Overview

Dataset statistics

Number of variables5
Number of observations2106
Missing cells13
Missing cells (%)0.1%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory82.4 KiB
Average record size in memory40.1 B

Variable types

Categorical1
DateTime1
Text3

Dataset

Description전라남도 여수시 관내 공중위생업소(2023.9.15 기준)에 대한 데이터(세탁업, 이용업, 미용업, 숙박업 등) 자료 제공
Author전라남도 여수시
URLhttps://www.data.go.kr/data/3074190/fileData.do

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates

Reproduction

Analysis started2023-12-12 09:21:09.440056
Analysis finished2023-12-12 09:21:10.542759
Duration1.1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

Distinct21
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
일반미용업
394 
숙박업(생활)
292 
미용업
269 
숙박업(일반)
264 
피부미용업
158 
Other values (16)
729 

Length

Max length23
Median length16
Mean length5.6006648
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 394
18.7%
숙박업(생활) 292
13.9%
미용업 269
12.8%
숙박업(일반) 264
12.5%
피부미용업 158
7.5%
세탁업 133
 
6.3%
건물위생관리업 117
 
5.6%
이용업 115
 
5.5%
네일미용업 104
 
4.9%
목욕장업 78
 
3.7%
Other values (11) 182
8.6%

Length

2023-12-12T18:21:10.643640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 418
18.3%
미용업 350
15.3%
숙박업(생활 292
12.8%
숙박업(일반 264
11.5%
피부미용업 204
8.9%
네일미용업 165
 
7.2%
세탁업 133
 
5.8%
건물위생관리업 117
 
5.1%
이용업 115
 
5.0%
화장ㆍ분장 81
 
3.5%
Other values (2) 149
 
6.5%

신고일자
Date

Distinct1706
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
Minimum1956-01-03 00:00:00
Maximum2023-09-14 00:00:00
2023-12-12T18:21:10.796916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:10.951873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업소명
Text

Distinct2058
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
2023-12-12T18:21:11.225245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length22
Mean length5.9553656
Min length1

Characters and Unicode

Total characters12542
Distinct characters674
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2018 ?
Unique (%)95.8%

Sample

1st row하나여인숙
2nd row호텔마띠유여수
3rd row여수, 스파앤하우스
4th row서울
5th row삼오
ValueCountFrequency (%)
헤어 58
 
2.0%
호스텔 42
 
1.4%
여수 33
 
1.1%
호텔 28
 
1.0%
네일 24
 
0.8%
비고리조트 19
 
0.6%
헤어샵 17
 
0.6%
주식회사 17
 
0.6%
뷰티 16
 
0.5%
더 15
 
0.5%
Other values (2273) 2673
90.9%
2023-12-12T18:21:11.667667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
839
 
6.7%
어 402
 
3.2%
헤 364
 
2.9%
스 351
 
2.8%
이 253
 
2.0%
텔 249
 
2.0%
리 247
 
2.0%
미 201
 
1.6%
수 179
 
1.4%
호 161
 
1.3%
Other values (664) 9296
74.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10270
81.9%
Space Separator 839
 
6.7%
Lowercase Letter 455
 
3.6%
Uppercase Letter 417
 
3.3%
Decimal Number 150
 
1.2%
Close Punctuation 147
 
1.2%
Open Punctuation 144
 
1.1%
Other Punctuation 111
 
0.9%
Dash Punctuation 6
 
< 0.1%
Modifier Symbol 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 402
 
3.9%
헤 364
 
3.5%
스 351
 
3.4%
이 253
 
2.5%
텔 249
 
2.4%
리 247
 
2.4%
미 201
 
2.0%
수 179
 
1.7%
호 161
 
1.6%
모 147
 
1.4%
Other values (587) 7716
75.1%
Lowercase Letter
ValueCountFrequency (%)
e 60
13.2%
a 55
12.1%
o 40
 
