{"uid":"cap___63rN1b2r5DkuMEH8dmS","slug":"numeric-precision-comparisons-grouped-summary-iqr-outlier-flags-46b868f0","name":"Numeric Precision Comparisons — Grouped Summary + IQR Outlier Flags","description":"Numeric precision comparisons — Grouped numeric summary + IQR outlier flags: Grouped numeric summary + IQR outlier flags for supplied numeric precision comparisons records (source_value, transformed_value). 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.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-20-094?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","source_value","transformed_value"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"source_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"transformed_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","source_value":10,"dataset_group":"group-a","transformed_value":1},{"date":"2026-10-02","row_id":"row-2","source_value":20,"dataset_group":"group-b","transformed_value":2},{"date":"2026-10-03","row_id":"row-3","source_value":15,"dataset_group":"group-a","transformed_value":3},{"date":"2026-10-04","row_id":"row-4","source_value":30,"dataset_group":"group-b","transformed_value":4}],"maxItems":200,"minItems":1},"field":{"enum":["source_value","transformed_value"],"type":"string","default":"source_value"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_vO3--XX0awObYJFjhq6pJ","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Computes grouped numeric summary statistics and IQR-based outlier flags for source_value vs transformed_value pairs, covering up to 200 rows across two independent reports.","exampleAgentPrompt":"Can you analyze these 150 rows of source vs transformed numeric values grouped by department — I want a grouped summary and IQR outlier flags to see which rows look suspicious after the transformation?","exampleUseCases":[{"title":"ETL pipeline accuracy audit","prompt":"I ran a data transformation on our sales figures and have 120 rows with original and transformed values split across two product groups. Can you give me a grouped numeric summary and flag any IQR outliers so I can spot where the transformation went wrong?"},{"title":"Financial data normalization review","prompt":"We normalized 80 rows of revenue data across three business units — can you compute a grouped summary and IQR outlier flags on the source vs normalized values so we can verify nothing looks off?"},{"title":"QA check on ML feature engineering","prompt":"I have 200 rows of feature-engineered numeric outputs versus raw inputs, labeled by dataset group and date. Run a grouped statistical summary and flag any IQR outliers in the source_value column so I can catch bad transformations before training."}],"resultDescription":"Two independent analytical reports: (1) a grouped numeric summary (e.g. mean, median, std dev, min/max) broken down by dataset_group for the selected field, and (2) IQR-based outlier flags identifying rows whose values fall beyond 1.5× the interquartile range, covering up to 200 input rows.","failureModes":["Fewer than 1 row provided — rejected by minItems constraint","More than 200 rows submitted — exceeds maxItems limit","Invalid date format (not YYYY-MM-DD) — schema validation failure","source_value or transformed_value outside ±1,000,000,000,000 range — rejected","row_id missing or empty — required field violation","Insufficient data within a group to compute IQR meaningfully — may produce null or degenerate statistics"],"whenToPreferThis":"Choose this endpoint when you need both grouped descriptive statistics and IQR outlier detection on paired numeric records (source vs transformed) in a single call, without needing external lookups, ML predictions, or live data. It is ideal for data quality audits, ETL validation, and transformation accuracy checks on batches up to 200 rows, especially when cost efficiency ($0.01 for 2 reports) matters.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-23T18:47:46.895Z","isFirstParty":false,"canonicalSlug":"numeric-precision-comparisons-grouped-summary-iqr-outlier-flags-46b868f0"}