Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells14449
Missing cells (%)10.3%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory1.2 MiB
Average record size in memory125.0 B

Variable types

Categorical4
Text5
Numeric5

Dataset

Description등록신청사업,영업구분,등록증번호,상호,법인여부,사업장 전화번호,소재지,우편번호,등록일자,유효기간만료일자,폐쇄일자,지점설립일자,본점여부,최근수정일자
Author서대문구
URLhttps://data.seoul.go.kr/dataList/OA-2519/S/1/datasetView.do

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
등록일자 is highly overall correlated with 유효기간만료일자 and 2 other fieldsHigh correlation
유효기간만료일자 is highly overall correlated with 등록일자 and 2 other fieldsHigh correlation
폐쇄일자 is highly overall correlated with 등록일자 and 2 other fieldsHigh correlation
최근수정일자 is highly overall correlated with 등록일자 and 2 other fieldsHigh correlation
본점여부 is highly imbalanced (94.3%)Imbalance
등록증번호 has 191 (1.9%) missing valuesMissing
사업장 전화번호 has 3447 (34.5%) missing valuesMissing
소재지 has 335 (3.4%) missing valuesMissing
우편번호 has 5622 (56.2%) missing valuesMissing
유효기간만료일자 has 2037 (20.4%) missing valuesMissing
폐쇄일자 has 1570 (15.7%) missing valuesMissing
지점설립일자 has 1247 (12.5%) missing valuesMissing

Reproduction

Analysis started2024-05-04 05:37:34.398529
Analysis finished2024-05-04 05:37:45.932068
Duration11.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대부업
6208 
대부중개업
3374 
<NA>
 
418

Length

Max length5
Median length3
Mean length3.7166
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대부업 6208
62.1%
대부중개업 3374
33.7%
<NA> 418
 
4.2%

Length

2024-05-04T05:37:46.205538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T05:37:46.543063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대부업 6208
62.1%
대부중개업 3374
33.7%
na 418
 
4.2%

영업구분
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
3738 
<NA>
2851 
타시군구이관
1230 
영업중
850 
유효기간만료
803 
Other values (2)
528 

Length

Max length6
Median length4
Mean length3.575
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row유효기간만료
2nd row타시군구이관
3rd row폐업
4th row직권취소
5th row<NA>

Common Values

ValueCountFrequency (%)
폐업 3738
37.4%
<NA> 2851
28.5%
타시군구이관 1230
 
12.3%
영업중 850
 
8.5%
유효기간만료 803
 
8.0%
직권취소 523
 
5.2%
갱신등록불가 5
 
0.1%

Length

2024-05-04T05:37:46.928882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T05:37:47.256669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 3738
37.4%
na 2851
28.5%
타시군구이관 1230
 
12.3%
영업중 850
 
8.5%
유효기간만료 803
 
8.0%
직권취소 523
 
5.2%
갱신등록불가 5
 
< 0.1%

등록증번호
Text

MISSING 

Distinct9759
Distinct (%)99.5%
Missing191
Missing (%)1.9%
Memory size156.2 KiB
2024-05-04T05:37:47.635849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length25
Mean length19.500459
Min length4

Characters and Unicode

Total characters191280
Distinct characters68
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9709 ?
Unique (%)99.0%

Sample

1st row2009-서울특별시-00908(대부중개업)
2nd row2014-서울강북-0026
3rd row2012-서울서초-0201(대부업)
4th row2011-서울노원-00006(대부업)
5th row2010-서울노원-00016 (대부업)
ValueCountFrequency (%)
2011-서울특별시 19
 
0.2%
2013-서울특별시 17
 
0.2%
2010-서울 13
 
0.1%
2014-서울특별시 12
 
0.1%
대부업 12
 
0.1%
2015-서울특별시 11
 
0.1%
2012-서울특별시 10
 
0.1%
2016-서울특별시 9
 
0.1%
2017-서울특별시 8
 
0.1%
성북구-00009 5
 
0.1%
Other values (9717) 9842
98.8%
2024-05-04T05:37:48.321945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 33778
17.7%
- 19602
 
10.2%
2 15709
 
8.2%
1 11852
 
6.2%
10855
 
5.7%
9786
 
5.1%
8447
 
4.4%
( 8186
 
4.3%
8143
 
4.3%
) 8139
 
4.3%
Other values (58) 56783
29.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 82476
43.1%
Other Letter 72727
38.0%
Dash Punctuation 19602
 
10.2%
Open Punctuation 8186
 
4.3%
Close Punctuation 8139
 
4.3%
Space Separator 150
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10855
14.9%
9786
13.5%
8447
11.6%
8143
11.2%
7919
10.9%
3476
 
4.8%
2871
 
3.9%
2497
 
3.4%
2492
 
3.4%
2492
 
3.4%
Other values (44) 13749
18.9%
Decimal Number
ValueCountFrequency (%)
0 33778
41.0%
2 15709
19.0%
1 11852
 
14.4%
3 3706
 
4.5%
8 3097
 
3.8%
4 3045
 
3.7%
9 2882
 
3.5%
7 2848
 
3.5%
6 2813
 
3.4%
5 2746
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 19602
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8186
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8139
100.0%
Space Separator
ValueCountFrequency (%)
150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 118553
62.0%
Hangul 72727
38.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10855
14.9%
9786
13.5%
8447
11.6%
8143
11.2%
7919
10.9%
3476
 
