{"uid":"cap_5V5e-GRcy1S_XlVOCz36F","slug":"relaystation-fillna-missing-value-imputation-0c3b0bba","name":"Relaystation FillNA – Missing Value Imputation","description":"$0.0002/MB. Fill empty cells by value, forward/backward fill, mean, median, or mode. 1¢ x402 min; remainder auto-credits — relaystation.ai/penny","url":"https://api.relaystation.ai/v1/data/fillna","method":"POST","headers":{},"bodySchema":{"type":"object","$schema":"https://json-schema.org/draft/2020-12/schema","required":["input"],"properties":{"input":{"type":"object","required":["type","method","bodyType","body"],"properties":{"body":{"properties":{}},"type":{"type":"string","const":"http"},"method":{"enum":["POST","PUT","PATCH"],"type":"string"},"bodyType":{"enum":["json","form-data","text"],"type":"string"}},"additionalProperties":false},"output":{"type":"object","required":["type"],"properties":{"type":{"type":"string"},"example":{"type":"object"}}}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"registry","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_Zouu7EfuYYIbzj8xdbl_L","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":null,"unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Fills empty/null cells in tabular data using value, forward-fill, backward-fill, mean, median, or mode strategies","exampleAgentPrompt":"I have a CSV with a bunch of empty cells in the 'age' and 'salary' columns — can you fill them using the mean of each column so the dataset is complete for modeling?","exampleUseCases":[{"title":"ML pipeline missing data cleanup","prompt":"Before I train my model, fill all null values in my training dataset using column means — here's the CSV with gaps in 'age', 'income', and 'score' columns."},{"title":"Time series forward-fill for gaps","prompt":"I have sensor readings exported as a table with some missing timestamps — forward-fill the empty cells so there are no gaps in the series."},{"title":"Survey data mode imputation","prompt":"I've got a survey results table with lots of blank responses in the 'region' and 'category' columns — fill those blanks using the most common value (mode) for each column."}],"resultDescription":"Returns the input dataset with all specified null/empty cells filled according to the chosen strategy (constant value, forward-fill, backward-fill, mean, median, or mode per column). Output is the cleaned tabular data ready for downstream use.","failureModes":["Invalid or unsupported fill strategy specified","Malformed or unparseable input data format","Column referenced for fill does not exist in dataset","Numeric strategy (mean/median) applied to non-numeric column","Payload too large or malformed causing processing error","Insufficient data to compute mean/median/mode (all nulls in column)"],"whenToPreferThis":"Choose this endpoint when you need a lightweight, pay-per-use missing value imputation step in a data pipeline without spinning up a full data science environment. Ideal for preprocessing CSVs before ML training, cleaning exported spreadsheets, or handling gaps in time series data. Best when you need a simple statistical fill strategy (mean, median, mode, ffill, bfill, or constant) rather than complex model-based imputation.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T12:50:44.731Z","isFirstParty":false}