Overview

Dataset statistics

Number of variables7
Number of observations191
Missing cells16
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.1 KiB
Average record size in memory59.7 B

Variable types

Numeric3
Categorical1
Text3

Dataset

Description인천광역시 미추홀구 관내에 소재한 세탁업에 대한 데이터로 관내 세탁업장에 관한 업종명, 업소명, 전화번호,위도, 경도 등을 제공합니다.
Author인천광역시 미추홀구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15061014&srcSe=7661IVAWM27C61E190

Alerts

업종명 has constant value ""Constant
전화번호 has 16 (8.4%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-05-03 19:41:13.439272
Analysis finished2024-05-03 19:41:18.329996
Duration4.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct191
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96
Minimum1
Maximum191
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-05-03T19:41:18.636435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.5
Q148.5
median96
Q3143.5
95-th percentile181.5
Maximum191
Range190
Interquartile range (IQR)95

Descriptive statistics

Standard deviation55.2811
Coefficient of variation (CV)0.57584479
Kurtosis-1.2
Mean96
Median Absolute Deviation (MAD)48
Skewness0
Sum18336
Variance3056
MonotonicityStrictly increasing
2024-05-03T19:41:19.295359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
2 1
 
0.5%
123 1
 
0.5%
124 1
 
0.5%
125 1
 
0.5%
126 1
 
0.5%
127 1
 
0.5%
128 1
 
0.5%
129 1
 
0.5%
130 1
 
0.5%
Other values (181) 181
94.8%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
191 1
0.5%
190 1
0.5%
189 1
0.5%
188 1
0.5%
187 1
0.5%
186 1
0.5%
185 1
0.5%
184 1
0.5%
183 1
0.5%
182 1
0.5%

업종명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
세탁업
191 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
세탁업 191
100.0%

Length

2024-05-03T19:41:19.706515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T19:41:20.052936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
세탁업 191
100.0%

업소명
Text

Distinct182
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-05-03T19:41:20.593812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length14
Mean length5.4397906
Min length3

Characters and Unicode

Total characters1039
Distinct characters196
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique174 ?
Unique (%)91.1%

Sample

1st row준세탁소
2nd row시장세탁소
3rd row그린세탁
4th row로얄세탁
5th row성실세탁
ValueCountFrequency (%)
세탁소 20
 
8.8%
세탁 6
 
2.6%
동아세탁 3
 
1.3%
대우사 2
 
0.9%
충남세탁 2
 
0.9%
크린 2
 
0.9%
대성세탁 2
 
0.9%
엄마손 2
 
0.9%
젠틀한 2
 
0.9%
인하빨래터 2
 
0.9%
Other values (182) 185
81.1%
2024-05-03T19:41:21.761347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 159
 
15.3%
탁 158
 
15.2%
소 78
 
7.5%
37
 
3.6%
사 35
 
3.4%
크 20
 
1.9%
린 19
 
1.8%
대 18
 
1.7%
동 17
 
1.6%
성 13
 
1.3%
Other values (186) 485
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 984
94.7%
Space Separator 37
 
3.6%
Uppercase Letter 5
 
0.5%
Close Punctuation 3
 
0.3%
Open Punctuation 3
 
0.3%
Decimal Number 3
 
0.3%
Other Punctuation 2
 
0.2%
Lowercase Letter 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 159
 
16.2%
탁 158
 
16.1%
소 78
 
7.9%
사 35
 
3.6%
크 20
 
2.0%
린 19
 
1.9%
대 18
 
1.8%
동 17
 
1.7%
성 13
 
1.3%
화 13
 
1.3%
Other values (173) 454
46.1%
Uppercase Letter
ValueCountFrequency (%)
T 2
40.0%
C 1
20.0%
K 1
20.0%
S 1
20.0%
Decimal Number
ValueCountFrequency (%)
1 2
66.7%
2 1
33.3%
Other Punctuation
ValueCountFrequency (%)
. 1
50.0%
& 1
50.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
50.0%
h 1
50.0%
Space Separator
ValueCountFrequency (%)
37
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 984
94.7%
Common 48
 
4.6%
Latin 7
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 159
 
16.2%
탁 158
 
16.1%
소 78
 
7.9%
사 35
 
3.6%
크 20
 
2.0%
린 19
 
1.9%
대 18
 
1.8%
동 17
 
1.7%
성 13
 
1.3%
화 13
 
1.3%
Other values (173) 454
46.1%
Common
ValueCountFrequency (%)
37
77.1%
) 3
 
6.2%
( 3
 
6.2%
1 2
 
4.2%
. 1
 
2.1%
2 1
 
2.1%
& 1
 
2.1%
Latin
ValueCountFrequency (%)
T 2
28.6%
C 1
14.3%
K 1
14.3%
S 1
14.3%
e 1
14.3%
h 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 984
94.7%
ASCII 55
 
