Overview

Dataset statistics

Number of variables6
Number of observations882
Missing cells676
Missing cells (%)12.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory42.3 KiB
Average record size in memory49.1 B

Variable types

Numeric1
Categorical1
Text3
DateTime1

Dataset

Description인천광역시 중구 관내에 위치한 공중위생업 현황에 대한 데이터 입니다.파일명 인천광역시_중구_공중위생업파일내용 업종명, 업소명, 업소소재지 등
Author인천광역시 중구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15066151&srcSe=7661IVAWM27C61E190

Alerts

데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 업종명High correlation
업종명 is highly overall correlated with 연번High correlation
소재지전화 has 676 (76.6%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-03-18 05:45:36.319147
Analysis finished2024-03-18 05:45:38.049750
Duration1.73 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct882
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean441.5
Minimum1
Maximum882
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-18T14:45:38.122426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile45.05
Q1221.25
median441.5
Q3661.75
95-th percentile837.95
Maximum882
Range881
Interquartile range (IQR)440.5

Descriptive statistics

Standard deviation254.75577
Coefficient of variation (CV)0.57702325
Kurtosis-1.2
Mean441.5
Median Absolute Deviation (MAD)220.5
Skewness0
Sum389403
Variance64900.5
MonotonicityStrictly increasing
2024-03-18T14:45:38.235202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
594 1
 
0.1%
583 1
 
0.1%
584 1
 
0.1%
585 1
 
0.1%
586 1
 
0.1%
587 1
 
0.1%
588 1
 
0.1%
589 1
 
0.1%
590 1
 
0.1%
Other values (872) 872
98.9%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
882 1
0.1%
881 1
0.1%
880 1
0.1%
879 1
0.1%
878 1
0.1%
877 1
0.1%
876 1
0.1%
875 1
0.1%
874 1
0.1%
873 1
0.1%

업종명
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
숙박업(일반)
184 
일반미용업
171 
숙박업(생활)
119 
미용업
82 
건물위생관리업
54 
Other values (15)
272 

Length

Max length23
Median length16
Mean length6.1870748
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
숙박업(일반) 184
20.9%
일반미용업 171
19.4%
숙박업(생활) 119
13.5%
미용업 82
9.3%
건물위생관리업 54
 
6.1%
피부미용업 52
 
5.9%
세탁업 43
 
4.9%
네일미용업 35
 
4.0%
이용업 33
 
3.7%
목욕장업 25
 
2.8%
Other values (10) 84
9.5%

Length

2024-03-18T14:45:38.376875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 187
18.7%
숙박업(일반 184
18.4%
미용업 135
13.5%
숙박업(생활 119
11.9%
피부미용업 79
7.9%
네일미용업 71
 
7.1%
건물위생관리업 54
 
5.4%
화장ㆍ분장 53
 
5.3%
세탁업 43
 
4.3%
이용업 33
 
3.3%
Other values (2) 43
 
4.3%

업소명
Text

Distinct873
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
2024-03-18T14:45:38.575375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length36
Mean length6.9705215
Min length1

Characters and Unicode

Total characters6148
Distinct characters540
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

Unique864 ?
Unique (%)98.0%

Sample

1st row위드여인숙
2nd row하얏트
3rd row세신여인숙
4th row올림포스호텔
5th row백야모텔
ValueCountFrequency (%)
헤어 22
 
1.9%
주식회사 11
 
0.9%
nail 9
 
0.8%
영종하늘도시점 8
 
0.7%
hair 8
 
0.7%
호텔 7
 
0.6%
6
 
0.5%
hotel 5
 
0.4%
네일 5
 
0.4%
미용실 5
 
0.4%
Other values (1026) 1099
92.7%
2024-03-18T14:45:38.926658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
303
 
4.9%
스 217
 
3.5%
텔 196
 
3.2%
어 178
 
2.9%
헤 159
 
2.6%
호 155
 
2.5%
이 143
 
2.3%
( 99
 
1.6%
) 99
 
1.6%
인 91
 
1.5%
Other values (530) 4508
73.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4812
78.3%
Lowercase Letter 413
 
