# Null Coverage Adjacent Row Changes and Explicit Missing-Value Fill

> Null Coverage Adjacent Row Changes and 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 2026-10-18).

Analyzes null coverage data by computing adjacent row-to-row changes and filling missing values using zero, mean, or median imputation across up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-03-162?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-18
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/null-coverage-adjacent-row-changes-and-explicit-missing-value-fill-9264495a
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_z7d5p_xCcrDzYgO7IreOx

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 null-coverage-adjacent-row-changes-and-explicit-missing-value-fill-9264495a -d '<json body>'
```

Example prompt: I have a null coverage dataset with up to 50 rows — each has a row_id, dataset_group, date, missing_values, and total_values — can you run two reports on it: one showing adjacent row-to-row changes and one filling the nulls in missing_values using median imputation?

## When to prefer this

Choose this endpoint when you need a combined null coverage analysis that simultaneously shows row-to-row changes and fills missing values in a single low-cost call ($0.01 USDC). Ideal for lightweight data quality pipelines where you want offline statistical imputation (zero, mean, or median) alongside temporal delta tracking without hitting external data sources or prediction APIs.

## Known failure modes

- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, date, missing_values, total_values) cause validation errors
- Invalid date format (not YYYY-MM-DD) causes schema rejection
- Numeric values outside allowed range (-1e12 to 1e12) rejected
- Invalid enum value for field or method parameter
- Empty rows array (minItems:1 violated) causes error

## How this service works

Null coverage — Adjacent row changes + Explicit missing-value fill: Adjacent row changes + Explicit missing-value fill for supplied null coverage records (missing_values, total_values). 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 the delta in the selected field between consecutive rows, and (2) an explicit missing-value fill report where nulls in the chosen field are replaced using the specified method (zero, mean, or median). Both reports are computed locally without external lookups, covering up to 200 input rows.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/null-coverage-adjacent-row-changes-and-explicit-missing-value-fill-9264495a/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)