# Rainfall Observations String Normalisation and Date-by-Category Pivot

> Rainfall Observations String Normalisation and Date-by-Category Pivot is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-30).

Performs string normalisation and date-by-category pivot analysis on supplied rainfall observation records (rainfall_mm, duration_hours) for up to 200 rows, returning two analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-weather-02-035?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-30
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/rainfall-observations-string-normalisation-and-date-by-category-pivot-a85105e0
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_NYubbKAcxTvYlmyXdWqdb

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 rainfall-observations-string-normalisation-and-date-by-category-pivot-a85105e0 -d '<json body>'
```

Example prompt: I have a list of up to 200 rainfall observation rows — each with a row ID, station group, date, rainfall_mm, and duration_hours — can you run string normalisation and a date-by-category pivot on the rainfall_mm field and give me both reports?

## When to prefer this

Choose this endpoint when you have raw rainfall observation records (up to 200 rows) that need both string normalisation of categorical labels and a date-by-category pivot in a single low-cost call. It is ideal for preprocessing weather station data for downstream analysis without requiring live data lookups, predictions, or external integrations. Prefer it over generic analytics platforms when you need two bundled local reports at fixed low cost ($0.01 USDC).

## Known failure modes

- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, station_group, date, rainfall_mm, duration_hours) in any row — validation error
- Date values not matching YYYY-MM-DD pattern — schema validation failure
- Invalid field enum value (must be 'rainfall_mm' or 'duration_hours') — rejected
- Numeric values outside allowed range — schema rejection
- Empty rows array (minItems:1 violated) — error returned

## How this service works

Rainfall observations — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied rainfall observations records (rainfall_mm, duration_hours). 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 derived from the supplied rainfall observation rows: (1) a string-normalised version of the records with standardised station_group labels, and (2) a date-by-category pivot table showing the selected field (rainfall_mm or duration_hours) aggregated by date and station group category. Both reports use local analytical engines with no external lookups.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/rainfall-observations-string-normalisation-and-date-by-category-pivot-a85105e0/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)