{"uid":"cap_mEuCp9oBclAuO0TP0he5H","slug":"data-ingestion-batches-iqr-outlier-flags-date-by-category-pivot-5c15c757","name":"Data Ingestion Batches — IQR Outlier Flags + Date by Category Pivot","description":"Data ingestion batches — IQR outlier flags + Date by category pivot: IQR outlier flags + Date by category pivot 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-01-178?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"}}},"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_Ui_bZKa6iNbnWynoXSAM4","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 to produce IQR-based outlier flags and a date-by-category pivot table for received_rows or accepted_rows.","exampleAgentPrompt":"Can you run IQR outlier detection and a date-by-category pivot on my ingestion batch records — I have rows with dataset_group, date, received_rows, and accepted_rows fields, and I want to analyze the accepted_rows column across up to 200 records?","exampleUseCases":[{"title":"ETL pipeline anomaly detection","prompt":"I have data ingestion batch logs with received_rows and accepted_rows per dataset group and date — can you flag which batches are statistical outliers using IQR on the received_rows column? Here are my records for the last month across groups A and B."},{"title":"Data quality pivot report by team","prompt":"Can you pivot my ingestion batch data by date and dataset_group so I can see accepted_rows broken down across categories, and also flag any outliers in the accepted_rows values? I've got about 150 rows covering October."},{"title":"Monitoring ingestion drop-off patterns","prompt":"Our data pipeline has been dropping rows lately — can you analyze these 80 ingestion batch records, flag IQR outliers in accepted_rows, and give me a date-by-category pivot so I can see which groups and dates look abnormal?"}],"resultDescription":"Returns two analytical reports: (1) IQR outlier flags for each row indicating whether the selected numeric field (received_rows or accepted_rows) falls outside the interquartile range, and (2) a pivot table organizing values by date and dataset_group category. Both reports are computed locally from the supplied data with no external lookups.","failureModes":["Missing required fields (row_id, dataset_group, date, received_rows, accepted_rows) cause validation errors","Date strings not matching YYYY-MM-DD format are rejected","Exceeding 200 rows returns an error — batch must be split","Null numeric values may affect IQR computation if too prevalent","Invalid field enum value (not received_rows or accepted_rows) causes rejection","Payment of 0.01 USDC must be included per x402 protocol or request is rejected"],"whenToPreferThis":"Choose this endpoint when you need both IQR-based statistical outlier detection and a date-by-category pivot in a single low-cost call for data ingestion batch records specifically containing received_rows and accepted_rows metrics. It is ideal for data engineers monitoring ETL pipeline health, data quality teams spot-checking batch acceptance rates, or analysts building lightweight anomaly reports without needing an external analytics platform. Prefer this over general-purpose analytics services when you want a deterministic, self-contained analysis of up to 200 ingestion records for $0.01.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:58:24.516Z","isFirstParty":false,"canonicalSlug":"data-ingestion-batches-iqr-outlier-flags-date-by-category-pivot-5c15c757"}