Overview

Dataset statistics

Number of variables5
Number of observations660
Missing cells303
Missing cells (%)9.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.9 KiB
Average record size in memory40.2 B

Variable types

Categorical1
Text4

Dataset

Description양평관내 이용업, 미용업, 세탁업, 목욕장업에 대한 데이터로 업종명, 업소명, 소재지도로명주소, 소재지지번주소 등의 정보를 제공합니다.
Author경기도 양평군
URLhttps://www.data.go.kr/data/15061190/fileData.do

Alerts

소재지도로명주소 has 27 (4.1%) missing valuesMissing
소재지전화 has 276 (41.8%) missing valuesMissing

Reproduction

Analysis started2023-12-12 13:09:39.877645
Analysis finished2023-12-12 13:09:40.732058
Duration0.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

Distinct18
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
숙박업(일반)
148 
일반미용업
107 
미용업
78 
숙박업(생활)
63 
건물위생관리업
54 
Other values (13)
210 

Length

Max length23
Median length16
Mean length5.5651515
Min length3

Unique

Unique2 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
숙박업(일반) 148
22.4%
일반미용업 107
16.2%
미용업 78
11.8%
숙박업(생활) 63
9.5%
건물위생관리업 54
 
8.2%
피부미용업 43
 
6.5%
세탁업 38
 
5.8%
이용업 34
 
5.2%
네일미용업 28
 
4.2%
종합미용업 26
 
3.9%
Other values (8) 41
 
6.2%

Length

2023-12-12T22:09:40.830970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
숙박업(일반 148
21.5%
일반미용업 111
16.1%
미용업 90
13.1%
숙박업(생활 63
9.1%
건물위생관리업 54
 
7.8%
피부미용업 50
 
7.3%
네일미용업 39
 
5.7%
세탁업 38
 
5.5%
이용업 34
 
4.9%
종합미용업 26
 
3.8%
Other values (2) 36
 
5.2%

업소명
Text

Distinct651
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
2023-12-12T22:09:41.156563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length20
Mean length5.8848485
Min length1

Characters and Unicode

Total characters3884
Distinct characters486
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

Unique644 ?
Unique (%)97.6%

Sample

1st row청호여관
2nd row광주여관
3rd row환희텔
4th row은하여인숙
5th row호수여관
ValueCountFrequency (%)
주식회사 8
 
1.1%
윤헤어 4
 
0.6%
헤어 4
 
0.6%
에스테틱 3
 
0.4%
hair 3
 
0.4%
nail 3
 
0.4%
네일 3
 
0.4%
미용실 3
 
0.4%
쉐르빌온천관광호텔 2
 
0.3%
리버텔 2
 
0.3%
Other values (684) 692
95.2%
2023-12-12T22:09:41.670964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 132
 
3.4%
헤 120
 
3.1%
스 99
 
2.5%
리 92
 
2.4%
이 89
 
2.3%
미 86
 
2.2%
용 86
 
2.2%
텔 78
 
2.0%
68
 
1.8%
) 57
 
1.5%
Other values (476) 2977
76.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3435
88.4%
Uppercase Letter 125
 
3.2%
Lowercase Letter 84
 
2.2%
Space Separator 68
 
1.8%
Close Punctuation 57
 
1.5%
Open Punctuation 53
 
1.4%
Decimal Number 48
 
1.2%
Other Punctuation 14
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 132
 
3.8%
헤 120
 
3.5%
스 99
 
2.9%
리 92
 
2.7%
이 89
 
2.6%
미 86
 
2.5%
용 86
 
2.5%
텔 78
 
2.3%
라 54
 
1.6%
모 52
 
1.5%
Other values (416) 2547
74.1%
Uppercase Letter
ValueCountFrequency (%)
A 19
15.2%
H 13
 
10.4%
O 10
 
8.0%
L 9
 
7.2%
N 9
 
7.2%
S 8
 
6.4%
C 7
 
5.6%
E 6
 
4.8%
I 5
 
4.0%
M 5
 
4.0%
Other values (14) 34
27.2%
Lowercase Letter
ValueCountFrequency (%)
e 12
14.3%
a 11
13.1%
i 11
13.1%
n 10
11.9%
r 9
10.7%
l 7
8.3%
h 4
 
