Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells19329
Missing cells (%)12.9%
Duplicate rows3
Duplicate rows (%)< 0.1%
Total size in memory1.3 MiB
Average record size in memory133.0 B

Variable types

Categorical4
Text6
Numeric5

Dataset

Description등록신청사업,영업구분,등록증번호,상호,법인여부,사업장 전화번호,소재지,소재지(도로명),우편번호,등록일자,유효기간만료일자,폐쇄일자,지점설립일자,본점여부,최근수정일자
Author종로구
URLhttps://data.seoul.go.kr/dataList/OA-9865/S/1/datasetView.do

Alerts

Dataset has 3 (< 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 189 (1.9%) missing valuesMissing
사업장 전화번호 has 3436 (34.4%) missing valuesMissing
소재지 has 312 (3.1%) missing valuesMissing
소재지(도로명) has 4794 (47.9%) missing valuesMissing
우편번호 has 5640 (56.4%) missing valuesMissing
유효기간만료일자 has 2078 (20.8%) missing valuesMissing
폐쇄일자 has 1604 (16.0%) missing valuesMissing
지점설립일자 has 1276 (12.8%) missing valuesMissing

Reproduction

Analysis started2024-05-11 07:03:22.526074
Analysis finished2024-05-11 07:03:32.528600
Duration10 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대부업
6268 
대부중개업
3313 
<NA>
 
419

Length

Max length5
Median length3
Mean length3.7045
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대부업 6268
62.7%
대부중개업 3313
33.1%
<NA> 419
 
4.2%

Length

2024-05-11T16:03:32.641622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T16:03:32.825006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대부업 6268
62.7%
대부중개업 3313
33.1%
na 419
 
4.2%

영업구분
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
3736 
<NA>
2903 
타시군구이관
1188 
영업중
833 
유효기간만료
780 
Other values (2)
560 

Length

Max length6
Median length4
Mean length3.5633
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row영업중
2nd row타시군구이관
3rd row직권취소
4th row폐업
5th row폐업

Common Values

ValueCountFrequency (%)
폐업 3736
37.4%
<NA> 2903
29.0%
타시군구이관 1188
 
11.9%
영업중 833
 
8.3%
유효기간만료 780
 
7.8%
직권취소 559
 
5.6%
갱신등록불가 1
 
< 0.1%

Length

2024-05-11T16:03:33.016725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T16:03:33.221114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 3736
37.4%
na 2903
29.0%
타시군구이관 1188
 
11.9%
영업중 833
 
8.3%
유효기간만료 780
 
7.8%
직권취소 559
 
5.6%
갱신등록불가 1
 
< 0.1%

등록증번호
Text

MISSING 

Distinct9768
Distinct (%)99.6%
Missing189
Missing (%)1.9%
Memory size156.2 KiB
2024-05-11T16:03:33.566429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length26
Mean length19.486393
Min length4

Characters and Unicode

Total characters191181
Distinct characters77
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

Unique9726 ?
Unique (%)99.1%

Sample

1st row2021-서울금천-0031
2nd row2021-서울동대문-0007
3rd row2011-서울강남-0021
4th row2012-서울광진-0055(대부업)
5th row2015-서울중구-0095(대부업)
ValueCountFrequency (%)
2011-서울특별시 17
 
0.2%
2013-서울특별시 17
 
0.2%
2015-서울특별시 16
 
0.2%
2010-서울 15
 
0.2%
2012-서울특별시 13
 
0.1%
2017-서울특별시 12
 
0.1%
대부업 10
 
0.1%
2016-서울특별시 10
 
0.1%
2014-서울특별시 9
 
0.1%
2018-서울특별시 7
 
0.1%
Other values (9721) 9842
98.7%
2024-05-11T16:03:34.141641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 33895
17.7%
- 19601
 
10.3%
2 15730
 
8.2%
1 11727
 
6.1%
10831
 
5.7%
9783
 
5.1%
8438
 
4.4%
( 8167
 
4.3%
8136
 
4.3%
) 8106
 
4.2%
Other values (67) 56767
29.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 82513
43.2%
Other Letter 72637
38.0%
Dash Punctuation 19601
 
10.3%
Open Punctuation 8167
 
4.3%
Close Punctuation 8106
 
4.2%
Space Separator 157
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10831
14.9%
9783
13.5%
8438
11.6%
8136
11.2%
7909
10.9%
3478
 
4.8%
2805
 
3.9%
2523
 
3.5%
2515
 
3.5%
2515
 
3.5%
Other values (53) 13704
18.9%
Decimal Number
ValueCountFrequency (%)
0 33895
41.1%
2 15730
19.1%
1 11727
 
14.2%
3 3751
 
4.5%
8 3063
 
3.7%
4 3038
 
3.7%
7 2888
 
3.5%
9 2822
 
3.4%
6 2813
 
3.4%
5 2786
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
- 19601
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8167
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8106
100.0%
Space Separator
ValueCountFrequency (%)
157
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 118544
62.0%
Hangul 72637
38.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10831
14.9%
9783
13.5%
8438
11.6%
8136
11.2%
7909
10.9%
3478
 
4.8%
2805
 
3.9%
2523
 
3.5%
2515
 
3.5%
2515
 
3.5%
Other values (53) 13704
18.9%
Common
ValueCountFrequency (%)
0 33895
28.6%
- 19601
16.5%
2 15730
13.3%
1 11727
 
9.9%
( 8167
 
6.9%
) 8106
 
6.8%
3 3751
 
3.2%
8 3063
 
2.6%
4 3038
 
2.6%
7 2888
 
2.4%
Other values (4) 8578
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118544
62.0%
Hangul 72637
38.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33895
28.6%
- 19601
16.5%
2 15730
13.3%
1 11727
 
