Overview

Dataset statistics

Number of variables6
Number of observations324
Missing cells94
Missing cells (%)4.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.3 KiB
Average record size in memory48.4 B

Variable types

Categorical2
Text4

Dataset

Description인천광역시 동구 공중위생업소(숙박업, 미용업, 이용업 등) 현황 데이터로, 업종명, 업소명, 영업소주소, 소재지전화번호, 업태명 등 항목을 게시하였습니다.
Author인천광역시 동구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15007014&srcSe=7661IVAWM27C61E190

Alerts

업종명 is highly overall correlated with 업태명High correlation
업태명 is highly overall correlated with 업종명High correlation
소재지전화 has 94 (29.0%) missing valuesMissing

Reproduction

Analysis started2024-03-18 04:12:22.307036
Analysis finished2024-03-18 04:12:23.914635
Duration1.61 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
미용업
84 
일반미용업
49 
숙박업(일반)
46 
건물위생관리업
33 
세탁업
30 
Other values (13)
82 

Length

Max length23
Median length16
Mean length4.9969136
Min length3

Unique

Unique5 ?
Unique (%)1.5%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 84
25.9%
일반미용업 49
15.1%
숙박업(일반) 46
14.2%
건물위생관리업 33
 
10.2%
세탁업 30
 
9.3%
이용업 29
 
9.0%
종합미용업 13
 
4.0%
피부미용업 11
 
3.4%
네일미용업 10
 
3.1%
목욕장업 6
 
1.9%
Other values (8) 13
 
4.0%

Length

2024-03-18T13:12:23.990137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 94
27.1%
일반미용업 56
16.1%
숙박업(일반 46
13.3%
건물위생관리업 33
 
9.5%
세탁업 30
 
8.6%
이용업 29
 
8.4%
네일미용업 15
 
4.3%
피부미용업 14
 
4.0%
종합미용업 13
 
3.7%
화장·분장 10
 
2.9%
Other values (2) 7
 
2.0%

업태명
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
일반미용업
145 
여인숙업
36 
건물위생관리업
33 
일반이용업
29 
일반세탁업
29 
Other values (12)
52 

Length

Max length8
Median length5
Mean length5
Min length2

Unique

Unique4 ?
Unique (%)1.2%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 145
44.8%
여인숙업 36
 
11.1%
건물위생관리업 33
 
10.2%
일반이용업 29
 
9.0%
일반세탁업 29
 
9.0%
네일아트업 11
 
3.4%
피부미용업 11
 
3.4%
여관업 7
 
2.2%
메이크업업 6
 
1.9%
기타 5
 
1.5%
Other values (7) 12
 
3.7%

Length

2024-03-18T13:12:24.095036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 145
44.5%
여인숙업 36
 
11.0%
건물위생관리업 33
 
10.1%
일반이용업 29
 
8.9%
일반세탁업 29
 
8.9%
네일아트업 11
 
3.4%
피부미용업 11
 
3.4%
기타 7
 
2.1%
여관업 7
 
2.1%
메이크업업 6
 
1.8%
Other values (7) 12
 
3.7%

업소명
Text

Distinct319
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-03-18T13:12:24.285745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length20
Mean length5.941358
Min length2

Characters and Unicode

Total characters1925
Distinct characters361
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

Unique314 ?
Unique (%)96.9%

Sample

1st row서산여인숙
2nd row명수여인숙
3rd row황주여인숙
4th row명신여인숙
5th row명성여인숙
ValueCountFrequency (%)
헤어 14
 
3.5%
미용실 6
 
1.5%
채움 3
 
0.8%
헤어샵 3
 
0.8%
사회적협동조합 3
 
0.8%
주식회사 3
 
0.8%
헤어샾 2
 
0.5%
5 2
 
0.5%
화평건강랜드 2
 
0.5%
모텔 2
 
0.5%
Other values (352) 359
90.0%
2024-03-18T13:12:24.606383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 78
 
