{"uid":"cap_kzRR3CITpJ7hb1CEAIIkv","slug":"dataset-partition-sizes-category-frequencies-equal-width-histogram-3d03fdaf","name":"Dataset Partition Sizes — Category Frequencies + Equal-Width Histogram","description":"Dataset partition sizes — Category frequencies + Equal-width histogram: Category frequencies + Equal-width histogram 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-07-106?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"bins":{"type":"integer","default":5,"maximum":30,"minimum":1},"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_8tSYrAoRn1BDE4Fb6RX--","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 (row_count or file_mb) by generating category frequency counts and an equal-width histogram in a single call.","exampleAgentPrompt":"Analyze my dataset partitions and give me category frequencies by dataset_group plus a 10-bin histogram of the row_count field — here are the records: [{\"row_id\":\"row-1\",\"dataset_group\":\"group-a\",\"date\":\"2026-10-01\",\"row_count\":10,\"file_mb\":1},{\"row_id\":\"row-2\",\"dataset_group\":\"group-b\",\"date\":\"2026-10-02\",\"row_count\":20,\"file_mb\":2}].","exampleUseCases":[{"title":"Partition skew detection in data lake","prompt":"I have 150 dataset partition records with row_count and file_mb values — can you generate category frequencies by dataset_group and a 10-bin histogram of row_count so I can spot if any groups are massively larger than others?"},{"title":"File size distribution audit","prompt":"Run a 5-bin equal-width histogram on the file_mb field for these partition records, and also give me how many partitions fall into each dataset_group category — I want to see if any partition groups have unusually large files."},{"title":"Data pipeline monitoring snapshot","prompt":"Here are my daily dataset partition stats for the last 30 days — give me category frequencies by dataset_group and a 20-bin histogram of row_count so I can understand the overall shape of partition sizes."}],"resultDescription":"Returns two analytical reports: (1) category frequency counts broken down by dataset_group, showing how many records fall into each group; and (2) an equal-width histogram of the chosen numeric field (row_count or file_mb) divided into the specified number of bins, with counts per bin. Both reports are computed locally from the supplied data with no external lookups.","failureModes":["More than 200 rows submitted — request will be rejected or truncated","Invalid date format (not YYYY-MM-DD) causes row validation failure","Missing required fields (row_id, dataset_group, date, row_count, file_mb) cause item rejection","bins value outside 1–30 range is rejected","Null numeric values in the target field may reduce histogram accuracy","Non-numeric values in row_count or file_mb fields cause parsing errors"],"whenToPreferThis":"Choose this endpoint when you have up to 200 dataset partition records and need both a categorical frequency breakdown (by dataset_group) and a numeric distribution histogram (of row_count or file_mb) in a single inexpensive call. It is ideal for data engineers auditing partition skew, monitoring data pipeline outputs, or exploring the shape of partition size distributions without needing a full analytics platform. It is not suitable for real-time streaming data, datasets exceeding 200 rows per call, or tasks requiring prediction, anomaly detection, or external data enrichment.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-23T18:54:31.427Z","isFirstParty":false,"canonicalSlug":"dataset-partition-sizes-category-frequencies-equal-width-histogram-3d03fdaf"}