{"uid":"cap_AkFP3pMG63srrYaIr1yZO","slug":"financial-statement-string-normalisation-missing-value-fill-8dbecf55","name":"Financial Statement String Normalisation & Missing-Value Fill","description":"Financial statement inputs — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill for supplied financial statement inputs records (revenue_amount, expense_amount). 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-finance-22-037?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","instrument_group","date","revenue_amount","expense_amount"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"expense_amount":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"revenue_amount":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"instrument_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","expense_amount":1,"revenue_amount":10,"instrument_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","expense_amount":2,"revenue_amount":20,"instrument_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","expense_amount":3,"revenue_amount":15,"instrument_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","expense_amount":4,"revenue_amount":30,"instrument_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["revenue_amount","expense_amount"],"type":"string","default":"revenue_amount"},"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_vzpiF3hSaxY6V3aoZEf4D","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 fields and fills missing revenue or expense values (zero, mean, or median) in up to 200 financial statement rows, returning two analytical reports.","exampleAgentPrompt":"Can you clean up my financial statement rows by normalising the strings and filling any missing revenue_amount values using median imputation? I have about 50 rows with fields like row_id, instrument_group, date, revenue_amount, and expense_amount.","exampleUseCases":[{"title":"Impute missing revenue in quarterly report","prompt":"I have 120 rows of quarterly financial data with some null revenue_amount entries — can you fill those missing values using the median method and normalise the strings across the dataset? Each row has a row_id, instrument_group, date, revenue_amount, and expense_amount."},{"title":"Zero-fill missing expense amounts before analysis","prompt":"Before I run my analysis, I need to fill any missing expense_amount fields with zero and normalise all string columns in my 80-row financial statement dataset. Can you process that for me and give me the two analytical reports?"},{"title":"Clean finance data for automated reporting pipeline","prompt":"As part of my automated reporting pipeline, please normalise the string fields and use mean imputation to fill missing revenue_amount values in these 200 financial statement rows with row_id, instrument_group, date, revenue_amount, and expense_amount."}],"resultDescription":"Returns two independent analytical reports covering the supplied financial statement rows after string normalisation has been applied and missing values in the specified field (revenue_amount or expense_amount) have been explicitly filled using the chosen method (zero, mean, or median). No external lookups or live predictions are included.","failureModes":["More than 200 rows submitted — exceeds maxItems limit","Missing required fields (row_id, instrument_group, date) cause validation errors","Date strings not matching YYYY-MM-DD pattern are rejected","Amount values outside ±1,000,000,000,000 are rejected","Invalid enum value for field or method parameters causes a 400 error","Empty rows array (minItems:1 violated) returns an error"],"whenToPreferThis":"Choose this endpoint when you need a lightweight, low-cost ($0.01 USDC) local analytical operation to normalise string fields and fill missing revenue or expense values in financial statement rows (up to 200 rows) without any live data lookup, prediction, or external action. It is well-suited for preprocessing financial data before downstream analysis or reporting pipelines.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T12:35:28.782Z","isFirstParty":false,"canonicalSlug":"financial-statement-string-normalisation-missing-value-fill-8dbecf55"}