Overview

Dataset statistics

Number of variables5
Number of observations73
Missing cells21
Missing cells (%)5.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory42.8 B

Variable types

Numeric1
Text4

Dataset

Description인천광역시 미추홀구 세차장 현황 데이터로 연번, 상호명, 유형, 도로명주소, 전화번호, 죄표값을 항목으로 가지고 있습니다.
Author인천광역시 미추홀구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15091400&srcSe=7661IVAWM27C61E190

Alerts

도로명주소 has 1 (1.4%) missing valuesMissing
전화번호 has 20 (27.4%) missing valuesMissing
연번 has unique valuesUnique
상호명 has unique valuesUnique
지번주소 has unique valuesUnique

Reproduction

Analysis started2024-03-18 04:51:59.091330
Analysis finished2024-03-18 04:52:01.263905
Duration2.17 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct73
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37
Minimum1
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size789.0 B
2024-03-18T13:52:01.347839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.6
Q119
median37
Q355
95-th percentile69.4
Maximum73
Range72
Interquartile range (IQR)36

Descriptive statistics

Standard deviation21.217131
Coefficient of variation (CV)0.57343598
Kurtosis-1.2
Mean37
Median Absolute Deviation (MAD)18
Skewness0
Sum2701
Variance450.16667
MonotonicityStrictly increasing
2024-03-18T13:52:01.489635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.4%
56 1
 
1.4%
54 1
 
1.4%
53 1
 
1.4%
52 1
 
1.4%
51 1
 
1.4%
50 1
 
1.4%
49 1
 
1.4%
48 1
 
1.4%
47 1
 
1.4%
Other values (63) 63
86.3%
ValueCountFrequency (%)
1 1
1.4%
2 1
1.4%
3 1
1.4%
4 1
1.4%
5 1
1.4%
6 1
1.4%
7 1
1.4%
8 1
1.4%
9 1
1.4%
10 1
1.4%
ValueCountFrequency (%)
73 1
1.4%
72 1
1.4%
71 1
1.4%
70 1
1.4%
69 1
1.4%
68 1
1.4%
67 1
1.4%
66 1
1.4%
65 1
1.4%
64 1
1.4%

상호명
Text

UNIQUE 

Distinct73
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size716.0 B
2024-03-18T13:52:01.734873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length19
Mean length7.9863014
Min length2

Characters and Unicode

Total characters583
Distinct characters172
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73 ?
Unique (%)100.0%

Sample

1st row디테일가드 광택코팅 프로샵
2nd row크리스탈스팀카워시
3rd row진솔자동차공업사&세차장
4th row세차의 고수
5th row워시블랑 학익점
ValueCountFrequency (%)
실내크리닝 3
 
2.8%
제이 2
 
1.8%
자동차 2
 
1.8%
손세차장 2
 
1.8%
에바크리닝 2
 
1.8%
디테일가드 1
 
0.9%
카앤클린 1
 
0.9%
송도점 1
 
0.9%
다함께세차차 1
 
0.9%
미건정공 1
 
0.9%
Other values (93) 93
85.3%
2024-03-18T13:52:02.027431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
차 45
 
7.7%
세 36
 
6.2%
36
 
6.2%
장 27
 
4.6%
카 15
 
2.6%
스 14
 
2.4%
점 11
 
1.9%
인 11
 
1.9%
일 10
 
1.7%
손 10
 
1.7%
Other values (162) 368
63.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 526
90.2%
Space Separator 36
 
6.2%
Decimal Number 13
 
2.2%
Uppercase Letter 5
 
0.9%
Other Punctuation 2
 
0.3%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
차 45
 
8.6%
세 36
 
6.8%
장 27
 
5.1%
카 15
 
2.9%
스 14
 
2.7%
점 11
 
2.1%
인 11
 
2.1%
일 10
 
1.9%
손 10
 
1.9%
리 10
 
1.9%
Other values (148) 337
64.1%
Decimal Number
ValueCountFrequency (%)
3 3
23.1%
5 3
23.1%
2 3
23.1%
4 2
15.4%
0 1
 