4.8%
2871
 
3.9%
2497
 
3.4%
2492
 
3.4%
2492
 
3.4%
Other values (44) 13749
18.9%
Common
ValueCountFrequency (%)
0 33778
28.5%
- 19602
16.5%
2 15709
13.3%
1 11852
 
10.0%
( 8186
 
6.9%
) 8139
 
6.9%
3 3706
 
3.1%
8 3097
 
2.6%
4 3045
 
2.6%
9 2882
 
2.4%
Other values (4) 8557
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118553
62.0%
Hangul 72727
38.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33778
28.5%
- 19602
16.5%
2 15709
13.3%
1 11852
 
10.0%
( 8186
 
6.9%
) 8139
 
6.9%
3 3706
 
3.1%
8 3097
 
2.6%
4 3045
 
2.6%
9 2882
 
2.4%
Other values (4) 8557
 
7.2%
Hangul
ValueCountFrequency (%)
10855
14.9%
9786
13.5%
8447
11.6%
8143
11.2%
7919
10.9%
3476
 
4.8%
2871
 
3.9%
2497
 
3.4%
2492
 
3.4%
2492
 
3.4%
Other values (44) 13749
18.9%

상호
Text

Distinct8671
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-04T05:37:48.931394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length28
Mean length7.7563
Min length1

Characters and Unicode

Total characters77563
Distinct characters778
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

Unique7598 ?
Unique (%)76.0%

Sample

1st rowASSET ZONE 대부중개
2nd row골드라인(Goldline)대부
3rd row비앤비대부
4th row리드대부기획
5th row양주대부
ValueCountFrequency (%)
주식회사 840
 
7.0%
대부중개 326
 
2.7%
대부 268
 
2.2%
유한회사 50
 
0.4%
캐피탈 20
 
0.2%
대부업 20
 
0.2%
14
 
0.1%
loan 14
 
0.1%
대부중개업 13
 
0.1%
the 13
 
0.1%
Other values (8701) 10386
86.8%
2024-05-04T05:37:50.086043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8508
 
11.0%
8158
 
10.5%
2718
 
3.5%
2286
 
2.9%
2138
 
2.8%
2123
 
2.7%
1968
 
2.5%
) 1903
 
2.5%
( 1895
 
2.4%
1875
 
2.4%
Other values (768) 43991
56.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 67583
87.1%
Uppercase Letter 2438
 
3.1%
Space Separator 1968
 
2.5%
Close Punctuation 1903
 
2.5%
Open Punctuation 1895
 
2.4%
Lowercase Letter 1205
 
1.6%
Decimal Number 269
 
0.3%
Other Punctuation 256
 
0.3%
Dash Punctuation 30
 
< 0.1%
Other Symbol 12
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
8508
 
12.6%
8158
 
12.1%
2718
 
4.0%
2286
 
3.4%
2138
 
3.2%
2123
 
3.1%
1875
 
2.8%
1356
 
2.0%
1128
 
1.7%
1034
 
1.5%
Other values (693) 36259
53.7%
Uppercase Letter
ValueCountFrequency (%)
S 328
13.5%
K 210
 
8.6%
C 197
 
8.1%
M 180
 
7.4%
J 176
 
7.2%
H 126
 
5.2%
G 113
 
4.6%
B 110
 
4.5%
L 109
 
4.5%
N 102
 
4.2%
Other values (15) 787
32.3%
Lowercase Letter
ValueCountFrequency (%)
e 149
12.4%
n 141
11.7%
o 140
11.6%
a 109
 
9.0%
t 74
 
6.1%
i 74
 
6.1%
s 59
 
4.9%
l 57
 
4.7%
c 55
 
4.6%
r 49
 
4.1%
Other values (15) 298
24.7%
Decimal Number
ValueCountFrequency (%)
1 96
35.7%
2 43
16.0%
4 34
 
12.6%
9 22
 
8.2%
3 17
 
6.3%
5 15
 
5.6%
0 13
 
4.8%
7 11
 
4.1%
6 11
 
4.1%
8 7
 
2.6%
Other Punctuation
ValueCountFrequency (%)
. 129
50.4%
& 107
41.8%
? 8
 
3.1%
, 7
 
2.7%
* 2
 
0.8%
/ 1
 
0.4%
1
 
0.4%
@ 1
 
0.4%
Space Separator
ValueCountFrequency (%)
1968
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1903
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1895
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 30
100.0%
Other Symbol
ValueCountFrequency (%)
12
100.0%
Letter Number
ValueCountFrequency (%)
2
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 67588
87.1%
Common 6323
 
