{"uid":"cap_OBEsSVroJ7tHUKPsICdtW","slug":"company-headcount-observations-category-frequencies-explicit-missing-fa2472f7","name":"Company Headcount Observations — Category Frequencies + Explicit Missing-Value Fill","description":"Company headcount observations — Category frequencies + Explicit missing-value fill: Category frequencies + Explicit missing-value fill for supplied company headcount observations records (headcount, open_roles). 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-12-112?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","company_group","date","headcount","open_roles"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"headcount":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"open_roles":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"company_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","headcount":10,"open_roles":1,"company_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","headcount":20,"open_roles":2,"company_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","headcount":15,"open_roles":3,"company_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","headcount":30,"open_roles":4,"company_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["headcount","open_roles"],"type":"string","default":"headcount"},"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_nop4Pa6PbLfl-YHmysxPV","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":"Analyzes up to 200 company headcount observation rows to produce category frequency distributions and fill missing values in headcount or open_roles fields using zero, mean, or median imputation.","exampleAgentPrompt":"Analyze my company headcount observation records — I have up to 50 rows with company_group, date, headcount, and open_roles fields — give me category frequencies for company groups and fill any missing headcount values using median imputation.","exampleUseCases":[{"title":"Headcount data quality audit","prompt":"I have 120 rows of company headcount observations across groups like 'engineering', 'sales', and 'ops' with some nulls in the headcount column — can you show me how often each group appears and fill the missing headcount values using median?"},{"title":"Open roles gap filling for reporting","prompt":"My workforce dataset has missing open_roles values for some dates. Can you compute the category frequencies for my company groups and fill those gaps using mean imputation so I have a clean dataset for my report?"},{"title":"Zero-fill missing headcount before pipeline","prompt":"Before I run my analytics pipeline, I need to handle nulls in the headcount field across 80 rows of observations for group-a and group-b. Fill the missing values with zero and also give me the frequency breakdown by company group."}],"resultDescription":"Returns two analytical reports: (1) a category frequency table showing how often each company_group value appears in the dataset, and (2) a missing-value fill report for the specified field (headcount or open_roles) where nulls are replaced using the chosen method (zero, mean, or median). Results are computed locally without external lookups.","failureModes":["Input array exceeds 200 rows — request will be rejected","Missing required fields (row_id, company_group, date, headcount, open_roles) cause validation errors","Invalid date format (not YYYY-MM-DD) triggers schema rejection","Invalid method value outside zero/mean/median causes enum validation failure","Invalid field value outside headcount/open_roles causes enum validation failure","Empty rows array (minItems: 1 not met) returns error","Payment of 0.01 USDC not provided or rejected blocks execution"],"whenToPreferThis":"Choose this endpoint when you need both a category frequency summary and missing-value imputation for company headcount data in a single low-cost call. It is ideal for data cleaning pipelines where you want to understand group distributions and fill nulls in headcount or open_roles before downstream analysis. Prefer it over generic analytics tools when your data fits the headcount/open_roles schema and you want deterministic, locally-computed results without external data dependencies.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:45:34.449Z","isFirstParty":false,"canonicalSlug":"company-headcount-observations-category-frequencies-explicit-missing-fa2472f7"}