# Data Reconciliation: Two-Measure Correlation + Explicit Missing-Value Fill

> Data Reconciliation: Two-Measure Correlation + 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).

Computes correlation between source_total and target_total columns and fills missing values using a chosen method (zero, mean, or median) across up to 200 reconciliation rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-184?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-two-measure-correlation-explicit-missing-value-fill-c38ddf06
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_q-Gu54bl4FFMgqn3FZcej

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-two-measure-correlation-explicit-missing-value-fill-c38ddf06 -d '<json body>'
```

Example prompt: I have 150 rows of reconciliation data with source_total and target_total columns — some target_total values are null. Can you compute the correlation between the two measures and fill the missing target_total values using the median of existing values?

## When to prefer this

Choose this endpoint when you have tabular reconciliation data with two numeric measures (source and target totals) and need both a correlation analysis and missing-value imputation in a single call. It is ideal for financial reconciliation, audit preparation, and data quality checks where you supply your own data and need fast, local statistical computation without any external data lookup or live prediction. Prefer this over general analytics tools when you specifically need the source/target reconciliation pattern and want both reports together at low cost ($0.01 USDC for up to 200 rows).

## Known failure modes

- More than 200 rows submitted — request will be rejected or truncated
- Invalid date format (must match YYYY-MM-DD pattern)
- Both source_total and target_total are entirely null — correlation cannot be computed
- Unrecognized fill method value outside enum (zero, mean, median)
- Row missing required fields (row_id, dataset_group, date) — validation error
- All values in the target field are null — mean/median fill has no basis value

## How this service works

Data reconciliation — Two-measure correlation + Explicit missing-value fill: Two-measure correlation + 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 per call: (1) a Pearson or equivalent correlation between source_total and target_total across the supplied rows, and (2) a version of the dataset with missing values in the specified field filled using the chosen method (zero, mean, or median). Results are computed locally with no external lookups or predictions.

## 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-two-measure-correlation-explicit-missing-value-fill-c38ddf06/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)