{"uid":"cap_OPJevGNa1w7VkFzMEsLsJ","slug":"feature-availability-analytics-equal-width-histogram-date-by-category-53ee1423","name":"Feature Availability Analytics: Equal-Width Histogram + Date-by-Category Pivot","description":"Feature availability — Equal-width histogram + Date by category pivot: Equal-width histogram + Date by category pivot 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-10-173?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"}}},"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_WLPl7CbLRnMzGOGiNaKRx","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":"Generates two local analytical reports — an equal-width histogram and a date-by-category pivot table — for supplied feature availability (populated_features, expected_features) records, up to 200 rows.","exampleAgentPrompt":"Can you run a histogram and date-by-category pivot on my feature availability data? I have 4 rows — row-1 in group-a on 2026-10-01 with 10 populated and 1 expected, row-2 in group-b on 2026-10-02 with 20 populated and 2 expected, row-3 in group-a on 2026-10-03 with 15 populated and 3 expected, row-4 in group-b on 2026-10-04 with 30 populated and 4 expected — use 5 bins and analyze the populated_features field.","exampleUseCases":[{"title":"ML dataset completeness audit","prompt":"I want to see the distribution and a date-category breakdown of populated_features across my two dataset groups. Here are 6 rows: row-1 group-a 2026-09-01 pop=50 exp=60, row-2 group-b 2026-09-01 pop=40 exp=60, row-3 group-a 2026-09-02 pop=55 exp=60, row-4 group-b 2026-09-02 pop=35 exp=60, row-5 group-a 2026-09-03 pop=58 exp=60, row-6 group-b 2026-09-03 pop=30 exp=60. Use 6 bins on the populated_features field."},{"title":"Expected feature gap analysis","prompt":"Can you histogram and pivot my expected_features data by date and group? Rows: row-1 group-x 2026-11-01 pop=100 exp=120, row-2 group-y 2026-11-01 pop=80 exp=90, row-3 group-x 2026-11-02 pop=110 exp=120, row-4 group-y 2026-11-02 pop=75 exp=90. Use 4 bins analyzing the expected_features field."},{"title":"Daily feature population trend by team","prompt":"Run a histogram with 8 bins and a date-by-category pivot on the populated_features for my team data: row-1 team-alpha 2026-12-01 pop=200 exp=250, row-2 team-beta 2026-12-01 pop=180 exp=250, row-3 team-alpha 2026-12-02 pop=220 exp=250, row-4 team-beta 2026-12-02 pop=160 exp=250, row-5 team-alpha 2026-12-03 pop=240 exp=250, row-6 team-beta 2026-12-03 pop=150 exp=250."}],"resultDescription":"Returns two independent analytical reports computed locally: (1) an equal-width histogram showing the frequency distribution of values in the selected field (populated_features or expected_features) divided into the requested number of bins, and (2) a date-by-category pivot table aggregating the selected field values grouped by date and dataset_group. Results cover up to 200 input rows. No external lookups or predictions are made.","failureModes":["More than 200 rows submitted — request will be rejected or truncated","Invalid date format (not YYYY-MM-DD) causes row rejection","bins parameter outside 1–30 range causes validation error","All values null for selected field produces empty histogram","Missing required row fields (row_id, dataset_group, date, populated_features, expected_features) cause row-level errors","Payment of 0.01 USDC not included causes 402 rejection"],"whenToPreferThis":"Choose this endpoint when you need both a frequency histogram and a date-by-category pivot table computed together on feature availability data (populated_features or expected_features) in a single low-cost call. It is ideal for data quality audits, ML feature completeness monitoring, and dataset group comparisons when you already have the raw records and need fast local analytics without external lookups or predictions. Prefer this over general-purpose analytics tools when working with feature store or dataset metadata containing row_id, dataset_group, and date dimensions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:47:01.472Z","isFirstParty":false,"canonicalSlug":"feature-availability-analytics-equal-width-histogram-date-by-category-53ee1423"}