4.8%
s 4
 
4.8%
o 3
 
3.6%
p 2
 
2.4%
Other values (7) 11
13.1%
Decimal Number
ValueCountFrequency (%)
1 12
25.0%
2 8
16.7%
4 7
14.6%
5 4
 
8.3%
9 4
 
8.3%
7 3
 
6.2%
8 3
 
6.2%
3 3
 
6.2%
0 2
 
4.2%
6 2
 
4.2%
Other Punctuation
ValueCountFrequency (%)
& 7
50.0%
# 3
21.4%
' 1
 
7.1%
, 1
 
7.1%
! 1
 
7.1%
. 1
 
7.1%
Space Separator
ValueCountFrequency (%)
68
100.0%
Close Punctuation
ValueCountFrequency (%)
) 57
100.0%
Open Punctuation
ValueCountFrequency (%)
( 53
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3434
88.4%
Common 240
 
6.2%
Latin 209
 
5.4%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 132
 
3.8%
헤 120
 
3.5%
스 99
 
2.9%
리 92
 
2.7%
이 89
 
2.6%
미 86
 
2.5%
용 86
 
2.5%
텔 78
 
2.3%
라 54
 
1.6%
모 52
 
1.5%
Other values (415) 2546
74.1%
Latin
ValueCountFrequency (%)
A 19
 
9.1%
H 13
 
6.2%
e 12
 
5.7%
a 11
 
5.3%
i 11
 
5.3%
O 10
 
4.8%
n 10
 
4.8%
L 9
 
4.3%
r 9
 
4.3%
N 9
 
4.3%
Other values (31) 96
45.9%
Common
ValueCountFrequency (%)
68
28.3%
) 57
23.8%
( 53
22.1%
1 12
 
5.0%
2 8
 
3.3%
& 7
 
2.9%
4 7
 
2.9%
5 4
 
1.7%
9 4
 
1.7%
7 3
 
1.2%
Other values (9) 17
 
7.1%
Han
ValueCountFrequency (%)
美 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3434
88.4%
ASCII 449
 
11.6%
CJK 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 132
 
3.8%
헤 120
 
3.5%
스 99
 
2.9%
리 92
 
2.7%
이 89
 
2.6%
미 86
 
2.5%
용 86
 
2.5%
텔 78
 
2.3%
라 54
 
1.6%
모 52
 
1.5%
Other values (415) 2546
74.1%
ASCII
ValueCountFrequency (%)
68
 
15.1%
) 57
 
12.7%
( 53
 
11.8%
A 19
 
4.2%
H 13
 
2.9%
1 12
 
2.7%
e 12
 
2.7%
a 11
 
2.4%
i 11
 
2.4%
O 10
 
2.2%
Other values (50) 183
40.8%
CJK
ValueCountFrequency (%)
美 1
100.0%

소재지도로명주소
Text

MISSING 

Distinct610
Distinct (%)96.4%
Missing27
Missing (%)4.1%
Memory size5.3 KiB
2023-12-12T22:09:42.062634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length81
Median length48
Mean length23.562401
Min length17

Characters and Unicode

Total characters14915
Distinct characters227
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

Unique591 ?
Unique (%)93.4%

Sample

1st row경기도 양평군 양평읍 양평시장길17번길 4
2nd row경기도 양평군 양평읍 해오름길 20
3rd row경기도 양평군 용문면 용문산로 652-1
4th row경기도 양평군 양서면 양수로150번길 17
5th row경기도 양평군 양평읍 양근로 189-6
ValueCountFrequency (%)
경기도 633
 
17.6%
양평군 633
 
17.6%
양평읍 217
 
6.0%
1층 133
 
3.7%
용문면 118
 
3.3%
강하면 45
 
1.3%
강남로 44
 
1.2%
2층 40
 
1.1%
옥천면 40
 
1.1%
서종면 39
 
1.1%
Other values (673) 1656
46.0%
2023-12-12T22:09:42.623556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2965
19.9%
양 1023
 
6.9%
평 954
 
6.4%
1 700
 
4.7%
군 651
 
4.4%
경 644
 
4.3%
기 637
 
4.3%
도 636
 
4.3%
면 417
 
2.8%
길 400
 
2.7%
Other values (217) 5888
39.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8815
59.1%
Space Separator 2965
 
