[빅.분.기] 작업형2유형 - One Hot Encoding

Machine Learning #3 : 범주형 변수 변황 (one hot encoding)¶

자료 출처 : Datacampus "빅데이터 분석기사 자격증 과정 실기" 책 예제 : https://www.datacampus.co.kr/board/read.jsp?id=98394&code=notice

one hot encoding 예시¶

출처 : https://minjejeon.github.io/learningstock/2017/06/05/easy-one-hot-encoding.html

data/library import¶

In [8]:

import warnings
warnings.filterwarnings('ignore')

In [12]:

import pandas as pd
data=pd.read_csv('vote.csv')
print(data.info())
data.head()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 211 entries, 0 to 210
Data columns (total 10 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   gender           211 non-null    int64  
 1   region           211 non-null    int64  
 2   edu              211 non-null    int64  
 3   income           211 non-null    int64  
 4   age              211 non-null    int64  
 5   score_gov        211 non-null    int64  
 6   score_progress   211 non-null    int64  
 7   score_intention  211 non-null    float64
 8   vote             211 non-null    int64  
 9   parties          211 non-null    int64  
dtypes: float64(1), int64(9)
memory usage: 16.6 KB
None

Out[12]:

	gender	region	edu	income	age	score_gov	score_progress	score_intention	vote	parties
0	1	4	3	3	3	2	2	4.0	1	2
1	1	5	2	3	3	2	4	3.0	0	3
2	1	3	1	2	4	1	3	2.8	1	4
3	2	1	2	1	3	5	4	2.6	1	1
4	1	1	1	2	4	4	3	2.4	1	1

data set 나누기¶

In [13]:

x1=data[['gender','region']]
xy=data[data.columns.tolist()[2:]]

In [17]:

xy

Out[17]:

	edu	income	age	score_gov	score_progress	score_intention	vote	parties
0	3	3	3	2	2	4.0	1	2
1	2	3	3	2	4	3.0	0	3
2	1	2	4	1	3	2.8	1	4
3	2	1	3	5	4	2.6	1	1
4	1	2	4	4	3	2.4	1	1
...	...	...	...	...	...	...	...	...
206	1	4	4	3	3	1.8	1	2
207	2	1	2	3	4	2.6	1	4
208	2	1	2	3	3	2.6	1	2
209	2	3	4	3	2	4.0	1	4
210	2	2	2	3	3	3.8	1	2

211 rows × 8 columns

범주형 변수 실제 값을 변환하기¶

In [14]:

x1['gender']=x1['gender'].replace({1:"male",2:"female"})
x1.head()

Out[14]:

	gender	region
0	male	4
1	male	5
2	male	3
3	female	1
4	male	1

In [15]:

x1['region']=x1['region'].replace([1,2,3,4,5],['Sudo',"Chungcheung","Honam","Youngnam","Others"])
x1.head()

Out[15]:

	gender	region
0	male	Youngnam
1	male	Others
2	male	Honam
3	female	Sudo
4	male	Sudo

범주형 변수 변환 : pd.get_dummies()이용¶

In [16]:

x1_dum=pd.get_dummies(x1)
x1_dum.head()

Out[16]:

	gender_female	gender_male	region_Chungcheung	region_Honam	region_Others	region_Sudo	region_Youngnam
0	0	1	0	0	0	0	1
1	0	1	0	0	1	0	0
2	0	1	0	1	0	0	0
3	1	0	0	0	0	1	0
4	0	1	0	0	0	1	0

In [18]:

Fvote=pd.concat([x1_dum,xy],axis=1)
Fvote.head()

Out[18]:

	gender_female	gender_male	region_Chungcheung	region_Honam	region_Others	region_Sudo	region_Youngnam	edu	income	age	score_gov	score_progress	score_intention	vote	parties
0	0	1	0	0	0	0	1	3	3	3	2	2	4.0	1	2
1	0	1	0	0	1	0	0	2	3	3	2	4	3.0	0	3
2	0	1	0	1	0	0	0	1	2	4	1	3	2.8	1	4
3	1	0	0	0	0	1	0	2	1	3	5	4	2.6	1	1
4	0	1	0	0	0	1	0	1	2	4	4	3	2.4	1	1

자료 저장하기¶

In [19]:

Fvote.to_excel("onehotencoding_Fvote.xlsx")

In [20]:

Fvote.to_csv("onehotencoding_Fvote.csv")

In [ ]: