ExternalIODemo.hs

{-# LANGUAGE OverloadedStrings #-}
-- | Hanalyze.DataIO.External のデモ。
--
-- Hackage 'dataframe' ライブラリ経由で CSV を読み込み:
-- - 列ごとの自動型推論結果
-- - 欠損値の検出
-- - imputeMean で欠損補完
import qualified Data.Text as T

import Hanalyze.DataIO.CSV         (loadCSV)
import Hanalyze.DataIO.Preprocess  (countMissing, imputeMean)
import qualified DataFrame                 as DX
import qualified DataFrame.Internal.DataFrame as DXD
import qualified DataFrame.Internal.Column as DXC
import Text.Printf        (printf)

testCSV :: String
testCSV = unlines
  [ "name,age,score,group"
  , "Alice,30,95.5,A"
  , "Bob,25,88.0,B"
  , "Carol,35,,A"            -- score 欠損
  , "Dave,,77.2,B"            -- age 欠損
  , "Eve,42,NA,C"             -- score "NA"
  ]

main :: IO ()
main = do
  let path = "/tmp/external_demo.csv"
  writeFile path testCSV

  putStrLn "=================================="
  putStrLn " Hanalyze.DataIO.External Demo"
  putStrLn "=================================="
  putStrLn ""

  putStrLn "--- loadCSV (Hackage dataframe) ---"
  Right df <- loadCSV path
  printDFTypes df
  putStrLn ""

  putStrLn "--- countMissing ---"
  mapM_ (\(c, m) ->
    if m > 0 then printf "  %s: %d missing\n" (T.unpack c) m
             else printf "  %s: complete\n"   (T.unpack c))
    (countMissing df)
  putStrLn ""

  putStrLn "--- imputeMean \"score\" ---"
  case imputeMean "score" df of
    Just df3 -> do
      printDFTypes df3
      printf "  → score is now numeric (mean-imputed for NA rows)\n"
    Nothing -> putStrLn "  imputeMean failed"
  putStrLn ""

  putStrLn "Done."

printDFTypes :: DXD.DataFrame -> IO ()
printDFTypes df = do
  let (rows, ncols) = DX.dimensions df
  printf "  Rows: %d, Columns: %d\n" rows ncols
  mapM_ (\n -> case DXD.getColumn n df of
           Just c  -> printf "    %-10s : %s (len=%d)\n"
                        (T.unpack n)
                        (DXC.columnTypeString c)
                        (DXC.columnLength c)
           Nothing -> printf "    %-10s : <missing>\n" (T.unpack n))
        (DX.columnNames df)