Overview

Dataset statistics

Number of variables9
Number of observations28
Missing cells11
Missing cells (%)4.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory77.7 B

Variable types

Numeric1
Categorical4
Text3
DateTime1

Dataset

Description서울특별시 용산구 대부업등록 현황(시군구명, 등록신청사업, 상호, 법인여부, 사업장전화, 소재지(도로명), 등록일자, 관리기관)에 대한 데이터를 제공합니다.
URLhttps://www.data.go.kr/data/15090470/fileData.do

Alerts

시군구명 has constant value ""Constant
등록신청사업 is highly overall correlated with 관리기관High correlation
관리기관 is highly overall correlated with 순번 and 2 other fieldsHigh correlation
법인여부 is highly overall correlated with 관리기관High correlation
순번 is highly overall correlated with 관리기관High correlation
관리기관 is highly imbalanced (77.8%)Imbalance
사업장 전화번호 has 10 (35.7%) missing valuesMissing
등록일자 has 1 (3.6%) missing valuesMissing
순번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 21:44:16.448294
Analysis finished2023-12-12 21:44:17.208836
Duration0.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.5
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-13T06:44:17.269194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.35
Q17.75
median14.5
Q321.25
95-th percentile26.65
Maximum28
Range27
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation8.2259751
Coefficient of variation (CV)0.56730863
Kurtosis-1.2
Mean14.5
Median Absolute Deviation (MAD)7
Skewness0
Sum406
Variance67.666667
MonotonicityStrictly increasing
2023-12-13T06:44:17.387216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1 1
 
3.6%
16 1
 
3.6%
28 1
 
3.6%
27 1
 
3.6%
26 1
 
3.6%
25 1
 
3.6%
24 1
 
3.6%
23 1
 
3.6%
22 1
 
3.6%
21 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
1 1
3.6%
2 1
3.6%
3 1
3.6%
4 1
3.6%
5 1
3.6%
6 1
3.6%
7 1
3.6%
8 1
3.6%
9 1
3.6%
10 1
3.6%
ValueCountFrequency (%)
28 1
3.6%
27 1
3.6%
26 1
3.6%
25 1
3.6%
24 1
3.6%
23 1
3.6%
22 1
3.6%
21 1
3.6%
20 1
3.6%
19 1
3.6%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size356.0 B
서울특별시 용산구
28 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시 용산구
2nd row서울특별시 용산구
3rd row서울특별시 용산구
4th row서울특별시 용산구
5th row서울특별시 용산구

Common Values

ValueCountFrequency (%)
서울특별시 용산구 28
100.0%

Length

2023-12-13T06:44:17.491492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:44:17.571685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 28
50.0%
용산구 28
50.0%

등록신청사업
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size356.0 B
대부업
23 
대부중개업
5 

Length

Max length5
Median length3
Mean length3.3571429
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대부업 23
82.1%
대부중개업 5
 
17.9%

Length

2023-12-13T06:44:17.669986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:44:17.764989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대부업 23
82.1%
대부중개업 5
 
17.9%

상호
Text

Distinct24
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-12-13T06:44:17.911788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12.5
Mean length10.571429
Min length4

Characters and Unicode

Total characters296
Distinct characters83
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

Unique20 ?
Unique (%)71.4%

Sample

1st row스피드론 캐쉬 대부
2nd row주식회사 포인트자산관리대부
3rd row주식회사 제트앤아이대부파트너스
4th row주식회사 엘앤피캐피탈대부
5th row주식회사 엘앤피캐피탈대부
ValueCountFrequency (%)
주식회사 13
28.3%
대부 3
 
6.5%
주)위드미캐피탈대부 2
 
4.3%
so(에스오)캐피탈대부 2
 
4.3%
준파트너스대부 2
 
4.3%
엘앤피캐피탈대부 2
 
4.3%
트레비홀딩스 1
 
2.2%
캐쉬 1
 
2.2%
스피드론 1
 
2.2%
용산대부 1
 
2.2%
Other values (18) 18
39.1%
2023-12-13T06:44:18.213563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
대 28
 
9.5%
부 28
 
9.5%
18
 
6.1%
주 17
 
5.7%
사 15
 
5.1%
회 14
 
4.7%
식 14
 
4.7%
스 11
 
3.7%
피 10
 
3.4%
트 9
 
3.0%
Other values (73) 132
44.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 264
89.2%
Space Separator 18
 
