Overview

Dataset statistics

Number of variables6
Number of observations49
Missing cells26
Missing cells (%)8.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory51.7 B

Variable types

Numeric1
Categorical1
Text4

Dataset

Description인천광역시 미추홀구의 대부업 에 대한 데이터로 유형, 상호명, 도로명주소, 지번주소, 전화번호 등의 항목을 제공합니다.
Author인천광역시 미추홀구
URLhttps://www.data.go.kr/data/15016233/fileData.do

Alerts

연번 is highly overall correlated with 유형High correlation
유형 is highly overall correlated with 연번High correlation
전화번호 has 26 (53.1%) missing valuesMissing
연번 has unique valuesUnique
상호명 has unique valuesUnique

Reproduction

Analysis started2024-04-29 22:24:10.873966
Analysis finished2024-04-29 22:24:13.013097
Duration2.14 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2024-04-30T07:24:13.087237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.4
Q113
median25
Q337
95-th percentile46.6
Maximum49
Range48
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.28869
Coefficient of variation (CV)0.57154761
Kurtosis-1.2
Mean25
Median Absolute Deviation (MAD)12
Skewness0
Sum1225
Variance204.16667
MonotonicityStrictly increasing
2024-04-30T07:24:13.228430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1 1
 
2.0%
38 1
 
2.0%
28 1
 
2.0%
29 1
 
2.0%
30 1
 
2.0%
31 1
 
2.0%
32 1
 
2.0%
33 1
 
2.0%
34 1
 
2.0%
35 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
1 1
2.0%
2 1
2.0%
3 1
2.0%
4 1
2.0%
5 1
2.0%
6 1
2.0%
7 1
2.0%
8 1
2.0%
9 1
2.0%
10 1
2.0%
ValueCountFrequency (%)
49 1
2.0%
48 1
2.0%
47 1
2.0%
46 1
2.0%
45 1
2.0%
44 1
2.0%
43 1
2.0%
42 1
2.0%
41 1
2.0%
40 1
2.0%

유형
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
대부업
30 
대부중개업
19 

Length

Max length5
Median length3
Mean length3.7755102
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대부업 30
61.2%
대부중개업 19
38.8%

Length

2024-04-30T07:24:13.348737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:24:13.445087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대부업 30
61.2%
대부중개업 19
38.8%

상호명
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2024-04-30T07:24:13.601296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length9.0612245
Min length4

Characters and Unicode

Total characters444
Distinct characters105
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)100.0%

Sample

1st rowKK대부
2nd row장수대부
3rd row금융파이낸셜대부
4th row연형란대부(주)
5th row주식회사 엘케이대부
ValueCountFrequency (%)
주식회사 10
 
15.4%
대부중개 4
 
6.2%
kk대부 1
 
1.5%
jk컴퍼니대부중개 1
 
1.5%
금성전당포대부 1
 
1.5%
경기사 1
 
1.5%
전당포 1
 
1.5%
대부 1
 
1.5%
에이치씨 1
 
1.5%
오라클대부중개 1
 
1.5%
Other values (43) 43
66.2%
2024-04-30T07:24:13.917307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
대 51
 
11.5%
부 49
 
11.0%
중 19
 
4.3%
개 19
 
4.3%
주 17
 
3.8%
이 16
 
3.6%
16
 
3.6%
사 15
 
3.4%
식 11
 
2.5%
회 11
 
2.5%
Other values (95) 220
49.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 406
91.4%
Space Separator 16
 
3.6%
Uppercase Letter 12
 
2.7%
Open Punctuation 5
 
1.1%
Close Punctuation 5
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 51
 
12.6%
부 49
 
12.1%
중 19
 
4.7%
개 19
 
4.7%
주 17
 
4.2%
이 16
 
3.9%
사 15
 
3.7%
식 11
 
2.7%
회 11
 
2.7%
포 9
 
2.2%
Other values (86) 189
46.6%
Uppercase Letter
ValueCountFrequency (%)
K 4
33.3%
J 2
16.7%
I 2
16.7%
T 2
16.7%
D 1
 
