데이터 전처리

데이터 분석
공개

2025년 1월 2일

데이터 전처리의 의미

  1. 데이터 클리닝
  2. 데이터 통합
  3. 데이터 변환
  4. 데이터 축소
  5. 불균형 데이터 처리
  6. 데이터 분할

이상치 확인 및 정제

이상치 확인

import pandas as pd
import matplotlib.pyplot as plt
from pandas.core.common import random_state
from sklearn.datasets import load_wine

wine_load = load_wine()
wine = pd.DataFrame(wine_load.data, columns=wine_load.feature_names)
wine['class'] = wine_load.target
wine['class'] = wine['class'].map({0: 'class_0', 1: 'class_1', 2: 'class_2'})

plt.boxplot(wine['color_intensity'], whis=1.5)
plt.title('Boxplot of color_intensity')
plt.show()

import numpy as np

def outliers_iqr(dt, col):
    q1, q3 = np.percentile(dt[col], [25, 75])
    iqr = q3 - q1
    lower_bound = q1 - (iqr * 1.5)
    upper_bound = q3 + (iqr * 1.5)
    return dt[(dt[col] < lower_bound) | (dt[col] > upper_bound)]

outliers = outliers_iqr(wine, 'color_intensity')
outliers
alcohol malic_acid ash alcalinity_of_ash magnesium total_phenols flavanoids nonflavanoid_phenols proanthocyanins color_intensity hue od280/od315_of_diluted_wines proline class
151 12.79 2.67 2.48 22.0 112.0 1.48 1.36 0.24 1.26 10.80 0.48 1.47 480.0 class_2
158 14.34 1.68 2.70 25.0 98.0 2.80 1.31 0.53 2.70 13.00 0.57 1.96 660.0 class_2
159 13.48 1.67 2.64 22.5 89.0 2.60 1.10 0.52 2.29 11.75 0.57 1.78 620.0 class_2
166 13.45 3.70 2.60 23.0 111.0 1.70 0.92 0.43 1.46 10.68 0.85 1.56 695.0 class_2

이상치 정제

  1. 이상치 제거
drop_outliers = wine.drop(index=outliers.index)

print("Original:", wine.shape)
print("Drop outliers:", drop_outliers.shape)
Original: (178, 14)
Drop outliers: (174, 14)
  1. 이상치 대체

이상치를 NULL로 만든 후, 결측치와 함께 대체

wine.loc[outliers.index, 'color_intensity'] = np.NaN

wine['color_intensity'].fillna(wine['color_intensity'].mean(), inplace=True)
wine.loc[outliers.index, 'color_intensity']
151    4.908678
158    4.908678
159    4.908678
166    4.908678
Name: color_intensity, dtype: float64

범주형 데이터 처리

from sklearn.datasets import load_iris

iris = load_iris()
iris = pd.DataFrame(iris.data, columns=iris.feature_names)
iris['Class'] = load_iris().target
iris['Class'] = iris['Class'].map({0: 'Setosa', 
                                   1:'Versicolour', 
                                   2: 'Virginica'})

데이터 분할

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(iris.drop(
  columns='Class'), iris['Class'], test_size=0.2, random_state=1004)
print('X_train: ', X_train.shape, 'X_test: ', X_test.shape)
print('y_train: ', y_train.shape, 'y_test: ', y_test.shape)
X_train:  (120, 4) X_test:  (30, 4)
y_train:  (120,) y_test:  (30,)
X_train.head(3)
sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)
87 6.3 2.3 4.4 1.3
67 5.8 2.7 4.1 1.0
131 7.9 3.8 6.4 2.0 
y_train.head(3)
87     Versicolour
67     Versicolour
131      Virginica
Name: Class, dtype: object
iris['Class'].value_counts()
Class
Setosa         50
Versicolour    50
Virginica      50
Name: count, dtype: int64
y_train.value_counts()
Class
Versicolour    41
Setosa         40
Virginica      39
Name: count, dtype: int64

데이터 스케일링

Standard Scaler

  • 평균이 0, 분산이 1이 되도록 변환
  • 이상치에 민감하다.
  • 회귀분석보다는 분류분석에 적합
from sklearn.preprocessing import RobustScaler, StandardScaler

StdScaler = StandardScaler()

StdScaler.fit(X_train)
X_train_sc = StdScaler.transform(X_train)
X_test_sc = StdScaler.transform(X_test)

Min-Max Scaler

  • 0 ~ 1 사이의 값으로 변환
  • 이상치에 민감하다.
  • 회귀분석에 적합
from sklearn.preprocessing import MinMaxScaler

MinMaxScaler = MinMaxScaler()

MinMaxScaler.fit(X_train)
X_train_sc = MinMaxScaler.transform(X_train)

X_test_sc = MinMaxScaler.transform(X_test)

Max Abs Scaler

  • -1 ~ 1 사이의 값으로 변환
  • 이상치에 민감하다.
  • 회귀분석에 적합
from sklearn.preprocessing import MaxAbsScaler

MaxAbsScaler = MaxAbsScaler()

MaxAbsScaler.fit(X_train)
X_train_sc = MaxAbsScaler.transform(X_train)

X_test_sc = MaxAbsScaler.transform(X_test)

Robust Scaler

  • 중앙값을 0으로 설정하고, IQR을 사용하여 잉상치 영향을 최소화함
from sklearn.preprocessing import RobustScaler

RobustScaler = RobustScaler()

RobustScaler.fit(X_train)
X_train_sc = RobustScaler.transform(X_train)

X_test_sc = RobustScaler.transform(X_test)

다시 완본으로 변경

  • scaler.inverse_transform()
pd.DataFrame(X_train_sc).head(3)
0 1 2 3
0 0.384615 -1.4 0.028369 0.000000
1 0.000000 -0.6 -0.056738 -0.200000
2 1.615385 1.6 0.595745 0.466667 
X_original = RobustScaler.inverse_transform(X_train_sc)

pd.DataFrame(X_original).head(3)
0 1 2 3
0 6.3 2.3 4.4 1.3
1 5.8 2.7 4.1 1.0
2 7.9 3.8 6.4 2.0

차원 축소

features = []
x = iris.drop(columns='Class')

x = StandardScaler().fit_transform(x)

pd.DataFrame(x).head(3)
0 1 2 3
0 -0.900681 1.019004 -1.340227 -1.315444
1 -1.143017 -0.131979 -1.340227 -1.315444
2 -1.385353 0.328414 -1.397064 -1.315444 
from sklearn.decomposition import PCA

pca = PCA(n_components=4)
pca_fit = pca.fit(x)

print(pca.singular_values_)
print(pca.explained_variance_ratio_.cumsum())
[20.92306556 11.7091661   4.69185798  1.76273239]
[0.72962445 0.95813207 0.99482129 1.        ]
plt.title('Scree Plot')
plt.plot(pca.explained_variance_ratio_, 'o-')
plt.show()

데이터 불균형 문제 처리

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