6.1%
Uppercase Letter 6
 
2.0%
Close Punctuation 4
 
1.4%
Open Punctuation 4
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 28
 
10.6%
부 28
 
10.6%
주 17
 
6.4%
사 15
 
5.7%
회 14
 
5.3%
식 14
 
5.3%
스 11
 
4.2%
피 10
 
3.8%
트 9
 
3.4%
캐 8
 
3.0%
Other values (66) 110
41.7%
Uppercase Letter
ValueCountFrequency (%)
O 2
33.3%
S 2
33.3%
K 1
16.7%
W 1
16.7%
Space Separator
ValueCountFrequency (%)
18
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 264
89.2%
Common 26
 
8.8%
Latin 6
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 28
 
10.6%
부 28
 
10.6%
주 17
 
6.4%
사 15
 
5.7%
회 14
 
5.3%
식 14
 
5.3%
스 11
 
4.2%
피 10
 
3.8%
트 9
 
3.4%
캐 8
 
3.0%
Other values (66) 110
41.7%
Latin
ValueCountFrequency (%)
O 2
33.3%
S 2
33.3%
K 1
16.7%
W 1
16.7%
Common
ValueCountFrequency (%)
18
69.2%
) 4
 
15.4%
( 4
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 264
89.2%
ASCII 32
 
10.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
대 28
 
10.6%
부 28
 
10.6%
주 17
 
6.4%
사 15
 
5.7%
회 14
 
5.3%
식 14
 
5.3%
스 11
 
4.2%
피 10
 
3.8%
트 9
 
3.4%
캐 8
 
3.0%
Other values (66) 110
41.7%
ASCII
ValueCountFrequency (%)
18
56.2%
) 4
 
12.5%
( 4
 
12.5%
O 2
 
6.2%
S 2
 
6.2%
K 1
 
3.1%
W 1
 
3.1%

법인여부
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size356.0 B
법인
16 
개인
12 

Length

Max length3
Median length2
Mean length2.4285714
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row개인
2nd row법인
3rd row법인
4th row법인
5th row법인

Common Values

ValueCountFrequency (%)
법인 16
57.1%
개인 12
42.9%

Length

2023-12-13T06:44:18.343027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:44:18.462481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
법인 16
57.1%
개인 12
42.9%

사업장 전화번호
Text

MISSING 

Distinct15
Distinct (%)83.3%
Missing10
Missing (%)35.7%
Memory size356.0 B
2023-12-13T06:44:18.604024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.611111
Min length9

Characters and Unicode

Total characters209
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

Unique12 ?
Unique (%)66.7%

Sample

1st row080-4466-8700
2nd row02-794-0655
3rd row070-8220-2990
4th row070-8220-2990
5th row1833-4437
ValueCountFrequency (%)
070-8220-2990 2
 
11.1%
02-3785-0777 2
 
11.1%
070-4152-0088 2
 
11.1%
080-4466-8700 1
 
5.6%
02-794-0655 1
 
5.6%
1833-4437 1
 
5.6%
1600-1491 1
 
5.6%
02-542-2570 1
 
5.6%
02-6324-0022 1
 
5.6%
02-718-1332 1
 
5.6%
Other values (5) 5
27.8%
2023-12-13T06:44:19.163166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 45
21.5%
- 34
16.3%
2 25
12.0%
7 22
10.5%
8 17
 
8.1%
3 14
 
6.7%
4 13
 
6.2%
9 11
 
5.3%
5 11
 
5.3%
1 10
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 175
83.7%
Dash Punctuation 34
 
16.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 45
25.7%
2 25
14.3%
7 22
12.6%
8 17
 
9.7%
3 14
 
8.0%
4 13
 
7.4%
9 11
 
6.3%
5 11
 
6.3%
1 10
 
5.7%
6 7
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 209
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 45
21.5%
- 34
16.3%
2 25
12.0%
7 22
10.5%
8 17
 
8.1%
3 14
 
6.7%
4 13
 
6.2%
9 11
 
5.3%
5 11
 
5.3%
1 10
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 209
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 45
21.5%
- 34
16.3%
2 25
12.0%
7 22
10.5%
8 17
 
8.1%
3 14
 
6.7%
4 13
 
6.2%
9 11
 
5.3%
5 11
 
5.3%
1 10
 
4.8%

소재지(도로명)
Text

Distinct24
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-12-13T06:44:19.449362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length43
Mean length37.25
Min length27

Characters and Unicode

Total characters1043
Distinct characters97
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)71.4%

Sample

1st row서울특별시 용산구 원효로 211 501호 (원효로2가 원효오피스텔)
2nd row서울특별시 용산구 우사단로 2-1 5층 (보광동)
3rd row서울특별시 용산구 한강대로 95 408호 (한강로2가 래미안용산 더 센트럴)
4th row서울특별시 용산구 한강대로39길 34-4 위너스타워 제8층 제805호 (한강로2가)
5th row서울특별시 용산구 한강대로39길 34-4 위너스타워 제8층 제805호 (한강로2가)
ValueCountFrequency (%)
서울특별시 28
 