4.1%
헤 77
 
4.0%
75
 
3.9%
미 67
 
3.5%
용 55
 
2.9%
이 50
 
2.6%
실 47
 
2.4%
인 47
 
2.4%
여 37
 
1.9%
리 36
 
1.9%
Other values (351) 1356
70.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1729
89.8%
Space Separator 75
 
3.9%
Lowercase Letter 34
 
1.8%
Uppercase Letter 32
 
1.7%
Close Punctuation 20
 
1.0%
Open Punctuation 20
 
1.0%
Other Punctuation 11
 
0.6%
Decimal Number 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 78
 
4.5%
헤 77
 
4.5%
미 67
 
3.9%
용 55
 
3.2%
이 50
 
2.9%
실 47
 
2.7%
인 47
 
2.7%
여 37
 
2.1%
리 36
 
2.1%
숙 35
 
2.0%
Other values (312) 1200
69.4%
Uppercase Letter
ValueCountFrequency (%)
I 4
12.5%
S 4
12.5%
N 3
9.4%
G 3
9.4%
R 2
 
6.2%
E 2
 
6.2%
T 2
 
6.2%
M 2
 
6.2%
A 2
 
6.2%
J 2
 
6.2%
Other values (6) 6
18.8%
Lowercase Letter
ValueCountFrequency (%)
i 4
11.8%
t 4
11.8%
e 4
11.8%
o 3
8.8%
h 3
8.8%
l 3
8.8%
m 3
8.8%
a 3
8.8%
n 2
5.9%
r 2
5.9%
Other values (3) 3
8.8%
Other Punctuation
ValueCountFrequency (%)
# 4
36.4%
& 4
36.4%
. 2
18.2%
, 1
 
9.1%
Decimal Number
ValueCountFrequency (%)
5 2
50.0%
8 1
25.0%
6 1
25.0%
Space Separator
ValueCountFrequency (%)
75
100.0%
Close Punctuation
ValueCountFrequency (%)
) 20
100.0%
Open Punctuation
ValueCountFrequency (%)
( 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1728
89.8%
Common 130
 
6.8%
Latin 66
 
3.4%
Han 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 78
 
4.5%
헤 77
 
4.5%
미 67
 
3.9%
용 55
 
3.2%
이 50
 
2.9%
실 47
 
2.7%
인 47
 
2.7%
여 37
 
2.1%
리 36
 
2.1%
숙 35
 
2.0%
Other values (311) 1199
69.4%
Latin
ValueCountFrequency (%)
i 4
 
6.1%
t 4
 
6.1%
e 4
 
6.1%
I 4
 
6.1%
S 4
 
6.1%
N 3
 
4.5%
G 3
 
4.5%
o 3
 
4.5%
h 3
 
4.5%
l 3
 
4.5%
Other values (19) 31
47.0%
Common
ValueCountFrequency (%)
75
57.7%
) 20
 
15.4%
( 20
 
15.4%
# 4
 
3.1%
& 4
 
3.1%
. 2
 
1.5%
5 2
 
1.5%
, 1
 
0.8%
8 1
 
0.8%
6 1
 
0.8%
Han
ValueCountFrequency (%)
美 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1728
89.8%
ASCII 196
 
10.2%
CJK 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 78
 
4.5%
헤 77
 
4.5%
미 67
 
3.9%
용 55
 
3.2%
이 50
 
2.9%
실 47
 
2.7%
인 47
 
2.7%
여 37
 
2.1%
리 36
 
2.1%
숙 35
 
2.0%
Other values (311) 1199
69.4%
ASCII
ValueCountFrequency (%)
75
38.3%
) 20
 
10.2%
( 20
 
10.2%
i 4
 
2.0%
t 4
 
2.0%
e 4
 
2.0%
I 4
 
2.0%
# 4
 
2.0%
S 4
 
2.0%
& 4
 
2.0%
Other values (29) 53
27.0%
CJK
ValueCountFrequency (%)
美 1
100.0%

영업소주소(도로명)
Text

Distinct320
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-03-18T13:12:24.836977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length44
Mean length29.475309
Min length15