8.8%
i 39
 
8.6%
r 29
 
6.4%
l 27
 
5.9%
n 27
 
5.9%
h 25
 
5.5%
t 25
 
5.5%
y 24
 
5.3%
Other values (14) 104
22.9%
Uppercase Letter
ValueCountFrequency (%)
S 39
 
9.4%
H 33
 
7.9%
A 32
 
7.7%
T 27
 
6.5%
L 25
 
6.0%
O 24
 
5.8%
J 24
 
5.8%
I 23
 
5.5%
E 22
 
5.3%
B 22
 
5.3%
Other values (14) 146
35.0%
Decimal Number
ValueCountFrequency (%)
1 34
22.7%
2 25
16.7%
3 21
14.0%
5 13
 
8.7%
7 13
 
8.7%
4 12
 
8.0%
8 9
 
6.0%
9 9
 
6.0%
6 8
 
5.3%
0 6
 
4.0%
Other Punctuation
ValueCountFrequency (%)
& 31
27.9%
# 23
20.7%
. 22
19.8%
, 18
16.2%
: 9
 
8.1%
' 4
 
3.6%
? 1
 
0.9%
! 1
 
0.9%
; 1
 
0.9%
· 1
 
0.9%
Close Punctuation
ValueCountFrequency (%)
) 146
99.3%
] 1
 
0.7%
Open Punctuation
ValueCountFrequency (%)
( 143
99.3%
[ 1
 
0.7%
Space Separator
ValueCountFrequency (%)
839
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10264
81.8%
Common 1400
 
11.2%
Latin 872
 
7.0%
Han 6
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 402
 
3.9%
헤 364
 
3.5%
스 351
 
3.4%
이 253
 
2.5%
텔 249
 
2.4%
리 247
 
2.4%
미 201
 
2.0%
수 179
 
1.7%
호 161
 
1.6%
모 147
 
1.4%
Other values (583) 7710
75.1%
Latin
ValueCountFrequency (%)
e 60
 
6.9%
a 55
 
6.3%
o 40
 
4.6%
S 39
 
4.5%
i 39
 
4.5%
H 33
 
3.8%
A 32
 
3.7%
r 29
 
3.3%
T 27
 
3.1%
l 27
 
3.1%
Other values (38) 491
56.3%
Common
ValueCountFrequency (%)
839
59.9%
) 146
 
10.4%
( 143
 
10.2%
1 34
 
2.4%
& 31
 
2.2%
2 25
 
1.8%
# 23
 
1.6%
. 22
 
1.6%
3 21
 
1.5%
, 18
 
1.3%
Other values (19) 98
 
7.0%
Han
ValueCountFrequency (%)
美 3
50.0%
共 1
 
16.7%
感 1
 
16.7%
夜 1
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10262
81.8%
ASCII 2271
 
18.1%
CJK 6
 
< 0.1%
Compat Jamo 2
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
839
36.9%
) 146
 
6.4%
( 143
 
6.3%
e 60
 
2.6%
a 55
 
2.4%
o 40
 
1.8%
S 39
 
1.7%
i 39
 
1.7%
1 34
 
1.5%
H 33
 
1.5%
Other values (66) 843
37.1%
Hangul
ValueCountFrequency (%)
어 402
 
3.9%
헤 364
 
3.5%
스 351
 
3.4%
이 253
 
2.5%
텔 249
 
2.4%
리 247
 
2.4%
미 201
 
2.0%
수 179
 
1.7%
호 161
 
1.6%
모 147
 
1.4%
Other values (581) 7708
75.1%
CJK
ValueCountFrequency (%)
美 3
50.0%
共 1
 
16.7%
感 1
 
16.7%
夜 1
 
16.7%
Compat Jamo
ValueCountFrequency (%)
ㅅ 1
50.0%
ㅎ 1
50.0%
None
ValueCountFrequency (%)
· 1
100.0%

영업소 주소(도로명)
Text

Distinct1962
Distinct (%)93.6%
Missing9
Missing (%)0.4%
Memory size16.6 KiB
2023-12-12T18:21:12.072043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length52
Mean length26.13257
Min length18

Characters and Unicode

Total characters54800
Distinct characters282
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

Unique1848 ?
Unique (%)88.1%

Sample

1st row전라남도 여수시 교동시장4길 16-3 (교동)
2nd row전라남도 여수시 오동도로 20 (공화동)
3rd row전라남도 여수시 동문로 10-13 (중앙동)
4th row전라남도 여수시 동문로 124-3 (공화동)
5th row전라남도 여수시 서교연등천길 7 (서교동)
ValueCountFrequency (%)
전라남도 2097
 