7.7%
1 1
 
7.7%
Uppercase Letter
ValueCountFrequency (%)
N 1
20.0%
E 1
20.0%
O 1
20.0%
A 1
20.0%
W 1
20.0%
Space Separator
ValueCountFrequency (%)
36
100.0%
Other Punctuation
ValueCountFrequency (%)
& 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 526
90.2%
Common 52
 
8.9%
Latin 5
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
차 45
 
8.6%
세 36
 
6.8%
장 27
 
5.1%
카 15
 
2.9%
스 14
 
2.7%
점 11
 
2.1%
인 11
 
2.1%
일 10
 
1.9%
손 10
 
1.9%
리 10
 
1.9%
Other values (148) 337
64.1%
Common
ValueCountFrequency (%)
36
69.2%
3 3
 
5.8%
5 3
 
5.8%
2 3
 
5.8%
4 2
 
3.8%
& 2
 
3.8%
0 1
 
1.9%
- 1
 
1.9%
1 1
 
1.9%
Latin
ValueCountFrequency (%)
N 1
20.0%
E 1
20.0%
O 1
20.0%
A 1
20.0%
W 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 526
90.2%
ASCII 57
 
9.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
차 45
 
8.6%
세 36
 
6.8%
장 27
 
5.1%
카 15
 
2.9%
스 14
 
2.7%
점 11
 
2.1%
인 11
 
2.1%
일 10
 
1.9%
손 10
 
1.9%
리 10
 
1.9%
Other values (148) 337
64.1%
ASCII
ValueCountFrequency (%)
36
63.2%
3 3
 
5.3%
5 3
 
5.3%
2 3
 
5.3%
4 2
 
3.5%
& 2
 
3.5%
N 1
 
1.8%
E 1
 
1.8%
O 1
 
1.8%
0 1
 
1.8%
Other values (4) 4
 
7.0%

지번주소
Text

UNIQUE 

Distinct73
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size716.0 B
2024-03-18T13:52:02.269514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length36
Mean length23.369863
Min length17

Characters and Unicode

Total characters1706
Distinct characters97
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

Unique73 ?
Unique (%)100.0%

Sample

1st row인천광역시 미추홀구 관교동 317-10 1층 디테일가드
2nd row인천광역시 미추홀구 문학동 155-34 1층 101호
3rd row인천광역시 미추홀구 도화동 104-6
4th row인천광역시 미추홀구 주안동 22-22 1층 세차장
5th row인천광역시 미추홀구 학익동 401-45
ValueCountFrequency (%)
인천광역시 73
21.7%
미추홀구 73
21.7%
주안동 22
 
6.5%
학익동 16
 
4.8%
1층 13
 
3.9%
용현동 13
 
3.9%
도화동 8
 
2.4%
숭의동 6
 
1.8%
관교동 4
 
1.2%
문학동 3
 
0.9%
Other values (103) 105
31.2%
2024-03-18T13:52:02.729364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
266
 
15.6%
1 81
 
4.7%
인 77
 
4.5%
동 75
 
4.4%
시 74
 
4.3%
홀 73
 
4.3%
구 73
 
4.3%
천 73
 
4.3%
추 73
 
4.3%
미 73
 
4.3%
Other values (87) 768
45.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 985
57.7%
Decimal Number 369
 
21.6%
Space Separator 266
 
15.6%
Dash Punctuation 68
 
4.0%
Lowercase Letter 10
 
0.6%
Close Punctuation 2
 
0.1%
Open Punctuation 2
 
0.1%
Other Punctuation 2
 
0.1%
Uppercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 77
 
7.8%
동 75
 
7.6%
시 74
 
7.5%
홀 73
 
7.4%
구 73
 
7.4%
천 73
 
7.4%
추 73
 
7.4%
미 73
 
7.4%
역 73
 
7.4%
광 73
 
7.4%
Other values (60) 248
25.2%
Decimal Number
ValueCountFrequency (%)
1 81
22.0%
2 52
14.1%
6 51
13.8%
4 36
9.8%
8 34
9.2%
5 31
 
