# Numeric Precision Comparisons — Base-100 Series + Explicit Missing-Value Fill

> Numeric Precision Comparisons — Base-100 Series + 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).

Analyzes supplied numeric pairs (source_value, transformed_value) with base-100 indexing and explicit missing-value fill, producing two analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-20-169?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-base-100-series-explicit-missing-value-b8ce1e9a
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_qPYIlw6cT3C342_q58qht

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-base-100-series-explicit-missing-value-b8ce1e9a -d '<json body>'
```

Example prompt: I have a dataset of 50 rows with source_value and transformed_value columns grouped into 'group-a' and 'group-b' — can you run a base-100 series anchored at row index 0 and fill any missing source_value entries using median, then give me both analytical reports?

## When to prefer this

Choose this endpoint when you need to (1) index a numeric time series to a base-100 scale from a specific anchor row, and/or (2) explicitly fill missing values in numeric columns using zero, mean, or median — and want both analyses in a single $0.01 call on up to 200 rows of supplied data. It requires no external data access and is best suited for offline or batch data quality and normalization workflows where you already have the source and transformed value pairs.

## Known failure modes

- Rows array exceeds 200 items — request rejected
- Invalid date format (not YYYY-MM-DD) — validation error
- base_index out of bounds for the supplied row count — error
- source_value or transformed_value outside ±1,000,000,000,000 — rejected
- Empty rows array — validation error
- Invalid method enum value (not zero/mean/median) — error

## How this service works

Numeric precision comparisons — Base-100 series + Explicit missing-value fill: Base-100 series + 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

Returns two independent analytical reports: (1) a base-100 indexed series showing how source_value and/or transformed_value evolve relative to a chosen baseline row, and (2) an explicit missing-value fill report indicating which rows had nulls and what values were substituted (using zero, mean, or median). Both reports are computed locally from the supplied data with no external lookups.

## 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"
  },
  "base_index": {
   "type": "integer",
   "default": 0,
   "maximum": 199,
   "minimum": 0
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/numeric-precision-comparisons-base-100-series-explicit-missing-value-b8ce1e9a/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)