8.2%
Latin 3645
 
4.7%
Han 7
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
8508
 
12.6%
8158
 
12.1%
2718
 
4.0%
2286
 
3.4%
2138
 
3.2%
2123
 
3.1%
1875
 
2.8%
1356
 
2.0%
1128
 
1.7%
1034
 
1.5%
Other values (687) 36264
53.7%
Latin
ValueCountFrequency (%)
S 328
 
9.0%
K 210
 
5.8%
C 197
 
5.4%
M 180
 
4.9%
J 176
 
4.8%
e 149
 
4.1%
n 141
 
3.9%
o 140
 
3.8%
H 126
 
3.5%
G 113
 
3.1%
Other values (41) 1885
51.7%
Common
ValueCountFrequency (%)
1968
31.1%
) 1903
30.1%
( 1895
30.0%
. 129
 
2.0%
& 107
 
1.7%
1 96
 
1.5%
2 43
 
0.7%
4 34
 
0.5%
- 30
 
0.5%
9 22
 
0.3%
Other values (13) 96
 
1.5%
Han
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 67576
87.1%
ASCII 9965
 
12.8%
None 13
 
< 0.1%
CJK 7
 
< 0.1%
Number Forms 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
8508
 
12.6%
8158
 
12.1%
2718
 
4.0%
2286
 
3.4%
2138
 
3.2%
2123
 
3.1%
1875
 
2.8%
1356
 
2.0%
1128
 
1.7%
1034
 
1.5%
Other values (686) 36252
53.6%
ASCII
ValueCountFrequency (%)
1968
19.7%
) 1903
19.1%
( 1895
19.0%
S 328
 
3.3%
K 210
 
2.1%
C 197
 
2.0%
M 180
 
1.8%
J 176
 
1.8%
e 149
 
1.5%
n 141
 
1.4%
Other values (62) 2818
28.3%
None
ValueCountFrequency (%)
12
92.3%
1
 
7.7%
Number Forms
ValueCountFrequency (%)
2
100.0%
CJK
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%

법인여부
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
개인
7183 
법인
2817 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
개인 7183
71.8%
법인 2817
 
28.2%

Length

2024-05-04T05:37:50.369030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T05:37:50.534879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 7183
71.8%
법인 2817
 
28.2%
Distinct5797
Distinct (%)88.5%
Missing3447
Missing (%)34.5%
Memory size156.2 KiB
2024-05-04T05:37:50.896688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length40
Mean length10.586602
Min length1

Characters and Unicode

Total characters69374
Distinct characters27
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5181 ?
Unique (%)79.1%

Sample

1st row02-903-4200
2nd row없음
3rd row0216881147
4th row02-785-2017
5th row024039851
ValueCountFrequency (%)
02 278
 
3.8%
66
 
0.9%
070 38
 
0.5%
010 11
 
0.1%
432 9
 
0.1%
1599 6
 
0.1%
1566 6
 
0.1%
703 5
 
0.1%
2212 5
 
0.1%
63880505 5
 
0.1%
Other values (6099) 6913
94.2%
2024-05-04T05:37:51.764834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11320
16.3%
2 10217
14.7%
- 7065
10.2%
5 5829
8.4%
7 5443
7.8%
1 5122
7.4%
6 5040
7.3%
3 4811
6.9%
4 4751
6.8%
8 4651
6.7%
Other values (17) 5125
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 61147
88.1%
Dash Punctuation 7065
 
10.2%
Space Separator 869
 
1.3%
Other Punctuation 166
 
0.2%
Close Punctuation 65
 
0.1%
Math Symbol 31
 
< 0.1%
Open Punctuation 22
 
< 0.1%
Other Letter 5
 
< 0.1%
Uppercase Letter 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11320
18.5%
2 10217
16.7%
5 5829
9.5%
7 5443
8.9%
1 5122
8.4%
6 5040
8.2%
3 4811
7.9%
4 4751
7.8%
8 4651
7.6%
9 3963
 
6.5%
Other Letter
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Other Punctuation
ValueCountFrequency (%)
* 106
63.9%
/ 40
 
24.1%
. 20
 
12.0%
Uppercase Letter
ValueCountFrequency (%)
K 2
50.0%
T 1
25.0%
S 1
25.0%
Math Symbol
ValueCountFrequency (%)
~ 30
96.8%
× 1
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 7065
100.0%
Space Separator
ValueCountFrequency (%)
869
100.0%
Close Punctuation
ValueCountFrequency (%)
) 65
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69365
> 99.9%
Hangul 5
 
< 0.1%
Latin 4
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11320
16.3%
2 10217
14.7%
- 7065
10.2%
5 5829
8.4%
7 5443
7.8%
1 5122
7.4%
6 5040
7.3%
3 4811
6.9%
4 4751
6.8%
8 4651
6.7%
Other values (9) 5116
7.4%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Latin
ValueCountFrequency (%)
K 2
50.0%
T 1
25.0%
S 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69368
> 99.9%
Hangul 5
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11320
16.3%
2 10217
14.7%
- 7065
10.2%
5 5829
8.4%
7 5443
7.8%
1 5122
7.4%
6 5040
7.3%
3 4811
6.9%
4 4751
6.8%
8 4651
6.7%
Other values (11) 5119
7.4%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
None
ValueCountFrequency (%)
× 1
100.0%

소재지
Text

MISSING 

Distinct8622
Distinct (%)89.2%
Missing335
Missing (%)3.4%
Memory size156.2 KiB
2024-05-04T05:37:52.330393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length65
Median length50
Mean length31.442421
Min length15