Characters and Unicode

Total characters9550
Distinct characters166
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

Unique316 ?
Unique (%)97.5%

Sample

1st row인천광역시 동구 화도진로68번길 3-6 (화평동)
2nd row인천광역시 동구 송화로2번길 1-8 (화평동)
3rd row인천광역시 동구 화도진로52번길 9-3 (송현동)
4th row인천광역시 동구 금곡로11번길 8-7 (금곡동)
5th row인천광역시 동구 화도진로56번길 6-3 (송현동)
ValueCountFrequency (%)
동구 325
 
16.4%
인천광역시 324
 
16.3%
송림동 128
 
6.5%
1층 90
 
4.5%
송현동 75
 
3.8%
송현로 33
 
1.7%
2층 27
 
1.4%
화수동 25
 
1.3%
화평동 22
 
1.1%
화도진로 21
 
1.1%
Other values (406) 913
46.0%
2024-03-18T13:12:25.189894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1659
 
17.4%
동 701
 
7.3%
1 437
 
4.6%
인 355
 
3.7%
천 347
 
3.6%
시 330
 
3.5%
송 329
 
3.4%
) 329
 
3.4%
( 328
 
3.4%
구 327
 
3.4%
Other values (156) 4408
46.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5438
56.9%
Space Separator 1659
 
17.4%
Decimal Number 1451
 
15.2%
Close Punctuation 329
 
3.4%
Open Punctuation 328
 
3.4%
Other Punctuation 239
 
2.5%
Dash Punctuation 78
 
0.8%
Uppercase Letter 20
 
0.2%
Math Symbol 4
 
< 0.1%
Lowercase Letter 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 701
 
12.9%
인 355
 
6.5%
천 347
 
6.4%
시 330
 
6.1%
송 329
 
6.1%
구 327
 
6.0%
역 326
 
6.0%
로 324
 
6.0%
광 324
 
6.0%
림 176
 
3.2%
Other values (130) 1899
34.9%
Decimal Number
ValueCountFrequency (%)
1 437
30.1%
2 192
13.2%
3 187
12.9%
0 127
 
8.8%
4 112
 
7.7%
6 96
 
6.6%
8 95
 
6.5%
5 71
 
4.9%
7 68
 
4.7%
9 66
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
B 10
50.0%
A 8
40.0%
C 1
 
5.0%
L 1
 
5.0%
Lowercase Letter
ValueCountFrequency (%)
b 1
25.0%
m 1
25.0%
a 1
25.0%
s 1
25.0%
Other Punctuation
ValueCountFrequency (%)
, 235
98.3%
@ 2
 
0.8%
. 2
 
0.8%
Space Separator
ValueCountFrequency (%)
1659
100.0%
Close Punctuation
ValueCountFrequency (%)
) 329
100.0%
Open Punctuation
ValueCountFrequency (%)
( 328
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 78
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5438
56.9%
Common 4088
42.8%
Latin 24
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 701
 
12.9%
인 355
 
6.5%
천 347
 
6.4%
시 330
 
6.1%
송 329
 
6.1%
구 327
 
6.0%
역 326
 
6.0%
로 324
 
6.0%
광 324
 
6.0%
림 176
 
3.2%
Other values (130) 1899
34.9%
Common
ValueCountFrequency (%)
1659
40.6%
1 437
 
10.7%
) 329
 
8.0%
( 328
 
8.0%
, 235
 
5.7%
2 192
 
4.7%
3 187
 
4.6%
0 127
 
3.1%
4 112
 
2.7%
6 96
 
2.3%
Other values (8) 386
 
9.4%
Latin
ValueCountFrequency (%)
B 10
41.7%
A 8
33.3%
b 1
 
4.2%
C 1
 
4.2%
L 1
 
4.2%
m 1
 
4.2%
a 1
 
4.2%
s 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5438
56.9%
ASCII 4112
43.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1659
40.3%
1 437
 
