{"uid":"cap_g-Qw0thUTShb1E17dKAeK","slug":"water-service-coverage-daily-sum-series-with-missing-value-fill-a1890220","name":"Water Service Coverage Daily Sum Series with Missing Value Fill","description":"Water service coverage — Daily sum series + Explicit missing-value fill: Daily sum series + Explicit missing-value fill for supplied water service coverage records (served_households, total_households). 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-economics-19-145?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","region","date","served_households","total_households"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"region":{"type":"string","default":"","maxLength":80},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"total_households":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"served_households":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","region":"group-a","row_id":"row-1","total_households":1,"served_households":10},{"date":"2026-10-02","region":"group-b","row_id":"row-2","total_households":2,"served_households":20},{"date":"2026-10-03","region":"group-a","row_id":"row-3","total_households":3,"served_households":15},{"date":"2026-10-04","region":"group-b","row_id":"row-4","total_households":4,"served_households":30}],"maxItems":200,"minItems":1},"field":{"enum":["served_households","total_households"],"type":"string","default":"served_households"},"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_HYSwCHPTT5Uaj1dIretg5","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":"Computes daily sum time series and fills missing values for water service coverage data (served_households, total_households) across regions, returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"I have a dataset of daily water service records for two regions with some missing served_households values — can you compute the daily sum series and fill the missing values using median imputation? Here are up to 200 rows with row_id, region, date, served_households, and total_households.","exampleUseCases":[{"title":"Municipal water coverage gap analysis","prompt":"I've got daily water service records for three districts over the past quarter and some served_households entries are null — can you compute the daily sum series and fill those gaps using median values so I can see complete coverage trends?"},{"title":"Utility reporting with zero-fill for missing days","prompt":"I need a daily total of total_households served across all regions in my dataset, and wherever data is missing I want it filled with zero — can you run that aggregation and give me two reports covering both metrics?"},{"title":"Regional household water access trend preparation","prompt":"Before I hand off this water service dataset to our analytics team, can you compute the daily sum series for served_households and use mean imputation to fill any missing entries? The dataset has about 150 rows across multiple regions."}],"resultDescription":"Two analytical reports: (1) a daily sum series aggregating the specified field (served_households or total_households) by date, and (2) an explicit missing-value fill report showing imputed values using the chosen method (zero, mean, or median), covering up to 200 input rows.","failureModes":["Missing required fields (row_id, region, date) cause validation errors","Date values not matching YYYY-MM-DD pattern are rejected","Input array exceeding 200 rows is rejected","Invalid enum value for method or field returns error","Numeric values outside ±1,000,000,000,000 range cause validation failure","Empty rows array (minItems:1 not satisfied) returns error"],"whenToPreferThis":"Choose this endpoint when you need both a daily sum time series and explicit missing-value imputation on water service household data in a single call. It is purpose-built for water utility coverage records with the specific fields served_households and total_households, and returns two reports simultaneously for the cost of one call. Prefer it over generic analytics tools when working with regional, date-keyed household coverage data at up to 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-29T13:16:27.486Z","isFirstParty":false,"canonicalSlug":"water-service-coverage-daily-sum-series-with-missing-value-fill-a1890220"}