9.9%
( 8167
 
6.9%
) 8106
 
6.8%
3 3751
 
3.2%
8 3063
 
2.6%
4 3038
 
2.6%
7 2888
 
2.4%
Other values (4) 8578
 
7.2%
Hangul
ValueCountFrequency (%)
10831
14.9%
9783
13.5%
8438
11.6%
8136
11.2%
7909
10.9%
3478
 
4.8%
2805
 
3.9%
2523
 
3.5%
2515
 
3.5%
2515
 
3.5%
Other values (53) 13704
18.9%

상호
Text

Distinct8702
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T16:03:34.707087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length26
Mean length7.7221
Min length1

Characters and Unicode

Total characters77221
Distinct characters784
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7670 ?
Unique (%)76.7%

Sample

1st row(주)엘앤비파이낸셜대부
2nd row주식회사 코너스톤캐피탈대부
3rd row바로바대부
4th row친구기획대부
5th row제이더블유캐피탈대부(주)
ValueCountFrequency (%)
주식회사 866
 
7.3%
대부중개 296
 
2.5%
대부 283
 
2.4%
유한회사 54
 
0.5%
대부업 21
 
0.2%
캐피탈 18
 
0.2%
14
 
0.1%
대부중개업 13
 
0.1%
미래 12
 
0.1%
미래대부 10
 
0.1%
Other values (8712) 10352
86.7%
2024-05-11T16:03:35.579243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8538
 
11.1%
8180
 
10.6%
2693
 
3.5%
2278
 
2.9%
2070
 
2.7%
2045
 
2.6%
1943
 
2.5%
1897
 
2.5%
) 1822
 
2.4%
( 1815
 
2.4%
Other values (774) 43940
56.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 67795
87.8%
Uppercase Letter 2191
 
2.8%
Space Separator 1943
 
2.5%
Close Punctuation 1822
 
2.4%
Open Punctuation 1815
 
2.4%
Lowercase Letter 1128
 
1.5%
Decimal Number 266
 
0.3%
Other Punctuation 225
 
0.3%
Dash Punctuation 28
 
< 0.1%
Other Symbol 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
8538
 
12.6%
8180
 
12.1%
2693
 
4.0%
2278
 
3.4%
2070
 
3.1%
2045
 
3.0%
1897
 
2.8%
1374
 
2.0%
1121
 
1.7%
1071
 
1.6%
Other values (699) 36528
53.9%
Uppercase Letter
ValueCountFrequency (%)
S 323
14.7%
J 181
 
8.3%
C 179
 
8.2%
K 178
 
8.1%
M 160
 
7.3%
H 115
 
5.2%
G 90
 
4.1%
B 90
 
4.1%
L 89
 
4.1%
A 82
 
3.7%
Other values (16) 704
32.1%
Lowercase Letter
ValueCountFrequency (%)
n 139
12.3%
e 133
11.8%
o 112
9.9%
a 108
 
9.6%
i 76
 
6.7%
s 68
 
6.0%
t 63
 
5.6%
c 56
 
5.0%
l 52
 
4.6%
r 46
 
4.1%
Other values (15) 275
24.4%
Decimal Number
ValueCountFrequency (%)
1 91
34.2%
2 43
16.2%
4 34
 
12.8%
9 22
 
8.3%
3 19
 
7.1%
5 19
 
7.1%
0 14
 
5.3%
6 11
 
4.1%
7 8
 
3.0%
8 5
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 113
50.2%
& 100
44.4%
? 6
 
2.7%
/ 2
 
0.9%
, 2
 
0.9%
1
 
0.4%
* 1
 
0.4%
Math Symbol
ValueCountFrequency (%)
< 1
50.0%
> 1
50.0%
Space Separator
ValueCountFrequency (%)
1943
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1822
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1815
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 28
100.0%
Other Symbol
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 67790
87.8%
Common 6101
 
7.9%
Latin 3319
 
4.3%
Han 11
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
8538
 
12.6%
8180
 
12.1%
2693
 
4.0%
2278
 
3.4%
2070
 
3.1%
2045
 
3.0%
1897
 
2.8%
1374
 
2.0%
1121
 
1.7%
1071
 
1.6%
Other values (689) 36523
53.9%
Latin
ValueCountFrequency (%)
S 323
 
9.7%
J 181
 
5.5%
C 179
 
5.4%
K 178
 
5.4%
M 160
 
4.8%
n 139
 
4.2%
e 133
 
4.0%
H 115
 
3.5%
o 112
 
3.4%
a 108
 
3.3%
Other values (41) 1691
50.9%
Common
ValueCountFrequency (%)
1943
31.8%
) 1822
29.9%
( 1815
29.7%
. 113
 
1.9%
& 100
 
1.6%
1 91
 
1.5%
2 43
 
0.7%
4 34
 
0.6%
- 28
 
0.5%
9 22
 
0.4%
Other values (13) 90
 
1.5%
Han
ValueCountFrequency (%)
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 67784
87.8%
ASCII 9419
 
12.2%
CJK 11
 
< 0.1%
None 7
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
8538
 
12.6%
8180
 
12.1%
2693
 
4.0%
2278
 
3.4%
2070
 
3.1%
2045
 
3.0%
1897
 
2.8%
1374
 
2.0%
1121
 
1.7%
1071
 
1.6%
Other values (688) 36517
53.9%
ASCII
ValueCountFrequency (%)
1943
20.6%
) 1822
19.3%
( 1815
19.3%
S 323
 
3.4%
J 181
 
1.9%
C 179
 
1.9%
K 178
 
1.9%
M 160
 
1.7%
n 139
 
1.5%
e 133
 
1.4%
Other values (63) 2546
27.0%
None
ValueCountFrequency (%)
6
85.7%
1
 
14.3%
CJK
ValueCountFrequency (%)
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%

법인여부
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
개인
7206 
법인
2794 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
개인 7206
72.1%
법인 2794
 
