{"uid":"cap_lE6ebzDHPOSSGaKZaM4kx","slug":"transformation-throughput-analytics-string-normalisation-explicit-e9bc8bd3","name":"Transformation Throughput Analytics — String Normalisation + Explicit Missing-Value Fill","description":"Transformation throughput — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill for supplied transformation throughput records (processed_rows, duration_seconds). 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-13-037?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","processed_rows","duration_seconds"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"processed_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"duration_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","processed_rows":10,"duration_seconds":1},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","processed_rows":20,"duration_seconds":2},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","processed_rows":15,"duration_seconds":3},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","processed_rows":30,"duration_seconds":4}],"maxItems":200,"minItems":1},"field":{"enum":["processed_rows","duration_seconds"],"type":"string","default":"processed_rows"},"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_NBGaK2MYb29Ag7cDB0QHs","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 values (zero, mean, or median) in transformation throughput records containing processed_rows and duration_seconds metrics, returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"I have 80 rows of ETL throughput records with dataset_group labels and some missing processed_rows values — can you normalise the string fields and fill those gaps using median imputation, and give me two analytical reports on the results?","exampleUseCases":[{"title":"ETL pipeline audit data cleanup","prompt":"I've got 120 rows from our nightly ETL runs with some null processed_rows values and inconsistent dataset_group strings — please normalise the strings and fill the missing processed_rows using median, and give me the two analytical reports."},{"title":"Duration metric imputation before reporting","prompt":"Before I send our weekly throughput summary, can you clean up these 60 transformation records by normalising the string fields and filling any missing duration_seconds values using mean imputation?"},{"title":"Zero-fill missing counts for dashboard","prompt":"I need to prep 45 transformation throughput rows for my dashboard — the processed_rows column has some nulls that should be filled with zero, and the string fields need normalising. Can you run the two-report analysis?"}],"resultDescription":"Returns two analytical reports based on the supplied transformation throughput rows (up to 200): one covering string normalisation of fields like dataset_group, and one covering explicit missing-value fill for the chosen field (processed_rows or duration_seconds) using the selected method (zero, mean, or median). No external lookups or predictions are made; all processing is local and deterministic.","failureModes":["More than 200 rows submitted — request will be rejected or truncated","Missing required fields (row_id, date, processed_rows, duration_seconds) in any row — validation error","Invalid date format (not YYYY-MM-DD) — schema validation failure","Invalid enum value for field or method — rejected with validation error","Empty rows array — minimum 1 row required","Numeric values outside allowed range (±1,000,000,000,000) — validation error"],"whenToPreferThis":"Choose this endpoint when you need a combined string normalisation and missing-value imputation pass on transformation throughput records (processed_rows, duration_seconds) in a single paid call, and you want two independent analytical reports without any external API calls, live lookups, or ML predictions. It is well-suited for ETL audit pipelines, data quality preprocessing, and dashboard preparation where deterministic, local computation suffices for up to 200 rows at very low cost ($0.01 USDC).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T00:54:10.639Z","isFirstParty":false,"canonicalSlug":"transformation-throughput-analytics-string-normalisation-explicit-e9bc8bd3"}