10.6%
) 329
 
8.0%
( 328
 
8.0%
, 235
 
5.7%
2 192
 
4.7%
3 187
 
4.5%
0 127
 
3.1%
4 112
 
2.7%
6 96
 
2.3%
Other values (16) 410
 
10.0%
Hangul
ValueCountFrequency (%)
동 701
 
12.9%
인 355
 
6.5%
천 347
 
6.4%
시 330
 
6.1%
송 329
 
6.1%
구 327
 
6.0%
역 326
 
6.0%
로 324
 
6.0%
광 324
 
6.0%
림 176
 
3.2%
Other values (130) 1899
34.9%

영업소주소(지번)
Text

Distinct303
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-03-18T13:12:25.566782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length38
Mean length21.679012
Min length15

Characters and Unicode

Total characters7024
Distinct characters137
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

Unique288 ?
Unique (%)88.9%

Sample

1st row인천광역시 동구 화평동 72-3
2nd row인천광역시 동구 화평동 527-16
3rd row인천광역시 동구 송현동 72-177
4th row인천광역시 동구 금곡동 35-10
5th row인천광역시 동구 송현동 72-173
ValueCountFrequency (%)
동구 325
21.3%
인천광역시 324
21.2%
송림동 143
 
9.4%
송현동 87
 
5.7%
화수동 31
 
2.0%
화평동 24
 
1.6%
1층 21
 
1.4%
만석동 21
 
1.4%
상가 16
 
1.0%
154 15
 
1.0%
Other values (377) 519
34.0%
2024-03-18T13:12:25.998570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1206
17.2%
동 692
 
9.9%
인 347
 
4.9%
천 339
 
4.8%
시 329
 
4.7%
역 325
 
4.6%
구 325
 
4.6%
광 324
 
4.6%
1 285
 
4.1%
- 255
 
3.6%
Other values (127) 2597
37.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3967
56.5%
Decimal Number 1522
 
21.7%
Space Separator 1206
 
17.2%
Dash Punctuation 255
 
3.6%
Open Punctuation 20
 
0.3%
Close Punctuation 20
 
0.3%
Uppercase Letter 18
 
0.3%
Other Punctuation 12
 
0.2%
Lowercase Letter 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 692
17.4%
인 347
8.7%
천 339
8.5%
시 329
8.3%
역 325
8.2%
구 325
8.2%
광 324
8.2%
송 242
 
6.1%
림 153
 
3.9%
현 94
 
2.4%
Other values (103) 797
20.1%
Decimal Number
ValueCountFrequency (%)
1 285
18.7%
2 245
16.1%
5 186
12.2%
6 156
10.2%
3 135
8.9%
0 121
8.0%
4 113
 
7.4%
7 107
 
7.0%
9 96
 
6.3%
8 78
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
B 11
61.1%
A 5
27.8%
C 1
 
5.6%
L 1
 
5.6%
Lowercase Letter
ValueCountFrequency (%)
b 1
25.0%
s 1
25.0%
a 1
25.0%
m 1
25.0%
Other Punctuation
ValueCountFrequency (%)
, 10
83.3%
@ 2
 
16.7%
Space Separator
ValueCountFrequency (%)
1206
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 255
100.0%
Open Punctuation
ValueCountFrequency (%)
( 20
100.0%
Close Punctuation
ValueCountFrequency (%)
) 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3967
56.5%
Common 3035
43.2%
Latin 22
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 692
17.4%
인 347
8.7%
천 339
8.5%
시 329
8.3%
역 325
8.2%
구 325
8.2%
광 324
8.2%
송 242
 
6.1%
림 153
 
3.9%
현 94
 
2.4%
Other values (103) 797
20.1%
Common
ValueCountFrequency (%)
1206
39.7%
1 285
 