27.9%

Length

2024-05-11T16:03:35.821105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T16:03:35.980133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 7206
72.1%
법인 2794
 
27.9%
Distinct5851
Distinct (%)89.1%
Missing3436
Missing (%)34.4%
Memory size156.2 KiB
2024-05-11T16:03:36.357246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length38
Mean length10.612279
Min length1

Characters and Unicode

Total characters69659
Distinct characters35
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

Unique5268 ?
Unique (%)80.3%

Sample

1st row1833-7458
2nd row025178843
3rd row02-773-6789
4th row02-535-3476
5th row02-6394-5030
ValueCountFrequency (%)
02 287
 
3.9%
57
 
0.8%
070 41
 
0.6%
1566 6
 
0.1%
02-6140-5753 5
 
0.1%
010 5
 
0.1%
703 5
 
0.1%
02-737-2882 5
 
0.1%
0 5
 
0.1%
1688 5
 
0.1%
Other values (6156) 6948
94.3%
2024-05-11T16:03:36.939291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11327
16.3%
2 10286
14.8%
- 7057
10.1%
5 5889
8.5%
7 5479
7.9%
1 5077
7.3%
6 5006
7.2%
3 4903
7.0%
4 4765
6.8%
8 4698
6.7%
Other values (25) 5172
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 61386
88.1%
Dash Punctuation 7057
 
10.1%
Space Separator 899
 
1.3%
Other Punctuation 165
 
0.2%
Close Punctuation 73
 
0.1%
Math Symbol 30
 
< 0.1%
Open Punctuation 29
 
< 0.1%
Other Letter 18
 
< 0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2
 
11.1%
2
 
11.1%
2
 
11.1%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
Other values (5) 5
27.8%
Decimal Number
ValueCountFrequency (%)
0 11327
18.5%
2 10286
16.8%
5 5889
9.6%
7 5479
8.9%
1 5077
8.3%
6 5006
8.2%
3 4903
8.0%
4 4765
7.8%
8 4698
7.7%
9 3956
 
6.4%
Other Punctuation
ValueCountFrequency (%)
* 101
61.2%
/ 46
27.9%
. 18
 
10.9%
Uppercase Letter
ValueCountFrequency (%)
K 1
50.0%
T 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 7057
100.0%
Space Separator
ValueCountFrequency (%)
899
100.0%
Close Punctuation
ValueCountFrequency (%)
) 73
100.0%
Math Symbol
ValueCountFrequency (%)
~ 30
100.0%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69639
> 99.9%
Hangul 18
 
< 0.1%
Latin 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11327
16.3%
2 10286
14.8%
- 7057
10.1%
5 5889
8.5%
7 5479
7.9%
1 5077
7.3%
6 5006
7.2%
3 4903
7.0%
4 4765
6.8%
8 4698
6.7%
Other values (8) 5152
7.4%
Hangul
ValueCountFrequency (%)
2
 
11.1%
2
 
11.1%
2
 
11.1%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
Other values (5) 5
27.8%
Latin
ValueCountFrequency (%)
K 1
50.0%
T 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69641
> 99.9%
Hangul 18
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11327
16.3%
2 10286
14.8%
- 7057
10.1%
5 5889
8.5%
7 5479
7.9%
1 5077
7.3%
6 5006
7.2%
3 4903
7.0%
4 4765
6.8%
8 4698
6.7%
Other values (10) 5154
7.4%
Hangul
ValueCountFrequency (%)
2
 
11.1%
2
 
11.1%
2
 
11.1%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
Other values (5) 5
27.8%

소재지
Text

MISSING 

Distinct8639
Distinct (%)89.2%
Missing312
Missing (%)3.1%
Memory size156.2 KiB
2024-05-11T16:03:37.476042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length62
Median length47
Mean length31.401115
Min length15

Characters and Unicode

Total characters304214
Distinct characters623
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7908 ?
Unique (%)81.6%

Sample

1st row서울특별시 금천구 가산동 459번지 7호
2nd row서울특별시 동대문구 장안동 466번지 9호
3rd row서울특별시 강남구 논현동 19번지 2호 성현빌딩 607호
4th row서울특별시 광진구 중곡동 161번지 24호 동아빌딩-306
5th row서울특별시 중구 명동1가 1번지 1호 YWC연합회-411
ValueCountFrequency (%)
서울특별시 9683
 
17.0%
강남구 1655
 
2.9%
서초구 903
 
1.6%
1호 735
 
1.3%
역삼동 679
 
1.2%
송파구 601
 
1.1%
중구 579
 
1.0%
서초동 547
 
1.0%
영등포구 449
 
0.8%
2호 440
 
0.8%
Other values (9482) 40736
71.5%
2024-05-11T16:03:38.218235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
67632
22.2%
1 13555
 
4.5%
11954
 
3.9%
11090
 
3.6%
10459
 
3.4%
9948
 
3.3%
9731
 
3.2%
9696
 
3.2%
9685
 
3.2%
2 8675
 
2.9%
Other values (613) 141789
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 166271
54.7%
Space Separator 67632
22.2%
Decimal Number 63101
 
20.7%
Dash Punctuation 5430
 
1.8%
Uppercase Letter 1184
 
0.4%
Other Punctuation 246
 
0.1%
Lowercase Letter 126
 
< 0.1%
Close Punctuation 93
 
< 0.1%
Open Punctuation 92
 
< 0.1%
Letter Number 28
 
< 0.1%
Other values (2) 11
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
11954
 
7.2%
11090
 
6.7%
10459
 
6.3%
9948
 
6.0%
9731
 
5.9%
9696
 
5.8%
9685
 
5.8%
8594
 
5.2%
8428
 
5.1%
7907
 
4.8%
Other values (536) 68779
41.4%
Uppercase Letter
ValueCountFrequency (%)
B 275
23.2%
A 221
18.7%
S 90
 
