or professional assessments. Prefer alternatives if you need more than 200 rows, real-time data enrichment, or more complex statistical modeling.

## Known failure modes

- Fewer than 1 or more than 200 rows provided — request rejected
- Invalid date format (not YYYY-MM-DD) — validation error
- sample_value or sample_weight outside allowed range (-1e12 to 1e12) — validation error
- Unknown fill method specified (not zero, mean, or median) — validation error
- All values in target field are null — mean/median fill may be undefined

## How this service works

Sampling observations — Numeric distribution + Explicit missing-value fill: Numeric distribution + Explicit missing-value fill for supplied sampling observations records (sample_value, sample_weight). 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 analytical reports: (1) a numeric distribution summary (e.g. min, max, mean, median, std dev, percentiles) for the specified field across all supplied rows, and (2) a version of the dataset with missing values in the target field explicitly filled using the chosen method (zero, mean, or median). Both reports are computed locally with no external lookups.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/sampling-observations-numeric-distribution-and-missing-value-fill-08bb1dc4/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)