19.9%
Decimal Number 2501
 
16.8%
Other Punctuation 340
 
2.3%
Dash Punctuation 165
 
1.1%
Open Punctuation 55
 
0.4%
Close Punctuation 55
 
0.4%
Math Symbol 10
 
0.1%
Uppercase Letter 9
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
양 1023
 
11.6%
평 954
 
10.8%
군 651
 
7.4%
경 644
 
7.3%
기 637
 
7.2%
도 636
 
7.2%
면 417
 
4.7%
길 400
 
4.5%
로 335
 
3.8%
문 235
 
2.7%
Other values (197) 2883
32.7%
Decimal Number
ValueCountFrequency (%)
1 700
28.0%
2 339
13.6%
3 260
 
10.4%
0 204
 
8.2%
4 198
 
7.9%
5 184
 
7.4%
6 180
 
7.2%
7 148
 
5.9%
8 148
 
5.9%
9 140
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
B 6
66.7%
A 2
 
22.2%
D 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
, 339
99.7%
. 1
 
0.3%
Space Separator
ValueCountFrequency (%)
2965
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 165
100.0%
Open Punctuation
ValueCountFrequency (%)
( 55
100.0%
Close Punctuation
ValueCountFrequency (%)
) 55
100.0%
Math Symbol
ValueCountFrequency (%)
~ 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8815
59.1%
Common 6091
40.8%
Latin 9
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
양 1023
 
11.6%
평 954
 
10.8%
군 651
 
7.4%
경 644
 
7.3%
기 637
 
7.2%
도 636
 
7.2%
면 417
 
4.7%
길 400
 
4.5%
로 335
 
3.8%
문 235
 
2.7%
Other values (197) 2883
32.7%
Common
ValueCountFrequency (%)
2965
48.7%
1 700
 
11.5%
, 339
 
5.6%
2 339
 
5.6%
3 260
 
4.3%
0 204
 
3.3%
4 198
 
3.3%
5 184
 
3.0%
6 180
 
3.0%
- 165
 
2.7%
Other values (7) 557
 
9.1%
Latin
ValueCountFrequency (%)
B 6
66.7%
A 2
 
22.2%
D 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8815
59.1%
ASCII 6100
40.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2965
48.6%
1 700
 
11.5%
, 339
 
5.6%
2 339
 
5.6%
3 260
 
4.3%
0 204
 
3.3%
4 198
 
3.2%
5 184
 
3.0%
6 180
 
3.0%
- 165
 
2.7%
Other values (10) 566
 
9.3%
Hangul
ValueCountFrequency (%)
양 1023
 
11.6%
평 954
 
10.8%
군 651
 
7.4%
경 644
 
7.3%
기 637
 
7.2%
도 636
 
7.2%
면 417
 
4.7%
길 400
 
4.5%
로 335
 
3.8%
문 235
 
2.7%
Other values (197) 2883
32.7%

소재지지번주소
Text

Distinct607
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
2023-12-12T22:09:42.920606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length41
Mean length23.274242
Min length18

Characters and Unicode

Total characters15361
Distinct characters182
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

Unique565 ?
Unique (%)85.6%

Sample

1st row경기도 양평군 양평읍 양근리 410-9
2nd row경기도 양평군 양평읍 양근리 314-9
3rd row경기도 양평군 용문면 신점리 520-3
4th row경기도 양평군 양동면 쌍학리 179
5th row경기도 양평군 양서면 양수리 553-6
ValueCountFrequency (%)
경기도 660
18.8%
양평군 660
18.8%
양평읍 226
 
6.4%
양근리 131
 
3.7%
용문면 120
 
3.4%
다문리 73
 
2.1%
강하면 45
 
1.3%
공흥리 45
 
1.3%
양서면 42
 
1.2%
서종면 40
 
1.1%
Other values (727) 1464
41.8%
2023-12-12T22:09:43.432051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3487
22.7%
양 1116
 
7.3%
평 956
 
6.2%
군 678
 
4.4%
도 665
 
4.3%
리 661
 
4.3%
경 660
 
4.3%
기 660
 
4.3%
- 556
 
3.6%
1 494
 
3.2%
Other values (172) 5428
35.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8419
54.8%
Space Separator 3487
22.7%
Decimal Number 2823
 
