# Sampling Observations — Grouped Numeric Summary + Explicit Missing-Value Fill

> Sampling Observations — Grouped Numeric Summary + Explicit 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 grouped numeric summary statistics and fills missing values (zero, mean, or median) for up to 200 sampling observation rows with sample_value and sample_weight fields, returning two analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-15-099?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-grouped-numeric-summary-explicit-missing-value-1cda2aa0
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_TpAcbulTRvX6H-QbFHZjW

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-grouped-numeric-summary-explicit-missing-value-1cda2aa0 -d '<json body>'
```

Example prompt: I have 150 sampling observation rows with sample_value and sample_weight fields — some sample_values are null. Can you group them by dataset_group, fill the missing sample_values using the median, and give me a grouped numeric summary?

## When to prefer this

Choose this endpoint when you have tabular sampling observation data with sample_value and sample_weight columns (up to 200 rows), need grouped numeric summaries by category, and want missing values imputed before aggregation — all in a single low-cost call. Prefer this over general-purpose analytics when the dataset fits the exact schema (row_id, dataset_group, date, sample_value, sample_weight) and you need both missing-value fill and grouped summary in one step without any live data fetching or external enrichment.

## Known failure modes

- More than 200 rows submitted — request will be rejected or truncated
- Invalid date format (not YYYY-MM-DD) causes row rejection
- Empty rows array returns an error
- sample_value or sample_weight values outside ±1,000,000,000,000 range are rejected
- Unknown fill method (not zero, mean, median) causes a validation error
- Missing required fields (row_id, dataset_group, date) cause row-level failures

## How this service works

Sampling observations — Grouped numeric summary + Explicit missing-value fill: Grouped numeric summary + 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 grouped numeric summary of sample_value/sample_weight statistics broken down by dataset_group, and (2) the dataset with missing values in the chosen field explicitly filled using the selected method (zero, mean, or median). All computation is performed locally on the supplied data 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-grouped-numeric-summary-explicit-missing-value-1cda2aa0/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)