7.6%
D 86
 
7.3%
C 53
 
4.5%
K 53
 
4.5%
I 50
 
4.2%
T 44
 
3.7%
L 38
 
3.2%
G 33
 
2.8%
Other values (16) 241
20.4%
Lowercase Letter
ValueCountFrequency (%)
e 22
17.5%
i 15
11.9%
n 14
11.1%
t 12
9.5%
r 9
 
7.1%
l 6
 
4.8%
u 6
 
4.8%
c 6
 
4.8%
o 5
 
4.0%
y 5
 
4.0%
Other values (11) 26
20.6%
Decimal Number
ValueCountFrequency (%)
1 13555
21.5%
2 8675
13.7%
0 7878
12.5%
3 6848
10.9%
4 5819
9.2%
5 5028
 
8.0%
6 4555
 
7.2%
7 3933
 
6.2%
9 3430
 
5.4%
8 3380
 
5.4%
Other Punctuation
ValueCountFrequency (%)
/ 96
39.0%
, 87
35.4%
. 56
22.8%
3
 
1.2%
& 2
 
0.8%
@ 1
 
0.4%
# 1
 
0.4%
Letter Number
ValueCountFrequency (%)
19
67.9%
5
 
17.9%
4
 
14.3%
Math Symbol
ValueCountFrequency (%)
~ 8
80.0%
> 1
 
10.0%
< 1
 
10.0%
Close Punctuation
ValueCountFrequency (%)
) 92
98.9%
] 1
 
1.1%
Open Punctuation
ValueCountFrequency (%)
( 91
98.9%
[ 1
 
1.1%
Space Separator
ValueCountFrequency (%)
67632
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5430
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 166272
54.7%
Common 136604
44.9%
Latin 1338
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
11954
 
7.2%
11090
 
6.7%
10459
 
6.3%
9948
 
6.0%
9731
 
5.9%
9696
 
5.8%
9685
 
5.8%
8594
 
5.2%
8428
 
5.1%
7907
 
4.8%
Other values (537) 68780
41.4%
Latin
ValueCountFrequency (%)
B 275
20.6%
A 221
16.5%
S 90
 
6.7%
D 86
 
6.4%
C 53
 
4.0%
K 53
 
4.0%
I 50
 
3.7%
T 44
 
3.3%
L 38
 
2.8%
G 33
 
2.5%
Other values (40) 395
29.5%
Common
ValueCountFrequency (%)
67632
49.5%
1 13555
 
9.9%
2 8675
 
6.4%
0 7878
 
5.8%
3 6848
 
5.0%
4 5819
 
4.3%
- 5430
 
4.0%
5 5028
 
3.7%
6 4555
 
3.3%
7 3933
 
2.9%
Other values (16) 7251
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 166271
54.7%
ASCII 137911
45.3%
Number Forms 28
 
< 0.1%
None 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
67632
49.0%
1 13555
 
9.8%
2 8675
 
6.3%
0 7878
 
5.7%
3 6848
 
5.0%
4 5819
 
4.2%
- 5430
 
3.9%
5 5028
 
3.6%
6 4555
 
3.3%
7 3933
 
2.9%
Other values (62) 8558
 
6.2%
Hangul
ValueCountFrequency (%)
11954
 
7.2%
11090
 
6.7%
10459
 
6.3%
9948
 
6.0%
9731
 
5.9%
9696
 
5.8%
9685
 
5.8%
8594
 
5.2%
8428
 
5.1%
7907
 
4.8%
Other values (536) 68779
41.4%
Number Forms
ValueCountFrequency (%)
19
67.9%
5
 
17.9%
4
 
14.3%
None
ValueCountFrequency (%)
3
75.0%
1
 
25.0%

소재지(도로명)
Text

MISSING 

Distinct4766
Distinct (%)91.5%
Missing4794
Missing (%)47.9%
Memory size156.2 KiB
2024-05-11T16:03:38.881150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length62
Median length52
Mean length37.024779
Min length19

Characters and Unicode

Total characters192751
Distinct characters609
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4368 ?
Unique (%)83.9%

Sample

1st row서울특별시 금천구 가산디지털1로 205-27, 오211호 (가산동)
2nd row서울특별시 동대문구 천호대로 451, 907호 (장안동)
3rd row서울특별시 중구 명동길 73, 411호 (명동1가, YWCA연합회)
4th row서울특별시 서초구 서운로 160, 902호 (서초동, 팔레스빌딩)
5th row서울특별시 강남구 선릉로125길 8, 301호 (논현동)
ValueCountFrequency (%)
서울특별시 5203
 
14.1%
강남구 962
 
2.6%
서초구 554
 
1.5%
2층 469
 
1.3%
역삼동 387
 
1.1%
서초동 367
 
1.0%
3층 365
 
1.0%
송파구 332
 
0.9%
4층 309
 
0.8%
영등포구 306
 
0.8%
Other values (6599) 27529
74.8%
2024-05-11T16:03:39.763905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31585
 
16.4%
1 7404
 
3.8%
, 7115
 
3.7%
6835
 
3.5%
6791
 
3.5%
5734
 
3.0%
5733
 
3.0%
5408
 
2.8%
5248
 
2.7%
) 5241
 
2.7%
Other values (599) 105657
54.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 107238
55.6%
Decimal Number 34259
 
17.8%
Space Separator 31585
 
16.4%
Other Punctuation 7132
 
3.7%
Close Punctuation 5242
 
2.7%
Open Punctuation 5241
 
2.7%
Dash Punctuation 1007
 
0.5%
Uppercase Letter 885
 
0.5%
Lowercase Letter 125
 
0.1%
Letter Number 25
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6835
 
6.4%
6791
 
6.3%
5734
 
5.3%
5733
 
5.3%
5408
 
5.0%
5248
 
4.9%
5216
 
4.9%
5204
 
4.9%
4193
 
3.9%
2728
 
2.5%
Other values (527) 54148
50.5%
Uppercase Letter
ValueCountFrequency (%)
B 165
18.6%
A 126
14.2%
S 75
 