8.3%
O 1
 
8.3%
Space Separator
ValueCountFrequency (%)
16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 406
91.4%
Common 26
 
5.9%
Latin 12
 
2.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 51
 
12.6%
부 49
 
12.1%
중 19
 
4.7%
개 19
 
4.7%
주 17
 
4.2%
이 16
 
3.9%
사 15
 
3.7%
식 11
 
2.7%
회 11
 
2.7%
포 9
 
2.2%
Other values (86) 189
46.6%
Latin
ValueCountFrequency (%)
K 4
33.3%
J 2
16.7%
I 2
16.7%
T 2
16.7%
D 1
 
8.3%
O 1
 
8.3%
Common
ValueCountFrequency (%)
16
61.5%
( 5
 
19.2%
) 5
 
19.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 406
91.4%
ASCII 38
 
8.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
대 51
 
12.6%
부 49
 
12.1%
중 19
 
4.7%
개 19
 
4.7%
주 17
 
4.2%
이 16
 
3.9%
사 15
 
3.7%
식 11
 
2.7%
회 11
 
2.7%
포 9
 
2.2%
Other values (86) 189
46.6%
ASCII
ValueCountFrequency (%)
16
42.1%
( 5
 
13.2%
) 5
 
13.2%
K 4
 
10.5%
J 2
 
5.3%
I 2
 
5.3%
T 2
 
5.3%
D 1
 
2.6%
O 1
 
2.6%

도로명주소
Text

Distinct39
Distinct (%)79.6%
Missing0
Missing (%)0.0%
Memory size524.0 B
2024-04-30T07:24:14.158194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length38
Mean length34.612245
Min length22

Characters and Unicode

Total characters1696
Distinct characters102
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

Unique30 ?
Unique (%)61.2%

Sample

1st row인천광역시 미추홀구 경인로 452, 센트레뷰 1동 2101호 (주안동)
2nd row인천광역시 미추홀구 인주대로123번길 15, 신영오피스텔 301호 (용현동)
3rd row인천광역시 미추홀구 주안로 68, 에코이뷰 101호 (주안동)
4th row인천광역시 미추홀구 경원대로852번길 15, 203호 (주안동)
5th row인천광역시 미추홀구 주안로 68, 705호 (주안동)
ValueCountFrequency (%)
인천광역시 49
 
14.8%
미추홀구 49
 
14.8%
주안동 38
 
11.4%
2층 9
 
2.7%
주안로 9
 
2.7%
경인로 9
 
2.7%
경원대로 5
 
1.5%
주안로56번길 5
 
1.5%
5 5
 
1.5%
현성빌딩 5
 
1.5%
Other values (100) 149
44.9%
2024-04-30T07:24:14.520660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
283
 
16.7%
인 64
 
3.8%
주 57
 
3.4%
안 56
 
3.3%
, 53
 
3.1%
홀 52
 
3.1%
추 51
 
3.0%
동 51
 
3.0%
미 51
 
3.0%
시 51
 
3.0%
Other values (92) 927
54.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 965
56.9%
Decimal Number 286
 
16.9%
Space Separator 283
 
16.7%
Other Punctuation 53
 
3.1%
Close Punctuation 49
 
2.9%
Open Punctuation 49
 
2.9%
Dash Punctuation 10
 
0.6%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 64
 
6.6%
주 57
 
5.9%
안 56
 
5.8%
홀 52
 
5.4%
추 51
 
5.3%
동 51
 
5.3%
미 51
 
5.3%
시 51
 
5.3%
광 49
 
5.1%
구 49
 
5.1%
Other values (76) 434
45.0%
Decimal Number
ValueCountFrequency (%)
1 49
17.1%
2 44
15.4%
0 39
13.6%
4 34
11.9%
5 30
10.5%
6 28
9.8%
3 21
7.3%
8 20
7.0%
7 11
 