14.0%
용산구 28
 
14.0%
한강대로 6
 
3.0%
한강로2가 5
 
2.5%
이촌로 4
 
2.0%
갈월동 4
 
2.0%
한남동 3
 
1.5%
202호 3
 
1.5%
한강로3가 3
 
1.5%
고려에이트리움 3
 
1.5%
Other values (85) 113
56.5%
2023-12-13T06:44:19.839552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
172
 
16.5%
1 42
 
4.0%
로 42
 
4.0%
2 39
 
3.7%
산 33
 
3.2%
용 32
 
3.1%
한 30
 
2.9%
서 29
 
2.8%
특 28
 
2.7%
별 28
 
2.7%
Other values (87) 568
54.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 606
58.1%
Decimal Number 197
 
18.9%
Space Separator 172
 
16.5%
Open Punctuation 28
 
2.7%
Close Punctuation 28
 
2.7%
Dash Punctuation 8
 
0.8%
Uppercase Letter 4
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 42
 
6.9%
산 33
 
5.4%
용 32
 
5.3%
한 30
 
5.0%
서 29
 
4.8%
특 28
 
4.6%
별 28
 
4.6%
시 28
 
4.6%
구 28
 
4.6%
울 28
 
4.6%
Other values (72) 300
49.5%
Decimal Number
ValueCountFrequency (%)
1 42
21.3%
2 39
19.8%
0 24
12.2%
3 20
10.2%
5 16
 
8.1%
4 15
 
7.6%
6 14
 
7.1%
8 13
 
6.6%
9 13
 
6.6%
7 1
 
0.5%
Space Separator
ValueCountFrequency (%)
172
100.0%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 606
58.1%
Common 433
41.5%
Latin 4
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 42
 
6.9%
산 33
 
5.4%
용 32
 
5.3%
한 30
 
5.0%
서 29
 
4.8%
특 28
 
4.6%
별 28
 
4.6%
시 28
 
4.6%
구 28
 
4.6%
울 28
 
4.6%
Other values (72) 300
49.5%
Common
ValueCountFrequency (%)
172
39.7%
1 42
 
9.7%
2 39
 
9.0%
( 28
 
6.5%
) 28
 
6.5%
0 24
 
5.5%
3 20
 
4.6%
5 16
 
3.7%
4 15
 
3.5%
6 14
 
3.2%
Other values (4) 35
 
8.1%
Latin
ValueCountFrequency (%)
B 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 606
58.1%
ASCII 437
41.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
172
39.4%
1 42
 
9.6%
2 39
 
8.9%
( 28
 
6.4%
) 28
 
6.4%
0 24
 
5.5%
3 20
 
4.6%
5 16
 
3.7%
4 15
 
3.4%
6 14
 
3.2%
Other values (5) 39
 
8.9%
Hangul
ValueCountFrequency (%)
로 42
 
6.9%
산 33
 
5.4%
용 32
 
5.3%
한 30
 
5.0%
서 29
 
4.8%
특 28
 
4.6%
별 28
 
4.6%
시 28
 
4.6%
구 28
 
4.6%
울 28
 
4.6%
Other values (72) 300
49.5%

등록일자
Date

MISSING 

Distinct23
Distinct (%)85.2%
Missing1
Missing (%)3.6%
Memory size356.0 B
Minimum2009-07-30 00:00:00
Maximum2023-05-04 00:00:00
2023-12-13T06:44:19.978660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:44:20.108100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)

관리기관
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size356.0 B
용산구청 지역경제과
27 
<NA>
 
1

Length

Max length10
Median length10
Mean length9.7857143
Min length4

Unique

Unique1 ?
Unique (%)3.6%

Sample

1st row용산구청 지역경제과
2nd row용산구청 지역경제과
3rd row용산구청 지역경제과
4th row용산구청 지역경제과
5th row용산구청 지역경제과

Common Values

ValueCountFrequency (%)
용산구청 지역경제과 27
96.4%
<NA> 1
 
3.6%

Length

2023-12-13T06:44:20.228748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:44:20.322063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
용산구청 27
49.1%
지역경제과 27
49.1%
na 1
 
1.8%

Interactions

2023-12-13T06:44:16.821563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:44:20.391099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번등록신청사업상호법인여부사업장 전화번호소재지(도로명)등록일자
순번1.0000.0001.0000.7221.0001.0001.000
등록신청사업0.0001.0000.0000.0000.0000.0000.000
상호1.0000.0001.0001.0001.0001.0001.000
법인여부0.7220.0001.0001.0001.0001.0001.000
사업장 전화번호1.0000.0001.0001.0001.0001.0001.000
소재지(도로명)1.0000.0001.0001.0001.0001.0001.000
등록일자1.0000.0001.0001.0001.0001.0001.000
2023-12-13T06:44:20.486701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록신청사업관리기관법인여부
등록신청사업1.0001.0000.000
관리기관1.0001.0001.000
법인여부0.0001.0001.000
2023-12-13T06:44:20.587942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번등록신청사업법인여부관리기관
순번1.0000.0000.4611.000
등록신청사업0.0001.0000.0001.000
법인여부0.4610.0001.0001.000
관리기관1.0001.0001.0001.000