5.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 159
 
16.2%
탁 158
 
16.1%
소 78
 
7.9%
사 35
 
3.6%
크 20
 
2.0%
린 19
 
1.9%
대 18
 
1.8%
동 17
 
1.7%
성 13
 
1.3%
화 13
 
1.3%
Other values (173) 454
46.1%
ASCII
ValueCountFrequency (%)
37
67.3%
) 3
 
5.5%
( 3
 
5.5%
1 2
 
3.6%
T 2
 
3.6%
C 1
 
1.8%
. 1
 
1.8%
K 1
 
1.8%
S 1
 
1.8%
e 1
 
1.8%
Other values (3) 3
 
5.5%

도로명주소
Text

Distinct190
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-05-03T19:41:22.431517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length61
Median length48
Mean length30.858639
Min length23

Characters and Unicode

Total characters5894
Distinct characters147
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

Unique189 ?
Unique (%)99.0%

Sample

1st row인천광역시 미추홀구 경인로7번길 18 (숭의동)
2nd row인천광역시 미추홀구 석정로423번길 20 (주안동,1층)
3rd row인천광역시 미추홀구 수봉남로17번길 20 (용현동)
4th row인천광역시 미추홀구 미추홀대로587번길 19 (주안동)
5th row인천광역시 미추홀구 인주대로446번길 11-12 (주안동)
ValueCountFrequency (%)
인천광역시 191
 
17.5%
미추홀구 191
 
17.5%
주안동 69
 
6.3%
1층 46
 
4.2%
용현동 38
 
3.5%
도화동 23
 
2.1%
숭의동 18
 
1.7%
학익동 13
 
1.2%
상가동 9
 
0.8%
문학동 8
 
0.7%
Other values (346) 484
44.4%
2024-05-03T19:41:23.787893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
899
 
15.3%
인 244
 
4.1%
1 227
 
3.9%
동 221
 
3.7%
홀 207
 
3.5%
미 204
 
3.5%
추 203
 
3.4%
천 194
 
3.3%
구 194
 
3.3%
( 193
 
3.3%
Other values (137) 3108
52.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3525
59.8%
Decimal Number 941
 
16.0%
Space Separator 899
 
15.3%
Open Punctuation 193
 
3.3%
Close Punctuation 193
 
3.3%
Other Punctuation 99
 
1.7%
Dash Punctuation 34
 
0.6%
Uppercase Letter 8
 
0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 244
 
6.9%
동 221
 
6.3%
홀 207
 
5.9%
미 204
 
5.8%
추 203
 
5.8%
천 194
 
5.5%
구 194
 
5.5%
시 192
 
5.4%
역 191
 
5.4%
광 191
 
5.4%
Other values (116) 1484
42.1%
Decimal Number
ValueCountFrequency (%)
1 227
24.1%
2 120
12.8%
3 112
11.9%
4 87
 
9.2%
0 80
 
8.5%
5 67
 
7.1%
6 66
 
7.0%
8 63
 
6.7%
9 60
 
6.4%
7 59
 
6.3%
Uppercase Letter
ValueCountFrequency (%)
A 3
37.5%
C 2
25.0%
B 1
 
12.5%
S 1
 
12.5%
K 1
 
12.5%
Space Separator
ValueCountFrequency (%)
899
100.0%
Open Punctuation
ValueCountFrequency (%)
( 193
100.0%
Close Punctuation
ValueCountFrequency (%)
) 193
100.0%
Other Punctuation
ValueCountFrequency (%)
, 99
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 34
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3525
59.8%
Common 2361
40.1%
Latin 8
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 244
 
6.9%
동 221
 
6.3%
홀 207
 
5.9%
미 204
 
5.8%
추 203
 
5.8%
천 194
 
5.5%
구 194
 
5.5%
시 192
 
5.4%
역 191
 
5.4%
광 191
 
5.4%
Other values (116) 1484
42.1%
Common
ValueCountFrequency (%)
899
38.1%
1 227
 
9.6%
( 193
 
8.2%
) 193
 
8.2%
2 120
 
5.1%
3 112
 
4.7%
, 99
 
4.2%
4 87
 
3.7%
0 80
 
3.4%
5 67
 
2.8%
Other values (6) 284
 
12.0%
Latin
ValueCountFrequency (%)
A 3
37.5%
C 2
25.0%
B 1
 
12.5%
S 1
 
12.5%
K 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3525
59.8%
ASCII 2369
40.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
899
37.9%
1 227
 
