# Physics Scenes Data Cleaner — String Normalisation + Missing Value Fill

> Physics Scenes Data Cleaner — String Normalisation + 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 2026-11-02).

Normalises strings and fills missing values in physics simulation scene records (body_count, step_ms) across up to 200 rows, returning two analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-game-08-037?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-11-02
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/physics-scenes-data-cleaner-string-normalisation-missing-value-fill-c4949a77
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_N0r2mQSycGhZKHHZ0O3k0

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 physics-scenes-data-cleaner-string-normalisation-missing-value-fill-c4949a77 -d '<json body>'
```

Example prompt: Can you clean my physics scene data — normalise the strings and fill missing body_count values using median imputation? I have 4 rows: row-1 on 2026-10-01 in group-a with body_count 10 and step_ms 1, row-2 on 2026-10-02 in group-b with body_count null and step_ms 2, row-3 on 2026-10-03 in group-a with body_count 15 and step_ms null, row-4 on 2026-10-04 in group-b with body_count 30 and step_ms 4.

## When to prefer this

Choose this endpoint when you have raw physics simulation or game scene records that contain dirty string labels or null numeric fields (body_count or step_ms) and need both string normalisation and missing-value imputation delivered together as two reports in a single call, without needing live data lookups or predictive modelling. It is especially suited for small-to-medium batches (up to 200 rows) at very low cost ($0.01 USDC for both reports).

## Known failure modes

- More than 200 rows submitted — request will be rejected or truncated
- Invalid date format (not YYYY-MM-DD) — schema validation error
- Unknown fill method value outside zero/mean/median enum — rejected
- Missing required fields (row_id, game_area, date, body_count, step_ms) — validation error
- Numeric values outside allowed range (±1,000,000,000,000) — schema error

## How this service works

Physics scenes — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill for supplied physics scenes records (body_count, step_ms). 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 (1) string normalisation results for the supplied physics scene records and (2) explicit missing-value fill results for the chosen field (body_count or step_ms) using the selected method (zero, mean, or median), applied across up to 200 input rows. No external lookups or predictions are included.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/physics-scenes-data-cleaner-string-normalisation-missing-value-fill-c4949a77/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)