{"uid":"cap_yJ7rNKju2-37rPoaD5Rsy","slug":"customer-segment-observations-string-normalisation-explicit-missing-d4f26b61","name":"Customer Segment Observations — String Normalisation + Explicit Missing-Value Fill","description":"Customer segment observations — String normalisation + Explicit missing-value fill: String normalisation + 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-16-037?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_T23EZgsytWnL20_v9ThLw","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":"Normalises string fields and fills missing values (account_count, revenue_amount) in customer segment observation records using zero, mean, or median imputation, returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you clean my customer segment data — normalise the company_group strings and fill missing revenue_amount values using median imputation? I have about 150 rows with fields: row_id, company_group, date, account_count, and revenue_amount.","exampleUseCases":[{"title":"Imputing missing revenue in segment data","prompt":"I have 120 customer segment rows with some null revenue_amount entries. Can you fill those missing values using median imputation and also normalise the company_group strings for me?"},{"title":"Zero-filling missing account counts","prompt":"My segment observation dataset has blank account_count values scattered across about 80 rows. Please fill those nulls with zero and clean up the company group labels."},{"title":"Pre-analysis data prep for segment report","prompt":"Before I run my segment analysis, I need to clean the data — normalise company_group text and use mean imputation on the account_count field across my 200 customer segment records."}],"resultDescription":"Returns two independent analytical reports per call covering: (1) string-normalised company_group fields and (2) explicitly filled missing values for the chosen numeric field (account_count or revenue_amount) using the specified method (zero, mean, or median), applied across up to 200 input rows. No external lookups or predictions are included.","failureModes":["More than 200 rows submitted — exceeds maxItems limit","Invalid date format (not YYYY-MM-DD) causes validation error","row_id missing or empty string fails minLength constraint","Invalid method value outside zero/mean/median enum","Invalid field value outside account_count/revenue_amount enum","Numeric values outside ±1,000,000,000,000 range rejected"],"whenToPreferThis":"Choose this endpoint when you need lightweight, local, deterministic data cleaning on customer segment observation records — specifically string normalisation of categorical fields (company_group) combined with missing-value imputation for numeric metrics (account_count or revenue_amount). Ideal for preprocessing pipelines where you want two bundled reports at low cost ($0.01) without any external API calls, predictions, or live data lookups, and your dataset fits within 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-25T00:51:55.171Z","isFirstParty":false,"canonicalSlug":"customer-segment-observations-string-normalisation-explicit-missing-d4f26b61"}