# Feature Availability Analytics: Equal-Width Histogram + Missing-Value Fill

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

Computes an equal-width histogram and applies explicit missing-value fill on feature availability records (populated_features, expected_features) for up to 200 rows, returning 2 analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-175?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-missing-value-fill-fbe51fa9
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_l7YZR194liNsHr-IF5bq9

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-missing-value-fill-fbe51fa9 -d '<json body>'
```

Example prompt: Analyze my feature availability data — here are 50 rows with populated_features and expected_features — give me an equal-width histogram with 10 bins and fill any missing populated_features values using the median.

## When to prefer this

Choose this endpoint when you need to quickly profile feature availability data with a histogram and impute missing values in a single call, without any external data lookups or ML model involvement. Ideal for data quality checks before ML training or ETL validation where both distribution analysis and null handling are needed together on small-to-medium datasets (≤200 rows).

## Known failure modes

- More than 200 rows submitted — request will be rejected
- Missing required fields (row_id, date, populated_features, expected_features) on any row — validation error
- bins value outside 1–30 range — rejected
- Invalid date format (must be YYYY-MM-DD) — parse error
- All values null with fill method mean/median — imputation undefined
- field enum value not recognized — error

## How this service works

Feature availability — Equal-width histogram + Explicit missing-value fill: Equal-width histogram + Explicit missing-value fill 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 analytical reports: (1) an equal-width histogram breaking the selected field into the specified number of bins with counts per bin, and (2) a report showing missing-value fills applied to null entries using the chosen method (zero, mean, or median). Both reports are computed locally from the supplied rows.

## 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"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/feature-availability-analytics-equal-width-histogram-missing-value-fill-fbe51fa9/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)