Characters and Unicode

Total characters303891
Distinct characters633
Distinct categories13 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7874 ?
Unique (%)81.5%

Sample

1st row서울특별시 중랑구 중화동 306-41
2nd row서울특별시 강북구 수유동 229번지 18호 -205
3rd row서울특별시 서초구 잠원동 44번지 20호 B103호
4th row서울특별시 노원구 공릉동 744번지 건영장미아파트 101-110
5th row서울특별시 노원구 하계동 256번지 5호 1 한신아파트-606
ValueCountFrequency (%)
서울특별시 9661
 
17.0%
강남구 1657
 
2.9%
서초구 939
 
1.6%
역삼동 734
 
1.3%
1호 722
 
1.3%
송파구 590
 
1.0%
서초동 578
 
1.0%
중구 530
 
0.9%
2호 453
 
0.8%
강북구 434
 
0.8%
Other values (9424) 40615
71.4%
2024-05-04T05:37:53.166993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
67464
22.2%
1 13511
 
4.4%
12022
 
4.0%
11130
 
3.7%
10430
 
3.4%
9922
 
3.3%
9705
 
3.2%
9668
 
3.2%
9662
 
3.2%
2 8622
 
2.8%
Other values (623) 141755
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 166035
54.6%
Space Separator 67464
22.2%
Decimal Number 63229
 
20.8%
Dash Punctuation 5452
 
1.8%
Uppercase Letter 1113
 
0.4%
Other Punctuation 235
 
0.1%
Lowercase Letter 144
 
< 0.1%
Close Punctuation 98
 
< 0.1%
Open Punctuation 94
 
< 0.1%
Letter Number 18
 
< 0.1%
Other values (3) 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
12022
 
7.2%
11130
 
6.7%
10430
 
6.3%
9922
 
6.0%
9705
 
5.8%
9668
 
5.8%
9662
 
5.8%
8501
 
5.1%
8402
 
5.1%
7924
 
4.8%
Other values (545) 68669
41.4%
Uppercase Letter
ValueCountFrequency (%)
B 271
24.3%
A 217
19.5%
S 80
 
7.2%
D 71
 
6.4%
T 52
 
4.7%
I 48
 
4.3%
K 47
 
4.2%
C 42
 
3.8%
L 34
 
3.1%
G 34
 
3.1%
Other values (16) 217
19.5%
Lowercase Letter
ValueCountFrequency (%)
e 30
20.8%
n 13
9.0%
r 12
 
8.3%
i 12
 
8.3%
l 9
 
6.2%
o 8
 
5.6%
w 8
 
5.6%
t 7
 
4.9%
a 7
 
4.9%
k 6
 
4.2%
Other values (12) 32
22.2%
Decimal Number
ValueCountFrequency (%)
1 13511
21.4%
2 8622
13.6%
0 7923
12.5%
3 6984
11.0%
4 5800
9.2%
5 4970
 
7.9%
6 4591
 
7.3%
7 4072
 
6.4%
8 3410
 
5.4%
9 3346
 
5.3%
Other Punctuation
ValueCountFrequency (%)
, 85
36.2%
/ 83
35.3%
. 61
26.0%
; 2
 
0.9%
1
 
0.4%
& 1
 
0.4%
* 1
 
0.4%
@ 1
 
0.4%
Letter Number
ValueCountFrequency (%)
12
66.7%
4
 
22.2%
2
 
11.1%
Math Symbol
ValueCountFrequency (%)
~ 5
71.4%
> 1
 
14.3%
< 1
 
14.3%
Space Separator
ValueCountFrequency (%)
67464
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5452
100.0%
Close Punctuation
ValueCountFrequency (%)
) 98
100.0%
Open Punctuation
ValueCountFrequency (%)
( 94
100.0%
Other Number
ValueCountFrequency (%)
½ 1
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 166034
54.6%
Common 136580
44.9%
Latin 1275
 
0.4%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
12022
 
7.2%
11130
 
6.7%
10430
 
6.3%
9922
 
6.0%
9705
 
5.8%
9668
 
5.8%
9662
 
5.8%
8501
 
5.1%
8402
 
5.1%
7924
 
4.8%
Other values (544) 68668
41.4%
Latin
ValueCountFrequency (%)
B 271
21.3%
A 217
17.0%
S 80
 
6.3%
D 71
 
5.6%
T 52
 
4.1%
I 48
 
3.8%
K 47
 
3.7%
C 42
 
3.3%
L 34
 
2.7%
G 34
 
2.7%
Other values (41) 379
29.7%
Common
ValueCountFrequency (%)
67464
49.4%
1 13511
 
9.9%
2 8622
 
6.3%
0 7923
 
5.8%
3 6984
 
5.1%
4 5800
 
4.2%
- 5452
 
4.0%
5 4970
 
3.6%
6 4591
 
3.4%
7 4072
 
3.0%
Other values (16) 7191
 
5.3%
Han
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 166033
54.6%
ASCII 137835
45.4%
Number Forms 18
 
< 0.1%
None 3
 
< 0.1%
CJK 1
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
67464
48.9%
1 13511
 