3.8%
9 10
 
3.5%
Space Separator
ValueCountFrequency (%)
283
100.0%
Other Punctuation
ValueCountFrequency (%)
, 53
100.0%
Close Punctuation
ValueCountFrequency (%)
) 49
100.0%
Open Punctuation
ValueCountFrequency (%)
( 49
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 965
56.9%
Common 730
43.0%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 64
 
6.6%
주 57
 
5.9%
안 56
 
5.8%
홀 52
 
5.4%
추 51
 
5.3%
동 51
 
5.3%
미 51
 
5.3%
시 51
 
5.3%
광 49
 
5.1%
구 49
 
5.1%
Other values (76) 434
45.0%
Common
ValueCountFrequency (%)
283
38.8%
, 53
 
7.3%
1 49
 
6.7%
) 49
 
6.7%
( 49
 
6.7%
2 44
 
6.0%
0 39
 
5.3%
4 34
 
4.7%
5 30
 
4.1%
6 28
 
3.8%
Other values (5) 72
 
9.9%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 965
56.9%
ASCII 731
43.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
283
38.7%
, 53
 
7.3%
1 49
 
6.7%
) 49
 
6.7%
( 49
 
6.7%
2 44
 
6.0%
0 39
 
5.3%
4 34
 
4.7%
5 30
 
4.1%
6 28
 
3.8%
Other values (6) 73
 
10.0%
Hangul
ValueCountFrequency (%)
인 64
 
6.6%
주 57
 
5.9%
안 56
 
5.8%
홀 52
 
5.4%
추 51
 
5.3%
동 51
 
5.3%
미 51
 
5.3%
시 51
 
5.3%
광 49
 
5.1%
구 49
 
5.1%
Other values (76) 434
45.0%

지번주소
Text

Distinct35
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Memory size524.0 B
2024-04-30T07:24:14.739633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length29
Mean length24.22449
Min length20

Characters and Unicode

Total characters1187
Distinct characters71
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)51.0%

Sample

1st row인천광역시 미추홀구 주안동 1542-7 센트레뷰
2nd row인천광역시 미추홀구 용현동 459-32
3rd row인천광역시 미추홀구 주안동 267-9
4th row인천광역시 미추홀구 주안동 952-1
5th row인천광역시 미추홀구 주안동 267-9
ValueCountFrequency (%)
인천광역시 49
20.4%
미추홀구 49
20.4%
주안동 38
15.8%
현성빌딩 5
 
2.1%
265-1 4
 
1.7%
1호 4
 
1.7%
용현동 4
 
1.7%
267-9 4
 
1.7%
학익동 4
 
1.7%
35번지 2
 
0.8%
Other values (62) 77
32.1%
2024-04-30T07:24:15.090595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
191
 
16.1%
인 53
 
4.5%
시 50
 
4.2%
동 49
 
4.1%
광 49
 
4.1%
역 49
 
4.1%
미 49
 
4.1%
추 49
 
4.1%
홀 49
 
4.1%
구 49
 
4.1%
Other values (61) 550
46.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 747
62.9%
Decimal Number 219
 
18.4%
Space Separator 191
 
16.1%
Dash Punctuation 30
 
2.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 53
 
7.1%
시 50
 
6.7%
동 49
 
6.6%
광 49
 
6.6%
역 49
 
6.6%
미 49
 
6.6%
추 49
 
6.6%
홀 49
 
6.6%
구 49
 
6.6%
천 49
 
6.6%
Other values (49) 252
33.7%
Decimal Number
ValueCountFrequency (%)
1 46
21.0%
2 30
13.7%
6 27
12.3%
5 22
10.0%
3 20
9.1%
4 20
9.1%
9 16
 
7.3%
0 15
 
6.8%
8 13
 
5.9%
7 10
 
4.6%
Space Separator
ValueCountFrequency (%)
191
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 747
62.9%
Common 440
37.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 53
 
