{"uid":"cap_Pqv2UDpchPk2GGGh1LS9L","slug":"transaction-fee-observations-adjacent-row-changes-explicit-missing-59b108b8","name":"Transaction Fee Observations — Adjacent Row Changes + Explicit Missing-Value Fill","description":"Transaction fee observations — Adjacent row changes + Explicit missing-value fill: Adjacent row changes + Explicit missing-value fill for supplied transaction fee observations records (fee_units, transaction_count). 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-03-162?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","instrument_group","date","fee_units","transaction_count"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"fee_units":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"instrument_group":{"type":"string","default":"","maxLength":80},"transaction_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","fee_units":10,"instrument_group":"group-a","transaction_count":1},{"date":"2026-10-02","row_id":"row-2","fee_units":20,"instrument_group":"group-b","transaction_count":2},{"date":"2026-10-03","row_id":"row-3","fee_units":15,"instrument_group":"group-a","transaction_count":3},{"date":"2026-10-04","row_id":"row-4","fee_units":30,"instrument_group":"group-b","transaction_count":4}],"maxItems":200,"minItems":1},"field":{"enum":["fee_units","transaction_count"],"type":"string","default":"fee_units"},"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_8Vum6GvOu4XF_6cvL1N3l","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":"Analyzes up to 200 transaction fee observation rows by computing adjacent row changes and filling missing values using zero, mean, or median imputation for fee_units or transaction_count fields.","exampleAgentPrompt":"I have a list of daily transaction fee records with some missing fee_units values — can you fill the nulls using median imputation and also show me the adjacent row-to-row changes? I'll pass you up to 50 rows with fields row_id, instrument_group, date, fee_units, and transaction_count.","exampleUseCases":[{"title":"Clean fee data before reporting","prompt":"I have 80 rows of daily transaction fee observations for two instrument groups and some fee_units entries are null — fill the missing values using the median method and also give me the adjacent row change analysis for fee_units."},{"title":"Detect fee spikes across consecutive days","prompt":"Can you run adjacent row change analysis on my transaction_count field across 120 daily fee records? I want to see how much transaction_count changes from one row to the next, and fill any nulls with zero."},{"title":"Impute gaps in fee time series","prompt":"I've got a 40-row dataset of transaction fee observations — several fee_units values are missing. Please fill the blanks using mean imputation and compute the sequential row-to-row differences so I can spot anomalies."}],"resultDescription":"Returns two analytical reports: (1) an adjacent row change report showing the difference between consecutive fee_units or transaction_count values, and (2) an explicit missing-value fill report showing imputed replacements for null entries using the chosen method (zero, mean, or median). Up to 200 rows processed per call.","failureModes":["Input array exceeds 200 rows — request rejected","Invalid date format not matching YYYY-MM-DD pattern — validation error","Field value out of range (beyond ±1,000,000,000,000) — schema rejection","Empty rows array (minItems:1 violated) — request rejected","Invalid method enum value (not zero/mean/median) — schema validation failure","Invalid field enum value (not fee_units/transaction_count) — schema validation failure"],"whenToPreferThis":"Choose this endpoint when you have a batch of up to 200 transaction fee observation rows and need both missing-value imputation (zero, mean, or median) and adjacent row change analysis in a single call without requiring any live data lookup or external connections. Ideal for local, offline-style analytical processing of finance/fee time series data where you control the input dataset.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-26T12:33:27.414Z","isFirstParty":false,"canonicalSlug":"transaction-fee-observations-adjacent-row-changes-explicit-missing-59b108b8"}