9.8%
2 8622
 
6.3%
0 7923
 
5.7%
3 6984
 
5.1%
4 5800
 
4.2%
- 5452
 
4.0%
5 4970
 
3.6%
6 4591
 
3.3%
7 4072
 
3.0%
Other values (62) 8446
 
6.1%
Hangul
ValueCountFrequency (%)
12022
 
7.2%
11130
 
6.7%
10430
 
6.3%
9922
 
6.0%
9705
 
5.8%
9668
 
5.8%
9662
 
5.8%
8501
 
5.1%
8402
 
5.1%
7924
 
4.8%
Other values (543) 68667
41.4%
Number Forms
ValueCountFrequency (%)
12
66.7%
4
 
22.2%
2
 
11.1%
None
ValueCountFrequency (%)
½ 1
33.3%
1
33.3%
1
33.3%
CJK
ValueCountFrequency (%)
1
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
1
100.0%

우편번호
Real number (ℝ)

MISSING 

Distinct1365
Distinct (%)31.2%
Missing5622
Missing (%)56.2%
Infinite0
Infinite (%)0.0%
Mean136286.02
Minimum2519
Maximum429842
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:37:53.552542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2519
5-th percentile100864.85
Q1132020
median136066
Q3142890.75
95-th percentile157031
Maximum429842
Range427323
Interquartile range (IQR)10870.75

Descriptive statistics

Standard deviation15757.031
Coefficient of variation (CV)0.11561737
Kurtosis55.070428
Mean136286.02
Median Absolute Deviation (MAD)5222.5
Skewness1.3221873
Sum5.966602 × 108
Variance2.4828401 × 108
MonotonicityNot monotonic
2024-05-04T05:37:53.958319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135080 155
 
1.6%
137070 130
 
1.3%
157010 64
 
0.6%
135010 57
 
0.6%
142070 49
 
0.5%
151015 48
 
0.5%
158070 46
 
0.5%
152050 38
 
0.4%
158050 37
 
0.4%
135090 35
 
0.4%
Other values (1355) 3719
37.2%
(Missing) 5622
56.2%
ValueCountFrequency (%)
2519 1
< 0.1%
4537 1
< 0.1%
4538 1
< 0.1%
4550 1
< 0.1%
5510 1
< 0.1%
7220 1
< 0.1%
14538 1
< 0.1%
100011 2
< 0.1%
100012 2
< 0.1%
100013 2
< 0.1%
ValueCountFrequency (%)
429842 1
 
< 0.1%
410380 1
 
< 0.1%
158881 1
 
< 0.1%
158877 1
 
< 0.1%
158871 2
 
< 0.1%
158864 2
 
< 0.1%
158860 6
0.1%
158859 4
< 0.1%
158857 1
 
< 0.1%
158849 1
 
< 0.1%

등록일자
Real number (ℝ)

HIGH CORRELATION 

Distinct3530
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20136967
Minimum20060308
Maximum20240503
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:37:54.356559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20060308
5-th percentile20070906
Q120091128
median20130328
Q320170727
95-th percentile20230216
Maximum20240503
Range180195
Interquartile range (IQR)79599

Descriptive statistics

Standard deviation48878.733
Coefficient of variation (CV)0.0024273136
Kurtosis-0.91526932
Mean20136967
Median Absolute Deviation (MAD)39619.5
Skewness0.44708488
Sum2.0136967 × 1011
Variance2.3891306 × 109
MonotonicityNot monotonic
2024-05-04T05:37:54.796012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20080731 30
 
0.3%
20080814 28
 
0.3%
20081222 21
 
0.2%
20080818 19
 
0.2%
20090514 16
 
0.2%
20090213 16
 
0.2%
20090325 15
 
0.1%
20080822 14
 
0.1%
20080806 14
 
0.1%
20080424 14
 
0.1%
Other values (3520) 9813
98.1%
ValueCountFrequency (%)
20060308 1
 
< 0.1%
20060310 1
 
< 0.1%
20060323 3
< 0.1%
20060324 1
 
< 0.1%
20060327 1
 
< 0.1%
20060405 2
 
< 0.1%
20060407 6
0.1%
20060410 1
 
< 0.1%
20060418 3
< 0.1%
20060425 2
 
< 0.1%
ValueCountFrequency (%)
20240503 1
 
< 0.1%
20240430 3
< 0.1%
20240426 1
 
< 0.1%
20240425 3
< 0.1%
20240424 4
< 0.1%
20240422 3
< 0.1%
20240419 3
< 0.1%
20240418 1
 
< 0.1%
20240416 1
 
< 0.1%
20240415 1
 
< 0.1%

유효기간만료일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3294
Distinct (%)41.4%
Missing2037
Missing (%)20.4%
Infinite0
Infinite (%)0.0%
Mean20181471
Minimum20091116
Maximum20270503
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:37:55.176769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20091116
5-th percentile20120327
Q120141029
median20180105
Q320220103
95-th percentile20260502
Maximum20270503
Range179387
Interquartile range (IQR)79074

Descriptive statistics

Standard deviation44379.429
Coefficient of variation (CV)0.0021990186
Kurtosis-0.98230882
Mean20181471
Median Absolute Deviation (MAD)39078
Skewness0.31727725
Sum1.6070505 × 1011
Variance1.9695337 × 109
MonotonicityNot monotonic
2024-05-04T05:37:55.454270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20110831 18
 
0.2%
20140831 12
 
0.1%
20190705 12
 
0.1%
20140711 11
 
0.1%
20190720 11
 
0.1%
20140328 11
 
0.1%
20190711 10
 
0.1%
20170922 10
 
0.1%
20231204 10
 
0.1%
20120514 10
 
0.1%
Other values (3284) 7848
78.5%
(Missing) 2037
 
20.4%
ValueCountFrequency (%)
20091116 2
< 0.1%
20100122 1
< 0.1%
20100323 1
< 0.1%
20100405 1
< 0.1%
20100410 1
< 0.1%
20100418 1
< 0.1%
20100419 1
< 0.1%
20100426 1
< 0.1%
20100501 1
< 0.1%
20100522 2
< 0.1%
ValueCountFrequency (%)
20270503 1
 
< 0.1%
20270430 3
< 0.1%
20270426 1
 
< 0.1%
20270425 3
< 0.1%
20270424 4
< 0.1%
20270422 1
 
< 0.1%
20270421 2
< 0.1%
20270419 3
< 0.1%
20270418 1
 
< 0.1%
20270416 1
 
< 0.1%

폐쇄일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3103
Distinct (%)36.8%
Missing1570
Missing (%)15.7%
Infinite0
Infinite (%)0.0%
Mean20142280
Minimum20060920
Maximum20240503
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:37:56.050018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20060920
5-th percentile20090908
Q120110413
median20130809
Q320170528
95-th percentile20220913
Maximum20240503
Range179583
Interquartile range (IQR)60115.25

Descriptive statistics

Standard deviation40853.74
Coefficient of variation (CV)0.0020282579
Kurtosis-0.59231199
Mean20142280
Median Absolute Deviation (MAD)29882
Skewness0.66376059
Sum1.6979942 × 1011
Variance1.669028 × 109
MonotonicityNot monotonic
2024-05-04T05:37:56.482369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20091116 220
 
2.2%
20100927 70
 
0.7%
20101213 22
 
0.2%
20170124 19
 
0.2%
20110425 19
 
0.2%
20110901 19
 
0.2%
20160725 18
 
0.2%
20110420 17
 
0.2%
20101126 15
 
0.1%
20121218 15
 
0.1%
Other values (3093) 7996
80.0%
(Missing) 1570
 
15.7%
ValueCountFrequency (%)
20060920 2
< 0.1%
20071030 1
 
< 0.1%
20081023 1
 
< 0.1%
20090219 1
 
< 0.1%
20090305 1
 
< 0.1%
20090309 2
< 0.1%
20090311 4
< 0.1%
20090312 2
< 0.1%
20090313 3
< 0.1%
20090316 2
< 0.1%
ValueCountFrequency (%)
20240503 1
 
< 0.1%
20240501 1
 
< 0.1%
20240430 2
< 0.1%
20240426 1
 
< 0.1%
20240424 1
 
< 0.1%
20240423 1
 
< 0.1%
20240422 4
< 0.1%
20240419 1
 
< 0.1%
20240418 4
< 0.1%
20240417 2
< 0.1%

지점설립일자
Text

MISSING 

Distinct3570
Distinct (%)40.8%
Missing1247
Missing (%)12.5%
Memory size156.2 KiB
2024-05-04T05:37:57.128814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters70024
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1347 ?
Unique (%)15.4%

Sample

1st row20090528
2nd row20140703
3rd row20121113
4th row20110125
5th row20100222
ValueCountFrequency (%)
20090820 23
 
0.3%
20090514 21
 
0.2%
20090528 17
 
0.2%
20090611 16
 
0.2%
20090511 16
 
0.2%
20090821 15
 
0.2%
20090605 15
 
0.2%
20160720 14
 
0.2%
20100407 13
 
0.1%
20090529 13
 
0.1%
Other values (3560) 8590
98.1%
2024-05-04T05:37:57.947970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 22644
32.3%
2 15855
22.6%
1 14109
20.1%
3 2858
 
4.1%
9 2708
 
3.9%
7 2539
 
3.6%
6 2473
 
3.5%
5 2353
 
3.4%
4 2245
 
3.2%
8 2228
 
3.2%
Other values (7) 12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70012
> 99.9%
Space Separator 6
 
< 0.1%
Lowercase Letter 4
 
< 0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22644
32.3%
2 15855
22.6%
1 14109
20.2%
3 2858
 
4.1%
9 2708
 
3.9%
7 2539
 
3.6%
6 2473
 
3.5%
5 2353
 
3.4%
4 2245
 
3.2%
8 2228
 
3.2%
Lowercase Letter
ValueCountFrequency (%)
a 1
25.0%
y 1
25.0%
p 1
25.0%
r 1
25.0%
Uppercase Letter
ValueCountFrequency (%)
M 1
50.0%
A 1
50.0%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70018
> 99.9%
Latin 6
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22644
32.3%
2 15855
22.6%
1 14109
20.2%
3 2858
 