8.5%
C 53
 
6.0%
I 47
 
5.3%
T 44
 
5.0%
K 43
 
4.9%
G 40
 
4.5%
E 37
 
4.2%
L 37
 
4.2%
Other values (15) 218
24.6%
Lowercase Letter
ValueCountFrequency (%)
e 23
18.4%
r 14
11.2%
t 14
11.2%
n 13
10.4%
i 10
8.0%
o 9
 
7.2%
w 8
 
6.4%
c 8
 
6.4%
y 5
 
4.0%
l 4
 
3.2%
Other values (8) 17
13.6%
Decimal Number
ValueCountFrequency (%)
1 7404
21.6%
2 5192
15.2%
0 4478
13.1%
3 4073
11.9%
4 2930
 
8.6%
5 2694
 
7.9%
6 2243
 
6.5%
7 1894
 
5.5%
8 1837
 
5.4%
9 1514
 
4.4%
Other Punctuation
ValueCountFrequency (%)
, 7115
99.8%
. 8
 
0.1%
/ 3
 
< 0.1%
2
 
< 0.1%
& 2
 
< 0.1%
@ 1
 
< 0.1%
# 1
 
< 0.1%
Letter Number
ValueCountFrequency (%)
15
60.0%
5
 
20.0%
5
 
20.0%
Math Symbol
ValueCountFrequency (%)
~ 10
83.3%
> 1
 
8.3%
< 1
 
8.3%
Close Punctuation
ValueCountFrequency (%)
) 5241
> 99.9%
] 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 5240
> 99.9%
[ 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
31585
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1007
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 107238
55.6%
Common 84478
43.8%
Latin 1035
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
6835
 
6.4%
6791
 
6.3%
5734
 
5.3%
5733
 
5.3%
5408
 
5.0%
5248
 
4.9%
5216
 
4.9%
5204
 
4.9%
4193
 
3.9%
2728
 
2.5%
Other values (527) 54148
50.5%
Latin
ValueCountFrequency (%)
B 165
15.9%
A 126
 
12.2%
S 75
 
7.2%
C 53
 
5.1%
I 47
 
4.5%
T 44
 
4.3%
K 43
 
4.2%
G 40
 
3.9%
E 37
 
3.6%
L 37
 
3.6%
Other values (36) 368
35.6%
Common
ValueCountFrequency (%)
31585
37.4%
1 7404
 
8.8%
, 7115
 
8.4%
) 5241
 
6.2%
( 5240
 
6.2%
2 5192
 
6.1%
0 4478
 
5.3%
3 4073
 
4.8%
4 2930
 
3.5%
5 2694
 
3.2%
Other values (16) 8526
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 107238
55.6%
ASCII 85486
44.4%
Number Forms 25
 
< 0.1%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31585
36.9%
1 7404
 
8.7%
, 7115
 
8.3%
) 5241
 
6.1%
( 5240
 
6.1%
2 5192
 
6.1%
0 4478
 
5.2%
3 4073
 
4.8%
4 2930
 
3.4%
5 2694
 
3.2%
Other values (58) 9534
 
11.2%
Hangul
ValueCountFrequency (%)
6835
 
6.4%
6791
 
6.3%
5734
 
5.3%
5733
 
5.3%
5408
 
5.0%
5248
 
4.9%
5216
 
4.9%
5204
 
4.9%
4193
 
3.9%
2728
 
2.5%
Other values (527) 54148
50.5%
Number Forms
ValueCountFrequency (%)
15
60.0%
5
 
20.0%
5
 
20.0%
None
ValueCountFrequency (%)
2
100.0%

우편번호
Real number (ℝ)

MISSING 

Distinct1390
Distinct (%)31.9%
Missing5640
Missing (%)56.4%
Infinite0
Infinite (%)0.0%
Mean136481.92
Minimum3163
Maximum429842
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T16:03:40.017147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3163
5-th percentile100865.9
Q1132040
median136034
Q3143200
95-th percentile157220
Maximum429842
Range426679
Interquartile range (IQR)11160

Descriptive statistics

Standard deviation15679.081
Coefficient of variation (CV)0.11488028
Kurtosis55.253411
Mean136481.92
Median Absolute Deviation (MAD)5032.5
Skewness1.4900312
Sum5.9506116 × 108
Variance2.4583359 × 108
MonotonicityNot monotonic
2024-05-11T16:03:40.251425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135080 165
 
1.7%
137070 131
 
1.3%
135010 60
 
0.6%
157010 58
 
0.6%
151015 53
 
0.5%
152050 51
 
0.5%
158070 49
 
0.5%
135090 37
 
0.4%
158090 37
 
0.4%
142070 36
 
0.4%
Other values (1380) 3683
36.8%
(Missing) 5640
56.4%
ValueCountFrequency (%)
3163 1
 
< 0.1%
4538 1
 
< 0.1%
5510 1
 
< 0.1%
7220 1
 
< 0.1%
7327 1
 
< 0.1%
14538 1
 
< 0.1%
100011 4
< 0.1%
100012 4
< 0.1%
100013 2
< 0.1%
100014 2
< 0.1%
ValueCountFrequency (%)
429842 1
 
< 0.1%
410762 1
 
< 0.1%
158877 1
 
< 0.1%
158871 1
 
< 0.1%
158864 5
0.1%
158863 1
 
< 0.1%
158860 7
0.1%
158859 5
0.1%
158841 1
 
< 0.1%
158838 1
 
< 0.1%

등록일자
Real number (ℝ)

