Overview

Dataset statistics

Number of variables7
Number of observations490
Missing cells3
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.9 KiB
Average record size in memory56.3 B

Variable types

Categorical3
Text3
DateTime1

Dataset

Description충청북도 음성군 공중위생업 현황에 대한 데이터로 업체의 (건물위생관리업, 목욕장업, 미용업, 세탁업, 이용업) 상호명, 주소, 전화번호를 안내하는 데이터입니다.
URLhttps://www.data.go.kr/data/15006946/fileData.do

Alerts

기준일자 has constant value ""Constant
업종명 is highly overall correlated with 업태명High correlation
업태명 is highly overall correlated with 업종명High correlation

Reproduction

Analysis started2023-12-12 16:27:04.761818
Analysis finished2023-12-12 16:27:05.624314
Duration0.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
일반미용업
167 
숙박업(일반)
75 
세탁업
44 
이용업
39 
종합미용업
34 
Other values (14)
131 

Length

Max length23
Median length5
Mean length5.6387755
Min length3

Unique

Unique3 ?
Unique (%)0.6%

Sample

1st row숙박업(일반)
2nd row숙박업(일반)
3rd row숙박업(일반)
4th row숙박업(일반)
5th row숙박업(일반)

Common Values

ValueCountFrequency (%)
일반미용업 167
34.1%
숙박업(일반) 75
15.3%
세탁업 44
 
9.0%
이용업 39
 
8.0%
종합미용업 34
 
6.9%
건물위생관리업 32
 
6.5%
피부미용업 29
 
5.9%
네일미용업 22
 
4.5%
미용업 12
 
2.4%
목욕장업 11
 
2.2%
Other values (9) 25
 
5.1%

Length

2023-12-13T01:27:05.719441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 177
32.8%
숙박업(일반 75
13.9%
세탁업 44
 
8.1%
네일미용업 41
 
7.6%
이용업 39
 
7.2%
피부미용업 37
 
6.9%
종합미용업 34
 
6.3%
건물위생관리업 32
 
5.9%
미용업 30
 
5.6%
화장ㆍ분장 18
 
3.3%
Other values (2) 13
 
2.4%
Distinct484
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T01:27:06.035651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length18
Mean length5.9183673
Min length2

Characters and Unicode

Total characters2900
Distinct characters431
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

Unique478 ?
Unique (%)97.6%

Sample

1st row온천장여관
2nd row제일여인숙
3rd row로얄모텔
4th row월드파크
5th row음성파크
ValueCountFrequency (%)
헤어샵 6
 
1.0%
hair 5
 
0.9%
헤어 4
 
0.7%
충북혁신도시점 3
 
0.5%
네일 3
 
0.5%
그린파크 2
 
0.3%
헤어클리닉 2
 
0.3%
청담 2
 
0.3%
아침 2
 
0.3%
탈모야안녕 2
 
0.3%
Other values (533) 547
94.6%
2023-12-13T01:27:06.444114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
125
 
4.3%
121
 
4.2%
88
 
3.0%
81
 
2.8%
78
 
2.7%
64
 
2.2%
63
 
2.2%
55
 
1.9%
48
 
1.7%
47
 
1.6%
Other values (421) 2130
73.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2513
86.7%
Uppercase Letter 114
 
3.9%
Lowercase Letter 89
 
3.1%
Space Separator 88
 
3.0%
Open Punctuation 34
 
1.2%
Close Punctuation 34
 
1.2%
Other Punctuation 19
 
0.7%
Decimal Number 9
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
125
 
5.0%
121
 
4.8%
81
 
3.2%
78
 
3.1%
64
 
2.5%
63
 
2.5%
55
 
2.2%
48
 
1.9%
47
 
1.9%
40
 
1.6%
Other values (363) 1791
71.3%
Uppercase Letter
ValueCountFrequency (%)
A 12
 
10.5%
H 11
 
9.6%
I 10
 
8.8%
S 7
 
6.1%
J 6
 
5.3%
E 6
 
5.3%
M 6
 
5.3%
B 6
 
5.3%
N 5
 
4.4%
R 5
 
4.4%
Other values (14) 40
35.1%
Lowercase Letter
ValueCountFrequency (%)
i 12
13.5%
a 11
12.4%
r 10
11.2%
o 10
11.2%
e 9
10.1%
n 6
 