18.4%
Dash Punctuation 556
 
3.6%
Close Punctuation 29
 
0.2%
Open Punctuation 29
 
0.2%
Other Punctuation 9
 
0.1%
Math Symbol 5
 
< 0.1%
Uppercase Letter 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
양 1116
13.3%
평 956
11.4%
군 678
 
8.1%
도 665
 
7.9%
리 661
 
7.9%
경 660
 
7.8%
기 660
 
7.8%
면 436
 
5.2%
문 227
 
2.7%
읍 226
 
2.7%
Other values (154) 2134
25.3%
Decimal Number
ValueCountFrequency (%)
1 494
17.5%
3 378
13.4%
2 337
11.9%
5 296
10.5%
4 279
9.9%
6 260
9.2%
7 244
8.6%
0 188
 
6.7%
9 176
 
6.2%
8 171
 
6.1%
Uppercase Letter
ValueCountFrequency (%)
B 3
75.0%
A 1
 
25.0%
Space Separator
ValueCountFrequency (%)
3487
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 556
100.0%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Other Punctuation
ValueCountFrequency (%)
, 9
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8419
54.8%
Common 6938
45.2%
Latin 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
양 1116
13.3%
평 956
11.4%
군 678
 
8.1%
도 665
 
7.9%
리 661
 
7.9%
경 660
 
7.8%
기 660
 
7.8%
면 436
 
5.2%
문 227
 
2.7%
읍 226
 
2.7%
Other values (154) 2134
25.3%
Common
ValueCountFrequency (%)
3487
50.3%
- 556
 
8.0%
1 494
 
7.1%
3 378
 
5.4%
2 337
 
4.9%
5 296
 
4.3%
4 279
 
4.0%
6 260
 
3.7%
7 244
 
3.5%
0 188
 
2.7%
Other values (6) 419
 
6.0%
Latin
ValueCountFrequency (%)
B 3
75.0%
A 1
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8419
54.8%
ASCII 6942
45.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3487
50.2%
- 556
 
8.0%
1 494
 
7.1%
3 378
 
5.4%
2 337
 
4.9%
5 296
 
4.3%
4 279
 
4.0%
6 260
 
3.7%
7 244
 
3.5%
0 188
 
2.7%
Other values (8) 423
 
6.1%
Hangul
ValueCountFrequency (%)
양 1116
13.3%
평 956
11.4%
군 678
 
8.1%
도 665
 
7.9%
리 661
 
7.9%
경 660
 
7.8%
기 660
 
7.8%
면 436
 
5.2%
문 227
 
2.7%
읍 226
 
2.7%
Other values (154) 2134
25.3%

소재지전화
Text

MISSING 

Distinct373
Distinct (%)97.1%
Missing276
Missing (%)41.8%
Memory size5.3 KiB
2023-12-12T22:09:44.075287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.007812
Min length11

Characters and Unicode

Total characters4611
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

Unique363 ?
Unique (%)94.5%

Sample

1st row031-771-2803
2nd row031-772-2389
3rd row031-771-1751
4th row031-773-5885
5th row031-772-6063
ValueCountFrequency (%)
031-552-3881 3
 
0.8%
031-774-4101 2
 
0.5%
031-771-8331 2
 
0.5%
031-772-2347 2
 
0.5%
031-770-9700 2
 
0.5%
031-770-8888 2
 
0.5%
031-773-3064 2
 
0.5%
031-774-0083 2
 
0.5%
031-771-0001 2
 
0.5%
031-774-4321 2
 
0.5%
Other values (363) 363
94.5%
2023-12-12T22:09:44.598290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 905
19.6%
- 768
16.7%
3 621
13.5%
1 616
13.4%
0 587
12.7%
2 247
 
5.4%
5 213
 
4.6%
4 200
 
4.3%
8 160
 
3.5%
9 157
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3843
83.3%
Dash Punctuation 768
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 905
23.5%
3 621
16.2%
1 616
16.0%
0 587
15.3%
2 247
 
6.4%
5 213
 
5.5%
4 200
 
5.2%
8 160
 
4.2%
9 157
 
4.1%
6 137
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4611
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
7 905
19.6%
- 768
16.7%
3 621
13.5%
1 616
13.4%
0 587
12.7%
2 247
 
