Overview

Dataset statistics

Number of variables5
Number of observations950
Missing cells493
Missing cells (%)10.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory37.2 KiB
Average record size in memory40.1 B

Variable types

Categorical1
Text4

Dataset

Description전북특별자치도익산시 관내 의 모든 미용업소, 이용업소의 상호와 주소가 있는 데이터 제공하고 있습니다. 활용가능한 데이터 입니다..
Author전북특별자치도 익산시
URLhttps://www.data.go.kr/data/3079167/fileData.do

Alerts

업종명 is highly imbalanced (51.3%)Imbalance
업소소재지(도로명) has 165 (17.4%) missing valuesMissing
소재지전화 has 328 (34.5%) missing valuesMissing

Reproduction

Analysis started2024-03-14 09:37:21.287261
Analysis finished2024-03-14 09:37:22.809524
Duration1.52 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

IMBALANCE 

Distinct11
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size7.5 KiB
미용업
543 
미용업(일반)
254 
미용업(피부)
94 
미용업(손톱ㆍ발톱)
 
27
미용업(종합)
 
10
Other values (6)
 
22

Length

Max length31
Median length3
Mean length5.08
Min length3

Unique

Unique4 ?
Unique (%)0.4%

Sample

1st row미용업
2nd row미용업
3rd row미용업
4th row미용업
5th row미용업

Common Values

ValueCountFrequency (%)
미용업 543
57.2%
미용업(일반) 254
26.7%
미용업(피부) 94
 
9.9%
미용업(손톱ㆍ발톱) 27
 
2.8%
미용업(종합) 10
 
1.1%
미용업(피부), 미용업(손톱ㆍ발톱) 10
 
1.1%
미용업(일반), 미용업(손톱ㆍ발톱) 8
 
0.8%
미용업(화장ㆍ분장) 1
 
0.1%
미용업(일반), 미용업(화장ㆍ분장) 1
 
0.1%
미용업(피부), 미용업(화장ㆍ분장) 1
 
0.1%

Length

2024-03-14T18:37:22.924828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 543
55.9%
미용업(일반 264
27.2%
미용업(피부 105
 
10.8%
미용업(손톱ㆍ발톱 46
 
4.7%
미용업(종합 10
 
1.0%
미용업(화장ㆍ분장 4
 
0.4%

업소명
Text

Distinct918
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Memory size7.5 KiB
2024-03-14T18:37:23.937022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length17
Mean length5.5031579
Min length2

Characters and Unicode

Total characters5228
Distinct characters507
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique889 ?
Unique (%)93.6%

Sample

1st row여원미용실
2nd row양지미장원
3rd row현대헤어
4th row중앙미용실
5th row샘미용실
ValueCountFrequency (%)
헤어 19
 
1.8%
헤어샵 10
 
0.9%
미용실 9
 
0.8%
스킨케어 5
 
0.5%
미소헤어 3
 
0.3%
뷰티 3
 
0.3%
미 3
 
0.3%
스타일 3
 
0.3%
수 3
 
0.3%
3
 
0.3%
Other values (966) 1019
94.4%
2024-03-14T18:37:25.240826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 437
 
8.4%
헤 416
 
8.0%
미 267
 
5.1%
실 204
 
3.9%
용 197
 
3.8%
리 147
 
2.8%
130
 
2.5%
스 121
 
2.3%
샵 114
 
2.2%
이 92
 
1.8%
Other values (497) 3103
59.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4869
93.1%
Space Separator 130
 
2.5%
Lowercase Letter 75
 
1.4%
Uppercase Letter 70
 
1.3%
Other Punctuation 28
 
0.5%
Close Punctuation 21
 
0.4%
Open Punctuation 20
 
0.4%
Decimal Number 14
 
0.3%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 437
 
9.0%
헤 416
 
8.5%
미 267
 
5.5%
실 204
 
4.2%
용 197
 
4.0%
리 147
 
3.0%
스 121
 
2.5%
샵 114
 
2.3%
이 92
 
1.9%
머 57
 
1.2%
Other values (443) 2817
57.9%
Uppercase Letter
ValueCountFrequency (%)
J 10
14.3%
Y 8
11.4%
S 7
10.0%
E 6
 