6.7%
u 4
 
4.5%
l 4
 
4.5%
c 3
 
3.4%
s 3
 
3.4%
Other values (10) 17
19.1%
Decimal Number
ValueCountFrequency (%)
5 3
33.3%
1 2
22.2%
2 1
 
11.1%
0 1
 
11.1%
8 1
 
11.1%
9 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
& 11
57.9%
. 3
 
15.8%
, 2
 
10.5%
: 2
 
10.5%
# 1
 
5.3%
Space Separator
ValueCountFrequency (%)
88
100.0%
Open Punctuation
ValueCountFrequency (%)
( 34
100.0%
Close Punctuation
ValueCountFrequency (%)
) 34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2511
86.6%
Latin 203
 
7.0%
Common 184
 
6.3%
Han 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
125
 
5.0%
121
 
4.8%
81
 
3.2%
78
 
3.1%
64
 
2.5%
63
 
2.5%
55
 
2.2%
48
 
1.9%
47
 
1.9%
40
 
1.6%
Other values (361) 1789
71.2%
Latin
ValueCountFrequency (%)
i 12
 
5.9%
A 12
 
5.9%
a 11
 
5.4%
H 11
 
5.4%
r 10
 
4.9%
o 10
 
4.9%
I 10
 
4.9%
e 9
 
4.4%
S 7
 
3.4%
n 6
 
3.0%
Other values (34) 105
51.7%
Common
ValueCountFrequency (%)
88
47.8%
( 34
 
18.5%
) 34
 
18.5%
& 11
 
6.0%
5 3
 
1.6%
. 3
 
1.6%
, 2
 
1.1%
: 2
 
1.1%
1 2
 
1.1%
# 1
 
0.5%
Other values (4) 4
 
2.2%
Han
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2511
86.6%
ASCII 387
 
13.3%
CJK 2
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
125
 
5.0%
121
 
4.8%
81
 
3.2%
78
 
3.1%
64
 
2.5%
63
 
2.5%
55
 
2.2%
48
 
1.9%
47
 
1.9%
40
 
1.6%
Other values (361) 1789
71.2%
ASCII
ValueCountFrequency (%)
88
22.7%
( 34
 
8.8%
) 34
 
8.8%
i 12
 
3.1%
A 12
 
3.1%
a 11
 
2.8%
& 11
 
2.8%
H 11
 
2.8%
r 10
 
2.6%
o 10
 
2.6%
Other values (48) 154
39.8%
CJK
ValueCountFrequency (%)
1
50.0%
1
50.0%
Distinct460
Distinct (%)94.1%
Missing1
Missing (%)0.2%
Memory size4.0 KiB
2023-12-13T01:27:06.733804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length44
Mean length23.840491
Min length18

Characters and Unicode

Total characters11658
Distinct characters174
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

Unique432 ?
Unique (%)88.3%

Sample

1st row충청북도 음성군 감곡면 장감로131번길 5
2nd row충청북도 음성군 감곡면 장감로143번길 3-6
3rd row충청북도 음성군 음성읍 중앙로 126-9
4th row충청북도 음성군 대소면 오태로 76-6
5th row충청북도 음성군 음성읍 음성천서길 167
ValueCountFrequency (%)
충청북도 489
18.0%
음성군 489
18.0%
금왕읍 112
 
4.1%
음성읍 98
 
3.6%
대소면 85
 
3.1%
맹동면 78
 
2.9%
감곡면 59
 
2.2%
1층 31
 
1.1%
시장로 31
 
1.1%
무극로 30
 
1.1%
Other values (551) 1212
44.7%
2023-12-13T01:27:07.240551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2225
19.1%
688
 
5.9%
627
 
5.4%
1 507
 
4.3%
497
 
4.3%
496
 
4.3%
492
 
4.2%
492
 
4.2%
489
 
4.2%
406
 
3.5%
Other values (164) 4739
40.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7195
61.7%
Space Separator 2225
 
19.1%
Decimal Number 2021
 
17.3%
Dash Punctuation 141
 
1.2%
Close Punctuation 32
 
0.3%
Open Punctuation 32
 
0.3%
Uppercase Letter 5
 
< 0.1%
Other Punctuation 3
 
< 0.1%
Lowercase Letter 3
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
688
 
9.6%
627
 
8.7%
497
 
6.9%
496
 
6.9%
492
 
6.8%
492
 
6.8%
489
 
6.8%
406
 
5.6%
279
 
3.9%
210
 
2.9%
Other values (142) 2519
35.0%
Decimal Number
ValueCountFrequency (%)
1 507
25.1%
2 312
15.4%
3 210
10.4%
0 197
 
9.7%
4 161
 
8.0%
6 158
 
7.8%
5 154
 
7.6%
7 127
 
6.3%
8 106
 
5.2%
9 89
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
B 2
40.0%
S 2
40.0%
D 1
20.0%
Lowercase Letter
ValueCountFrequency (%)
c 1
33.3%
g 1
33.3%
v 1
33.3%
Space Separator
ValueCountFrequency (%)
2225
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 141
100.0%
Close Punctuation
ValueCountFrequency (%)
) 32
100.0%
Open Punctuation
ValueCountFrequency (%)
( 32
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7195
61.7%
Common 4455
38.2%
Latin 8
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
688
 
9.6%
627
 
8.7%
497
 
6.9%
496
 
6.9%
492
 
6.8%
492
 
6.8%
489
 
6.8%
406
 
5.6%
279
 
3.9%
210
 
2.9%
Other values (142) 2519
35.0%
Common
ValueCountFrequency (%)
2225
49.9%
1 507
 
11.4%
2 312
 
7.0%
3 210
 
4.7%
0 197
 
4.4%
4 161
 
3.6%
6 158
 
3.5%
5 154
 
3.5%
- 141
 
3.2%
7 127
 
2.9%
Other values (6) 263
 
5.9%
Latin
ValueCountFrequency (%)
B 2
25.0%
S 2
25.0%
D 1
12.5%
c 1
12.5%
g 1
12.5%
v 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7195
61.7%
ASCII 4463
38.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2225
49.9%
1 507
 
11.4%
2 312
 
7.0%
3 210
 
4.7%
0 197
 
4.4%
4 161
 
3.6%
6 158
 
3.5%
5 154
 
3.5%
- 141
 
3.2%
7 127
 
2.8%
Other values (12) 271
 
6.1%
Hangul
ValueCountFrequency (%)
688
 
9.6%
627
 
8.7%
497
 
6.9%
496
 
6.9%
492
 
6.8%
492
 
6.8%
489
 
6.8%
406
 
5.6%
279
 
3.9%
210
 
2.9%
Other values (142) 2519
35.0%
Distinct431
Distinct (%)88.3%
Missing2
Missing (%)0.4%
Memory size4.0 KiB
2023-12-13T01:27:07.478487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length40
Mean length23.856557
Min length20

Characters and Unicode

Total characters11642
Distinct characters150
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique380 ?
Unique (%)77.9%

Sample

1st row충청북도 음성군 감곡면 왕장리 436-1
2nd row충청북도 음성군 감곡면 왕장리 461
3rd row충청북도 음성군 음성읍 읍내리 530-1
4th row충청북도 음성군 대소면 오산리 114-24
5th row충청북도 음성군 음성읍 읍내리 540-3
ValueCountFrequency (%)
충청북도 488
19.3%
음성군 488
19.3%
금왕읍 112
 
4.4%
음성읍 97
 
3.8%
읍내리 86
 
3.4%
대소면 85
 
3.4%
무극리 83
 
3.3%
맹동면 79
 
3.1%
감곡면 59
 
2.3%
동성리 43
 
1.7%
Other values (525) 910
36.0%
2023-12-13T01:27:07.845303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2508
21.5%
703
 
6.0%
600
 
5.2%
494
 
4.2%
490
 
4.2%
490
 
4.2%
490
 
4.2%
488
 
4.2%
488
 
4.2%
1 388
 
3.3%
Other values (140) 4503
38.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6692
57.5%
Space Separator 2508
 
21.5%
Decimal Number 2070
 
17.8%
Dash Punctuation 362
 
3.1%
Close Punctuation 4
 
< 0.1%
Open Punctuation 4
 
< 0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
703
 
10.5%
600
 
9.0%
494
 
7.4%
490
 
7.3%
490
 
7.3%
490
 
7.3%
488
 
7.3%
488
 
7.3%
295
 
4.4%
279
 
4.2%
Other values (125) 1875
28.0%
Decimal Number
ValueCountFrequency (%)
1 388
18.7%
4 264
12.8%
2 263
12.7%
5 251
12.1%
3 246
11.9%
0 172
8.3%
7 140
 
6.8%
6 121
 
5.8%
8 120
 
5.8%
9 105
 
5.1%
Space Separator
ValueCountFrequency (%)
2508
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 362
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6692
57.5%
Common 4948
42.5%
Latin 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
703
 
10.5%
600
 
9.0%
494
 
7.4%
490
 
7.3%
490
 
7.3%
490
 
7.3%
488
 
7.3%
488
 
7.3%
295
 
4.4%
279
 
4.2%
Other values (125) 1875
28.0%
Common
ValueCountFrequency (%)
2508
50.7%
1 388
 
7.8%
- 362
 
7.3%
4 264
 
5.3%
2 263
 
5.3%
5 251
 
5.1%
3 246
 
5.0%
0 172
 
3.5%
7 140
 
2.8%
6 121
 
2.4%
Other values (4) 233
 
4.7%
Latin
ValueCountFrequency (%)
B 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6692
57.5%
ASCII 4950
42.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2508
50.7%
1 388
 
7.8%
- 362
 
7.3%
4 264
 
5.3%
2 263
 
5.3%
5 251
 
5.1%
3 246
 
5.0%
0 172
 
3.5%
7 140
 
2.8%
6 121
 
2.4%
Other values (5) 235
 
4.7%
Hangul
ValueCountFrequency (%)
703
 
10.5%
600
 
9.0%
494
 
7.4%
490
 
7.3%
490
 
7.3%
490
 
7.3%
488
 
7.3%
488
 
7.3%
295
 
4.4%
279
 
4.2%
Other values (125) 1875
28.0%
Distinct458
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum1973-01-04 00:00:00
Maximum2023-08-17 00:00:00
2023-12-13T01:27:07.978206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:08.108892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태명
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
일반미용업
213 
여관업
64 
일반세탁업
42 
피부미용업
39 
일반이용업
39 
Other values (16)
93 

Length

Max length14
Median length5
Mean length4.8693878
Min length2

Unique

Unique6 ?
Unique (%)1.2%

Sample

1st row여관업
2nd row여인숙업
3rd row여관업
4th row여관업
5th row여관업

Common Values

ValueCountFrequency (%)
일반미용업 213
43.5%
여관업 64
 
13.1%
일반세탁업 42
 
8.6%
피부미용업 39
 
8.0%
일반이용업 39
 
8.0%
건물위생관리업 32
 
6.5%
네일아트업 29
 
5.9%
공동탕업 6
 
1.2%
일반호텔 5
 
1.0%
메이크업업 3
 
0.6%
Other values (11) 18
 
3.7%

Length

2023-12-13T01:27:08.268190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 213
43.1%
여관업 64
 
13.0%
일반세탁업 42
 
8.5%
피부미용업 39
 
7.9%
일반이용업 39
 
7.9%
건물위생관리업 32
 
6.5%
네일아트업 29
 
5.9%
기타 7
 
1.4%
공동탕업 6
 
1.2%
일반호텔 5
 
1.0%
Other values (11) 18
 
3.6%

기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-08-18
490 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-08-18
2nd row2023-08-18
3rd row2023-08-18
4th row2023-08-18
5th row2023-08-18

Common Values

ValueCountFrequency (%)
2023-08-18 490
100.0%

Length

2023-12-13T01:27:08.463787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:27:08.588992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-08-18 490
100.0%

Correlations

2023-12-13T01:27:08.663966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명업태명
업종명1.0000.955
업태명0.9551.000
2023-12-13T01:27:08.787896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업태명업종명
업태명1.0000.684
업종명0.6841.000
2023-12-13T01:27:08.913066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명업태명
업종명1.0000.684
업태명0.6841.000

Missing values

2023-12-13T01:27:05.345540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:27:05.470441image/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.
2023-12-13T01:27:05.569523image/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