17.6%
여수시 2097
 
17.6%
1층 284
 
2.4%
신기동 206
 
1.7%
돌산읍 176
 
1.5%
학동 169
 
1.4%
2층 166
 
1.4%
문수동 125
 
1.0%
여서동 117
 
1.0%
상가동 106
 
0.9%
Other values (1585) 6389
53.5%
2023-12-12T18:21:12.722719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9838
18.0%
여 2435
 
4.4%
수 2422
 
4.4%
남 2421
 
4.4%
동 2260
 
4.1%
시 2226
 
4.1%
1 2220
 
4.1%
라 2197
 
4.0%
도 2146
 
3.9%
전 2115
 
3.9%
Other values (272) 24520
44.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31488
57.5%
Space Separator 9838
 
18.0%
Decimal Number 8081
 
14.7%
Open Punctuation 1818
 
3.3%
Close Punctuation 1818
 
3.3%
Other Punctuation 978
 
1.8%
Dash Punctuation 735
 
1.3%
Uppercase Letter 24
 
< 0.1%
Math Symbol 17
 
< 0.1%
Lowercase Letter 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
여 2435
 
7.7%
수 2422
 
7.7%
남 2421
 
7.7%
동 2260
 
7.2%
시 2226
 
7.1%
라 2197
 
7.0%
도 2146
 
6.8%
전 2115
 
6.7%
길 1136
 
3.6%
로 967
 
3.1%
Other values (247) 11163
35.5%
Decimal Number
ValueCountFrequency (%)
1 2220
27.5%
2 1342
16.6%
3 987
12.2%
4 605
 
7.5%
5 600
 
7.4%
0 570
 
7.1%
6 560
 
6.9%
7 463
 
5.7%
8 387
 
4.8%
9 347
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 952
97.3%
@ 24
 
2.5%
& 1
 
0.1%
/ 1
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
A 17
70.8%
C 4
 
16.7%
B 2
 
8.3%
M 1
 
4.2%
Lowercase Letter
ValueCountFrequency (%)
e 2
66.7%
c 1
33.3%
Space Separator
ValueCountFrequency (%)
9838
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1818
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1818
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 735
100.0%
Math Symbol
ValueCountFrequency (%)
~ 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 31488
57.5%
Common 23285
42.5%
Latin 27
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
여 2435
 
7.7%
수 2422
 
7.7%
남 2421
 
7.7%
동 2260
 
7.2%
시 2226
 
7.1%
라 2197
 
7.0%
도 2146
 
6.8%
전 2115
 
6.7%
길 1136
 
3.6%
로 967
 
3.1%
Other values (247) 11163
35.5%
Common
ValueCountFrequency (%)
9838
42.3%
1 2220
 
9.5%
( 1818
 
7.8%
) 1818
 
7.8%
2 1342
 
5.8%
3 987
 
4.2%
, 952
 
4.1%
- 735
 
3.2%
4 605
 
2.6%
5 600
 
2.6%
Other values (9) 2370
 
10.2%
Latin
ValueCountFrequency (%)
A 17
63.0%
C 4
 
14.8%
B 2
 
7.4%
e 2
 
7.4%
M 1
 
3.7%
c 1
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 31488
57.5%
ASCII 23312
42.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9838
42.2%
1 2220
 
9.5%
( 1818
 
7.8%
) 1818
 
7.8%
2 1342
 
5.8%
3 987
 
4.2%
, 952
 
4.1%
- 735
 
3.2%
4 605
 
2.6%
5 600
 
2.6%
Other values (15) 2397
 
10.3%
Hangul
ValueCountFrequency (%)
여 2435
 
7.7%
수 2422
 
7.7%
남 2421
 
7.7%
동 2260
 
7.2%
시 2226
 
7.1%
라 2197
 
7.0%
도 2146
 
6.8%
전 2115
 
6.7%
길 1136
 
3.6%
로 967
 
3.1%
Other values (247) 11163
35.5%

영업소 주소(지번)
Text

Distinct1861
Distinct (%)88.5%
Missing4
Missing (%)0.2%
Memory size16.6 KiB
2023-12-12T18:21:13.121308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length40
Mean length20.875833
Min length15

