diff --git a/CHANGELOG.md b/CHANGELOG.md
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # Revision history for dataframe
 
-## 0.4.0.2
+## 0.4.0.3
 * Improved performance for folds and reductions.
 * Improve standalone mean and correlation functions.
 * Remove buggy boxedness check in aggregations.
diff --git a/app/Benchmark.hs b/app/Benchmark.hs
--- a/app/Benchmark.hs
+++ b/app/Benchmark.hs
@@ -1,6 +1,5 @@
 {-# LANGUAGE NumericUnderscores #-}
 {-# LANGUAGE OverloadedStrings #-}
-{-# LANGUAGE TemplateHaskell #-}
 {-# LANGUAGE TypeApplications #-}
 
 import Data.Time
@@ -9,36 +8,31 @@
 import qualified DataFrame.Functions as F
 import System.Random.Stateful
 
-import Data.Text (Text)
-import DataFrame ((|>))
-import DataFrame.DecisionTree
-import DataFrame.Functions ((.=))
-
-$(F.declareColumnsFromCsvFile "../../Downloads/playground-series-s5e11/train.csv")
-
 main :: IO ()
 main = do
-    train <- D.readCsv "../../Downloads/playground-series-s5e11/train.csv"
-    -- Create a new symbol for loan paid back since we are changing the type.
-    let (loanPaidBack, train') =
-            train
-                |> D.deriveWithExpr
-                    (F.name loan_paid_back)
-                    (F.lift (round @Double @Int) loan_paid_back)
-
-    let model = fitDecisionTree (TreeConfig 15 2) loanPaidBack (train' |> D.exclude ["id"])
-    let trainPred = D.derive "prediction" model train'
-    print $
-        trainPred
-            |> D.groupBy [F.name loanPaidBack, "prediction"]
-            |> D.aggregate ["count" .= F.count loanPaidBack]
-            |> D.sortBy [D.Desc "prediction", D.Desc (F.name loanPaidBack)]
-
-    test <- D.readCsv "../../Downloads/playground-series-s5e11/test.csv"
-    let withPredictions = D.derive "prediction" model test
-    D.writeCsv
-        "predictions.csv"
-        ( withPredictions
-            |> D.select ["id", "prediction"]
-            |> D.rename "prediction" (F.name loan_paid_back)
-        )
+    let n = 100_000_000
+    g <- newIOGenM =<< newStdGen
+    let range = (0 :: Double, 1 :: Double)
+    startGeneration <- getCurrentTime
+    ns <- VU.replicateM n (uniformRM range g)
+    xs <- VU.replicateM n (uniformRM range g)
+    ys <- VU.replicateM n (uniformRM range g)
+    let df = D.fromUnnamedColumns (map D.fromUnboxedVector [ns, xs, ys])
+    print df
+    endGeneration <- getCurrentTime
+    let generationTime = diffUTCTime endGeneration startGeneration
+    putStrLn $ "Data generation Time: " ++ show generationTime
+    startCalculation <- getCurrentTime
+    print $ D.mean (F.col @Double "0") df
+    print $ D.variance (F.col @Double "1") df
+    print $ D.correlation "1" "2" df
+    endCalculation <- getCurrentTime
+    let calculationTime = diffUTCTime endCalculation startCalculation
+    putStrLn $ "Calculation Time: " ++ show calculationTime
+    startFilter <- getCurrentTime
+    print $ D.filter (F.col @Double "0") (> 0.971) df D.|> D.take 10
+    endFilter <- getCurrentTime
+    let filterTime = diffUTCTime endFilter startFilter
+    putStrLn $ "Filter Time: " ++ show filterTime
+    let totalTime = diffUTCTime endFilter startGeneration
+    putStrLn $ "Total Time: " ++ show totalTime
diff --git a/dataframe.cabal b/dataframe.cabal
--- a/dataframe.cabal
+++ b/dataframe.cabal
@@ -1,6 +1,6 @@
 cabal-version:      2.4
 name:               dataframe
-version:            0.4.0.2
+version:            0.4.0.3
 
 synopsis: A fast, safe, and intuitive DataFrame library.