8.6%
B 5
 
7.1%
V 5
 
7.1%
I 4
 
5.7%
L 4
 
5.7%
G 3
 
4.3%
N 2
 
2.9%
Other values (9) 16
22.9%
Lowercase Letter
ValueCountFrequency (%)
o 11
14.7%
a 10
13.3%
i 6
8.0%
n 6
8.0%
s 6
8.0%
b 6
8.0%
l 5
 
6.7%
h 5
 
6.7%
e 4
 
5.3%
u 3
 
4.0%
Other values (8) 13
17.3%
Decimal Number
ValueCountFrequency (%)
1 3
21.4%
0 3
21.4%
2 2
14.3%
6 2
14.3%
4 1
 
7.1%
5 1
 
7.1%
7 1
 
7.1%
8 1
 
7.1%
Other Punctuation
ValueCountFrequency (%)
& 16
57.1%
. 6
 
21.4%
# 3
 
10.7%
, 2
 
7.1%
' 1
 
3.6%
Space Separator
ValueCountFrequency (%)
130
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 20
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4861
93.0%
Common 214
 
4.1%
Latin 145
 
2.8%
Han 8
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 437
 
9.0%
헤 416
 
8.6%
미 267
 
5.5%
실 204
 
4.2%
용 197
 
4.1%
리 147
 
3.0%
스 121
 
2.5%
샵 114
 
2.3%
이 92
 
1.9%
머 57
 
1.2%
Other values (441) 2809
57.8%
Latin
ValueCountFrequency (%)
o 11
 
7.6%
a 10
 
6.9%
J 10
 
6.9%
Y 8
 
5.5%
S 7
 
4.8%
i 6
 
4.1%
n 6
 
4.1%
s 6
 
4.1%
E 6
 
4.1%
b 6
 
4.1%
Other values (27) 69
47.6%
Common
ValueCountFrequency (%)
130
60.7%
) 21
 
9.8%
( 20
 
9.3%
& 16
 
7.5%
. 6
 
2.8%
1 3
 
1.4%
# 3
 
1.4%
0 3
 
1.4%
, 2
 
0.9%
2 2
 
0.9%
Other values (7) 8
 
3.7%
Han
ValueCountFrequency (%)
美 7
87.5%
場 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4861
93.0%
ASCII 359
 
6.9%
CJK 8
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 437
 
9.0%
헤 416
 
8.6%
미 267
 
5.5%
실 204
 
4.2%
용 197
 
4.1%
리 147
 
3.0%
스 121
 
2.5%
샵 114
 
2.3%
이 92
 
1.9%
머 57
 
1.2%
Other values (441) 2809
57.8%
ASCII
ValueCountFrequency (%)
130
36.2%
) 21
 
5.8%
( 20
 
5.6%
& 16
 
4.5%
o 11
 
3.1%
a 10
 
2.8%
J 10
 
2.8%
Y 8
 
2.2%
S 7
 
1.9%
i 6
 
1.7%
Other values (44) 120
33.4%
CJK
ValueCountFrequency (%)
美 7
87.5%
場 1
 
12.5%

업소소재지(도로명)
Text

MISSING 

Distinct768
Distinct (%)97.8%
Missing165
Missing (%)17.4%
Memory size7.5 KiB
2024-03-14T18:37:26.218717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length44
Mean length24.289172
Min length13

Characters and Unicode

Total characters19067
Distinct characters196
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique752 ?
Unique (%)95.8%

Sample

1st row 익산시 익산대로14길 36-2 (중앙동3가)
2nd row 익산시 고현로7길 7, 상가동 1층 111호 (모현동1가, 현대아파트)
3rd row 익산시 중앙로 22-26 (중앙동1가)
4th row 익산시 중앙로4길 45 (주현동)
5th row 익산시 인북로8길 3 (인화동1가)
ValueCountFrequency (%)
익산시 785
 
20.9%
1층 225
 
6.0%
영등동 154
 
4.1%
모현동1가 93
 
2.5%
신동 89
 
2.4%
2층 57
 
1.5%
부송동 57
 
1.5%
어양동 54
 
1.4%
동산동 43
 
1.1%
상가동 32
 
0.9%
Other values (787) 2175
57.8%
2024-03-14T18:37:27.475945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3764
19.7%
1 1336
 
