# Numeric Precision Comparisons: Adjacent Row Changes + Explicit Missing-Value Fill

> Numeric Precision Comparisons: 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/median) for up to 200 rows of source_value/transformed_value numeric data, returning 2 analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-20-162?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/numeric-precision-comparisons-adjacent-row-changes-explicit-missing-81129801
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_NuAS92k5xYIdYz9XbbbUl

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 numeric-precision-comparisons-adjacent-row-changes-explicit-missing-81129801 -d '<json body>'
```

Example prompt: I have a dataset of up to 50 rows with source_value and transformed_value columns — can you compute the adjacent row changes and fill any missing source_value entries using median imputation?

## When to prefer this

Choose this endpoint when you need both adjacent row delta computation and missing-value imputation in a single call on a small-to-medium dataset (≤200 rows) without any external lookups or live data. Ideal for batch data quality workflows, ETL validation, or offline numeric analysis where you supply the full dataset and want two complementary reports cheaply ($0.01 USDC per call).

## Known failure modes

- More than 200 rows submitted — request rejected or truncated
- Invalid date format (not YYYY-MM-DD) causes row-level validation error
- Values outside ±1,000,000,000,000 range cause schema rejection
- Missing required fields (row_id, dataset_group, date) cause validation failure
- Invalid fill method enum value causes request rejection

## How this service works

Numeric precision comparisons — Adjacent row changes + Explicit missing-value fill: Adjacent row changes + Explicit missing-value fill for supplied numeric precision comparisons records (source_value, transformed_value). 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 change report showing delta between each consecutive row for the specified field, and (2) a missing-value fill report with nulls replaced using the chosen method (zero, mean, or median). Covers up to 200 rows per call.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/numeric-precision-comparisons-adjacent-row-changes-explicit-missing-81129801/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)