{"uid":"cap_Bejns6nu7-1in6HbUGkgV","slug":"customer-segment-observations-date-category-pivot-missing-value-fill-e16eb2c1","name":"Customer Segment Observations — Date×Category Pivot + Missing-Value Fill","description":"Customer segment observations — Date by category pivot + Explicit missing-value fill: Date by category pivot + Explicit missing-value fill for supplied customer segment observations records (account_count, revenue_amount). 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-company-16-189?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","company_group","date","account_count","revenue_amount"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"account_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"company_group":{"type":"string","default":"","maxLength":80},"revenue_amount":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","account_count":10,"company_group":"group-a","revenue_amount":1},{"date":"2026-10-02","row_id":"row-2","account_count":20,"company_group":"group-b","revenue_amount":2},{"date":"2026-10-03","row_id":"row-3","account_count":15,"company_group":"group-a","revenue_amount":3},{"date":"2026-10-04","row_id":"row-4","account_count":30,"company_group":"group-b","revenue_amount":4}],"maxItems":200,"minItems":1},"field":{"enum":["account_count","revenue_amount"],"type":"string","default":"account_count"},"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_TTKqncFiGMwjgqLMlRmfU","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":"Pivots customer segment observation rows by date and company group, then fills missing values using zero, mean, or median imputation, for account_count or revenue_amount fields.","exampleAgentPrompt":"Can you pivot my customer segment observations by date and company group for the account_count field — I have up to 50 rows with some nulls, so please fill missing values using median imputation?","exampleUseCases":[{"title":"Revenue pivot with zero fill","prompt":"I have monthly revenue_amount records for several customer groups with some missing entries — can you pivot them by date and company group, filling any gaps with zero?"},{"title":"Account count matrix for reporting","prompt":"Take these customer segment rows and produce a date-by-company-group pivot table for account_count, filling missing cells with the mean value across each column."},{"title":"Segment gap analysis before modeling","prompt":"I need to clean up my segment observation dataset before feeding it into a model — pivot it by date and company_group for revenue_amount and impute any missing values using the median."}],"resultDescription":"Returns up to 2 analytical reports: a date-by-category pivot table for the specified field (account_count or revenue_amount) with missing values explicitly filled using the chosen method (zero, mean, or median). The output is computed locally from the supplied rows with no external lookups.","failureModes":["Row count exceeds 200 limit — request rejected","Invalid date format not matching YYYY-MM-DD pattern","Missing required fields (row_id, company_group, date, account_count, revenue_amount) in any row","Invalid enum value for field or method parameters","Empty rows array — no data to process"],"whenToPreferThis":"Choose this endpoint when you need to reshape flat customer segment observation records into a date-by-category pivot table and handle missing values in a single step, especially when working with account_count or revenue_amount metrics across company groups. Ideal for pre-processing segment data before reporting or modeling when null imputation is required alongside pivoting.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T12:48:57.574Z","isFirstParty":false,"canonicalSlug":"customer-segment-observations-date-category-pivot-missing-value-fill-e16eb2c1"}