4.1%
9 2708
 
3.9%
7 2539
 
3.6%
6 2473
 
3.5%
5 2353
 
3.4%
4 2245
 
3.2%
8 2228
 
3.2%
Latin
ValueCountFrequency (%)
M 1
16.7%
a 1
16.7%
y 1
16.7%
A 1
16.7%
p 1
16.7%
r 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70024
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22644
32.3%
2 15855
22.6%
1 14109
20.1%
3 2858
 
4.1%
9 2708
 
3.9%
7 2539
 
3.6%
6 2473
 
3.5%
5 2353
 
3.4%
4 2245
 
3.2%
8 2228
 
3.2%
Other values (7) 12
 
< 0.1%

본점여부
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
본점
9935 
지점
 
65

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
본점 9935
99.4%
지점 65
 
0.7%

Length

2024-05-04T05:37:58.198496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T05:37:58.378975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
본점 9935
99.4%
지점 65
 
0.7%

최근수정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct3183
Distinct (%)31.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20153159
Minimum20090518
Maximum20240503
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:37:58.647597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20090518
5-th percentile20091117
Q120111017
median20140919
Q320190403
95-th percentile20231005
Maximum20240503
Range149985
Interquartile range (IQR)79386.25

Descriptive statistics

Standard deviation45828.165
Coefficient of variation (CV)0.0022739941
Kurtosis-1.0710815
Mean20153159
Median Absolute Deviation (MAD)30605
Skewness0.43336889
Sum2.0153159 × 1011
Variance2.1002207 × 109
MonotonicityNot monotonic
2024-05-04T05:37:58.917100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20091117 90
 
0.9%
20091118 56
 
0.6%
20100330 52
 
0.5%
20091116 50
 
0.5%
20090609 48
 
0.5%
20100927 41
 
0.4%
20100517 36
 
0.4%
20110425 35
 
0.4%
20130621 34
 
0.3%
20090611 29
 
0.3%
Other values (3173) 9529
95.3%
ValueCountFrequency (%)
20090518 1
 
< 0.1%
20090519 4
 
< 0.1%
20090521 5
 
0.1%
20090601 3
 
< 0.1%
20090602 3
 
< 0.1%
20090603 7
 
0.1%
20090604 22
0.2%
20090605 2
 
< 0.1%
20090608 3
 
< 0.1%
20090609 48
0.5%
ValueCountFrequency (%)
20240503 6
0.1%
20240502 5
0.1%
20240501 1
 
< 0.1%
20240430 5
0.1%
20240429 1
 
< 0.1%
20240426 2
 
< 0.1%
20240425 7
0.1%
20240424 5
0.1%
20240423 4
< 0.1%
20240422 9
0.1%

Interactions

2024-05-04T05:37:43.487989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:37:38.209626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:37:39.316340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:37:40.578007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:37:41.850814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:37:43.768600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:37:38.453469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:37:39.574177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:37:40.798600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:37:42.115223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:37:44.009717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:37:38.682343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:37:39.875916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:37:40.991698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:37:42.394051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:37:44.211278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:37:38.869431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:37:40.066895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:37:41.196339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:37:42.843916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:37:44.427448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:37:39.044680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:37:40.294139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:37:41.563910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:37:43.185820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T05:37:59.120058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록신청사업영업구분법인여부우편번호등록일자유효기간만료일자폐쇄일자본점여부최근수정일자
등록신청사업1.0000.1010.0300.0000.2420.1720.1800.0000.190
영업구분0.1011.0000.2920.0430.6240.6310.2050.0410.547
법인여부0.0300.2921.0000.0760.3730.3150.2940.1990.376
우편번호0.0000.0430.0761.0000.2350.2270.2050.0000.310
등록일자0.2420.6240.3730.2351.0000.9990.9340.0710.939
유효기간만료일자0.1720.6310.3150.2270.9991.0000.8320.0550.839
폐쇄일자0.1800.2050.2940.2050.9340.8321.0000.0550.988
본점여부0.0000.0410.1990.0000.0710.0550.0551.0000.112
최근수정일자0.1900.5470.3760.3100.9390.8390.9880.1121.000
2024-05-04T05:37:59.406700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영업구분법인여부본점여부등록신청사업
영업구분1.0000.2100.0290.072
법인여부0.2101.0000.1270.019
본점여부0.0290.1271.0000.000
등록신청사업0.0720.0190.0001.000
2024-05-04T05:37:59.682147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호등록일자유효기간만료일자폐쇄일자최근수정일자등록신청사업영업구분법인여부본점여부
우편번호1.0000.0270.0180.0480.0360.0000.0310.0420.000
등록일자0.0271.0000.9970.9620.9660.1860.3900.2860.055
유효기간만료일자0.0180.9971.0000.9640.9660.1320.3970.2420.042
폐쇄일자0.0480.9620.9641.0000.9910.1800.1190.2250.042
최근수정일자0.0360.9660.9660.9911.0000.1450.3080.2880.085
등록신청사업0.0000.1860.1320.1800.1451.0000.0720.0190.000
영업구분0.0310.3900.3970.1190.3080.0721.0000.2100.029
법인여부0.0420.2860.2420.2250.2880.0190.2101.0000.127
본점여부0.0000.0550.0420.0420.0850.0000.0290.1271.000