업종명업소명도로명주소지번주소영업자시작일업태명기준일자
0숙박업(일반)온천장여관충청북도 음성군 감곡면 장감로131번길 5충청북도 음성군 감곡면 왕장리 436-12021-06-23여관업2023-08-18
1숙박업(일반)제일여인숙충청북도 음성군 감곡면 장감로143번길 3-6충청북도 음성군 감곡면 왕장리 4612006-10-30여인숙업2023-08-18
2숙박업(일반)로얄모텔충청북도 음성군 음성읍 중앙로 126-9충청북도 음성군 음성읍 읍내리 530-12004-02-14여관업2023-08-18
3숙박업(일반)월드파크충청북도 음성군 대소면 오태로 76-6충청북도 음성군 대소면 오산리 114-242022-12-23여관업2023-08-18
4숙박업(일반)음성파크충청북도 음성군 음성읍 음성천서길 167충청북도 음성군 음성읍 읍내리 540-32002-11-11여관업2023-08-18
5숙박업(일반)아비숑모텔충청북도 음성군 금왕읍 음성로 1189충청북도 음성군 금왕읍 무극리 121-12015-06-02여관업2023-08-18
6숙박업(일반)대원파크충청북도 음성군 음성읍 시장로115번길 5충청북도 음성군 음성읍 읍내리 3082022-11-11여관업2023-08-18
7숙박업(일반)롯데여관충청북도 음성군 감곡면 장감로132번길 32-2충청북도 음성군 감곡면 왕장리 5172008-01-03여관업2023-08-18
8숙박업(일반)파라다이스모텔충청북도 음성군 대소면 오태로 62-25충청북도 음성군 대소면 오산리 114-282007-04-27여관업2023-08-18
9숙박업(일반)J모텔충청북도 음성군 음성읍 음성천동길 108충청북도 음성군 음성읍 읍내리 353-102011-06-13여관업2023-08-18
업종명업소명도로명주소지번주소영업자시작일업태명기준일자
480일반미용업, 네일미용업, 화장ㆍ분장 미용업헤어아트충청북도 음성군 대소면 오산로 77충청북도 음성군 대소면 오산리 144-72016-06-09일반미용업2023-08-18
481일반미용업, 네일미용업, 화장ㆍ분장 미용업헤어살롱 다인충청북도 음성군 맹동면 장성로 41 1층 103호충청북도 음성군 맹동면 동성리 5142018-01-15일반미용업2023-08-18
482일반미용업, 네일미용업, 화장ㆍ분장 미용업헤어스토리충청북도 음성군 음성읍 시장로 54 중앙상가충청북도 음성군 음성읍 읍내리 348-15 중앙상가2018-07-24일반미용업2023-08-18
483일반미용업, 네일미용업, 화장ㆍ분장 미용업이루코헤어 본점 충북혁신도시점충청북도 음성군 맹동면 사예3길 18 201호충청북도 음성군 맹동면 동성리 4842019-03-20일반미용업2023-08-18
484일반미용업, 네일미용업, 화장ㆍ분장 미용업헤어뉴스충청북도 음성군 대소면 오태로 155 105호충청북도 음성군 대소면 태생리 5262019-06-18일반미용업2023-08-18
485일반미용업, 네일미용업, 화장ㆍ분장 미용업오:누아충청북도 음성군 맹동면 대하로 226 2층 204호충청북도 음성군 맹동면 두성리 13822019-09-20일반미용업2023-08-18
486일반미용업, 네일미용업, 화장ㆍ분장 미용업제나뷰티충청북도 음성군 금왕읍 탑골길 17충청북도 음성군 금왕읍 무극리 7032022-09-06네일아트업2023-08-18
487피부미용업, 네일미용업, 화장ㆍ분장 미용업e끌림네일&에스테틱충청북도 음성군 맹동면 원중로 1438 DS타워 2층 204호충청북도 음성군 맹동면 동성리 4882018-06-18피부미용업2023-08-18
488피부미용업, 네일미용업, 화장ㆍ분장 미용업네일집충청북도 음성군 대소면 대금로 217-1충청북도 음성군 대소면 오류리 610-1132018-09-14네일아트업2023-08-18
489피부미용업, 네일미용업, 화장ㆍ분장 미용업오늘은뷰티앤충청북도 음성군 맹동면 산학로 35 106호충청북도 음성군 맹동면 두성리 13662022-09-06피부미용업2023-08-18