Overview

Dataset statistics

Number of variables6
Number of observations369
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.4 KiB
Average record size in memory48.4 B

Variable types

Categorical2
Text4

Dataset

Description충청남도 논산시 미용업에 대한 공공데이터입니다. 해당데이터는 업소명, 행정동, 주소, 전화번호 정보를 제공하고 있습니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=389&beforeMenuCd=DOM_000000201001001000&publicdatapk=15054218

Alerts

업종구분 is highly imbalanced (58.2%)Imbalance
행정동 is highly imbalanced (51.3%)Imbalance

Reproduction

Analysis started2024-01-09 19:55:28.828298
Analysis finished2024-01-09 19:55:29.491872
Duration0.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종구분
Categorical

IMBALANCE 

Distinct12
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
미용업(일반)
271 
미용업(피부)
45 
미용업(종합)
 
15
미용업(네일아트)
 
14
미용업(네일아트)+미용업(피부)
 
6
Other values (7)
 
18

Length

Max length27
Median length7
Mean length7.7425474
Min length7

Unique

Unique3 ?
Unique (%)0.8%

Sample

1st row미용업(일반)
2nd row미용업(일반)
3rd row미용업(네일아트)+미용업(피부)
4th row미용업(일반)
5th row미용업(일반)

Common Values

ValueCountFrequency (%)
미용업(일반) 271
73.4%
미용업(피부) 45
 
12.2%
미용업(종합) 15
 
4.1%
미용업(네일아트) 14
 
3.8%
미용업(네일아트)+미용업(피부) 6
 
1.6%
미용업(네일아트)+미용업(메이크업) 5
 
1.4%
미용업(일반)+미용업(메이크업) 4
 
1.1%
미용업(일반)+미용업(피부) 3
 
0.8%
미용업(일반)+미용업(네일아트) 3
 
0.8%
미용업(메이크업)+미용업(피부) 1
 
0.3%
Other values (2) 2
 
0.5%

Length

2024-01-10T04:55:29.619906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업(일반 271
73.4%
미용업(피부 45
 
12.2%
미용업(종합 15
 
4.1%
미용업(네일아트 14
 
3.8%
미용업(네일아트)+미용업(피부 6
 
1.6%
미용업(네일아트)+미용업(메이크업 5
 
1.4%
미용업(일반)+미용업(메이크업 4
 
1.1%
미용업(일반)+미용업(피부 3
 
0.8%
미용업(일반)+미용업(네일아트 3
 
0.8%
미용업(메이크업)+미용업(피부 1
 
0.3%
Other values (2) 2
 
0.5%

업소명
Text

Distinct361
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
2024-01-10T04:55:30.111545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15
Mean length5.0542005
Min length1

Characters and Unicode

Total characters1865
Distinct characters342
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique353 ?
Unique (%)95.7%

Sample

1st row1050클리닉헤어
2nd rowBo톡스헤어
3rd rowJUNO에스테틱
4th rowJYP헤어
5th rowJ헤어아트
ValueCountFrequency (%)
마실헤어 2
 
0.5%
현대미용실 2
 
0.5%
피부관리실 2
 
0.5%
여심헤어샵 2
 
0.5%
뷰티호텔 2
 
0.5%
미용실 2
 
0.5%
신신미용실 2
 
0.5%
우리미용실 2
 
0.5%
서울미용실 2
 
0.5%
스타미용실 2
 
0.5%
Other values (365) 365
94.8%
2024-01-10T04:55:30.641126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 151
 
8.1%
헤 148
 
7.9%
미 106
 
5.7%
실 81
 
4.3%
용 77
 
4.1%
스 46
 
2.5%
샵 43
 
2.3%
리 39
 
2.1%
이 35
 
1.9%
아 30
 
1.6%
Other values (332) 1109
59.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1801
96.6%
Uppercase Letter 22
 
