{"uid":"cap_g-0ojBoZAbCR-VKEMNKmI","slug":"promotion-observations-string-normalisation-explicit-missing-value-fill-a8851622","name":"Promotion Observations — String Normalisation + Explicit Missing-Value Fill","description":"Promotion observations — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill for supplied promotion observations records (discount_amount, units_sold). 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-retail-05-037?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","store_group","date","discount_amount","units_sold"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"units_sold":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"store_group":{"type":"string","default":"","maxLength":80},"discount_amount":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","units_sold":1,"store_group":"group-a","discount_amount":10},{"date":"2026-10-02","row_id":"row-2","units_sold":2,"store_group":"group-b","discount_amount":20},{"date":"2026-10-03","row_id":"row-3","units_sold":3,"store_group":"group-a","discount_amount":15},{"date":"2026-10-04","row_id":"row-4","units_sold":4,"store_group":"group-b","discount_amount":30}],"maxItems":200,"minItems":1},"field":{"enum":["discount_amount","units_sold"],"type":"string","default":"discount_amount"},"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_43cvmlwVmylKRRlW7nsvI","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 strings and fills missing values (zero, mean, or median) for promotion observation records containing discount_amount and units_sold, producing 2 analytical reports for up to 200 rows.","exampleAgentPrompt":"Clean up my promotion observations data — I have 150 rows with store_group, date, discount_amount, and units_sold fields, some discount_amount values are missing. Fill the missing discount_amount values using median imputation and normalise the strings, then give me the 2 reports.","exampleUseCases":[{"title":"Imputing missing discount amounts for retail promos","prompt":"I have a batch of 80 promotion records from our retail stores — some discount_amount entries are null. Can you fill those missing values using the mean and normalise all the string fields like store_group? I need both clean-data reports back."},{"title":"Zero-filling missing units sold before campaign review","prompt":"Before our weekly promo review, I need to fill in any missing units_sold figures with zero across my 120 promotion observation rows and clean up the store group strings. Can you run string normalisation and zero-fill on the units_sold field and give me the two reports?"},{"title":"Standardising promotion data from multiple store groups","prompt":"We pulled promotion data from three store groups over the past month — about 60 rows total — but the store_group strings are inconsistent and some discount_amount values are missing. Can you normalise the strings and fill those gaps using median, then send me the two analytical reports?"}],"resultDescription":"Two analytical reports covering the submitted promotion observation rows: one reflecting string-normalised records and one with explicit missing-value fills applied to the chosen field (discount_amount or units_sold) using the selected method (zero, mean, or median). Covers up to 200 input rows with no external lookups or predictions.","failureModes":["More than 200 rows submitted — request rejected with validation error","Invalid date format (not YYYY-MM-DD) — schema validation failure","Missing required fields (row_id, store_group, date) — rejected","Field enum value not in [discount_amount, units_sold] — rejected","Method enum value not in [zero, mean, median] — rejected","Numeric values outside allowed range (-1e12 to 1e12) — validation error"],"whenToPreferThis":"Use this endpoint when you have promotion observation records (with discount_amount and units_sold) that need string normalisation and missing-value imputation in a single call, and you want two separate analytical reports for up to 200 rows at low cost. Prefer this over general-purpose data cleaning services when your data fits the retail promotion schema (store_group, date, discount_amount, units_sold) and you need both normalisation and fill operations reported together without sending data to external predictive or ML services.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T12:47:21.996Z","isFirstParty":false,"canonicalSlug":"promotion-observations-string-normalisation-explicit-missing-value-fill-a8851622"}