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Dataset Partition Sizes — IQR Outlier Flags + Missing-Value Fill
x402
Dataset Partition Sizes — IQR Outlier Flags + 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).
Analyzes dataset partition size records (row_count, file_mb) to flag IQR-based outliers and fill missing values using a chosen imputation method, returning two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Dataset partition sizes — IQR outlier flags + Explicit missing-value fill: IQR outlier flags + Explicit missing-value fill for supplied dataset partition sizes records (row_count, file_mb). 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: (1) IQR-based outlier flags identifying which records have row_count or file_mb values beyond the interquartile range boundaries, and (2) a missing-value fill report showing which records had nulls and what values were imputed using the chosen method (zero, mean, or median). All analysis is performed locally with no external lookups.
Example use cases
ETL pipeline data quality check
“I've got 80 partition size records from our ETL pipeline — each has a row_id, dataset_group, date, row_count, and file_mb. Can you flag any IQR outliers in row_count and fill any missing row_count values using median?”
File size anomaly detection for storage audit
“Run an IQR outlier analysis on file_mb across these 120 dataset partition records and fill any null file_mb values with the mean — I need to spot which partitions are abnormally large or small.”
Missing data imputation before ML feature engineering
“Before I feed these partition size records into my model, can you fill all missing row_count entries using zero and also flag any outlier row_counts based on IQR? I have about 150 rows with row_id, date, dataset_group, row_count, and file_mb.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-07-180?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 dataset partition size records and need both IQR outlier detection and missing-value imputation in a single low-cost call. Ideal for data quality pipelines, pre-processing steps before ML or analytics, and storage audits where you need statistical anomaly flagging without external dependencies or live data lookups.
Side effects
- Input array exceeds 200 rows — request will be rejected
- Invalid date format (must be YYYY-MM-DD) causes validation error
- Missing required fields (row_id, dataset_group, date) cause schema validation failure
- Numeric values outside allowed range trigger validation errors
- Empty rows array rejected by minItems:1 constraint
- Invalid method or field enum value causes request failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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