7.1%
시 50
 
6.7%
동 49
 
6.6%
광 49
 
6.6%
역 49
 
6.6%
미 49
 
6.6%
추 49
 
6.6%
홀 49
 
6.6%
구 49
 
6.6%
천 49
 
6.6%
Other values (49) 252
33.7%
Common
ValueCountFrequency (%)
191
43.4%
1 46
 
10.5%
- 30
 
6.8%
2 30
 
6.8%
6 27
 
6.1%
5 22
 
5.0%
3 20
 
4.5%
4 20
 
4.5%
9 16
 
3.6%
0 15
 
3.4%
Other values (2) 23
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 747
62.9%
ASCII 440
37.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
191
43.4%
1 46
 
10.5%
- 30
 
6.8%
2 30
 
6.8%
6 27
 
6.1%
5 22
 
5.0%
3 20
 
4.5%
4 20
 
4.5%
9 16
 
3.6%
0 15
 
3.4%
Other values (2) 23
 
5.2%
Hangul
ValueCountFrequency (%)
인 53
 
7.1%
시 50
 
6.7%
동 49
 
6.6%
광 49
 
6.6%
역 49
 
6.6%
미 49
 
6.6%
추 49
 
6.6%
홀 49
 
6.6%
구 49
 
6.6%
천 49
 
6.6%
Other values (49) 252
33.7%

전화번호
Text

MISSING 

Distinct16
Distinct (%)69.6%
Missing26
Missing (%)53.1%
Memory size524.0 B
2024-04-30T07:24:15.270731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length10.826087
Min length9

Characters and Unicode

Total characters249
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

Unique9 ?
Unique (%)39.1%

Sample

1st row1688-2569
2nd row1660-3660
3rd row032-887-7773
4th row1600-6531
5th row1544-8348
ValueCountFrequency (%)
1660-3660 2
 
8.7%
032-887-7773 2
 
8.7%
1544-8348 2
 
8.7%
032-719-8293 2
 
8.7%
032-442-3457 2
 
8.7%
032-875-2553 2
 
8.7%
032-876-3848 2
 
8.7%
032-423-4686 1
 
4.3%
1600-6531 1
 
4.3%
1566-8532 1
 
4.3%
Other values (6) 6
26.1%
2024-04-30T07:24:15.544275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 37
14.9%
3 34
13.7%
6 28
11.2%
8 27
10.8%
2 26
10.4%
0 25
10.0%
5 18
7.2%
4 18
7.2%
7 17
6.8%
1 13
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 212
85.1%
Dash Punctuation 37
 
14.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 34
16.0%
6 28
13.2%
8 27
12.7%
2 26
12.3%
0 25
11.8%
5 18
8.5%
4 18
8.5%
7 17
8.0%
1 13
 
6.1%
9 6
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 37
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 249
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 37
14.9%
3 34
13.7%
6 28
11.2%
8 27
10.8%
2 26
10.4%
0 25
10.0%
5 18
7.2%
4 18
7.2%
7 17
6.8%
1 13
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 249
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 37
14.9%
3 34
13.7%
6 28
11.2%
8 27
10.8%
2 26
10.4%
0 25
10.0%
5 18
7.2%
4 18
7.2%
7 17
6.8%
1 13
 
5.2%

Interactions

2024-04-30T07:24:12.730848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T07:24:15.635008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번유형상호명도로명주소지번주소전화번호
연번1.0000.9981.0000.0000.7040.477
유형0.9981.0001.0000.0000.0000.000
상호명1.0001.0001.0001.0001.0001.000
도로명주소0.0000.0001.0001.0001.0001.000
지번주소0.7040.0001.0001.0001.0000.999
전화번호0.4770.0001.0001.0000.9991.000
2024-04-30T07:24:15.719926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번유형
연번1.0000.872
유형0.8721.000

Missing values

2024-04-30T07:24:12.874776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T07:24:12.971488image/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

