# Sampling Observations — Adjacent Row Changes + Explicit Missing-Value Fill

> Sampling Observations — Adjacent Row Changes + 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 adjacent row changes and fills missing values (zero, mean, or median) for up to 200 rows of weighted sampling observation data, returning 2 analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-15-162?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-adjacent-row-changes-explicit-missing-value-fill-30e30eb4
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_IclS8wx8lps8tElR9PUv7

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-adjacent-row-changes-explicit-missing-value-fill-30e30eb4 -d '<json body>'
```

Example prompt: I have a dataset of 150 sampling observations with dates, sample values, and sample weights — some values are null. Can you compute the adjacent row changes and fill the missing sample_value entries using the median method?

## When to prefer this

Choose this endpoint when you have up to 200 rows of weighted sampling observations and need both adjacent row-change detection and missing-value imputation in a single call. Ideal for survey data, sensor readings, or longitudinal studies where nulls need filling and sequential deltas matter. Prefer this over general-purpose analytics tools when you want a low-cost ($0.01), self-contained computation that does not require any external data or live lookups.

## Known failure modes

- More than 200 rows submitted — request rejected due to maxItems constraint
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- sample_value or sample_weight out of allowed range (±1 trillion) — validation error
- Empty rows array — minItems:1 violation
- Unrecognized field or method enum value — schema rejection

## How this service works

Sampling observations — Adjacent row changes + Explicit missing-value fill: Adjacent row changes + 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

Two analytical reports: (1) adjacent row changes showing the delta between consecutive rows for the selected field, and (2) missing-value fill results where null entries are replaced using the chosen method (zero, mean, or median). Both reports are computed locally from 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-adjacent-row-changes-explicit-missing-value-fill-30e30eb4/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)