1.2%
Space Separator 16
 
0.9%
Lowercase Letter 13
 
0.7%
Decimal Number 5
 
0.3%
Close Punctuation 3
 
0.2%
Open Punctuation 3
 
0.2%
Other Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 151
 
8.4%
헤 148
 
8.2%
미 106
 
5.9%
실 81
 
4.5%
용 77
 
4.3%
스 46
 
2.6%
샵 43
 
2.4%
리 39
 
2.2%
이 35
 
1.9%
아 30
 
1.7%
Other values (302) 1045
58.0%
Uppercase Letter
ValueCountFrequency (%)
J 4
18.2%
O 3
13.6%
S 3
13.6%
K 2
9.1%
T 1
 
4.5%
N 1
 
4.5%
D 1
 
4.5%
Y 1
 
4.5%
U 1
 
4.5%
P 1
 
4.5%
Other values (4) 4
18.2%
Lowercase Letter
ValueCountFrequency (%)
s 4
30.8%
e 4
30.8%
h 1
 
7.7%
r 1
 
7.7%
i 1
 
7.7%
n 1
 
7.7%
o 1
 
7.7%
Decimal Number
ValueCountFrequency (%)
0 2
40.0%
3 1
20.0%
1 1
20.0%
5 1
20.0%
Other Punctuation
ValueCountFrequency (%)
& 1
50.0%
' 1
50.0%
Space Separator
ValueCountFrequency (%)
16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1800
96.5%
Latin 35
 
1.9%
Common 29
 
1.6%
Han 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 151
 
8.4%
헤 148
 
8.2%
미 106
 
5.9%
실 81
 
4.5%
용 77
 
4.3%
스 46
 
2.6%
샵 43
 
2.4%
리 39
 
2.2%
이 35
 
1.9%
아 30
 
1.7%
Other values (301) 1044
58.0%
Latin
ValueCountFrequency (%)
s 4
 
11.4%
e 4
 
11.4%
J 4
 
11.4%
O 3
 
8.6%
S 3
 
8.6%
K 2
 
5.7%
h 1
 
2.9%
T 1
 
2.9%
N 1
 
2.9%
r 1
 
2.9%
Other values (11) 11
31.4%
Common
ValueCountFrequency (%)
16
55.2%
) 3
 
10.3%
( 3
 
10.3%
0 2
 
6.9%
& 1
 
3.4%
3 1
 
3.4%
1 1
 
3.4%
5 1
 
3.4%
' 1
 
3.4%
Han
ValueCountFrequency (%)
韓 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1800
96.5%
ASCII 64
 
3.4%
CJK 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 151
 
8.4%
헤 148
 
8.2%
미 106
 
5.9%
실 81
 
4.5%
용 77
 
4.3%
스 46
 
2.6%
샵 43
 
2.4%
리 39
 
2.2%
이 35
 
1.9%
아 30
 
1.7%
Other values (301) 1044
58.0%
ASCII
ValueCountFrequency (%)
16
25.0%
s 4
 
6.2%
e 4
 
6.2%
J 4
 
6.2%
O 3
 
4.7%
) 3
 
4.7%
S 3
 
4.7%
( 3
 
4.7%
0 2
 
3.1%
K 2
 
3.1%
Other values (20) 20
31.2%
CJK
ValueCountFrequency (%)
韓 1
100.0%

행정동
Categorical

IMBALANCE 

Distinct13
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
취암동
240 
연무읍
41 
강경읍
40 
부창동
 
23
연산면
 
8
Other values (8)
 
17

Length

Max length4
Median length3
Mean length3.0054201
Min length3

Unique

Unique3 ?
Unique (%)0.8%

Sample

1st row취암동
2nd row취암동
3rd row취암동
4th row취암동
5th row강경읍

Common Values

ValueCountFrequency (%)
취암동 240
65.0%
연무읍 41
 
11.1%
강경읍 40
 
10.8%
부창동 23
 
6.2%
연산면 8
 
2.2%
양촌면 6
 
1.6%
성동면 2
 
0.5%
상월면 2
 
0.5%
가야곡면 2
 
0.5%
광석면 2
 
0.5%
Other values (3) 3
 
0.8%

Length

2024-01-10T04:55:30.793206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취암동 240
65.0%
연무읍 41
 