Missing values

2023-12-13T06:44:16.923373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:44:17.056597image/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.
2023-12-13T06:44:17.154105image/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

순번시군구명등록신청사업상호법인여부사업장 전화번호소재지(도로명)등록일자관리기관
01서울특별시 용산구대부업스피드론 캐쉬 대부개인080-4466-8700서울특별시 용산구 원효로 211 501호 (원효로2가 원효오피스텔)2023-05-04용산구청 지역경제과
12서울특별시 용산구대부업주식회사 포인트자산관리대부법인<NA>서울특별시 용산구 우사단로 2-1 5층 (보광동)2023-04-05용산구청 지역경제과
23서울특별시 용산구대부업주식회사 제트앤아이대부파트너스법인02-794-0655서울특별시 용산구 한강대로 95 408호 (한강로2가 래미안용산 더 센트럴)2023-01-02용산구청 지역경제과
34서울특별시 용산구대부중개업주식회사 엘앤피캐피탈대부법인070-8220-2990서울특별시 용산구 한강대로39길 34-4 위너스타워 제8층 제805호 (한강로2가)2022-12-09용산구청 지역경제과
45서울특별시 용산구대부업주식회사 엘앤피캐피탈대부법인070-8220-2990서울특별시 용산구 한강대로39길 34-4 위너스타워 제8층 제805호 (한강로2가)2022-12-09용산구청 지역경제과
56서울특별시 용산구대부업아주대부개인<NA>서울특별시 용산구 한강대로52길 39 202호 (한강로1가)2020-07-13용산구청 지역경제과
67서울특별시 용산구대부중개업주식회사 준파트너스대부법인<NA>서울특별시 용산구 한강대로 259 고려에이트리움 16층 1616호 (갈월동)2022-02-11용산구청 지역경제과
78서울특별시 용산구대부업주식회사 준파트너스대부법인<NA>서울특별시 용산구 한강대로 259 고려에이트리움 16층 1616호 (갈월동)2022-02-11용산구청 지역경제과
89서울특별시 용산구대부업씨브이씨대부주식회사법인1833-4437서울특별시 용산구 한강대로 296 참빛빌딩 3층 (남영동)2022-01-25용산구청 지역경제과
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순번시군구명등록신청사업상호법인여부사업장 전화번호소재지(도로명)등록일자관리기관
1819서울특별시 용산구대부업주식회사 갓파더대부법인070-8688-0251서울특별시 용산구 청파로 213 105호 (문배동 이안용산프리미어)2020-12-01용산구청 지역경제과
1920서울특별시 용산구대부업주식회사 골드만인베스트먼트대부법인02-733-9810서울특별시 용산구 한강대로44길 19 201호 (한강로2가)2020-11-02용산구청 지역경제과
2021서울특별시 용산구대부중개업SO(에스오)캐피탈대부개인02-3785-0777서울특별시 용산구 유엔빌리지길 188-12 B01호 (한남동)2020-06-19용산구청 지역경제과
2122서울특별시 용산구대부업SO(에스오)캐피탈대부개인02-3785-0777서울특별시 용산구 유엔빌리지길 188-12 B01호 (한남동)2020-06-19용산구청 지역경제과
2223서울특별시 용산구대부중개업(주)위드미캐피탈대부법인070-4152-0088서울특별시 용산구 이촌로 5 603호 (한강로3가 한강그랜드오피스텔)2018-10-29용산구청 지역경제과
2324서울특별시 용산구대부업(주)위드미캐피탈대부법인070-4152-0088서울특별시 용산구 이촌로 5 603호 (한강로3가 한강그랜드오피스텔)2018-10-29용산구청 지역경제과
2425서울특별시 용산구대부업월드컨설팅 대부개인02-704-9933서울특별시 용산구 원효로41가길 19 (원효로3가)2013-02-05용산구청 지역경제과
2526서울특별시 용산구대부업신진대부개인02-795-4358서울특별시 용산구 한강대로43길 8 103동 806호 (한강로2가 벽산메가트리움)2011-05-12용산구청 지역경제과
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2728서울특별시 용산구대부업로얄사대부업개인02-793-6400서울특별시 용산구 이촌로 245 203호 (이촌동 로얄상가)<NA><NA>