9.4%
- 255
 
8.4%
2 245
 
8.1%
5 186
 
6.1%
6 156
 
5.1%
3 135
 
4.4%
0 121
 
4.0%
4 113
 
3.7%
7 107
 
3.5%
Other values (6) 226
 
7.4%
Latin
ValueCountFrequency (%)
B 11
50.0%
A 5
22.7%
b 1
 
4.5%
C 1
 
4.5%
L 1
 
4.5%
s 1
 
4.5%
a 1
 
4.5%
m 1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3967
56.5%
ASCII 3057
43.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1206
39.5%
1 285
 
9.3%
- 255
 
8.3%
2 245
 
8.0%
5 186
 
6.1%
6 156
 
5.1%
3 135
 
4.4%
0 121
 
4.0%
4 113
 
3.7%
7 107
 
3.5%
Other values (14) 248
 
8.1%
Hangul
ValueCountFrequency (%)
동 692
17.4%
인 347
8.7%
천 339
8.5%
시 329
8.3%
역 325
8.2%
구 325
8.2%
광 324
8.2%
송 242
 
6.1%
림 153
 
3.9%
현 94
 
2.4%
Other values (103) 797
20.1%

소재지전화
Text

MISSING 

Distinct227
Distinct (%)98.7%
Missing94
Missing (%)29.0%
Memory size2.7 KiB
2024-03-18T13:12:26.220709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.026087
Min length9

Characters and Unicode

Total characters2766
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

Unique224 ?
Unique (%)97.4%

Sample

1st row032-772-6621
2nd row032-765-2770
3rd row032-773-0978
4th row032-773-6723
5th row032-765-2611
ValueCountFrequency (%)
032-762-3594 2
 
0.9%
032-227-5107 2
 
0.9%
032-773-0978 2
 
0.9%
032-765-5777 1
 
0.4%
032-773-5048 1
 
0.4%
032-863-0510 1
 
0.4%
032-766-9510 1
 
0.4%
032-772-6621 1
 
0.4%
032-772-7680 1
 
0.4%
032-777-3060 1
 
0.4%
Other values (217) 217
94.3%
2024-03-18T13:12:26.575014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 459
16.6%
7 402
14.5%
2 381
13.8%
0 359
13.0%
3 357
12.9%
6 259
9.4%
5 135
 
4.9%
1 114
 
4.1%
4 110
 
4.0%
9 100
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2307
83.4%
Dash Punctuation 459
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 402
17.4%
2 381
16.5%
0 359
15.6%
3 357
15.5%
6 259
11.2%
5 135
 
5.9%
1 114
 
4.9%
4 110
 
4.8%
9 100
 
4.3%
8 90
 
3.9%
Dash Punctuation
ValueCountFrequency (%)
- 459
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 459
16.6%
7 402
14.5%
2 381
13.8%
0 359
13.0%
3 357
12.9%
6 259
9.4%
5 135
 
4.9%
1 114
 
4.1%
4 110
 
4.0%
9 100
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 459
16.6%
7 402
14.5%
2 381
13.8%
0 359
13.0%
3 357
12.9%
6 259
9.4%
5 135
 
4.9%
1 114
 
4.1%
4 110
 
4.0%
9 100
 
3.6%

Correlations

2024-03-18T13:12:26.658026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명업태명
업종명1.0000.961
업태명0.9611.000
2024-03-18T13:12:26.725513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업태명업종명
업태명1.0000.736
업종명0.7361.000
2024-03-18T13:12:26.798709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명업태명
업종명1.0000.736
업태명0.7361.000

Missing values

2024-03-18T13:12:23.749472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T13:12:23.874461image/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