6.7%
Uppercase Letter 335
 
5.4%
Space Separator 303
 
4.9%
Open Punctuation 99
 
1.6%
Close Punctuation 99
 
1.6%
Other Punctuation 44
 
0.7%
Decimal Number 33
 
0.5%
Dash Punctuation 6
 
0.1%
Other Symbol 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 217
 
4.5%
텔 196
 
4.1%
어 178
 
3.7%
헤 159
 
3.3%
호 155
 
3.2%
이 143
 
3.0%
인 91
 
1.9%
미 91
 
1.9%
리 76
 
1.6%
일 64
 
1.3%
Other values (459) 3442
71.5%
Uppercase Letter
ValueCountFrequency (%)
A 35
 
10.4%
H 30
 
9.0%
O 29
 
8.7%
N 27
 
8.1%
E 25
 
7.5%
T 25
 
7.5%
L 21
 
6.3%
I 19
 
5.7%
B 15
 
4.5%
M 14
 
4.2%
Other values (15) 95
28.4%
Lowercase Letter
ValueCountFrequency (%)
e 55
13.3%
a 47
11.4%
i 43
10.4%
o 36
8.7%
r 30
 
7.3%
l 29
 
7.0%
h 27
 
6.5%
n 25
 
6.1%
t 22
 
5.3%
s 18
 
4.4%
Other values (14) 81
19.6%
Decimal Number
ValueCountFrequency (%)
2 10
30.3%
5 6
18.2%
0 4
 
12.1%
1 3
 
9.1%
9 3
 
9.1%
3 3
 
9.1%
7 2
 
6.1%
4 1
 
3.0%
6 1
 
3.0%
Other Punctuation
ValueCountFrequency (%)
& 19
43.2%
. 8
18.2%
, 7
 
15.9%
' 4
 
9.1%
# 4
 
9.1%
: 2
 
4.5%
Space Separator
ValueCountFrequency (%)
303
100.0%
Open Punctuation
ValueCountFrequency (%)
( 99
100.0%
Close Punctuation
ValueCountFrequency (%)
) 99
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Other Symbol
ValueCountFrequency (%)
° 2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4811
78.3%
Latin 749
 
12.2%
Common 587
 
9.5%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 217
 
4.5%
텔 196
 
4.1%
어 178
 
3.7%
헤 159
 
3.3%
호 155
 
3.2%
이 143
 
3.0%
인 91
 
1.9%
미 91
 
1.9%
리 76
 
1.6%
일 64
 
1.3%
Other values (458) 3441
71.5%
Latin
ValueCountFrequency (%)
e 55
 
7.3%
a 47
 
6.3%
i 43
 
5.7%
o 36
 
4.8%
A 35
 
4.7%
H 30
 
4.0%
r 30
 
4.0%
l 29
 
3.9%
O 29
 
3.9%
N 27
 
3.6%
Other values (40) 388
51.8%
Common
ValueCountFrequency (%)
303
51.6%
( 99
 
16.9%
) 99
 
16.9%
& 19
 
3.2%
2 10
 
1.7%
. 8
 
1.4%
, 7
 
1.2%
- 6
 
1.0%
5 6
 
1.0%
0 4
 
0.7%
Other values (11) 26
 
4.4%
Han
ValueCountFrequency (%)
愛 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4811
78.3%
ASCII 1333
 
21.7%
None 2
 
< 0.1%
Number Forms 1
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
303
22.7%
( 99
 