HIGH CORRELATION 

Distinct3533
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20136811
Minimum20060127
Maximum20240510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T16:03:40.495960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20060127
5-th percentile20070807
Q120091126
median20130308
Q320170801
95-th percentile20230302
Maximum20240510
Range180383
Interquartile range (IQR)79675.25

Descriptive statistics

Standard deviation48988.419
Coefficient of variation (CV)0.0024327794
Kurtosis-0.90743225
Mean20136811
Median Absolute Deviation (MAD)39590.5
Skewness0.46309017
Sum2.0136811 × 1011
Variance2.3998652 × 109
MonotonicityNot monotonic
2024-05-11T16:03:40.812722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20080731 21
 
0.2%
20080818 20
 
0.2%
20081222 20
 
0.2%
20090611 19
 
0.2%
20090520 17
 
0.2%
20080814 17
 
0.2%
20080806 17
 
0.2%
20090325 15
 
0.1%
20110711 14
 
0.1%
20090507 14
 
0.1%
Other values (3523) 9826
98.3%
ValueCountFrequency (%)
20060127 1
< 0.1%
20060306 2
< 0.1%
20060308 2
< 0.1%
20060310 2
< 0.1%
20060320 2
< 0.1%
20060323 2
< 0.1%
20060329 1
< 0.1%
20060405 2
< 0.1%
20060410 2
< 0.1%
20060412 1
< 0.1%
ValueCountFrequency (%)
20240510 1
 
< 0.1%
20240508 1
 
< 0.1%
20240507 3
< 0.1%
20240503 3
< 0.1%
20240430 1
 
< 0.1%
20240429 1
 
< 0.1%
20240425 2
< 0.1%
20240424 3
< 0.1%
20240422 1
 
< 0.1%
20240419 2
< 0.1%

유효기간만료일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3305
Distinct (%)41.7%
Missing2078
Missing (%)20.8%
Infinite0
Infinite (%)0.0%
Mean20181842
Minimum20091116
Maximum22180428
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T16:03:41.088712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20091116
5-th percentile20120334
Q120141107
median20171220
Q320220210
95-th percentile20260516
Maximum22180428
Range2089312
Interquartile range (IQR)79103.25

Descriptive statistics

Standard deviation49902.793
Coefficient of variation (CV)0.002472658
Kurtosis323.31664
Mean20181842
Median Absolute Deviation (MAD)30598.5
Skewness8.3320339
Sum1.5988055 × 1011
Variance2.4902888 × 109
MonotonicityNot monotonic
2024-05-11T16:03:41.357019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20140831 16
 
0.2%
20140711 14
 
0.1%
20141108 12
 
0.1%
20190705 12
 
0.1%
20150723 12
 
0.1%
20110831 11
 
0.1%
20190718 11
 
0.1%
20190711 11
 
0.1%
20120520 11
 
0.1%
20140502 10
 
0.1%
Other values (3295) 7802
78.0%
(Missing) 2078
 
20.8%
ValueCountFrequency (%)
20091116 1
< 0.1%
20091220 1
< 0.1%
20100112 1
< 0.1%
20100117 1
< 0.1%
20100122 1
< 0.1%
20100125 1
< 0.1%
20100308 1
< 0.1%
20100410 1
< 0.1%
20100411 2
< 0.1%
20100418 2
< 0.1%
ValueCountFrequency (%)
22180428 1
 
< 0.1%
20270510 1
 
< 0.1%
20270508 1
 
< 0.1%
20270507 2
< 0.1%
20270506 1
 
< 0.1%
20270503 3
< 0.1%
20270430 1
 
< 0.1%
20270429 1
 
< 0.1%
20270425 2
< 0.1%
20270424 3
< 0.1%

폐쇄일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3117
Distinct (%)37.1%
Missing1604
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean20142058
Minimum20081212
Maximum20240510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T16:03:41.589962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20081212
5-th percentile20090909
Q120110329
median20130726
Q320170415
95-th percentile20221122
Maximum20240510
Range159298
Interquartile range (IQR)60085.75

Descriptive statistics

Standard deviation41122.721
Coefficient of variation (CV)0.0020416345
Kurtosis-0.54364997
Mean20142058
Median Absolute Deviation (MAD)29799
Skewness0.70127365
Sum1.6911272 × 1011
Variance1.6910782 × 109
MonotonicityNot monotonic
2024-05-11T16:03:41.842373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20091116 208
 
2.1%
20100927 77
 
0.8%
20170124 22
 
0.2%
20110425 20
 
0.2%
20101213 18
 
0.2%
20160725 16
 
0.2%
20170125 16
 
0.2%
20120720 15
 
0.1%
20101228 15
 
0.1%
20110420 15
 
0.1%
Other values (3107) 7974
79.7%
(Missing) 1604
 
16.0%
ValueCountFrequency (%)
20081212 1
 
< 0.1%
20090128 1
 
< 0.1%
20090220 1
 
< 0.1%
20090305 1
 
< 0.1%
20090306 1
 
< 0.1%
20090307 2
 
< 0.1%
20090309 3
< 0.1%
20090311 5
0.1%
20090313 4
< 0.1%
20090317 3
< 0.1%
ValueCountFrequency (%)
20240510 2
< 0.1%
20240509 2
< 0.1%
20240507 2
< 0.1%
20240503 2
< 0.1%
20240502 1
 
< 0.1%
20240501 1
 
< 0.1%
20240430 4
< 0.1%
20240425 1
 
< 0.1%
20240424 1
 
< 0.1%
20240423 2
< 0.1%

지점설립일자
Text

MISSING 

Distinct3571
Distinct (%)40.9%
Missing1276
Missing (%)12.8%
Memory size156.2 KiB
2024-05-11T16:03:42.364217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters69792
Distinct characters14
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

