{"uid":"cap_7AR0W60q-M3bFCVQ96q6g","slug":"numeric-precision-comparisons-row-ordering-two-measure-correlation-04e19732","name":"Numeric Precision Comparisons — Row Ordering + Two-Measure Correlation","description":"Numeric precision comparisons — Numeric row ordering + Two-measure correlation: Numeric row ordering + Two-measure correlation 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-20-120?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_ELSoCoSXwNZKv1O33x6ph","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":"Performs numeric row ordering and Pearson/rank correlation analysis on paired source_value and transformed_value columns for up to 200 supplied rows, returning two independent analytical reports.","exampleAgentPrompt":"I have 4 rows of numeric precision data — row-1 (group-a, 2026-10-01, source=10, transformed=1), row-2 (group-b, 2026-10-02, source=20, transformed=2), row-3 (group-a, 2026-10-03, source=15, transformed=3), row-4 (group-b, 2026-10-04, source=30, transformed=4) — can you sort them by source_value and also give me the correlation between source_value and transformed_value?","exampleUseCases":[{"title":"ETL pipeline transformation validation","prompt":"I ran a transformation on 150 sensor readings and want to check how closely the transformed values track the originals — here are the row IDs, dates, dataset groups, and both numeric columns. Can you rank them by source_value and give me the correlation between source and transformed?"},{"title":"Finance data normalization QA","prompt":"I have 80 rows of financial figures where source_value is the raw ledger amount and transformed_value is after normalization. Sort them by source_value and tell me the two-measure correlation so I can see if the normalization is consistent."},{"title":"A/B experiment numeric scoring check","prompt":"I've got two groups of experiment scores — group-a and group-b — across 50 rows with dates, and I need them ordered by transformed_value plus a correlation report between source and transformed scores to verify the scoring pipeline worked correctly."}],"resultDescription":"Returns two independent analytical reports for the submitted rows: (1) a numeric row ordering/ranking based on the selected field, and (2) a two-measure correlation analysis between source_value and transformed_value — all computed locally with no external lookups or predictions.","failureModes":["Fewer than 1 row submitted — validation error","More than 200 rows submitted — rejected, exceeds limit","Invalid date format (not YYYY-MM-DD) — schema validation failure","source_value or transformed_value outside ±1,000,000,000,000 range — rejected","Missing required row fields (row_id, dataset_group, date) — schema error","All values null in a column — correlation may be undefined or return NaN"],"whenToPreferThis":"Choose this endpoint when you need both a numeric sort/ranking and a correlation analysis on the same batch of paired numeric records in a single call. It is ideal for ETL/data-pipeline QA, normalization validation, or experiment scoring checks where you have up to 200 rows with source and transformed numeric columns and want offline, locally-computed analysis without any external data enrichment or predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T06:40:27.045Z","isFirstParty":false,"canonicalSlug":"numeric-precision-comparisons-row-ordering-two-measure-correlation-04e19732"}