8.4%
3 28
 
7.6%
7 24
 
6.5%
0 20
 
5.4%
9 12
 
3.3%
Lowercase Letter
ValueCountFrequency (%)
i 2
20.0%
t 1
10.0%
g 1
10.0%
n 1
10.0%
l 1
10.0%
a 1
10.0%
e 1
10.0%
d 1
10.0%
y 1
10.0%
Other Punctuation
ValueCountFrequency (%)
, 1
50.0%
& 1
50.0%
Uppercase Letter
ValueCountFrequency (%)
W 1
50.0%
Z 1
50.0%
Space Separator
ValueCountFrequency (%)
266
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 68
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 985
57.7%
Common 709
41.6%
Latin 12
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 77
 
7.8%
동 75
 
7.6%
시 74
 
7.5%
홀 73
 
7.4%
구 73
 
7.4%
천 73
 
7.4%
추 73
 
7.4%
미 73
 
7.4%
역 73
 
7.4%
광 73
 
7.4%
Other values (60) 248
25.2%
Common
ValueCountFrequency (%)
266
37.5%
1 81
 
11.4%
- 68
 
9.6%
2 52
 
7.3%
6 51
 
7.2%
4 36
 
5.1%
8 34
 
4.8%
5 31
 
4.4%
3 28
 
3.9%
7 24
 
3.4%
Other values (6) 38
 
5.4%
Latin
ValueCountFrequency (%)
i 2
16.7%
t 1
8.3%
g 1
8.3%
n 1
8.3%
l 1
8.3%
a 1
8.3%
e 1
8.3%
d 1
8.3%
W 1
8.3%
Z 1
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 985
57.7%
ASCII 721
42.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
266
36.9%
1 81
 
11.2%
- 68
 
9.4%
2 52
 
7.2%
6 51
 
7.1%
4 36
 
5.0%
8 34
 
4.7%
5 31
 
4.3%
3 28
 
3.9%
7 24
 
3.3%
Other values (17) 50
 
6.9%
Hangul
ValueCountFrequency (%)
인 77
 
7.8%
동 75
 
7.6%
시 74
 
7.5%
홀 73
 
7.4%
구 73
 
7.4%
천 73
 
7.4%
추 73
 
7.4%
미 73
 
7.4%
역 73
 
7.4%
광 73
 
7.4%
Other values (60) 248
25.2%

도로명주소
Text

MISSING 

Distinct71
Distinct (%)98.6%
Missing1
Missing (%)1.4%
Memory size716.0 B
2024-03-18T13:52:03.044038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length30
Mean length23.277778
Min length17

Characters and Unicode

Total characters1676
Distinct characters127
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

Unique70 ?
Unique (%)97.2%

Sample

1st row인천광역시 미추홀구 인하로 402 1층 디테일가드
2nd row인천광역시 미추홀구 소성로 292 1층 101호
3rd row인천광역시 미추홀구 장고개로 8 진솔빌딩
4th row인천광역시 미추홀구 석정로 446 1층 세차장
5th row인천광역시 미추홀구 매소홀로 272
ValueCountFrequency (%)
인천광역시 72
21.5%
미추홀구 72
21.5%
1층 12
 
3.6%
소성로 5
 
1.5%
아암대로227번길 5
 
1.5%
석정로 5
 
1.5%
매소홀로 4
 
1.2%
아암대로253번길 3
 
0.9%
인하로 3
 
0.9%
인주대로330번길 3
 
0.9%
Other values (133) 151
45.1%
2024-03-18T13:52:03.417194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
263
 
15.7%
인 86
 
5.1%
홀 84
 
5.0%
미 77
 
4.6%
추 77
 
4.6%
시 73
 
4.4%
구 73
 
4.4%
로 72
 
4.3%
광 72
 
4.3%
역 72
 
4.3%
Other values (117) 727
43.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1094
65.3%
Decimal Number 291
 
17.4%
Space Separator 263
 
15.7%
Dash Punctuation 10
 
0.6%
Lowercase Letter 10
 
0.6%
Close Punctuation 2
 
0.1%
Open Punctuation 2
 
0.1%
Other Punctuation 2
 
0.1%
Uppercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 86
 
7.9%
홀 84
 
7.7%
미 77
 
7.0%
추 77
 
7.0%
시 73
 
6.7%
구 73
 
6.7%
로 72
 
6.6%
광 72
 
6.6%
역 72
 
6.6%
천 72
 
6.6%
Other values (90) 336
30.7%
Decimal Number
ValueCountFrequency (%)
1 48
16.5%
2 45
15.5%
3 38
13.1%
4 33
11.3%
7 31
10.7%
0 27
9.3%
8 21
7.2%
6 19
 