5.4%
5 213
 
4.6%
4 200
 
4.3%
8 160
 
3.5%
9 157
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4611
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 905
19.6%
- 768
16.7%
3 621
13.5%
1 616
13.4%
0 587
12.7%
2 247
 
5.4%
5 213
 
4.6%
4 200
 
4.3%
8 160
 
3.5%
9 157
 
3.4%

Missing values

2023-12-12T22:09:40.412676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:09:40.539065image/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.
2023-12-12T22:09:40.658873image/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

업종명업소명소재지도로명주소소재지지번주소소재지전화
0숙박업(일반)청호여관경기도 양평군 양평읍 양평시장길17번길 4경기도 양평군 양평읍 양근리 410-9031-771-2803
1숙박업(일반)광주여관경기도 양평군 양평읍 해오름길 20경기도 양평군 양평읍 양근리 314-9031-772-2389
2숙박업(일반)환희텔경기도 양평군 용문면 용문산로 652-1경기도 양평군 용문면 신점리 520-3031-771-1751
3숙박업(일반)은하여인숙<NA>경기도 양평군 양동면 쌍학리 179031-773-5885
4숙박업(일반)호수여관경기도 양평군 양서면 양수로150번길 17경기도 양평군 양서면 양수리 553-6031-772-6063
5숙박업(일반)쿨모텔경기도 양평군 양평읍 양근로 189-6경기도 양평군 양평읍 양근리 351-3<NA>
6숙박업(일반)뮤즈모텔경기도 양평군 양평읍 시민로 44경기도 양평군 양평읍 양근리 183-4031-771-3393
7숙박업(일반)리치모텔경기도 양평군 용문면 용문산로 652-11경기도 양평군 용문면 신점리 520-9031-773-3399
8숙박업(일반)대구여관경기도 양평군 양평읍 양평시장길18번길 3경기도 양평군 양평읍 양근리 409-1031-773-5767
9숙박업(일반)CF모텔경기도 양평군 양평읍 양평시장길7번길 8경기도 양평군 양평읍 양근리 304-2031-771-2667
업종명업소명소재지도로명주소소재지지번주소소재지전화
650화장ㆍ분장 미용업온나뷰티경기도 양평군 양평읍 중앙로37번길 3, 1층경기도 양평군 양평읍 공흥리 731-8<NA>
651피부미용업, 화장ㆍ분장 미용업더아이뷰티끄경기도 양평군 용문면 하진1길 38, 101호경기도 양평군 용문면 다문리 786-37<NA>
652네일미용업, 화장ㆍ분장 미용업유니네일샵(Yuni네일샵)경기도 양평군 용문면 다문북길 32, 1층경기도 양평군 용문면 다문리 701-2<NA>
653네일미용업, 화장ㆍ분장 미용업네일향기경기도 양평군 양평읍 양평시장길27번길 11, 1층경기도 양평군 양평읍 양근리 414-4 1통<NA>
654네일미용업, 화장ㆍ분장 미용업네일또와경기도 양평군 양평읍 관문길 68-4, 1층 109호경기도 양평군 양평읍 양근리 141-2<NA>
655네일미용업, 화장ㆍ분장 미용업썸경기도 양평군 강상면 양평대교길 89, 1층경기도 양평군 강상면 교평리 449-6<NA>
656네일미용업, 화장ㆍ분장 미용업체니뷰티경기도 양평군 용문면 용문로 855, 2층층경기도 양평군 용문면 광탄리 182-5<NA>
657일반미용업, 네일미용업, 화장ㆍ분장 미용업별빛헤어경기도 양평군 양평읍 시민로43번길 3-1경기도 양평군 양평읍 양근리 176-15<NA>
658일반미용업, 네일미용업, 화장ㆍ분장 미용업헤어바이엔(hair by N)경기도 양평군 용문면 다문북길 115, 유진빌딩경기도 양평군 용문면 다문리 353-4 유진빌딩<NA>
659피부미용업, 네일미용업, 화장ㆍ분장 미용업토브현뷰티살롱경기도 양평군 서종면 북한강로801번길 3, 1층경기도 양평군 서종면 문호리 731-3<NA>