7.0%
동 1057
 
5.5%
산 937
 
4.9%
익 850
 
4.5%
( 801
 
4.2%
) 801
 
4.2%
시 798
 
4.2%
로 762
 
4.0%
2 558
 
2.9%
Other values (186) 7403
38.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9050
47.5%
Decimal Number 3940
20.7%
Space Separator 3764
19.7%
Open Punctuation 801
 
4.2%
Close Punctuation 801
 
4.2%
Other Punctuation 527
 
2.8%
Dash Punctuation 180
 
0.9%
Uppercase Letter 2
 
< 0.1%
Math Symbol 1
 
< 0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1057
 
11.7%
산 937
 
10.4%
익 850
 
9.4%
시 798
 
8.8%
로 762
 
8.4%
길 536
 
5.9%
층 363
 
4.0%
가 243
 
2.7%
등 204
 
2.3%
영 181
 
2.0%
Other values (164) 3119
34.5%
Decimal Number
ValueCountFrequency (%)
1 1336
33.9%
2 558
14.2%
3 383
 
9.7%
0 331
 
8.4%
4 322
 
8.2%
5 241
 
6.1%
6 225
 
5.7%
7 191
 
4.8%
8 177
 
4.5%
9 176
 
4.5%
Other Punctuation
ValueCountFrequency (%)
, 486
92.2%
@ 38
 
7.2%
. 2
 
0.4%
/ 1
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
C 1
50.0%
A 1
50.0%
Space Separator
ValueCountFrequency (%)
3764
100.0%
Open Punctuation
ValueCountFrequency (%)
( 801
100.0%
Close Punctuation
ValueCountFrequency (%)
) 801
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 180
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
b 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10014
52.5%
Hangul 9050
47.5%
Latin 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1057
 
11.7%
산 937
 
10.4%
익 850
 
9.4%
시 798
 
8.8%
로 762
 
8.4%
길 536
 
5.9%
층 363
 
4.0%
가 243
 
2.7%
등 204
 
2.3%
영 181
 
2.0%
Other values (164) 3119
34.5%
Common
ValueCountFrequency (%)
3764
37.6%
1 1336
 
13.3%
( 801
 
8.0%
) 801
 
8.0%
2 558
 
5.6%
, 486
 
4.9%
3 383
 
3.8%
0 331
 
3.3%
4 322
 
3.2%
5 241
 
2.4%
Other values (9) 991
 
9.9%
Latin
ValueCountFrequency (%)
C 1
33.3%
A 1
33.3%
b 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10017
52.5%
Hangul 9050
47.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3764
37.6%
1 1336
 
13.3%
( 801
 
8.0%
) 801
 
8.0%
2 558
 
5.6%
, 486
 
4.9%
3 383
 
3.8%
0 331
 
3.3%
4 322
 
3.2%
5 241
 
2.4%
Other values (12) 994
 
9.9%
Hangul
ValueCountFrequency (%)
동 1057
 
11.7%
산 937
 
10.4%
익 850
 
9.4%
시 798
 
8.8%
로 762
 
8.4%
길 536
 
5.9%
층 363
 
4.0%
가 243
 
2.7%
등 204
 
2.3%
영 181
 
2.0%
Other values (164) 3119
34.5%

업소소재지(지번)
Text

Distinct912
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size7.5 KiB
2024-03-14T18:37:28.650205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length66
Median length47
Mean length23.046316
Min length15

Characters and Unicode

Total characters21894
Distinct characters196
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique879 ?
Unique (%)92.5%

Sample

1st row 익산시 중앙동3가 149번지 6호
2nd row 익산시 여산면 여산리 283번지
3rd row 익산시 모현동1가 303번지 1호
4th row 익산시 중앙동1가 80번지
5th row 익산시 주현동 85번지
ValueCountFrequency (%)
익산시 950
22.9%
영등동 194
 
4.7%
모현동1가 121
 
2.9%
1호 116
 
2.8%
신동 113
 
2.7%
1층 97
 
2.3%
부송동 88
 
2.1%
2호 75
 
1.8%
어양동 70
 
1.7%
동산동 61
 
1.5%
Other values (855) 2259
54.5%
2024-03-14T18:37:30.171004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6671
30.5%
1 1249
 