Missing values

2024-05-04T05:37:44.810843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T05:37:45.369360image/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.
2024-05-04T05:37:45.718238image/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

등록신청사업영업구분등록증번호상호법인여부사업장 전화번호소재지우편번호등록일자유효기간만료일자폐쇄일자지점설립일자본점여부최근수정일자
21583대부중개업유효기간만료2009-서울특별시-00908(대부중개업)ASSET ZONE 대부중개개인<NA>서울특별시 중랑구 중화동 306-41<NA>20090528201205282012052920090528본점20120529
13877대부업타시군구이관2014-서울강북-0026골드라인(Goldline)대부개인02-903-4200서울특별시 강북구 수유동 229번지 18호 -20514287820140703201707032015100620140703본점20151006
18933대부업폐업2012-서울서초-0201(대부업)비앤비대부개인<NA>서울특별시 서초구 잠원동 44번지 20호 B103호13703020121114201511142013050620121113본점20130506
18718대부업직권취소2011-서울노원-00006(대부업)리드대부기획개인<NA>서울특별시 노원구 공릉동 744번지 건영장미아파트 101-11013924020110125201401242013052920110125본점20130529
23775대부업<NA>2010-서울노원-00016 (대부업)양주대부개인<NA>서울특별시 노원구 하계동 256번지 5호 1 한신아파트-60613923020100222<NA>2011090720100222본점20110907
6584대부업폐업2016-서울송파-0015(대부업)윈윈베스트대부개인없음서울특별시 송파구 거여동 552번지 4호 -101<NA>20190214202202142020062920160404본점20200630
28394대부업<NA><NA>투데이대부금융개인0216881147서울특별시 관악구 신림동 1428번지 12호 대경빌딩 4층 408호15189120091204<NA>2010041920091204본점20100420
9586대부중개업<NA>2017-서울영등포-0931(대부중개업)주식회사 갓파더사대부법인02-785-2017서울특별시 영등포구 여의도동 26번지<NA>2017050220200502<NA>20170502본점20180115
15607대부업직권취소2013-서울구로-065(대부업)햇살대부개인<NA>서울특별시 구로구 신도림동 432번지 1호 101 신도림에스케이뷰-210215274920130911201609112014092320130911본점20140923
24400대부업<NA>2009-서울특별시-01510(대부업)파랑새개인024039851서울특별시 송파구 가락동 135-10번지 정진빌라-301<NA>20090424<NA>20110629<NA>본점20110630
등록신청사업영업구분등록증번호상호법인여부사업장 전화번호소재지우편번호등록일자유효기간만료일자폐쇄일자지점설립일자본점여부최근수정일자
18801대부업타시군구이관2008-서울특별시-00133(대부업)열린미래대부개인02-977-7879서울특별시 노원구 공릉동 404-24 2층<NA>20110624201406242013052020080814본점20130521
13684대부업폐업2015-서울광진-0030(대부업)와이에이대부개인02-545-1196<NA><NA>20150710201807102015112420150710본점20151124
11422대부중개업<NA>2015-서울송파-0049(대부중개업)(주)시소캐피탈대부중개법인1600-3017<NA>55102015062220180622<NA>20150622본점20170112
17527대부업폐업2012-서울중랑-0017(대부업)온누리대부개인<NA>서울특별시 중랑구 중화동 450번지 102 중화한신아파트-30513177220120312201503122013101020120312본점20131010
23691대부업<NA>2008-서울특별시-00764(대부업)베스트론센터개인025253469서울특별시 강남구 역삼동 603번지 7호 -70213508020080822201109142011091520050830본점20110916
4354대부업영업중2022-서울중랑-0011(대부업)가나할부금대부개인02-3423-2800서울특별시 중랑구 신내동 660번지 상가동 신내10단지신내아파트-207<NA>2022040420250404<NA>20190513본점20220512
7007대부업폐업2016-서울강남-0008(대부업)KM대부업개인<NA>서울특별시 강남구 역삼동 775번지 2호 초원빌딩-4695<NA>20181220202112202020011520160112본점20200115
14595대부업타시군구이관2014-서울강남-0086대부호자산관리(주)법인(02)514-0059서울특별시 강남구 신사동 664번지 7호13589720140522201705222015042720140522본점20150427
3559대부업타시군구이관2016-서울용산-00013오르피제펀샵대부개인02-792-2018<NA><NA>20220725202507252022112420160927본점20221124
5064대부중개업유효기간만료2018-서울강남-0193(대부중개업)(주)이피에스캐피탈대부법인02-538-5275서울특별시 강남구 역삼동 706번지 13호 윤익빌딩<NA>2018110920211109<NA>20181109본점20211110

Duplicate rows

Most frequently occurring

등록신청사업영업구분등록증번호상호법인여부사업장 전화번호소재지우편번호등록일자유효기간만료일자폐쇄일자지점설립일자본점여부최근수정일자# duplicates
0대부업폐업2006-서울특별시-00394(대부업)JSM캐피탈개인0222348157서울특별시 중구 신당동 236번지 89호<NA>20090818201208182006092020060907본점201201172