{"uid":"cap_RQNZbjLKpf_FmsZZHUu5J","slug":"feature-availability-analytics-grouped-numeric-summary-missing-value-c9bf9b88","name":"Feature Availability Analytics — Grouped Numeric Summary + Missing-Value Fill","description":"Feature availability — Grouped numeric summary + Explicit missing-value fill: Grouped numeric summary + Explicit missing-value fill 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-10-099?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"},"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_hm2-gyfw-ijuYaCnLr68Y","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 a grouped numeric summary and fills missing values for feature availability records (populated_features, expected_features) across up to 200 rows.","exampleAgentPrompt":"Summarize my feature availability records by dataset group and fill any missing populated_features values using the median — here are my rows: row-1 (group-a, 2026-10-01, populated=10, expected=1), row-2 (group-b, 2026-10-02, populated=20, expected=2), row-3 (group-a, 2026-10-03, populated=15, expected=3).","exampleUseCases":[{"title":"ML pipeline feature completeness audit","prompt":"I have up to 200 rows of feature availability data across two dataset groups for October 2026 — can you group them and give me a numeric summary of populated_features, then fill any nulls using the median?"},{"title":"Data quality check with zero-fill imputation","prompt":"Run a grouped numeric summary on my expected_features field and replace any missing values with zero so I can see where my datasets have gaps — here are my 50 rows split across group-a and group-b."},{"title":"Monthly feature population rate comparison","prompt":"I need to compare feature population rates across my dataset groups for Q4 2026 — summarize the populated_features by group and use mean imputation to handle any missing entries in my 150-row dataset."}],"resultDescription":"Two independent analytical reports: (1) a grouped numeric summary breaking down feature availability metrics (populated_features or expected_features) by dataset_group, and (2) an explicit missing-value fill report where nulls in the selected field are replaced using zero, mean, or median imputation. Both reports are computed locally with no external data fetching.","failureModes":["More than 200 rows submitted — request rejected, only up to 200 rows accepted","Invalid date format (not YYYY-MM-DD) causes schema validation failure","Missing required fields (row_id, dataset_group, date, populated_features, expected_features) causes request rejection","Invalid fill method or field enum value causes schema error","Payment of 0.01 USDC not provided or incorrect — x402 payment required"],"whenToPreferThis":"Choose this endpoint when you need lightweight, local grouped statistics and null imputation for feature availability records without any external lookups or predictions. Ideal for data pipeline health checks, ML feature store auditing, or quick dataset completeness diagnostics across up to 200 rows at minimal cost ($0.01 USDC for 2 reports).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:45.132Z","isFirstParty":false,"canonicalSlug":"feature-availability-analytics-grouped-numeric-summary-missing-value-c9bf9b88"}