{"uid":"cap_l7YZR194liNsHr-IF5bq9","slug":"feature-availability-analytics-equal-width-histogram-missing-value-fill-fbe51fa9","name":"Feature Availability Analytics: Equal-Width Histogram + Missing-Value Fill","description":"Feature availability — Equal-width histogram + Explicit missing-value fill: Equal-width histogram + 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-10-175?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","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_WA7sYjW4WNl0KTHTWJUFk","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 an equal-width histogram and applies explicit missing-value fill on feature availability records (populated_features, expected_features) for up to 200 rows, returning 2 analytical reports.","exampleAgentPrompt":"Analyze my feature availability data — here are 50 rows with populated_features and expected_features — give me an equal-width histogram with 10 bins and fill any missing populated_features values using the median.","exampleUseCases":[{"title":"Dataset completeness distribution audit","prompt":"I have 120 rows of feature availability records across two dataset groups from October. Can you bucket the populated_features values into 8 equal-width bins and show me the histogram, and also fill any nulls with the mean?"},{"title":"ML pipeline missing-value imputation","prompt":"Before I train my model, I need to clean up my feature availability dataset — fill any missing expected_features values with zero and also give me a 5-bin histogram so I can see how the data is spread across groups."},{"title":"Daily feature coverage trend analysis","prompt":"Here's my feature availability log for 200 rows dated across Q3 — can you run a histogram with 15 bins on the populated_features column and fill missing values using median so I can understand the coverage distribution?"}],"resultDescription":"Returns two analytical reports: (1) an equal-width histogram breaking the selected field into the specified number of bins with counts per bin, and (2) a report showing missing-value fills applied to null entries using the chosen method (zero, mean, or median). Both reports are computed locally from the supplied rows.","failureModes":["More than 200 rows submitted — request will be rejected","Missing required fields (row_id, date, populated_features, expected_features) on any row — validation error","bins value outside 1–30 range — rejected","Invalid date format (must be YYYY-MM-DD) — parse error","All values null with fill method mean/median — imputation undefined","field enum value not recognized — error"],"whenToPreferThis":"Choose this endpoint when you need to quickly profile feature availability data with a histogram and impute missing values in a single call, without any external data lookups or ML model involvement. Ideal for data quality checks before ML training or ETL validation where both distribution analysis and null handling are needed together on small-to-medium datasets (≤200 rows).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:46:18.191Z","isFirstParty":false,"canonicalSlug":"feature-availability-analytics-equal-width-histogram-missing-value-fill-fbe51fa9"}