# Survey Response Weights — Adjacent Row Changes & Missing Value Fill

> Survey Response Weights — Adjacent Row Changes & 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 2026-11-02).

Analyzes survey response weight records to detect adjacent row changes and fill explicit missing values, producing two analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-economics-24-162?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-11-02
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/survey-response-weights-adjacent-row-changes-missing-value-fill-fe4fa244
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_s9iNA2tezooPMPdLMXsZX

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 survey-response-weights-adjacent-row-changes-missing-value-fill-fe4fa244 -d '<json body>'
```

Example prompt: I have 50 rows of survey data with response_value and sample_weight columns — some response_value entries are missing. Can you fill those missing values using median imputation and also show me where adjacent rows changed in the response_value field?

## When to prefer this

Choose this endpoint when you have a batch of up to 200 survey records with response_value and sample_weight columns and need both missing value imputation and adjacent row change detection in a single low-cost call ($0.01 USDC). Ideal for survey panel data cleaning pipelines where you want two complementary analytical reports without an external data connection or prediction model.

## Known failure modes

- More than 200 rows submitted — request rejected with validation error
- Invalid date format (not YYYY-MM-DD) causes schema validation failure
- Numeric values outside allowed range (±1,000,000,000,000) rejected
- Missing required fields (row_id, region, date) cause row-level errors
- Invalid enum value for field or method parameters causes validation error
- Empty rows array (minItems:1 not met) returns error

## How this service works

Survey response weights — Adjacent row changes + Explicit missing-value fill: Adjacent row changes + Explicit missing-value fill for supplied survey response weights records (response_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) an adjacent row changes report showing where the selected field (response_value or sample_weight) changed between consecutive rows, and (2) a missing value fill report showing which null entries were filled and what values were substituted using the chosen method (zero, mean, or median). Both reports operate on the same submitted batch of up to 200 rows.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/survey-response-weights-adjacent-row-changes-missing-value-fill-fe4fa244/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)