7.4%
) 99
 
7.4%
e 55
 
4.1%
a 47
 
3.5%
i 43
 
3.2%
o 36
 
2.7%
A 35
 
2.6%
H 30
 
2.3%
r 30
 
2.3%
Other values (59) 556
41.7%
Hangul
ValueCountFrequency (%)
스 217
 
4.5%
텔 196
 
4.1%
어 178
 
3.7%
헤 159
 
3.3%
호 155
 
3.2%
이 143
 
3.0%
인 91
 
1.9%
미 91
 
1.9%
리 76
 
1.6%
일 64
 
1.3%
Other values (458) 3441
71.5%
None
ValueCountFrequency (%)
° 2
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%
CJK
ValueCountFrequency (%)
愛 1
100.0%

업소소재지(도로명)
Text

Distinct847
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
2024-03-18T14:45:39.202043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length70
Median length50
Mean length33.538549
Min length14

Characters and Unicode

Total characters29581
Distinct characters307
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique818 ?
Unique (%)92.7%

Sample

1st row인천광역시 중구 인중로174번길 17-1 (사동)
2nd row인천광역시 중구 신포로39번길 11 (송학동3가)
3rd row인천광역시 중구 도원로 2-1 (신흥동3가)
4th row인천광역시 중구 제물량로 257 (항동1가)
5th row인천광역시 중구 우현로 68-19 (용동)
ValueCountFrequency (%)
인천광역시 882
 
15.3%
중구 882
 
15.3%
1층 194
 
3.4%
운서동 183
 
3.2%
중산동 155
 
2.7%
을왕동 110
 
1.9%
2층 100
 
1.7%
운남동 48
 
0.8%
항동7가 48
 
0.8%
흰바위로 40
 
0.7%
Other values (1050) 3130
54.2%
2024-03-18T14:45:39.606218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4918
 
16.6%
1 1356
 
4.6%
중 1123
 
3.8%
동 984
 
3.3%
인 963
 
3.3%
시 959
 
3.2%
역 896
 
3.0%
천 895
 
3.0%
광 891
 
3.0%
구 882
 
3.0%
Other values (297) 15714
53.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 16480
55.7%
Decimal Number 5148
 
17.4%
Space Separator 4918
 
16.6%
Close Punctuation 870
 
2.9%
Open Punctuation 868
 
2.9%
Other Punctuation 851
 
2.9%
Dash Punctuation 226
 
0.8%
Math Symbol 99
 
0.3%
Uppercase Letter 93
 
0.3%
Lowercase Letter 27
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
중 1123
 
6.8%
동 984
 
6.0%
인 963
 
5.8%
시 959
 
5.8%
역 896
 
5.4%
천 895
 
5.4%
광 891
 
5.4%
구 882
 
5.4%
로 857
 
5.2%
층 550
 
3.3%
Other values (250) 7480
45.4%
Uppercase Letter
ValueCountFrequency (%)
B 16
17.2%
I 13
14.0%
S 9
9.7%
L 8
8.6%
A 7
7.5%
C 6
 
6.5%
K 6
 
6.5%
M 5
 
5.4%
H 5
 
5.4%
G 4
 
4.3%
Other values (8) 14
15.1%
Lowercase Letter
ValueCountFrequency (%)
y 5
18.5%
e 5
18.5%
k 3
11.1%
t 3
11.1%
c 2
 
7.4%
l 2
 
7.4%
i 2
 
7.4%
s 2
 
7.4%
u 1
 
3.7%
o 1
 
3.7%
Decimal Number
ValueCountFrequency (%)
1 1356
26.3%
2 839
16.3%
3 559
10.9%
0 484
 
9.4%
4 457
 
8.9%
5 379
 
7.4%
7 307
 
6.0%
6 287
 
5.6%
8 251
 
4.9%
9 229
 
4.4%
Other Punctuation
ValueCountFrequency (%)
, 849
99.8%
. 2
 
0.2%
Space Separator
ValueCountFrequency (%)
4918
100.0%
Close Punctuation
ValueCountFrequency (%)
) 870
100.0%
Open Punctuation
ValueCountFrequency (%)
( 868
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 226
100.0%
Math Symbol
ValueCountFrequency (%)
~ 99
100.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 16480
55.7%
Common 12980
43.9%
Latin 121
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
중 1123
 
