{"uid":"cap_Qtwisg78pRg56SPwBdfAP","slug":"dataset-partition-sizes-category-frequencies-iqr-outlier-flags-3bcbcfa9","name":"Dataset Partition Sizes — Category Frequencies + IQR Outlier Flags","description":"Dataset partition sizes — Category frequencies + IQR outlier flags: Category frequencies + IQR outlier flags for supplied dataset partition sizes records (row_count, file_mb). 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-07-107?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","row_count","file_mb"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"file_mb":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"row_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","file_mb":1,"row_count":10,"dataset_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","file_mb":2,"row_count":20,"dataset_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","file_mb":3,"row_count":15,"dataset_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","file_mb":4,"row_count":30,"dataset_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["row_count","file_mb"],"type":"string","default":"row_count"}}},"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_2k-CH0MMoiPqXIMMk9hme","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 dataset partition size records to produce category frequency distributions and IQR-based outlier flags for row_count or file_mb fields.","exampleAgentPrompt":"Can you analyze these dataset partition records and give me both the category frequencies by dataset_group and IQR outlier flags for the row_count field? I have up to 50 rows with fields like row_id, dataset_group, date, row_count, and file_mb.","exampleUseCases":[{"title":"Detecting oversized partitions in ETL pipeline","prompt":"I have a list of up to 80 dataset partition records with row_id, dataset_group, date, row_count, and file_mb — can you flag which file_mb values are IQR outliers and also show me how frequently each dataset_group appears?"},{"title":"Auditing row count anomalies by group","prompt":"Here are 120 partition size records from our data lake — please analyze the row_count field for IQR outlier flags and give me a frequency count of how many records belong to each dataset_group category."},{"title":"Data quality check on ingestion batches","prompt":"I want to quality-check these 40 partition size records — give me category frequencies across dataset_group values and flag any row_count entries that are statistical outliers using IQR."}],"resultDescription":"Returns two analytical reports: (1) category frequency counts showing how often each dataset_group value appears across the supplied rows, and (2) IQR-based outlier flags indicating which records have row_count or file_mb values outside the interquartile range fence. Both reports are computed locally on the supplied data with no external lookups.","failureModes":["Fewer than 1 row submitted — violates minItems constraint","More than 200 rows submitted — exceeds maxItems limit","Invalid date format (not YYYY-MM-DD) causes schema validation failure","Missing required fields (row_id, dataset_group, date, row_count, file_mb) cause rejection","Invalid field enum value other than 'row_count' or 'file_mb' causes error","Null numeric values may reduce IQR calculation reliability if too many are missing","Payment of 0.01 USDC not included causes 402 response"],"whenToPreferThis":"Choose this endpoint when you need both categorical distribution analysis and numeric outlier detection on dataset partition metadata in a single call. It is ideal for data engineers monitoring ETL pipeline health, auditing data lake partition sizes, or doing lightweight statistical QA on batch ingestion records — without needing an external analytics platform. Best when you have up to 200 records and want IQR-based flagging rather than z-score or ML-based anomaly detection.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:36.659Z","isFirstParty":false,"canonicalSlug":"dataset-partition-sizes-category-frequencies-iqr-outlier-flags-3bcbcfa9"}