5.7%
산 1049
 
4.8%
동 1031
 
4.7%
지 989
 
4.5%
시 960
 
4.4%
번 957
 
4.4%
익 953
 
4.4%
호 855
 
3.9%
2 640
 
2.9%
Other values (186) 6540
29.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9987
45.6%
Space Separator 6671
30.5%
Decimal Number 4932
22.5%
Dash Punctuation 86
 
0.4%
Open Punctuation 84
 
0.4%
Close Punctuation 84
 
0.4%
Other Punctuation 41
 
0.2%
Uppercase Letter 8
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 1049
10.5%
동 1031
10.3%
지 989
9.9%
시 960
 
9.6%
번 957
 
9.6%
익 953
 
9.5%
호 855
 
8.6%
가 296
 
3.0%
등 241
 
2.4%
영 203
 
2.0%
Other values (163) 2453
24.6%
Decimal Number
ValueCountFrequency (%)
1 1249
25.3%
2 640
13.0%
0 474
 
9.6%
3 434
 
8.8%
8 430
 
8.7%
6 389
 
7.9%
7 382
 
7.7%
5 373
 
7.6%
4 324
 
6.6%
9 237
 
4.8%
Other Punctuation
ValueCountFrequency (%)
, 19
46.3%
@ 11
26.8%
/ 6
 
14.6%
. 5
 
12.2%
Uppercase Letter
ValueCountFrequency (%)
A 4
50.0%
B 2
25.0%
K 1
 
12.5%
C 1
 
12.5%
Space Separator
ValueCountFrequency (%)
6671
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 86
100.0%
Open Punctuation
ValueCountFrequency (%)
( 84
100.0%
Close Punctuation
ValueCountFrequency (%)
) 84
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11899
54.3%
Hangul 9987
45.6%
Latin 8
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 1049
10.5%
동 1031
10.3%
지 989
9.9%
시 960
 
9.6%
번 957
 
9.6%
익 953
 
9.5%
호 855
 
8.6%
가 296
 
3.0%
등 241
 
2.4%
영 203
 
2.0%
Other values (163) 2453
24.6%
Common
ValueCountFrequency (%)
6671
56.1%
1 1249
 
10.5%
2 640
 
5.4%
0 474
 
4.0%
3 434
 
3.6%
8 430
 
3.6%
6 389
 
3.3%
7 382
 
3.2%
5 373
 
3.1%
4 324
 
2.7%
Other values (9) 533
 
4.5%
Latin
ValueCountFrequency (%)
A 4
50.0%
B 2
25.0%
K 1
 
12.5%
C 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11907
54.4%
Hangul 9987
45.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6671
56.0%
1 1249
 
10.5%
2 640
 
5.4%
0 474
 
4.0%
3 434
 
3.6%
8 430
 
3.6%
6 389
 
3.3%
7 382
 
3.2%
5 373
 
3.1%
4 324
 
2.7%
Other values (13) 541
 
4.5%
Hangul
ValueCountFrequency (%)
산 1049
10.5%
동 1031
10.3%
지 989
9.9%
시 960
 
9.6%
번 957
 
9.6%
익 953
 
9.5%
호 855
 
8.6%
가 296
 
3.0%
등 241
 
2.4%
영 203
 
2.0%
Other values (163) 2453
24.6%

소재지전화
Text

MISSING 

Distinct612
Distinct (%)98.4%
Missing328
Missing (%)34.5%
Memory size7.5 KiB
2024-03-14T18:37:31.103162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.004823
Min length12

Characters and Unicode

Total characters7467
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

Unique602 ?
Unique (%)96.8%

Sample

1st row063-855-6494
2nd row063-836-5128
3rd row063-855-4759
4th row063-841-1219
5th row063-855-7912
ValueCountFrequency (%)
063-833-8265 2
 
0.3%
063-853-0353 2
 
0.3%
063-841-4692 2
 
0.3%
063-833-0353 2
 
0.3%
063-835-9874 2
 
0.3%
063-832-3586 2
 
0.3%
063-862-2112 2
 
0.3%
063-917-3789 2
 
0.3%
063-835-5835 2
 
0.3%
063-857-7904 2
 
0.3%
Other values (602) 602
96.8%
2024-03-14T18:37:32.442101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1244
16.7%
3 1173
15.7%
6 929
12.4%
0 920
12.3%
8 894
12.0%
5 627
8.4%
1 386
 
