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Parking Utilization - Explicit Missing-Value Fill
x402
Parking Utilization - 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 2026-10-17).
Fills missing numeric values (occupied_spaces or available_spaces) in parking utilization records using zero, mean, or median imputation across up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Parking utilization - Explicit missing-value fill: Explicit missing-value fill for Parking utilization. Up to 200 supplied typed records. Local descriptive analytics; no enrichment or forecast. One of 25 new profiles across 20 shared operations in this industry. 0.01 USDC includes one bounded explicit missing-value fill report.
Output
A bounded report containing the original parking utilization records with missing values in the specified field (occupied_spaces or available_spaces) filled using the chosen method (zero, mean, or median), limited to up to 200 rows. No forecasting or external enrichment is applied — purely local descriptive imputation.
Example use cases
Fixing gaps in daily parking occupancy
“I have daily parking data for zones downtown-1 and downtown-2 from October 2026, but some occupied_spaces entries are missing — fill them in using the median so my occupancy report is complete.”
Zero-filling unavailable space counts
“My parking dataset has null available_spaces for a few dates in zone garage-north — can you fill those missing values with zero so I can run my utilization calculation?”
Mean imputation before dashboard refresh
“Before I refresh my parking dashboard, I need to fill the missing occupied_spaces values in my dataset of 150 records using the mean — the zones are lot-east and lot-west across Q4 dates.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ar-analytics-parking-utilization-fill-missing?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have tabular parking utilization records with missing occupied_spaces or available_spaces values and need a fast, bounded imputation using simple statistical methods (zero, mean, or median). It is ideal for data cleaning before dashboards, reports, or further analytics pipelines, and supports up to 200 records per call at very low cost. It does not perform forecasting, enrichment, or external lookups.
Side effects
- Empty rows array or exceeding 200 items returns a validation error
- Invalid date format (not YYYY-MM-DD) causes schema rejection
- Unknown field name outside enum causes bad request
- All values already present with no nulls returns unchanged data
- Extremely large numeric outliers may skew mean imputation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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