Characters and Unicode

Total characters43881
Distinct characters224
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

Unique1673 ?
Unique (%)79.6%

Sample

1st row전라남도 여수시 교동 622-18
2nd row전라남도 여수시 공화동 766 1~4층
3rd row전라남도 여수시 중앙동 474
4th row전라남도 여수시 공화동 685
5th row전라남도 여수시 서교동 532-2
ValueCountFrequency (%)
전라남도 2102
22.4%
여수시 2102
22.4%
신기동 213
 
2.3%
돌산읍 176
 
1.9%
학동 173
 
1.8%
문수동 129
 
1.4%
여서동 118
 
1.3%
웅천동 101
 
1.1%
선원동 90
 
1.0%
봉산동 86
 
0.9%
Other values (2012) 4078
43.5%
2023-12-12T18:21:13.698723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9331
21.3%
수 2300
 
5.2%
여 2260
 
5.2%
라 2199
 
5.0%
남 2129
 
4.9%
시 2126
 
4.8%
전 2109
 
4.8%
도 2105
 
4.8%
1 2001
 
4.6%
동 1870
 
4.3%
Other values (214) 15451
35.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 23753
54.1%
Space Separator 9331
 
21.3%
Decimal Number 9058
 
20.6%
Dash Punctuation 1605
 
3.7%
Close Punctuation 36
 
0.1%
Open Punctuation 36
 
0.1%
Other Punctuation 32
 
0.1%
Uppercase Letter 19
 
< 0.1%
Math Symbol 7
 
< 0.1%
Lowercase Letter 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 2300
 
9.7%
여 2260
 
9.5%
라 2199
 
9.3%
남 2129
 
9.0%
시 2126
 
9.0%
전 2109
 
8.9%
도 2105
 
8.9%
동 1870
 
7.9%
산 362
 
1.5%
리 324
 
1.4%
Other values (189) 5969
25.1%
Decimal Number
ValueCountFrequency (%)
1 2001
22.1%
2 1237
13.7%
4 903
10.0%
3 849
9.4%
7 755
 
8.3%
5 745
 
8.2%
0 708
 
7.8%
6 654
 
7.2%
8 647
 
7.1%
9 559
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
A 15
78.9%
M 1
 
5.3%
C 1
 
5.3%
D 1
 
5.3%
B 1
 
5.3%
Other Punctuation
ValueCountFrequency (%)
@ 17
53.1%
, 11
34.4%
/ 4
 
12.5%
Lowercase Letter
ValueCountFrequency (%)
a 2
50.0%
e 2
50.0%
Space Separator
ValueCountFrequency (%)
9331
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1605
100.0%
Close Punctuation
ValueCountFrequency (%)
) 36
100.0%
Open Punctuation
ValueCountFrequency (%)
( 36
100.0%
Math Symbol
ValueCountFrequency (%)
~ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 23753
54.1%
Common 20105
45.8%
Latin 23
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 2300
 
9.7%
여 2260
 
9.5%
라 2199
 
9.3%
남 2129
 
9.0%
시 2126
 
9.0%
전 2109
 
8.9%
도 2105
 
8.9%
동 1870
 
7.9%
산 362
 
1.5%
리 324
 
1.4%
Other values (189) 5969
25.1%
Common
ValueCountFrequency (%)
9331
46.4%
1 2001
 
10.0%
- 1605
 
8.0%
2 1237
 
6.2%
4 903
 
4.5%
3 849
 
4.2%
7 755
 
3.8%
5 745
 
3.7%
0 708
 
3.5%
6 654
 
3.3%
Other values (8) 1317
 
6.6%
Latin
ValueCountFrequency (%)
A 15
65.2%
a 2
 
8.7%
e 2
 
8.7%
M 1
 
4.3%
C 1
 
4.3%
D 1
 
4.3%
B 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 23753
54.1%
ASCII 20128
45.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9331
46.4%
1 2001
 
