{"uid":"cap_8UKVdQk4mmSkR-LH0lljY","slug":"transaction-fee-observations-numeric-interval-filter-explicit-missing-3ddc4e7e","name":"Transaction Fee Observations — Numeric Interval Filter + Explicit Missing-Value Fill","description":"Transaction fee observations — Numeric interval filter + Explicit missing-value fill: Numeric interval filter + 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-03-135?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"},"maximum":{"type":"number","default":100,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":0,"maximum":1000000000000,"minimum":-1000000000000}}},"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_X9NPZCtxjjG7sEhPwmC4M","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":"Filters transaction fee observation rows to a numeric interval and fills missing values in fee_units or transaction_count using zero, mean, or median imputation, returning up to 2 analytical reports for up to 200 rows.","exampleAgentPrompt":"I have a batch of transaction fee observation records with some missing fee_units values — can you filter them to only include rows where fee_units is between 0 and 500, and fill any missing fee_units values using the median? I have about 80 rows with fields: row_id, instrument_group, date, fee_units, and transaction_count.","exampleUseCases":[{"title":"Clean fee data before dashboard reporting","prompt":"I'm preparing a monthly fee report and my dataset has some null fee_units entries and outlier values. Can you filter fee_units to between 5 and 1000 and fill any missing fee_units with the mean? Here are my 120 transaction fee rows with row_id, instrument_group, date, fee_units, and transaction_count."},{"title":"Impute missing transaction counts for model input","prompt":"I need to prep my transaction_count field for a model — can you filter transaction_count to rows between 1 and 50000 and fill any nulls using the median? I'll supply about 150 rows with row_id, date, instrument_group, fee_units, and transaction_count."},{"title":"Zero-fill missing fees for reconciliation audit","prompt":"For my reconciliation audit I need all fee_units values clipped to a 0 to 10000 interval, with any missing fee_units replaced by zero. Can you run that on my 60 transaction fee observation records?"}],"resultDescription":"Returns up to 2 analytical reports covering the supplied transaction fee observation rows after applying the numeric interval filter (rows outside the min–max range are excluded or flagged) and explicit missing-value fill (nulls in the selected field replaced by zero, mean, or median). Reports are computed locally with no external lookups or live data.","failureModes":["Fewer than 1 row supplied — request rejected","More than 200 rows supplied — exceeds row limit","Invalid date format (not YYYY-MM-DD) — schema validation error","fee_units or transaction_count values outside ±1,000,000,000,000 — schema rejection","minimum greater than maximum — may produce empty filtered result set","Payment of 0.01 USDC not completed — endpoint returns 402 Payment Required"],"whenToPreferThis":"Choose this endpoint when you have up to 200 transaction fee observation records (with fee_units and transaction_count fields) that need both a numeric range filter and missing-value imputation in a single step. It is ideal for lightweight data cleaning before analytics or reporting without requiring an external data science environment. Prefer this over general-purpose ETL tools when working within an agentic payment workflow and needing a low-cost, deterministic, offline computation on small financial datasets.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T06:47:35.607Z","isFirstParty":false,"canonicalSlug":"transaction-fee-observations-numeric-interval-filter-explicit-missing-3ddc4e7e"}