{"uid":"cap_to7cU2vAOThzZOL3bi_j1","slug":"data-ingestion-batches-string-normalisation-explicit-missing-value-fill-381011f6","name":"Data Ingestion Batches — String Normalisation + Explicit Missing-Value Fill","description":"Data ingestion batches — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill for supplied data ingestion batches records (received_rows, accepted_rows). 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-01-037?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","received_rows","accepted_rows"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"accepted_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"received_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","accepted_rows":1,"dataset_group":"group-a","received_rows":10},{"date":"2026-10-02","row_id":"row-2","accepted_rows":2,"dataset_group":"group-b","received_rows":20},{"date":"2026-10-03","row_id":"row-3","accepted_rows":3,"dataset_group":"group-a","received_rows":15},{"date":"2026-10-04","row_id":"row-4","accepted_rows":4,"dataset_group":"group-b","received_rows":30}],"maxItems":200,"minItems":1},"field":{"enum":["received_rows","accepted_rows"],"type":"string","default":"received_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_Sbn-nA7V3s7Eww2CmK4em","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 in data ingestion batch records (received_rows, accepted_rows) for up to 200 rows, returning 2 analytical reports.","exampleAgentPrompt":"Can you normalise the string fields and fill any missing received_rows values using median imputation across these 4 ingestion batch records: row-1 group-a 2026-10-01 received=10 accepted=1, row-2 group-b 2026-10-02 received=20 accepted=null, row-3 group-a 2026-10-03 received=null accepted=3, row-4 group-b 2026-10-04 received=30 accepted=4? I want both analytical reports back.","exampleUseCases":[{"title":"Impute missing ingestion counts before warehouse load","prompt":"I have 50 rows of daily ingestion metrics with some null received_rows values — can you fill those missing counts using mean imputation and normalise the dataset_group strings so everything is consistent before I load it to the warehouse?"},{"title":"Audit pipeline acceptance rates with cleaned data","prompt":"My ingestion batch has 120 rows across groups alpha and beta with dates from last quarter, and accepted_rows has some nulls. Fill those nulls using median and normalise all the string fields, then give me the two summary reports so I can review acceptance rate patterns."},{"title":"Zero-fill missing row counts for monitoring dashboard","prompt":"I've got a batch of 30 ingestion records where received_rows is sometimes missing — fill those with zero, normalise the dataset group labels, and return both analytical reports so I can feed the cleaned data into my monitoring dashboard."}],"resultDescription":"Returns 2 independent analytical reports based on the submitted batch records (up to 200 rows), covering string-normalised fields and explicitly filled missing values for the chosen numeric field (received_rows or accepted_rows) using the selected fill method (zero, mean, or median). All processing uses local analytical engines with no external lookups.","failureModes":["Batch exceeds 200 rows — request rejected","Invalid date format not matching YYYY-MM-DD pattern — validation error","Numeric value outside allowed range (-1,000,000,000,000 to 1,000,000,000,000) — schema rejection","Empty rows array (minItems: 1 not met) — validation error","Invalid fill method or field enum value — schema rejection","Missing required fields (row_id, dataset_group, date) — validation error"],"whenToPreferThis":"Choose this endpoint when you need lightweight, low-cost ($0.01 USDC) local string normalisation and missing-value imputation on small-to-medium ingestion batch data (up to 200 rows) and want two bundled analytical reports without any live external lookups or predictions. Ideal for pre-warehouse cleaning of ingestion metrics (received_rows, accepted_rows) with a choice of zero, mean, or median fill strategy.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:44.262Z","isFirstParty":false,"canonicalSlug":"data-ingestion-batches-string-normalisation-explicit-missing-value-fill-381011f6"}