6.8%
동 984
 
6.0%
인 963
 
5.8%
시 959
 
5.8%
역 896
 
5.4%
천 895
 
5.4%
광 891
 
5.4%
구 882
 
5.4%
로 857
 
5.2%
층 550
 
3.3%
Other values (250) 7480
45.4%
Latin
ValueCountFrequency (%)
B 16
 
13.2%
I 13
 
10.7%
S 9
 
7.4%
L 8
 
6.6%
A 7
 
5.8%
C 6
 
5.0%
K 6
 
5.0%
y 5
 
4.1%
M 5
 
4.1%
e 5
 
4.1%
Other values (20) 41
33.9%
Common
ValueCountFrequency (%)
4918
37.9%
1 1356
 
10.4%
) 870
 
6.7%
( 868
 
6.7%
, 849
 
6.5%
2 839
 
6.5%
3 559
 
4.3%
0 484
 
3.7%
4 457
 
3.5%
5 379
 
2.9%
Other values (7) 1401
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 16480
55.7%
ASCII 13100
44.3%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4918
37.5%
1 1356
 
10.4%
) 870
 
6.6%
( 868
 
6.6%
, 849
 
6.5%
2 839
 
6.4%
3 559
 
4.3%
0 484
 
3.7%
4 457
 
3.5%
5 379
 
2.9%
Other values (36) 1521
 
11.6%
Hangul
ValueCountFrequency (%)
중 1123
 
6.8%
동 984
 
6.0%
인 963
 
5.8%
시 959
 
5.8%
역 896
 
5.4%
천 895
 
5.4%
광 891
 
5.4%
구 882
 
5.4%
로 857
 
5.2%
층 550
 
3.3%
Other values (250) 7480
45.4%
Number Forms
ValueCountFrequency (%)
Ⅰ 1
100.0%

소재지전화
Text

MISSING 

Distinct190
Distinct (%)92.2%
Missing676
Missing (%)76.6%
Memory size7.0 KiB
2024-03-18T14:45:39.848432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.980583
Min length11

Characters and Unicode

Total characters2468
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

Unique177 ?
Unique (%)85.9%

Sample

1st row032-762-5181
2nd row032-763-7003
3rd row032-764-7487
4th row032-763-4180
5th row032-882-5205
ValueCountFrequency (%)
032-743-3040 4
 
1.9%
032-722-0023 3
 
1.5%
032-777-8787 2
 
1.0%
02-3430-5546 2
 
1.0%
032-763-4180 2
 
1.0%
032-232-2000 2
 
1.0%
032-762-0346 2
 
1.0%
032-751-1146 2
 
1.0%
032-747-0008 2
 
1.0%
02-373-3137 2
 
1.0%
Other values (180) 183
88.8%
2024-03-18T14:45:40.189788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 412
16.7%
0 369
15.0%
3 337
13.7%
2 324
13.1%
7 284
11.5%
6 166
6.7%
1 139
 
5.6%
8 132
 
5.3%
4 126
 
5.1%
5 118
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2056
83.3%
Dash Punctuation 412
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 369
17.9%
3 337
16.4%
2 324
15.8%
7 284
13.8%
6 166
8.1%
1 139
 
6.8%
8 132
 
6.4%
4 126
 
6.1%
5 118
 
5.7%
9 61
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 412
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2468
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 412
16.7%
0 369
15.0%
3 337
13.7%
2 324
13.1%
7 284
11.5%
6 166
6.7%
1 139
 
5.6%
8 132
 
5.3%
4 126
 
5.1%
5 118
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2468
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 412
16.7%
0 369
15.0%
3 337
13.7%
2 324
13.1%
7 284
11.5%
6 166
6.7%
1 139
 