11.1%
강경읍 40
 
10.8%
부창동 23
 
6.2%
연산면 8
 
2.2%
양촌면 6
 
1.6%
성동면 2
 
0.5%
상월면 2
 
0.5%
가야곡면 2
 
0.5%
광석면 2
 
0.5%
Other values (3) 3
 
0.8%

소재지도로명주소
Text

Distinct333
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
2024-01-10T04:55:31.022750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length25
Mean length19.788618
Min length14

Characters and Unicode

Total characters7302
Distinct characters79
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique304 ?
Unique (%)82.4%

Sample

1st row충청남도 논산시 중앙로 369
2nd row충청남도 논산시 중앙로398번길 17
3rd row충청남도 논산시 번영로53번길 22
4th row충청남도 논산시 중앙로479번길 8
5th row충청남도 논산시 강경읍 계백로 126-1
ValueCountFrequency (%)
충청남도 369
23.3%
논산시 369
23.3%
연무읍 41
 
2.6%
강경읍 40
 
2.5%
중앙로 34
 
2.1%
계백로 23
 
1.5%
시민로 22
 
1.4%
안심로 18
 
1.1%
중앙로398번길 15
 
0.9%
시민로307번길 11
 
0.7%
Other values (348) 640
40.5%
2024-01-10T04:55:31.449891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1213
16.6%
시 443
 
6.1%
산 400
 
5.5%
논 378
 
5.2%
청 370
 
5.1%
충 369
 
5.1%
남 369
 
5.1%
도 369
 
5.1%
로 360
 
4.9%
1 311
 
4.3%
Other values (69) 2720
37.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4418
60.5%
Decimal Number 1535
 
21.0%
Space Separator 1213
 
16.6%
Dash Punctuation 136
 
1.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 443
10.0%
산 400
9.1%
논 378
 
8.6%
청 370
 
8.4%
충 369
 
8.4%
남 369
 
8.4%
도 369
 
8.4%
로 360
 
8.1%
길 197
 
4.5%
번 195
 
4.4%
Other values (57) 968
21.9%
Decimal Number
ValueCountFrequency (%)
1 311
20.3%
2 211
13.7%
3 183
11.9%
4 160
10.4%
9 139
9.1%
8 132
8.6%
0 127
8.3%
5 95
 
6.2%
6 90
 
5.9%
7 87
 
5.7%
Space Separator
ValueCountFrequency (%)
1213
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 136
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4418
60.5%
Common 2884
39.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 443
10.0%
산 400
9.1%
논 378
 
8.6%
청 370
 
8.4%
충 369
 
8.4%
남 369
 
8.4%
도 369
 
8.4%
로 360
 
8.1%
길 197
 
4.5%
번 195
 
4.4%
Other values (57) 968
21.9%
Common
ValueCountFrequency (%)
1213
42.1%
1 311
 
10.8%
2 211
 
7.3%
3 183
 
6.3%
4 160
 
5.5%
9 139
 
4.8%
- 136
 
4.7%
8 132
 
4.6%
0 127
 
4.4%
5 95
 
3.3%
Other values (2) 177
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4418
60.5%
ASCII 2884
39.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1213
42.1%
1 311
 
10.8%
2 211
 
7.3%
3 183
 
6.3%
4 160
 
5.5%
9 139
 
4.8%
- 136
 
4.7%
8 132
 
4.6%
0 127
 
4.4%
5 95
 
3.3%
Other values (2) 177
 
6.1%
Hangul
ValueCountFrequency (%)
시 443
10.0%
산 400
9.1%
논 378
 
8.6%
청 370
 
8.4%
충 369
 
8.4%
남 369
 
8.4%
도 369
 
8.4%
로 360
 
8.1%
길 197
 
4.5%
번 195
 
4.4%
Other values (57) 968
21.9%

소재지지번주소
Text

Distinct329
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
2024-01-10T04:55:32.099065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length18.696477
Min length15

Characters and Unicode

Total characters6899
Distinct characters73
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique299 ?
Unique (%)81.0%

Sample

1st row충청남도 논산시 취암동 372-84
2nd row충청남도 논산시 취암동 1061-5
3rd row충청남도 논산시 취암동 1074-9
4th row충청남도 논산시 화지동 107-10
5th row충청남도 논산시 강경읍 대흥리 19-3
ValueCountFrequency (%)
충청남도 369
23.3%
논산시 369
23.3%
취암동 111
 
