Label Encoding + One Hot Encoding

Encoding¶

OneHotEncoding¶

sklearn¶

• 변환하기 위해 모든 문자열 값이 숫자형 값으로 먼저 변환
• 입력값으로 2차원 데이터

Pandas¶

• get_dummies : 문자열만 Encoding
• 숫자를 astype(str)으로 바꾼 뒤

In [3]:

from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd

In [2]:

items = ['폰', '스마트폰', '갤럭시', '아이폰', '애플', '삼성', '애플']

In [6]:

# 먼저 숫자 값으로 변환하기 위해 Label Encoder
encoder = LabelEncoder()
encoder.fit(items)
labels = encoder.transform(items)

Out[6]:

array([5, 2, 0, 3, 4, 1, 4], dtype=int64)

In [8]:

# 2차원 데이터로 변환
labels = labels.reshape(-1,1)
labels

Out[8]:

array([[5],
       [2],
       [0],
       [3],
       [4],
       [1],
       [4]], dtype=int64)

In [9]:

#onehotencoding 적용
oh_encoder = OneHotEncoder()
oh_encoder.fit(labels)
oh_labels = oh_encoder.transform(labels)

oh_labels

Out[9]:

<7x6 sparse matrix of type '<class 'numpy.float64'>'
	with 7 stored elements in Compressed Sparse Row format>

In [12]:

print(oh_labels)

  (0, 5)	1.0
  (1, 2)	1.0
  (2, 0)	1.0
  (3, 3)	1.0
  (4, 4)	1.0
  (5, 1)	1.0
  (6, 4)	1.0

In [11]:

print(oh_labels.toarray())

[[0. 0. 0. 0. 0. 1.]
 [0. 0. 1. 0. 0. 0.]
 [1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 1. 0.]
 [0. 1. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0.]]

In [13]:

df = pd.DataFrame({'items' : ['폰', '스마트폰', '갤럭시', '아이폰', '애플', '삼성', '애플']})

In [14]:

pd.get_dummies(df)

Out[14]:

	items_갤럭시	items_삼성	items_스마트폰	items_아이폰	items_애플	items_폰
0	0	0	0	0	0	1
1	0	0	1	0	0	0
2	1	0	0	0	0	0
3	0	0	0	1	0	0
4	0	0	0	0	1	0
5	0	1	0	0	0	0
6	0	0	0	0	1	0