{"uid":"cap_Ke0NQ1s0PXzoUFXXROAWN","slug":"numeric-precision-comparisons-string-normalisation-explicit-missing-db5c3213","name":"Numeric Precision Comparisons — String Normalisation + Explicit Missing-Value Fill","description":"Numeric precision comparisons — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill 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-20-037?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"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"}}},"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_SfPB-dwNhR1jBJe4X9tC2","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":"Normalises string representations and fills missing values in numeric precision comparison datasets (source_value vs transformed_value), producing two analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you normalize the string representations and fill any missing source_value entries using median imputation across my 50-row dataset of source_value and transformed_value pairs grouped by 'group-a' and 'group-b', then give me the two analytical reports?","exampleUseCases":[{"title":"ETL pipeline null remediation","prompt":"I have 120 rows of source_value and transformed_value records from our ETL pipeline with some nulls in the transformed_value column — can you fill those missing entries using mean imputation, normalize the strings, and give me the two comparison reports?"},{"title":"Data quality audit before model training","prompt":"Before I train my model, I need to clean up this 80-row dataset comparing original and transformed numeric values — please fill any missing source_value entries with zero and normalize the strings so I can see the precision comparison reports."},{"title":"Finance reconciliation missing value check","prompt":"I've got 200 rows of financial source and transformed values split across two dataset groups — run string normalization and fill missing values using median, then give me both analytical reports so I can check where the numeric precision differences are."}],"resultDescription":"Two independent analytical reports covering: (1) string-normalised representations of the source_value and transformed_value fields, and (2) explicit missing-value fills for the chosen field using the selected method (zero, mean, or median). Reports are generated locally without external lookups and cover all submitted rows (up to 200).","failureModes":["More than 200 rows submitted — request rejected","Invalid date format (not YYYY-MM-DD) — validation error","source_value or transformed_value outside allowed range (±1,000,000,000,000) — validation error","Invalid fill method specified — schema validation error","Empty rows array — minimum item count not met","row_id missing or blank — required field error"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation and explicit missing-value imputation (zero, mean, or median) applied together to numeric precision comparison records in a single batch call producing two reports. It is best suited for small-to-medium datasets (up to 200 rows) where local, offline analysis suffices and no external data enrichment, prediction, or live lookup is required.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T06:42:17.575Z","isFirstParty":false,"canonicalSlug":"numeric-precision-comparisons-string-normalisation-explicit-missing-db5c3213"}