6.5%
5 19
 
6.5%
9 10
 
3.4%
Lowercase Letter
ValueCountFrequency (%)
i 2
20.0%
y 1
10.0%
d 1
10.0%
e 1
10.0%
t 1
10.0%
a 1
10.0%
l 1
10.0%
n 1
10.0%
g 1
10.0%
Other Punctuation
ValueCountFrequency (%)
& 1
50.0%
, 1
50.0%
Uppercase Letter
ValueCountFrequency (%)
W 1
50.0%
Z 1
50.0%
Space Separator
ValueCountFrequency (%)
263
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1094
65.3%
Common 570
34.0%
Latin 12
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 86
 
7.9%
홀 84
 
7.7%
미 77
 
7.0%
추 77
 
7.0%
시 73
 
6.7%
구 73
 
6.7%
로 72
 
6.6%
광 72
 
6.6%
역 72
 
6.6%
천 72
 
6.6%
Other values (90) 336
30.7%
Common
ValueCountFrequency (%)
263
46.1%
1 48
 
8.4%
2 45
 
7.9%
3 38
 
6.7%
4 33
 
5.8%
7 31
 
5.4%
0 27
 
4.7%
8 21
 
3.7%
6 19
 
3.3%
5 19
 
3.3%
Other values (6) 26
 
4.6%
Latin
ValueCountFrequency (%)
i 2
16.7%
W 1
8.3%
Z 1
8.3%
y 1
8.3%
d 1
8.3%
e 1
8.3%
t 1
8.3%
a 1
8.3%
l 1
8.3%
n 1
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1094
65.3%
ASCII 582
34.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
263
45.2%
1 48
 
8.2%
2 45
 
7.7%
3 38
 
6.5%
4 33
 
5.7%
7 31
 
5.3%
0 27
 
4.6%
8 21
 
3.6%
6 19
 
3.3%
5 19
 
3.3%
Other values (17) 38
 
6.5%
Hangul
ValueCountFrequency (%)
인 86
 
7.9%
홀 84
 
7.7%
미 77
 
7.0%
추 77
 
7.0%
시 73
 
6.7%
구 73
 
6.7%
로 72
 
6.6%
광 72
 
6.6%
역 72
 
6.6%
천 72
 
6.6%
Other values (90) 336
30.7%

전화번호
Text

MISSING 

Distinct53
Distinct (%)100.0%
Missing20
Missing (%)27.4%
Memory size716.0 B
2024-03-18T13:52:03.717356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length13.415094
Min length12

Characters and Unicode

Total characters711
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

Unique53 ?
Unique (%)100.0%

Sample

1st row0507-1412-0505
2nd row0507-1316-7407
3rd row0507-1305-6364
4th row0507-1305-6057
5th row032-263-4717
ValueCountFrequency (%)
0507-1369-2241 1
 
1.9%
032-422-9810 1
 
1.9%
0507-1375-8155 1
 
1.9%
0507-1322-5701 1
 
1.9%
032-887-9198 1
 
1.9%
032-865-1344 1
 
1.9%
0507-1341-5406 1
 
1.9%
0507-1326-6830 1
 
1.9%
0507-1372-8702 1
 
1.9%
0507-1307-6358 1
 
1.9%
Other values (43) 43
81.1%
2024-03-18T13:52:04.072968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 131
18.4%
- 106
14.9%
7 75
10.5%
5 70
9.8%
1 68
9.6%
3 68
9.6%
2 53
7.5%
4 49
 
6.9%
6 38
 
5.3%
8 35
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 605
85.1%
Dash Punctuation 106
 
14.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 131
21.7%
7 75
12.4%
5 70
11.6%
1 68
11.2%
3 68
11.2%
2 53
8.8%
4 49
 