5.2%
4 385
 
5.2%
2 345
 
4.6%
7 302
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6223
83.3%
Dash Punctuation 1244
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1173
18.8%
6 929
14.9%
0 920
14.8%
8 894
14.4%
5 627
10.1%
1 386
 
6.2%
4 385
 
6.2%
2 345
 
5.5%
7 302
 
4.9%
9 262
 
4.2%
Dash Punctuation
ValueCountFrequency (%)
- 1244
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7467
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1244
16.7%
3 1173
15.7%
6 929
12.4%
0 920
12.3%
8 894
12.0%
5 627
8.4%
1 386
 
5.2%
4 385
 
5.2%
2 345
 
4.6%
7 302
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7467
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1244
16.7%
3 1173
15.7%
6 929
12.4%
0 920
12.3%
8 894
12.0%
5 627
8.4%
1 386
 
5.2%
4 385
 
5.2%
2 345
 
4.6%
7 302
 
4.0%

Missing values

2024-03-14T18:37:22.150079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T18:37:22.472125image/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.
2024-03-14T18:37:22.729814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

업종명업소명업소소재지(도로명)업소소재지(지번)소재지전화
0미용업여원미용실익산시 익산대로14길 36-2 (중앙동3가)익산시 중앙동3가 149번지 6호063-855-6494
1미용업양지미장원<NA>익산시 여산면 여산리 283번지063-836-5128
2미용업현대헤어익산시 고현로7길 7, 상가동 1층 111호 (모현동1가, 현대아파트)익산시 모현동1가 303번지 1호<NA>
3미용업중앙미용실익산시 중앙로 22-26 (중앙동1가)익산시 중앙동1가 80번지063-855-4759
4미용업샘미용실익산시 중앙로4길 45 (주현동)익산시 주현동 85번지063-841-1219
5미용업해동미용실<NA>익산시 중앙동1가 83번지063-855-7912
6미용업문경미장원<NA>익산시 남중동 484번지 6호063-841-8810
7미용업아담미장원익산시 인북로8길 3 (인화동1가)익산시 인화동1가 50번지 8호063-841-6506
8미용업동산미용실익산시 서동로4길 30-1 (동산동)익산시 동산동 666번지 4호063-855-3128
9미용업수미용실익산시 익산대로10길 10 (평화동)익산시 평화동 5번지063-855-8777
업종명업소명업소소재지(도로명)업소소재지(지번)소재지전화
940미용업(피부), 미용업(손톱ㆍ발톱)호박네일&코코왁싱익산시 무왕로 1052, 2층 207호 (영등동)익산시 영등동 149번지 1호 207호063-831-4878
941미용업(피부), 미용업(손톱ㆍ발톱)희야꼬 네일익산시 동서로5길 107, 2층 (신동)익산시 신동 784번지 20호 K팰리스 2층<NA>
942미용업(피부), 미용업(손톱ㆍ발톱)나나네일익산시 선화로4길 37 (모현동1가)익산시 모현동1가 760번지 5호<NA>
943미용업(피부), 미용업(손톱ㆍ발톱)더나은네일익산시 고봉로30길 51, 1층 12호 (영등동)익산시 영등동 771번지 2호 비사벌상가 12호<NA>
944미용업(피부), 미용업(손톱ㆍ발톱)옹글리에익산시 익산대로 300-1, 1층 (남중동)익산시 남중동 388번지 20호<NA>
945미용업(피부), 미용업(손톱ㆍ발톱)그레이스 뷰티익산시 중앙로5길 5 (중앙동3가)익산시 중앙동3가 8번지 5호063-852-4141
946미용업(화장ㆍ분장)더블유미용실익산시 목천로 199 (평화동)익산시 평화동 250번지 2호 1층<NA>
947미용업(일반), 미용업(화장ㆍ분장)다온헤어샵익산시 서동로 31, 101-2호 (인화동2가)익산시 인화동2가 54번지 101-2호<NA>
948미용업(피부), 미용업(화장ㆍ분장)美&ME뷰티샵익산시 인북로62길 13 (신동)익산시 신동 809번지 25호<NA>
949미용업(일반), 미용업(손톱ㆍ발톱), 미용업(화장ㆍ분장)금샘네일익산시 부송1로 19, 상가동 1층 104호 (어양동)익산시 어양동 621번지 2호 상가동 104호<NA>