# Feature Availability Analytics: Equal-Width Histogram + Date-by-Category Pivot

> Feature Availability Analytics: Equal-Width Histogram + 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 2026-10-19).

Generates two local analytical reports — an equal-width histogram and a date-by-category pivot table — for supplied feature availability (populated_features, expected_features) records, up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-173?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/feature-availability-analytics-equal-width-histogram-date-by-category-53ee1423
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_OPJevGNa1w7VkFzMEsLsJ

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 feature-availability-analytics-equal-width-histogram-date-by-category-53ee1423 -d '<json body>'
```

Example prompt: Can you run a histogram and date-by-category pivot on my feature availability data? I have 4 rows — row-1 in group-a on 2026-10-01 with 10 populated and 1 expected, row-2 in group-b on 2026-10-02 with 20 populated and 2 expected, row-3 in group-a on 2026-10-03 with 15 populated and 3 expected, row-4 in group-b on 2026-10-04 with 30 populated and 4 expected — use 5 bins and analyze the populated_features field.

## When to prefer this

Choose this endpoint when you need both a frequency histogram and a date-by-category pivot table computed together on feature availability data (populated_features or expected_features) in a single low-cost call. It is ideal for data quality audits, ML feature completeness monitoring, and dataset group comparisons when you already have the raw records and need fast local analytics without external lookups or predictions. Prefer this over general-purpose analytics tools when working with feature store or dataset metadata containing row_id, dataset_group, and date dimensions.

## Known failure modes

- More than 200 rows submitted — request will be rejected or truncated
- Invalid date format (not YYYY-MM-DD) causes row rejection
- bins parameter outside 1–30 range causes validation error
- All values null for selected field produces empty histogram
- Missing required row fields (row_id, dataset_group, date, populated_features, expected_features) cause row-level errors
- Payment of 0.01 USDC not included causes 402 rejection

## How this service works

Feature availability — Equal-width histogram + Date by category pivot: Equal-width histogram + Date by category pivot for supplied feature availability records (populated_features, expected_features). 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 computed locally: (1) an equal-width histogram showing the frequency distribution of values in the selected field (populated_features or expected_features) divided into the requested number of bins, and (2) a date-by-category pivot table aggregating the selected field values grouped by date and dataset_group. Results cover up to 200 input rows. No external lookups or predictions are made.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "bins": {
   "type": "integer",
   "default": 5,
   "maximum": 30,
   "minimum": 1
  },
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "populated_features",
     "expected_features"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "expected_features": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "populated_features": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "dataset_group": "group-a",
     "expected_features": 1,
     "populated_features": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "dataset_group": "group-b",
     "expected_features": 2,
     "populated_features": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "dataset_group": "group-a",
     "expected_features": 3,
     "populated_features": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "dataset_group": "group-b",
     "expected_features": 4,
     "populated_features": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "populated_features",
    "expected_features"
   ],
   "type": "string",
   "default": "populated_features"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/feature-availability-analytics-equal-width-histogram-date-by-category-53ee1423/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)