8.1%
6 38
 
6.3%
8 35
 
5.8%
9 18
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 106
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 711
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 131
18.4%
- 106
14.9%
7 75
10.5%
5 70
9.8%
1 68
9.6%
3 68
9.6%
2 53
7.5%
4 49
 
6.9%
6 38
 
5.3%
8 35
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 711
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 131
18.4%
- 106
14.9%
7 75
10.5%
5 70
9.8%
1 68
9.6%
3 68
9.6%
2 53
7.5%
4 49
 
6.9%
6 38
 
5.3%
8 35
 
4.9%

Interactions

2024-03-18T13:52:00.886561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T13:52:04.151520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번상호명지번주소도로명주소전화번호
연번1.0001.0001.0000.9431.000
상호명1.0001.0001.0001.0001.000
지번주소1.0001.0001.0001.0001.000
도로명주소0.9431.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.000

Missing values

2024-03-18T13:52:01.048756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T13:52:01.125153image/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.
2024-03-18T13:52:01.210828image/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

연번상호명지번주소도로명주소전화번호
01디테일가드 광택코팅 프로샵인천광역시 미추홀구 관교동 317-10 1층 디테일가드인천광역시 미추홀구 인하로 402 1층 디테일가드0507-1412-0505
12크리스탈스팀카워시인천광역시 미추홀구 문학동 155-34 1층 101호인천광역시 미추홀구 소성로 292 1층 101호0507-1316-7407
23진솔자동차공업사&세차장인천광역시 미추홀구 도화동 104-6인천광역시 미추홀구 장고개로 8 진솔빌딩<NA>
34세차의 고수인천광역시 미추홀구 주안동 22-22 1층 세차장인천광역시 미추홀구 석정로 446 1층 세차장0507-1305-6364
45워시블랑 학익점인천광역시 미추홀구 학익동 401-45인천광역시 미추홀구 매소홀로 2720507-1305-6057
56주안넘버원손세차인천광역시 미추홀구 주안동 715-7인천광역시 미추홀구 한나루로 522032-263-4717
67W실내셀프세차장인천광역시 미추홀구 학익동 401-16 W 실내셀프세차장인천광역시 미추홀구 매소홀로271번길 7 W 실내셀프세차장0507-1444-3558
78고릴라 손세차장인천광역시 미추홀구 도화동 120-34 21세기 자동차 내 고릴라 손세차장인천광역시 미추홀구 석정로333번길 2 21세기 자동차 내 고릴라 손세차장0507-1470-7740
89주안셀프킹인천광역시 미추홀구 주안동 26-11 1층인천광역시 미추홀구 석정로 366 1층<NA>
910상상 디테일링인천광역시 미추홀구 주안동 1486-14 1층인천광역시 미추홀구 인주대로434번길 8 1층0507-1309-8408
연번상호명지번주소도로명주소전화번호
6364한국세차장인천광역시 미추홀구 학익동 587-99인천광역시 미추홀구 아암대로227번길 26<NA>
6465신용세차장인천광역시 미추홀구 관교동 13-8인천광역시 미추홀구 문화로 45<NA>
6566고려세차장인천광역시 미추홀구 학익동 587-86인천광역시 미추홀구 아암대로227번길 70-9032-831-5774
6667현대크리닝인천광역시 미추홀구 주안동 16-62인천광역시 미추홀구 길파로34번길 5-11<NA>
6768인일세차인천광역시 미추홀구 학익동 587-69인천광역시 미추홀구 아암대로253번길 21<NA>
6869인천세차장인천광역시 미추홀구 학익동 587-101인천광역시 미추홀구 아암대로253번길 88<NA>
6970르호봇세차장인천광역시 미추홀구 학익동 587-367인천광역시 미추홀구 아암대로227번길 38032-831-0365
7071부엉이출장세차인천광역시 미추홀구 도화동 1008인천광역시 미추홀구 숙골로88번길 560507-1322-4285
7172카젠 주안남부점 손세차장인천광역시 미추홀구 주안동 1421-19인천광역시 미추홀구 인주대로330번길 27032-863-4600
7273인우카센터손세차장인천광역시 미추홀구 주안동 1418-85인천광역시 미추홀구 인주대로330번길 30032-884-4224