9.9%
- 1605
 
8.0%
2 1237
 
6.1%
4 903
 
4.5%
3 849
 
4.2%
7 755
 
3.8%
5 745
 
3.7%
0 708
 
3.5%
6 654
 
3.2%
Other values (15) 1340
 
6.7%
Hangul
ValueCountFrequency (%)
수 2300
 
9.7%
여 2260
 
9.5%
라 2199
 
9.3%
남 2129
 
9.0%
시 2126
 
9.0%
전 2109
 
8.9%
도 2105
 
8.9%
동 1870
 
7.9%
산 362
 
1.5%
리 324
 
1.4%
Other values (189) 5969
25.1%

Missing values

2023-12-12T18:21:10.258755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:21:10.369021image/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.
2023-12-12T18:21:10.475960image/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

업종명신고일자업소명영업소 주소(도로명)영업소 주소(지번)
0숙박업(일반)1966-10-11하나여인숙전라남도 여수시 교동시장4길 16-3 (교동)전라남도 여수시 교동 622-18
1숙박업(일반)1966-12-03호텔마띠유여수전라남도 여수시 오동도로 20 (공화동)전라남도 여수시 공화동 766 1~4층
2숙박업(일반)1969-09-09여수, 스파앤하우스전라남도 여수시 동문로 10-13 (중앙동)전라남도 여수시 중앙동 474
3숙박업(일반)1970-11-11서울전라남도 여수시 동문로 124-3 (공화동)전라남도 여수시 공화동 685
4숙박업(일반)1970-12-19삼오전라남도 여수시 서교연등천길 7 (서교동)전라남도 여수시 서교동 532-2
5숙박업(일반)1970-11-19그린장전라남도 여수시 서교3길 6 (서교동)전라남도 여수시 서교동 422-1
6숙박업(일반)1971-10-21국제장전라남도 여수시 동문로 124-6 (공화동)전라남도 여수시 공화동 710
7숙박업(일반)1971-11-12팔도모텔전라남도 여수시 공화북2길 25-1 (공화동)전라남도 여수시 공화동 1087-1
8숙박업(일반)1972-08-08헤라모텔전라남도 여수시 봉산남8길 11-23 (봉산동)전라남도 여수시 봉산동 275-14
9숙박업(일반)1973-11-23신광전라남도 여수시 교동시장2길 6-2 (교동)전라남도 여수시 교동 622
업종명신고일자업소명영업소 주소(도로명)영업소 주소(지번)
2096건물위생관리업2022-07-14(유)청경환경전라남도 여수시 여수산단로 48 (봉계동)전라남도 여수시 봉계동 452-1
2097건물위생관리업2022-08-30미화컴퍼니전라남도 여수시 신기남1길 31, 3층 (신기동)전라남도 여수시 신기동 110-1
2098건물위생관리업2022-09-06(주)혜원전라남도 여수시 시청서4길 46-8, 2층 (학동)전라남도 여수시 학동 209-5
2099건물위생관리업2022-09-15우진시스템전라남도 여수시 동문로 25-1, 예수복음전도원 2층 (관문동)전라남도 여수시 관문동 402 예수복음전도원
2100건물위생관리업2022-09-30중앙환경방역전라남도 여수시 충무로 106 (연등동)전라남도 여수시 연등동 159
2101건물위생관리업2022-11-03삼일CS전라남도 여수시 대통2길 75-5, 1층 (화장동)전라남도 여수시 화장동 844-3
2102건물위생관리업2022-11-15상진산업개발(주)전라남도 여수시 성산3길 9, 1층 (화장동)전라남도 여수시 화장동 786-10
2103건물위생관리업2023-01-02바이드 파트너스 주식회사전라남도 여수시 박람회길 84-9, 제1층 101호 (덕충동)전라남도 여수시 덕충동 2024-7
2104건물위생관리업2023-02-16클린마스터전라남도 여수시 새터로 5, 2층 (신기동)전라남도 여수시 신기동 128-3
2105건물위생관리업2023-08-29삼환종합건설(주)전라남도 여수시 시청서4길 46-8, 미래빌딩 2층 (학동)전라남도 여수시 학동 209-5

Duplicate rows

Most frequently occurring

업종명신고일자업소명영업소 주소(도로명)영업소 주소(지번)# duplicates
0일반미용업2022-02-17라모드 헤어.네일전라남도 여수시 시청동5길 20, 206호 (학동)전라남도 여수시 학동 90-12