9.6%
( 193
 
8.1%
) 193
 
8.1%
2 120
 
5.1%
3 112
 
4.7%
, 99
 
4.2%
4 87
 
3.7%
0 80
 
3.4%
5 67
 
2.8%
Other values (11) 292
 
12.3%
Hangul
ValueCountFrequency (%)
인 244
 
6.9%
동 221
 
6.3%
홀 207
 
5.9%
미 204
 
5.8%
추 203
 
5.8%
천 194
 
5.5%
구 194
 
5.5%
시 192
 
5.4%
역 191
 
5.4%
광 191
 
5.4%
Other values (116) 1484
42.1%

전화번호
Text

MISSING 

Distinct174
Distinct (%)99.4%
Missing16
Missing (%)8.4%
Memory size1.6 KiB
2024-05-03T19:41:24.462141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters2100
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

Unique173 ?
Unique (%)98.9%

Sample

1st row032-887-9315
2nd row032-864-7633
3rd row032-887-0439
4th row032-863-6750
5th row032-424-7254
ValueCountFrequency (%)
032-442-7700 2
 
1.1%
032-433-8131 1
 
0.6%
032-868-1995 1
 
0.6%
032-432-2889 1
 
0.6%
032-863-9741 1
 
0.6%
032-873-8845 1
 
0.6%
032-866-6583 1
 
0.6%
032-876-2426 1
 
0.6%
032-425-0205 1
 
0.6%
032-201-8297 1
 
0.6%
Other values (164) 164
93.7%
2024-05-03T19:41:25.806019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 350
16.7%
2 316
15.0%
3 278
13.2%
0 260
12.4%
8 219
10.4%
6 163
7.8%
4 148
7.0%
7 142
6.8%
5 89
 
4.2%
1 70
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1750
83.3%
Dash Punctuation 350
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 316
18.1%
3 278
15.9%
0 260
14.9%
8 219
12.5%
6 163
9.3%
4 148
8.5%
7 142
8.1%
5 89
 
5.1%
1 70
 
4.0%
9 65
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 350
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 350
16.7%
2 316
15.0%
3 278
13.2%
0 260
12.4%
8 219
10.4%
6 163
7.8%
4 148
7.0%
7 142
6.8%
5 89
 
4.2%
1 70
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 350
16.7%
2 316
15.0%
3 278
13.2%
0 260
12.4%
8 219
10.4%
6 163
7.8%
4 148
7.0%
7 142
6.8%
5 89
 
4.2%
1 70
 
3.3%

위도
Real number (ℝ)

Distinct187
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.455502
Minimum37.436025
Maximum37.477184
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-05-03T19:41:26.475709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.436025
5-th percentile37.439625
Q137.449374
median37.456562
Q337.461771
95-th percentile37.468962
Maximum37.477184
Range0.04115881
Interquartile range (IQR)0.01239691

Descriptive statistics

Standard deviation0.0090563081
Coefficient of variation (CV)0.00024178846
Kurtosis-0.51924408
Mean37.455502
Median Absolute Deviation (MAD)0.00617991
Skewness-0.12110035
Sum7154.001
Variance8.2016717 × 10-5
MonotonicityNot monotonic
2024-05-03T19:41:27.021007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.46132153 3
 
1.6%
37.47031323 2
 
1.0%
37.45201495 2
 
1.0%
37.46347449 1
 
0.5%
37.46071033 1
 
0.5%
37.47005398 1
 
0.5%
37.45759345 1
 
0.5%
37.44563928 1
 
0.5%
37.43825393 1
 
0.5%
37.45842398 1
 
0.5%
Other values (177) 177
92.7%
ValueCountFrequency (%)
37.43602523 1
0.5%
37.43661464 1
0.5%
37.43741099 1
0.5%
37.43755102 1
0.5%
37.43757468 1
0.5%
37.43767257 1
0.5%
37.43825393 1
0.5%
37.43833846 1
0.5%
37.43884555 1
0.5%
37.43906396 1
0.5%
ValueCountFrequency (%)
37.47718404 1
0.5%
37.47714571 1
0.5%
37.47696529 1
0.5%
37.47130646 1
0.5%
37.47097428 1
0.5%
37.47031323 2
1.0%
37.47005398 1
0.5%
37.46967115 1
0.5%
37.46958846 1
0.5%
37.46833484 1
0.5%

경도
Real number (ℝ)

Distinct187
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.66944
Minimum126.6343
Maximum126.69826
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-05-03T19:41:27.745418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.6343
5-th percentile126.63994
Q1126.65793
median126.67021
Q3126.68292
95-th percentile126.69256
Maximum126.69826
Range0.0639604
Interquartile range (IQR)0.02498515

Descriptive statistics

Standard deviation0.015692335
Coefficient of variation (CV)0.00012388414
Kurtosis-0.81061982
Mean126.66944
Median Absolute Deviation (MAD)0.0126088
Skewness-0.31323851
Sum24193.862
Variance0.00024624936
MonotonicityNot monotonic
2024-05-03T19:41:28.191276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.6899169 3
 
