{"uid":"cap_o09xSbCFekQ_TH5hAtx6L","slug":"data-ingestion-batches-adjacent-row-changes-explicit-missing-value-fill-93af86ca","name":"Data Ingestion Batches — Adjacent Row Changes + Explicit Missing-Value Fill","description":"Data ingestion batches — Adjacent row changes + Explicit missing-value fill: Adjacent row changes + 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-01-162?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_pH81iCTzl4AsH-5YINqAQ","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 data ingestion batch records, computing adjacent row changes and filling missing values using a chosen method (zero, mean, or median) for received_rows or accepted_rows.","exampleAgentPrompt":"For my ingestion batch records across group-a and group-b from 2026-10-01 to 2026-10-04, run the adjacent row changes report and fill any missing values in received_rows using the median method — here are the 4 rows with row_id, dataset_group, date, received_rows, and accepted_rows.","exampleUseCases":[{"title":"ETL pipeline intake trend analysis","prompt":"I have 150 rows of daily ingestion batch data with row_id, dataset_group, date, received_rows, and accepted_rows — can you compute the adjacent row changes for received_rows and fill any nulls using the mean, then give me both reports?"},{"title":"Data quality audit with missing-value imputation","prompt":"My ingestion logs for dataset group 'prod-feeds' have some null accepted_rows values — run the missing-value fill using zero and also show me the adjacent row changes so I can spot where drops happened."},{"title":"Multi-group ingestion comparison","prompt":"Here are 80 ingestion batch records across three dataset groups — please analyze adjacent row changes for accepted_rows and fill missing entries using the median, so I can compare intake consistency across groups."}],"resultDescription":"Returns two analytical reports for the supplied ingestion batch rows: (1) an adjacent row changes report showing period-over-period differences in the selected field (received_rows or accepted_rows), and (2) an explicit missing-value fill report where nulls in the selected field are replaced using the specified method (zero, mean, or median). Both reports are computed locally on the supplied data with no external lookups.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, dataset_group, date, received_rows, accepted_rows) cause schema rejection","Invalid date format (not YYYY-MM-DD) triggers pattern validation failure","Numeric values outside ±1,000,000,000,000 are rejected","Invalid enum values for field or method return a 400-level error","Payment failure via x402 prevents execution"],"whenToPreferThis":"Choose this endpoint when you need both adjacent row-change analysis and missing-value imputation for ingestion batch metrics in a single call. It is ideal for small-to-medium datasets (up to 200 rows) where you want offline, reproducible computation on supplied data without sending data to an external model or live lookup service. Prefer it over general-purpose analytics when your data is structured around data ingestion pipeline metrics (received_rows, accepted_rows) grouped by dataset and date.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-17T00:46:34.599Z","isFirstParty":false,"canonicalSlug":"data-ingestion-batches-adjacent-row-changes-explicit-missing-value-fill-93af86ca"}