{"uid":"cap_CJQwAo8OFH0cxEzYz3kYM","slug":"travel-administration-numeric-interval-filter-explicit-missing-value-44851f41","name":"Travel Administration — Numeric Interval Filter + Explicit Missing-Value Fill","description":"Travel administration — Numeric interval filter + Explicit missing-value fill: Numeric interval filter + Explicit missing-value fill for supplied travel administration records (trip_count, recorded_cost). 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-operations-14-135?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","team_group","date","trip_count","recorded_cost"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"team_group":{"type":"string","default":"","maxLength":80},"trip_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"recorded_cost":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","team_group":"group-a","trip_count":10,"recorded_cost":1},{"date":"2026-10-02","row_id":"row-2","team_group":"group-b","trip_count":20,"recorded_cost":2},{"date":"2026-10-03","row_id":"row-3","team_group":"group-a","trip_count":15,"recorded_cost":3},{"date":"2026-10-04","row_id":"row-4","team_group":"group-b","trip_count":30,"recorded_cost":4}],"maxItems":200,"minItems":1},"field":{"enum":["trip_count","recorded_cost"],"type":"string","default":"trip_count"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"},"maximum":{"type":"number","default":100,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":0,"maximum":1000000000000,"minimum":-1000000000000}}},"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_1H5u-tbwddgNtujBkZDeQ","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":"Filters travel administration rows (trip_count or recorded_cost) to a specified numeric interval and fills missing values using zero, mean, or median imputation, returning up to 2 reports for up to 200 rows.","exampleAgentPrompt":"Filter my travel administration records so that trip_count stays between 5 and 50, and fill any missing trip_count values using median imputation — I have about 120 rows with fields like row_id, team_group, date, trip_count, and recorded_cost.","exampleUseCases":[{"title":"Clean travel cost data for reporting","prompt":"I have 150 rows of travel admin records with some missing recorded_cost values and a few outliers — can you filter recorded_cost to only keep values between 0 and 5000, and fill in the missing ones using the mean?"},{"title":"Prep trip count data for budget analysis","prompt":"Filter my travel dataset so trip_count is between 1 and 100, and replace any null trip_count entries with zero — here are my rows with row_id, team_group, date, trip_count, and recorded_cost fields."},{"title":"Impute missing values before team performance review","prompt":"Before our quarterly team review I need to clean up the travel data: keep only rows where trip_count falls between 10 and 200, and use median fill for any missing trip_count values across these 80 records."}],"resultDescription":"Returns up to 2 analytical reports covering: (1) rows filtered to the specified numeric interval for the chosen field (trip_count or recorded_cost), and (2) the dataset with missing values in the chosen field explicitly filled using the selected method (zero, mean, or median). Results are based entirely on the supplied input rows with no external lookups.","failureModes":["Input rows array is empty or missing — returns validation error","More than 200 rows supplied — request rejected","Invalid date format in rows — schema validation failure","Minimum value exceeds maximum value — may produce empty filtered result set","Field or method enum value not recognized — schema validation error","All values for chosen field are null — mean/median imputation may return zero or undefined"],"whenToPreferThis":"Choose this endpoint when you need to simultaneously apply numeric range filtering and missing-value imputation to travel administration data (trip_count or recorded_cost) in a single low-cost call. It is ideal for data cleaning pipelines where you want to bound outliers and handle nulls before downstream reporting, and when your dataset is 200 rows or fewer and you do not need live data lookups or predictive analytics.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T06:48:23.624Z","isFirstParty":false,"canonicalSlug":"travel-administration-numeric-interval-filter-explicit-missing-value-44851f41"}