7.0%
내동 57
 
3.6%
화지동 44
 
2.8%
연무읍 41
 
2.6%
강경읍 40
 
2.5%
반월동 27
 
1.7%
대흥리 26
 
1.6%
안심리 18
 
1.1%
Other values (365) 480
30.3%
2024-01-10T04:55:33.059704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1213
17.6%
산 429
 
6.2%
남 373
 
5.4%
도 371
 
5.4%
충 370
 
5.4%
청 370
 
5.4%
논 369
 
5.3%
시 369
 
5.3%
1 302
 
4.4%
동 282
 
4.1%
Other values (63) 2451
35.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3953
57.3%
Decimal Number 1459
 
21.1%
Space Separator 1213
 
17.6%
Dash Punctuation 274
 
4.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 429
10.9%
남 373
9.4%
도 371
9.4%
충 370
9.4%
청 370
9.4%
논 369
9.3%
시 369
9.3%
동 282
 
7.1%
암 112
 
2.8%
취 111
 
2.8%
Other values (51) 797
20.2%
Decimal Number
ValueCountFrequency (%)
1 302
20.7%
2 152
10.4%
0 148
10.1%
4 145
9.9%
6 132
9.0%
3 121
8.3%
5 118
 
8.1%
9 118
 
8.1%
8 112
 
7.7%
7 111
 
7.6%
Space Separator
ValueCountFrequency (%)
1213
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 274
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3953
57.3%
Common 2946
42.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 429
10.9%
남 373
9.4%
도 371
9.4%
충 370
9.4%
청 370
9.4%
논 369
9.3%
시 369
9.3%
동 282
 
7.1%
암 112
 
2.8%
취 111
 
2.8%
Other values (51) 797
20.2%
Common
ValueCountFrequency (%)
1213
41.2%
1 302
 
10.3%
- 274
 
9.3%
2 152
 
5.2%
0 148
 
5.0%
4 145
 
4.9%
6 132
 
4.5%
3 121
 
4.1%
5 118
 
4.0%
9 118
 
4.0%
Other values (2) 223
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3953
57.3%
ASCII 2946
42.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1213
41.2%
1 302
 
10.3%
- 274
 
9.3%
2 152
 
5.2%
0 148
 
5.0%
4 145
 
4.9%
6 132
 
4.5%
3 121
 
4.1%
5 118
 
4.0%
9 118
 
4.0%
Other values (2) 223
 
7.6%
Hangul
ValueCountFrequency (%)
산 429
10.9%
남 373
9.4%
도 371
9.4%
충 370
9.4%
청 370
9.4%
논 369
9.3%
시 369
9.3%
동 282
 
7.1%
암 112
 
2.8%
취 111
 
2.8%
Other values (51) 797
20.2%

전화번호
Text

Distinct299
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
2024-01-10T04:55:33.493653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.00813
Min length12

Characters and Unicode

Total characters4431
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique294 ?
Unique (%)79.7%

Sample

1st row000-000-0000
2nd row000-000-0000
3rd row041-732-5015
4th row041-734-2877
5th row041-745-1223
ValueCountFrequency (%)
000-000-0000 67
 
18.2%
041-733-2155 2
 
0.5%
041-735-0006 2
 
0.5%
041-734-6999 2
 
0.5%
041-734-6474 2
 
0.5%
041-741-1102 1
 
0.3%
041-735-4054 1
 
0.3%
041-745-3341 1
 
0.3%
041-735-9980 1
 
0.3%
041-733-4008 1
 
0.3%
Other values (289) 289
78.3%
2024-01-10T04:55:34.243789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1135
25.6%
- 738
16.7%
4 515
11.6%
1 434
 
9.8%
7 425
 
9.6%
3 375
 
8.5%
5 252
 
5.7%
2 175
 
3.9%
6 160
 
3.6%
8 116
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3693
83.3%
Dash Punctuation 738
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1135
30.7%
4 515
13.9%
1 434
 
11.8%
7 425
 
11.5%
3 375
 
10.2%
5 252
 
6.8%
2 175
 
4.7%
6 160
 
4.3%
8 116
 
3.1%
9 106
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
- 738
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4431
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1135
25.6%
- 738
16.7%
4 515
11.6%
1 434
 