Unique1386 ?
Unique (%)15.9%

Sample

1st row20210405
2nd row20190801
3rd row20110121
4th row20121228
5th row20130219
ValueCountFrequency (%)
20090520 23
 
0.3%
20090820 21
 
0.2%
20090611 21
 
0.2%
20090514 16
 
0.2%
20090507 16
 
0.2%
20090821 14
 
0.2%
20090511 14
 
0.2%
20090605 13
 
0.1%
20111108 13
 
0.1%
20120516 12
 
0.1%
Other values (3561) 8561
98.1%
2024-05-11T16:03:43.190607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 22671
32.5%
2 15935
22.8%
1 13871
19.9%
3 2871
 
4.1%
9 2637
 
3.8%
7 2593
 
3.7%
6 2443
 
3.5%
5 2361
 
3.4%
4 2224
 
3.2%
8 2180
 
3.1%
Other values (4) 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69786
> 99.9%
Space Separator 3
 
< 0.1%
Lowercase Letter 2
 
< 0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22671
32.5%
2 15935
22.8%
1 13871
19.9%
3 2871
 
4.1%
9 2637
 
3.8%
7 2593
 
3.7%
6 2443
 
3.5%
5 2361
 
3.4%
4 2224
 
3.2%
8 2180
 
3.1%
Lowercase Letter
ValueCountFrequency (%)
a 1
50.0%
r 1
50.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Uppercase Letter
ValueCountFrequency (%)
M 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69789
> 99.9%
Latin 3
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22671
32.5%
2 15935
22.8%
1 13871
19.9%
3 2871
 
4.1%
9 2637
 
3.8%
7 2593
 
3.7%
6 2443
 
3.5%
5 2361
 
3.4%
4 2224
 
3.2%
8 2180
 
3.1%
Latin
ValueCountFrequency (%)
M 1
33.3%
a 1
33.3%
r 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69792
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22671
32.5%
2 15935
22.8%
1 13871
19.9%
3 2871
 
4.1%
9 2637
 
3.8%
7 2593
 
3.7%
6 2443
 
3.5%
5 2361
 
3.4%
4 2224
 
3.2%
8 2180
 
3.1%
Other values (4) 6
 
< 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

2024-05-11T16:03:43.436342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T16:03:43.601903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
본점 9935
99.4%
지점 65
 
0.7%

최근수정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct3146
Distinct (%)31.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20153013
Minimum20090518
Maximum20240510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T16:03:43.791464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20090518
5-th percentile20091118
Q120111010
median20140901
Q320190410
95-th percentile20231023
Maximum20240510
Range149992
Interquartile range (IQR)79400.25

Descriptive statistics

Standard deviation46022.594
Coefficient of variation (CV)0.0022836582
Kurtosis-1.057902
Mean20153013
Median Absolute Deviation (MAD)30587
Skewness0.45123669
Sum2.0153013 × 1011
Variance2.1180792 × 109
MonotonicityNot monotonic
2024-05-11T16:03:44.030158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20091117 73
 
0.7%
20090609 59
 
0.6%
20091118 56
 
0.6%
20091116 44
 
0.4%
20100927 43
 
0.4%
20100330 43
 
0.4%
20090622 42
 
0.4%
20130621 40
 
0.4%
20110425 34
 
0.3%
20100517 33
 
0.3%
Other values (3136) 9533
95.3%
ValueCountFrequency (%)
20090518 2
 
< 0.1%
20090519 1
 
< 0.1%
20090521 4
 
< 0.1%
20090601 3
 
< 0.1%
20090603 8
 
0.1%
20090604 15
 
0.1%
20090605 4
 
< 0.1%
20090608 6
 
0.1%
20090609 59
0.6%
20090610 14
 
0.1%
ValueCountFrequency (%)
20240510 4
 
< 0.1%
20240509 4
 
< 0.1%
20240508 7
0.1%
20240507 7
0.1%
20240503 10
0.1%
20240502 7
0.1%
20240501 3
 
< 0.1%
20240430 6
0.1%
20240429 1
 
< 0.1%
20240426 2
 
< 0.1%

Interactions

2024-05-11T16:03:30.497770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:03:27.056596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:03:28.029875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:03:28.829930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:03:29.706250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:03:30.639946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:03:27.227083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:03:28.166374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:03:29.012887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:03:29.866581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:03:30.778617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:03:27.426908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:03:28.328904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:03:29.227532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:03:30.007370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:03:30.937877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:03:27.628299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:03:28.482610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:03:29.389546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:03:30.157881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:03:31.122587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:03:27.834713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:03:28.656516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:03:29.558803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:03:30.325450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T16:03:44.571694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록신청사업영업구분법인여부우편번호등록일자유효기간만료일자폐쇄일자본점여부최근수정일자
등록신청사업1.0000.0940.0000.0000.2330.0000.2180.0000.177
영업구분0.0941.0000.3130.0000.6110.0000.2850.0510.538
법인여부0.0000.3131.0000.1080.3540.0000.2690.1910.365
우편번호0.0000.0000.1081.0000.129NaN0.1240.0000.164
등록일자0.2330.6110.3540.1291.0000.0000.9380.0900.938
유효기간만료일자0.0000.0000.000NaN0.0001.0000.0000.0000.000
폐쇄일자0.2180.2850.2690.1240.9380.0001.0000.0450.986
본점여부0.0000.0510.1910.0000.0900.0000.0451.0000.112
최근수정일자0.1770.5380.3650.1640.9380.0000.9860.1121.000
2024-05-11T16:03:44.769485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영업구분본점여부등록신청사업법인여부
영업구분1.0000.0370.0680.225
본점여부0.0371.0000.0000.123
등록신청사업0.0680.0001.0000.000
법인여부0.2250.1230.0001.000
2024-05-11T16:03:44.956617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호등록일자유효기간만료일자폐쇄일자최근수정일자등록신청사업영업구분법인여부본점여부
우편번호1.0000.0350.0340.0490.0380.0000.0000.0670.000
등록일자0.0351.0000.9970.9610.9660.1790.3790.2720.069
유효기간만료일자0.0340.9971.0000.9630.9660.0000.0000.0000.000
폐쇄일자0.0490.9610.9631.0000.9910.1670.1220.2060.035
최근수정일자0.0380.9660.9660.9911.0000.1350.3010.2800.086
등록신청사업0.0000.1790.0000.1670.1351.0000.0680.0000.000
영업구분0.0000.3790.0000.1220.3010.0681.0000.2250.037
법인여부0.0670.2720.0000.2060.2800.0000.2251.0000.123
본점여부0.0000.0690.0000.0350.0860.0000.0370.1231.000