업종명업태명업소명영업소주소(도로명)영업소주소(지번)소재지전화
0숙박업(일반)여인숙업서산여인숙인천광역시 동구 화도진로68번길 3-6 (화평동)인천광역시 동구 화평동 72-3032-772-6621
1숙박업(일반)여인숙업명수여인숙인천광역시 동구 송화로2번길 1-8 (화평동)인천광역시 동구 화평동 527-16032-765-2770
2숙박업(일반)여인숙업황주여인숙인천광역시 동구 화도진로52번길 9-3 (송현동)인천광역시 동구 송현동 72-177032-773-0978
3숙박업(일반)여인숙업명신여인숙인천광역시 동구 금곡로11번길 8-7 (금곡동)인천광역시 동구 금곡동 35-10032-773-6723
4숙박업(일반)여인숙업명성여인숙인천광역시 동구 화도진로56번길 6-3 (송현동)인천광역시 동구 송현동 72-173032-765-2611
5숙박업(일반)여인숙업서울여인숙인천광역시 동구 솔빛로91번길 26-6 (송림동)인천광역시 동구 송림동 55-6<NA>
6숙박업(일반)여인숙업상신여인숙인천광역시 동구 화도진로56번길 6-4 (송현동)인천광역시 동구 송현동 72-181032-773-0978
7숙박업(일반)여인숙업황해여인숙인천광역시 동구 솔빛로 77 (송림동)인천광역시 동구 송림동 151-2<NA>
8숙박업(일반)여인숙업보경여인숙인천광역시 동구 금곡로81번길 29 (송림동)인천광역시 동구 송림동 59-11032-772-1774
9숙박업(일반)여인숙업신진여인숙인천광역시 동구 송화로 16-6, 신진여인숙 (화평동)인천광역시 동구 화평동 72-4 신진여인숙032-772-6392
업종명업태명업소명영업소주소(도로명)영업소주소(지번)소재지전화
314화장·분장 미용업메이크업업샵꾸밈(#GGumim)인천광역시 동구 새천년로34번길 9, 1층 (송림동)인천광역시 동구 송림동 101-51<NA>
315화장·분장 미용업메이크업업백설공주 래쉬인천광역시 동구 송현로 18, 2층 일부호 (송현동)인천광역시 동구 송현동 79-16032-227-5107
316화장·분장 미용업메이크업업에덴메이크업인천광역시 동구 송화로20번길 11, 2층 일부호 (화평동)인천광역시 동구 화평동 42<NA>
317일반미용업, 화장·분장 미용업일반미용업오땡큐 머리염색 전문점인천광역시 동구 금곡로 101, 1층 (송림동)인천광역시 동구 송림동 60-24<NA>
318일반미용업, 화장·분장 미용업일반미용업착한미용실인천광역시 동구 동산로 87-1, 1층 (송림동)인천광역시 동구 송림동 59-29032-777-1780
319피부미용업, 화장·분장 미용업메이크업업래쉬멜로인천광역시 동구 송현로 44, 솔빛마을 상가A동 2층 207호 (송현동)인천광역시 동구 송현동 154 솔빛마을 상가A동 207호<NA>
320일반미용업, 피부미용업, 화장·분장 미용업일반미용업남 헤어샵인천광역시 동구 송림로 24 (금곡동,두손빌딩114호)인천광역시 동구 금곡동 33-2 두손빌딩114호032-766-0102
321일반미용업, 네일미용업, 화장·분장 미용업일반미용업더 제이 헤어인천광역시 동구 화수로 46, 미륭 상가 2층 17호 (화수동)인천광역시 동구 화수동 2-7 미륭 상가<NA>
322일반미용업, 네일미용업, 화장·분장 미용업일반미용업남성여성커트전문점인천광역시 동구 샛골로161번길 26, 1층 일부호 (송림동)인천광역시 동구 송림동 55-199<NA>
323일반미용업, 네일미용업, 화장·분장 미용업메이크업업르미엘뷰티인천광역시 동구 샛골로162번길 26, 1층 (송림동)인천광역시 동구 송림동 50-49<NA>