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Brand Mention Inventories — Equal-Width Histogram + Explicit Missing-Value Fill
x402
Brand Mention Inventories — Equal-Width Histogram + 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 2026-10-20).
Generates an equal-width histogram and fills missing values for brand mention inventory data (mention_count, source_count) across up to 200 rows in two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Brand mention inventories — Equal-width histogram + Explicit missing-value fill: Equal-width histogram + Explicit missing-value fill for supplied brand mention inventories records (mention_count, source_count). 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 for the supplied brand mention inventory rows: (1) an equal-width histogram showing bin boundaries and row counts for the selected field (mention_count or source_count), and (2) the dataset with missing values explicitly filled using the chosen method (zero, mean, or median). Reports are computed locally with no external lookups.
Example use cases
Marketing team distribution analysis
“We have 120 rows of brand mention records across several company groups with some nulls in mention_count — can you build a 7-bin equal-width histogram on mention_count and fill missing values using median so we can see the distribution?”
Source diversity audit with zero-fill
“I've got a dataset of 80 brand mention inventory records and I want to understand how source_count is distributed — please create a 5-bin histogram on source_count and fill any missing values with zero.”
Multi-brand mention benchmarking
“Here are 200 rows of brand mention inventory data for groups A, B, and C — can you generate a 15-bin equal-width histogram on mention_count using mean imputation for missing values so I can compare the distributions?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-18-175?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both histogram binning and missing-value imputation for brand mention inventory data (mention_count or source_count) in a single paid call. Ideal for offline/batch analytical workflows on supplied datasets up to 200 rows where no live data lookup or external enrichment is needed. At $0.01 USDC for two reports, it is cost-effective for quick distribution analysis and data cleaning in one step.
Side effects
- Fewer than 1 or more than 200 rows provided — request rejected
- bins parameter outside 1–30 range — validation error
- Missing required row fields (row_id, date, company_group) — schema validation failure
- Invalid date format (not YYYY-MM-DD) — pattern validation error
- Numeric values outside allowed range (±1,000,000,000,000) — validation error
- Unrecognized fill method or field enum value — schema rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| bins | integer | |
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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