연번유형상호명도로명주소지번주소전화번호
01대부업KK대부인천광역시 미추홀구 경인로 452, 센트레뷰 1동 2101호 (주안동)인천광역시 미추홀구 주안동 1542-7 센트레뷰<NA>
12대부업장수대부인천광역시 미추홀구 인주대로123번길 15, 신영오피스텔 301호 (용현동)인천광역시 미추홀구 용현동 459-321688-2569
23대부업금융파이낸셜대부인천광역시 미추홀구 주안로 68, 에코이뷰 101호 (주안동)인천광역시 미추홀구 주안동 267-9<NA>
34대부업연형란대부(주)인천광역시 미추홀구 경원대로852번길 15, 203호 (주안동)인천광역시 미추홀구 주안동 952-1<NA>
45대부업주식회사 엘케이대부인천광역시 미추홀구 주안로 68, 705호 (주안동)인천광역시 미추홀구 주안동 267-9<NA>
56대부업트랜드머니캐피탈대부인천광역시 미추홀구 경인로 350-1, 1층 (주안동)인천광역시 미추홀구 주안동 505-41660-3660
67대부업게으른IT대부전당포인천광역시 미추홀구 경인로 422-1, 202호 (주안동)인천광역시 미추홀구 주안동 302-15<NA>
78대부업주식회사 코리아파트너스대부인천광역시 미추홀구 경원대로 873, 인성빌딩 402-9호 (주안동)인천광역시 미추홀구 주안동 988-2 인성빌딩<NA>
89대부업비엣코리아대부인천광역시 미추홀구 길파로 32, 2층 우측1호 (주안동)인천광역시 미추홀구 주안동 16-68<NA>
910대부업지오캐피탈대부인천광역시 미추홀구 경인로 392, 경인상가 2층 71호 (주안동)인천광역시 미추홀구 주안동 431-1 경인상가032-887-7773
연번유형상호명도로명주소지번주소전화번호
3940대부중개업주식회사 코리아파트너스대부중개인천광역시 미추홀구 경원대로 873, 인성빌딩 402-9호 (주안동)인천광역시 미추홀구 주안동 988-2 인성빌딩<NA>
4041대부중개업블루엔젤 대부중개인천광역시 미추홀구 소성로 181-24, 학익빌딩 2층 (학익동)인천광역시 미추홀구 학익동 244-16 학익빌딩<NA>
4142대부중개업주식회사 비케이존대부중개인천광역시 미추홀구 주안로56번길 5, 현성빌딩 4층 405호 (주안동)인천광역시 미추홀구 주안동 265-1 현성빌딩032-875-2553
4243대부중개업지오캐피탈대부중개인천광역시 미추홀구 경인로 392, 경인상가 2층 71호 (주안동)인천광역시 미추홀구 주안동 431-1 경인상가032-887-7773
4344대부중개업라이프대부중개주식회사인천광역시 미추홀구 염전로168번길 28, 도화 두손지젤시티 A동 1413호 (도화동)인천광역시 미추홀구 도화동 985-4 도화 두손지젤시티<NA>
4445대부중개업(주)이앤케이캐피탈대부중개인천광역시 미추홀구 주안로56번길 5, 현성빌딩 404호 (주안동)인천광역시 미추홀구 주안동 265-1 현성빌딩032-719-8293
4546대부중개업주식회사 한양대부중개인천광역시 미추홀구 주안로 108, 405호 (주안동, 경향빌딩)인천광역시 미추홀구 주안동 136번지 1호032-442-3457
4647대부중개업OK다이렉트 대부중개인천광역시 미추홀구 주안중로 25, 104호 (주안동, 신성쇼핑)인천광역시 미추홀구 주안동 169번지1566-8670
4748대부중개업한미캐피탈 대부중개인천광역시 미추홀구 주안로 116, 주안리가스퀘어 806호 (주안동)인천광역시 미추홀구 주안동 132 주안리가스퀘어1544-8348
4849대부중개업주식회사 신안건축도장대부중개인천광역시 미추홀구 학익소로 49-10, 4층 (학익동)인천광역시 미추홀구 학익동 35번지 17호032-876-3848