# Data Reconciliation – Numeric Distribution + Explicit Missing-Value Fill

> Data Reconciliation – Numeric Distribution + 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 2026-10-19).

Analyzes up to 200 data reconciliation rows (source_total vs target_total) by producing a numeric distribution report and filling missing values in a chosen field using zero, mean, or median imputation.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-085?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-19
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/data-reconciliation-numeric-distribution-explicit-missing-value-fill-2e4428cf
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_6MxWjnd91lrZ8_rcQXQRG

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 data-reconciliation-numeric-distribution-explicit-missing-value-fill-2e4428cf -d '<json body>'
```

Example prompt: Can you run a numeric distribution report and fill any missing source_total values using median imputation on my reconciliation dataset? I have rows with row_id, dataset_group, date, source_total, and target_total — up to 50 rows across group-a and group-b.

## When to prefer this

Choose this endpoint when you need a combined numeric distribution analysis and missing-value imputation for a batch of up to 200 reconciliation records with source and target totals. It is ideal for data quality pipelines, finance reconciliation audits, and ETL validation steps where you want both descriptive statistics and cleaned data in a single call. It is not suitable when you need live data fetching, predictive modeling, or more than 200 rows processed at once.

## Known failure modes

- More than 200 rows submitted — exceeds maxItems limit, request will be rejected
- Missing required fields (row_id, dataset_group, date, source_total, target_total) on any row — schema validation failure
- Date fields not matching YYYY-MM-DD pattern — schema validation error
- Numeric values outside ±1,000,000,000,000 range — schema validation failure
- Invalid method enum value (anything other than zero, mean, median) — rejected
- Invalid field enum value (anything other than source_total, target_total) — rejected
- Empty rows array — minItems constraint violation

## How this service works

Data reconciliation — Numeric distribution + Explicit missing-value fill: Numeric distribution + Explicit missing-value fill for supplied data reconciliation records (source_total, target_total). 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 numeric distribution summary of the selected field (source_total or target_total) across up to 200 reconciliation rows, and (2) a version of the dataset with missing values in the chosen field explicitly filled using the selected imputation method (zero, mean, or median). No external lookups or predictions are made — all computation is local.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-reconciliation-numeric-distribution-explicit-missing-value-fill-2e4428cf/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)