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Transport Ridership Weighted Mean & Missing Value Fill
x402
Transport Ridership Weighted Mean & 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).
Computes a secondary-measure weighted mean and fills missing values for supplied transport ridership records (passenger_count, service_count) across up to 200 rows, returning 2 analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Transport ridership — Secondary-measure weighted mean + Explicit missing-value fill: Secondary-measure weighted mean + Explicit missing-value fill for supplied transport ridership records (passenger_count, service_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 2 analytical reports for up to 200 supplied rows: (1) a secondary-measure weighted mean result for the specified field (passenger_count or service_count), and (2) a missing-value fill report where nulls are replaced using the chosen method (zero, mean, or median). No external data is fetched; all computation is local to the supplied rows.
Example use cases
Weekly transit ridership gap filling
“I've got 80 rows of weekly bus ridership data where some passenger_count entries are null — can you fill those missing values using median and also compute the service-count-weighted mean for passenger_count across all regions?”
Multi-region rail service analysis
“Here are 120 records of regional rail data with row_id, region, date, passenger_count, and service_count. Can you run a weighted mean on service_count using passenger_count as the weight, and fill any missing service_count values with zero?”
Transit planning data quality prep
“I'm preparing a ridership dataset for a planning report — 60 rows covering several city zones. Can you compute the passenger_count weighted mean weighted by service_count and replace any missing passenger_count values with the mean, so the data is clean for analysis?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-economics-05-187?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have tabular transport ridership data (passenger_count, service_count) that needs both a weighted-mean aggregation and missing-value imputation in a single call, at very low cost ($0.01 USDC for 2 reports). Best for batch offline analysis of up to 200 rows where no live data lookup or external prediction is needed.
Side effects
- More than 200 rows submitted — request rejected or truncated
- Missing required fields (row_id, region, date) — validation error
- Invalid date format not matching YYYY-MM-DD pattern — schema rejection
- All values for target field are null — weighted mean cannot be computed
- Numeric values outside allowed range (±1,000,000,000,000) — schema rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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