1.6%
126.6673576 2
 
1.0%
126.6450629 2
 
1.0%
126.6445769 1
 
0.5%
126.6860101 1
 
0.5%
126.673582 1
 
0.5%
126.6526138 1
 
0.5%
126.6915712 1
 
0.5%
126.6825099 1
 
0.5%
126.6480087 1
 
0.5%
Other values (177) 177
92.7%
ValueCountFrequency (%)
126.6342989 1
0.5%
126.6345065 1
0.5%
126.637758 1
0.5%
126.638282 1
0.5%
126.6383925 1
0.5%
126.6392535 1
0.5%
126.639379 1
0.5%
126.6395252 1
0.5%
126.6396114 1
0.5%
126.6398686 1
0.5%
ValueCountFrequency (%)
126.6982593 1
0.5%
126.6962105 1
0.5%
126.6954668 1
0.5%
126.6950059 1
0.5%
126.6946578 1
0.5%
126.6942227 1
0.5%
126.6941511 1
0.5%
126.6935442 1
0.5%
126.693418 1
0.5%
126.6932448 1
0.5%

Interactions

2024-05-03T19:41:16.477334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:41:14.189125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:41:15.172141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:41:16.887714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:41:14.527301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:41:15.837880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:41:17.258137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:41:14.819901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:41:16.157325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-03T19:41:28.483013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도
연번1.0000.3380.303
위도0.3381.0000.596
경도0.3030.5961.000
2024-05-03T19:41:28.727673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도
연번1.000-0.0700.053
위도-0.0701.000-0.140
경도0.053-0.1401.000

Missing values

2024-05-03T19:41:17.796625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T19:41:18.192323image/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

연번업종명업소명도로명주소전화번호위도경도
01세탁업준세탁소인천광역시 미추홀구 경인로7번길 18 (숭의동)032-887-931537.463474126.644577
12세탁업시장세탁소인천광역시 미추홀구 석정로423번길 20 (주안동,1층)032-864-763337.467499126.683052
23세탁업그린세탁인천광역시 미추홀구 수봉남로17번길 20 (용현동)032-887-043937.458336126.656205
34세탁업로얄세탁인천광역시 미추홀구 미추홀대로587번길 19 (주안동)032-863-675037.449668126.678556
45세탁업성실세탁인천광역시 미추홀구 인주대로446번길 11-12 (주안동)032-424-725437.450182126.686313
56세탁업스타크린인천광역시 미추홀구 독정이로 67 (숭의동)032-882-889337.461701126.651811
67세탁업광명사인천광역시 미추홀구 수봉로21번길 26 (숭의동)032-875-532537.46409126.657846
78세탁업신광세탁인천광역시 미추홀구 인주대로171번길 16-6 (용현동)032-875-691237.45594126.657234
89세탁업충남세탁인천광역시 미추홀구 수봉남로18번길 59 (용현동)032-864-845737.456562126.659687
910세탁업부흥세탁인천광역시 미추홀구 낙섬중로 110-2, 1층 (용현동)032-882-203737.457006126.641951
연번업종명업소명도로명주소전화번호위도경도
181182세탁업젠틀한 세탁인천광역시 미추홀구 매소홀로 550, 1층 (문학동)032-442-770037.438338126.682985
182183세탁업크린업플러스세탁인천광역시 미추홀구 경인로246번길 8, 1층 (도화동)032-213-703937.463393126.669132
183184세탁업명품수선전문세탁소인천광역시 미추홀구 장고개로 38, 1층 (도화동)032-866-270637.471306126.6676
184185세탁업신비마을명품세탁소인천광역시 미추홀구 주승로 160 (주안동)032-433-336237.444912126.688274
185186세탁업크린제이 씨티(C.T)산업인천광역시 미추홀구 노적산로 45, 인천학익 두산위브 1층 101호 (학익동)<NA>37.441476126.654978
186187세탁업명품사옷수선세탁소인천광역시 미추홀구 석바위로101번길 45, 1층 (주안동)<NA>37.462715126.683932
187188세탁업크린 앤 클리닝인천광역시 미추홀구 미추홀대로626번길 65, 102호 (주안동)032-432-957637.453225126.683695
188189세탁업크린매니저인천광역시 미추홀구 장고개로26번길 11, 1층 (도화동)<NA>37.470313126.667358
189190세탁업씨엔씨코리아인천광역시 미추홀구 장고개로26번길 11, 1층 (도화동)<NA>37.470313126.667358
190191세탁업젠틀한 세탁소인천광역시 미추홀구 인하로 191, 지하1층 (주안동)<NA>37.448939126.669175