9.8%
7 425
 
9.6%
3 375
 
8.5%
5 252
 
5.7%
2 175
 
3.9%
6 160
 
3.6%
8 116
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4431
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1135
25.6%
- 738
16.7%
4 515
11.6%
1 434
 
9.8%
7 425
 
9.6%
3 375
 
8.5%
5 252
 
5.7%
2 175
 
3.9%
6 160
 
3.6%
8 116
 
2.6%

Correlations

2024-01-10T04:55:34.521535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종구분행정동
업종구분1.0000.000
행정동0.0001.000
2024-01-10T04:55:34.706177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동업종구분
행정동1.0000.000
업종구분0.0001.000
2024-01-10T04:55:34.818774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종구분행정동
업종구분1.0000.000
행정동0.0001.000

Missing values

2024-01-10T04:55:29.288912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T04:55:29.431743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

업종구분업소명행정동소재지도로명주소소재지지번주소전화번호
0미용업(일반)1050클리닉헤어취암동충청남도 논산시 중앙로 369충청남도 논산시 취암동 372-84000-000-0000
1미용업(일반)Bo톡스헤어취암동충청남도 논산시 중앙로398번길 17충청남도 논산시 취암동 1061-5000-000-0000
2미용업(네일아트)+미용업(피부)JUNO에스테틱취암동충청남도 논산시 번영로53번길 22충청남도 논산시 취암동 1074-9041-732-5015
3미용업(일반)JYP헤어취암동충청남도 논산시 중앙로479번길 8충청남도 논산시 화지동 107-10041-734-2877
4미용업(일반)J헤어아트강경읍충청남도 논산시 강경읍 계백로 126-1충청남도 논산시 강경읍 대흥리 19-3041-745-1223
5미용업(일반)K헤어살롱취암동충청남도 논산시 해월로167번길 5충청남도 논산시 반월동 67-6041-736-8009
6미용업(일반)K헤어아트취암동충청남도 논산시 체육로 18-1충청남도 논산시 내동 968041-733-8422
7미용업(일반)+미용업(메이크업)Lee헤어취암동충청남도 논산시 중앙로398번길 29-3충청남도 논산시 취암동 1059-5041-732-5212
8미용업(일반)M헤어살롱취암동충청남도 논산시 시민로258번길 26-5충청남도 논산시 내동 273-2041-734-6999
9미용업(일반)S&H헤어스토리취암동충청남도 논산시 체육로 18-1충청남도 논산시 내동 968041-733-1920
업종구분업소명행정동소재지도로명주소소재지지번주소전화번호
359미용업(일반)화이트강경읍충청남도 논산시 강경읍 대흥로 25충청남도 논산시 강경읍 대흥리 16-8041-745-1793
360미용업(피부)화이트뷰티샵취암동충청남도 논산시 부창로 84충청남도 논산시 취암동 796041-734-7560
361미용업(일반)화이트헤어갤러리취암동충청남도 논산시 중앙로491번길 2-1충청남도 논산시 화지동 97-1041-733-9510
362미용업(일반)화지미용실취암동충청남도 논산시 중앙로492번길 9-6충청남도 논산시 화지동 87-54000-000-0000
363미용업(일반)황산강경읍충청남도 논산시 강경읍 황산길 28-1충청남도 논산시 강경읍 황산리 149-58041-745-5993
364미용업(일반)효빈미용실취암동충청남도 논산시 중앙로 498-6충청남도 논산시 화지동 87-125041-734-3460
365미용업(피부)후스킨케어취암동충청남도 논산시 시민로132번길 40-6충청남도 논산시 내동 1184041-735-3689
366미용업(피부)힐링진여화에스테틱취암동충청남도 논산시 중앙로480번길 6충청남도 논산시 반월동 160-1041-735-3383
367미용업(일반)힐링헤어취암동충청남도 논산시 시민로307번길 2충청남도 논산시 취암동 993041-745-4874
368미용업(일반)힐헤어연무읍충청남도 논산시 연무읍 안심로 97충청남도 논산시 연무읍 안심리 9-1041-741-4161