{"uid":"cap_QL6wsyaAxEoapS3Q0ILbW","slug":"feature-availability-analytics-numeric-distribution-category-15068b10","name":"Feature Availability Analytics — Numeric Distribution & Category Frequencies","description":"Feature availability — Numeric distribution + Category frequencies: Numeric distribution + Category frequencies for supplied feature availability records (populated_features, expected_features). 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-10-072?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","populated_features","expected_features"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"expected_features":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"populated_features":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","expected_features":1,"populated_features":10},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","expected_features":2,"populated_features":20},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","expected_features":3,"populated_features":15},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","expected_features":4,"populated_features":30}],"maxItems":200,"minItems":1},"field":{"enum":["populated_features","expected_features"],"type":"string","default":"populated_features"}}},"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_6aJ79afYXl-uoAroncO3o","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":"Computes numeric distribution statistics and category frequency breakdowns for feature availability records (populated_features and expected_features) across up to 200 rows.","exampleAgentPrompt":"Analyze the distribution and category frequencies for my feature availability data — here are 4 rows with date, row_id, dataset_group, populated_features, and expected_features — and report on the populated_features field.","exampleUseCases":[{"title":"Dataset feature completeness audit","prompt":"I have 150 rows of feature availability records from my ML pipeline with populated_features and expected_features counts per date and dataset group — can you compute the numeric distribution and category frequency breakdown for populated_features so I can see where my data has gaps?"},{"title":"Expected feature count profiling","prompt":"Run a distribution analysis on expected_features across these dataset records grouped by group-a and group-b — I need to understand the spread and frequency of expected feature counts before I train my model."},{"title":"Multi-group feature availability comparison","prompt":"Here's my feature availability data for 80 rows across three dataset groups from last month — analyze the numeric distribution and category frequencies for populated_features so I can compare completeness across groups."}],"resultDescription":"Returns two independent analytical reports: (1) a numeric distribution summary (e.g. min, max, mean, percentiles, histogram bins) for the selected field, and (2) a category frequency breakdown by dataset_group for the supplied feature availability records — all computed locally without external lookups.","failureModes":["Submitting more than 200 rows returns a validation error","Missing required fields (row_id, date, populated_features, expected_features) cause row rejection","Invalid date format (not YYYY-MM-DD) fails schema validation","Null values in both numeric fields may produce incomplete distribution statistics","Field enum value not matching 'populated_features' or 'expected_features' causes input rejection"],"whenToPreferThis":"Choose this endpoint when you need fast, offline statistical profiling of feature availability records — specifically numeric distribution and group-frequency reports — without any external data fetching, prediction, or live lookup. Ideal for data quality checks in ML pipelines where you supply your own rows and need two analytical views (distribution + category frequencies) in a single cheap call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T07:19:39.442Z","isFirstParty":false,"canonicalSlug":"feature-availability-analytics-numeric-distribution-category-15068b10"}