5.6%
8 132
 
5.3%
4 126
 
5.1%
5 118
 
4.8%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
Minimum2023-09-13 00:00:00
Maximum2023-09-13 00:00:00
2024-03-18T14:45:40.298964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:45:40.376892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-18T14:45:37.770908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T14:45:40.450315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명
연번1.0000.976
업종명0.9761.000
2024-03-18T14:45:40.541277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명
연번1.0000.739
업종명0.7391.000

Missing values

2024-03-18T14:45:37.916204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T14:45:38.007353image/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

연번업종명업소명업소소재지(도로명)소재지전화데이터기준일자
01숙박업(일반)위드여인숙인천광역시 중구 인중로174번길 17-1 (사동)<NA>2023-09-13
12숙박업(일반)하얏트인천광역시 중구 신포로39번길 11 (송학동3가)<NA>2023-09-13
23숙박업(일반)세신여인숙인천광역시 중구 도원로 2-1 (신흥동3가)<NA>2023-09-13
34숙박업(일반)올림포스호텔인천광역시 중구 제물량로 257 (항동1가)032-762-51812023-09-13
45숙박업(일반)백야모텔인천광역시 중구 우현로 68-19 (용동)032-763-70032023-09-13
56숙박업(일반)경산여인숙인천광역시 중구 큰우물로 28-44 (용동)<NA>2023-09-13
67숙박업(일반)한진여인숙인천광역시 중구 참외전로 149-64 (인현동)032-764-74872023-09-13
78숙박업(일반)청해여인숙인천광역시 중구 참외전로 172-7 (경동)032-763-41802023-09-13
89숙박업(일반)유성여인숙인천광역시 중구 월미로 190 (북성동1가)032-882-52052023-09-13
910숙박업(일반)유정여인숙인천광역시 중구 인현동 18<NA>2023-09-13
연번업종명업소명업소소재지(도로명)소재지전화데이터기준일자
872873피부미용업, 네일미용업, 화장ㆍ분장 미용업원네일인천광역시 중구 하늘중앙로225번길 3, 영종M타워 2층 201-3호 (중산동)<NA>2023-09-13
873874피부미용업, 네일미용업, 화장ㆍ분장 미용업블라썸뷰티라운지인천광역시 중구 모랫말로 6-9, 1층 101 일부호 (운서동)<NA>2023-09-13
874875피부미용업, 네일미용업, 화장ㆍ분장 미용업듀링뷰티(Duringbe.)인천광역시 중구 흰바위로 277, 상가3동 1층 108호 (운남동, 운서역푸르지오더스카이)<NA>2023-09-13
875876피부미용업, 네일미용업, 화장ㆍ분장 미용업라르떼네일인천광역시 중구 하늘별빛로65번길 11, 해솔프라자 403-1호 (중산동)<NA>2023-09-13
876877피부미용업, 네일미용업, 화장ㆍ분장 미용업문뷰티(MOON BEAUTY)인천광역시 중구 모랫말로16번길 8, 어반팰리스 1층 101호 (운서동)<NA>2023-09-13
877878피부미용업, 네일미용업, 화장ㆍ분장 미용업벨레자(BELLEZZA)인천광역시 중구 햇내로13번길 8, 프라임시티3 2층 206호 (운서동)<NA>2023-09-13
878879피부미용업, 네일미용업, 화장ㆍ분장 미용업네일정은인천광역시 중구 월촌길 28, 1층 102호 (중산동)<NA>2023-09-13
879880피부미용업, 네일미용업, 화장ㆍ분장 미용업태봉씨 뷰티살롱인천광역시 중구 하늘중앙로225번길 20, 스카이에비뉴2 9층 909,910호 (중산동)<NA>2023-09-13
880881피부미용업, 네일미용업, 화장ㆍ분장 미용업그리닷뷰티인천광역시 중구 하늘중앙로225번길 12, 정인타워 7층 701호 (중산동)<NA>2023-09-13
881882피부미용업, 네일미용업, 화장ㆍ분장 미용업그릿네일(GRIT NAIL)인천광역시 중구 하늘중앙로195번길 21, 2층 202호 (중산동)<NA>2023-09-13