# Promotion Observations — String Normalisation + Explicit Missing-Value Fill

> Promotion Observations — String Normalisation + 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 2026-10-24).

Normalises strings and fills missing values (zero, mean, or median) for promotion observation records containing discount_amount and units_sold, producing 2 analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-retail-05-037?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-24
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/promotion-observations-string-normalisation-explicit-missing-value-fill-a8851622
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_g-0ojBoZAbCR-VKEMNKmI

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 promotion-observations-string-normalisation-explicit-missing-value-fill-a8851622 -d '<json body>'
```

Example prompt: Clean up my promotion observations data — I have 150 rows with store_group, date, discount_amount, and units_sold fields, some discount_amount values are missing. Fill the missing discount_amount values using median imputation and normalise the strings, then give me the 2 reports.

## When to prefer this

Use this endpoint when you have promotion observation records (with discount_amount and units_sold) that need string normalisation and missing-value imputation in a single call, and you want two separate analytical reports for up to 200 rows at low cost. Prefer this over general-purpose data cleaning services when your data fits the retail promotion schema (store_group, date, discount_amount, units_sold) and you need both normalisation and fill operations reported together without sending data to external predictive or ML services.

## Known failure modes

- More than 200 rows submitted — request rejected with validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Missing required fields (row_id, store_group, date) — rejected
- Field enum value not in [discount_amount, units_sold] — rejected
- Method enum value not in [zero, mean, median] — rejected
- Numeric values outside allowed range (-1e12 to 1e12) — validation error

## How this service works

Promotion observations — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill for supplied promotion observations records (discount_amount, units_sold). 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 covering the submitted promotion observation rows: one reflecting string-normalised records and one with explicit missing-value fills applied to the chosen field (discount_amount or units_sold) using the selected method (zero, mean, or median). Covers up to 200 input rows with no external lookups or predictions.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/promotion-observations-string-normalisation-explicit-missing-value-fill-a8851622/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)