Missing values

2024-05-11T16:03:31.391642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T16:03:31.764983image/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.
2024-05-11T16:03:32.061092image/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 전화번호소재지소재지(도로명)우편번호등록일자유효기간만료일자폐쇄일자지점설립일자본점여부최근수정일자
545대부업영업중2021-서울금천-0031(주)엘앤비파이낸셜대부법인<NA>서울특별시 금천구 가산동 459번지 7호서울특별시 금천구 가산디지털1로 205-27, 오211호 (가산동)<NA>2024030820270308<NA>20210405본점20240311
4951대부업타시군구이관2021-서울동대문-0007주식회사 코너스톤캐피탈대부법인1833-7458서울특별시 동대문구 장안동 466번지 9호서울특별시 동대문구 천호대로 451, 907호 (장안동)<NA>20190801202208012021121420190801본점20211214
17277대부업직권취소2011-서울강남-0021바로바대부개인025178843서울특별시 강남구 논현동 19번지 2호 성현빌딩 607호<NA>1350102011012120140121<NA>20110121본점20131114
19532대부업폐업2012-서울광진-0055(대부업)친구기획대부개인<NA>서울특별시 광진구 중곡동 161번지 24호 동아빌딩-306<NA>14389820121228201512282013030620121228본점20130306
8310대부업폐업2015-서울중구-0095(대부업)제이더블유캐피탈대부(주)법인02-773-6789서울특별시 중구 명동1가 1번지 1호 YWC연합회-411서울특별시 중구 명동길 73, 411호 (명동1가, YWCA연합회)<NA>20151204201812042018120320130219본점20181203
4034대부업영업중2016-서울서초-0140(대부업)(주)리얼펀딩대부법인02-535-3476서울특별시 서초구 서초동 1316번지 4호 -902서울특별시 서초구 서운로 160, 902호 (서초동, 팔레스빌딩)<NA>2022080320250803<NA>20160922본점20220803
3645대부중개업영업중2022-서울강남-0095(대부중개업)주식회사 인에이블자산대부법인02-6394-5030서울특별시 강남구 논현동 242번지 44호서울특별시 강남구 선릉로125길 8, 301호 (논현동)<NA>2022051820250518<NA>20220518본점20221109
989대부업폐업2023-서울송파-0005(대부업)값진 대부개인02-425-3690서울특별시 송파구 방이동 48번지 5호 현대주상복합빌딩서울특별시 송파구 오금로11길 55, 2동 216호 (방이동, 현대주상복합빌딩)<NA>20230130202601302024011620230130본점20240116
13360대부업폐업2013-서울강남-0074(대부업)에이스대부개인<NA>서울특별시 강남구 논현동 36번지 한우리-102서울특별시 강남구 학동로25길 22, 102호 (논현동, 한우리)13581520130319201603192016020120130319본점20160201
14022대부중개업폐업2011-서울강남-0142(대부중개업)(주)새한자산대부평가관리법인02-511-3634서울특별시 강남구 역삼동 720번지 9호 스타팅빌딩 13층서울특별시 강남구 테헤란로38길 5, 13층 (역삼동, 스타팅빌딩)<NA>20110516201405162013081220110516본점20150828
등록신청사업영업구분등록증번호상호법인여부사업장 전화번호소재지소재지(도로명)우편번호등록일자유효기간만료일자폐쇄일자지점설립일자본점여부최근수정일자
25216대부업<NA>2008-서울특별시-01461(대부업)예스머니개인<NA>서울특별시 은평구 신사동 346-6<NA><NA>20080408<NA>2011033020080401본점20110331
23857대부업<NA>2009-서울특별시-02031(대부업)명성캐피탈개인<NA>서울특별시 성북구 하월곡동 산 2번지 11호<NA>13613020090415201204152011083120090821본점20110831
31대부중개업유효기간만료2021-서울구로-0005(대부중개업)주식회사 대림대부법인<NA>서울특별시 구로구 구로동 197번지 48호 에이스테크노타워Ⅲ서울특별시 구로구 디지털로29길 38, 에이스테크노타워Ⅲ 201-엔58호 (구로동)<NA>2021012920240129<NA>20210129본점20240508
22659대부중개업폐업2009-서울특별시-00483(대부중개업)JS론개인02 2068 1853서울특별시 영등포구 영등포동5가 81번지 10호 내오피스텔 4층-503<NA>150035200902272012022720120112<NA>본점20120112
1073대부중개업타시군구이관2022-서울중구-0006(대부중개업)주식회사 한국경우에셋대부법인02-6951-0976서울특별시 중구 순화동 5번지 2호 순화빌딩서울특별시 중구 서소문로 89, 순화빌딩 17층 LS-1705호 (순화동)<NA>20220303202503032024010220220303본점20240103
23635대부업<NA>2009-서울특별시-01143(대부업)상호주택금융대부개인029050551서울특별시 강북구 번동 446-13 가든타워 1516호<NA>14206020090611<NA>2011092620090611본점20110926
31140<NA><NA>2006-서울특별시-00092한미실업개인8378845서울특별시 관악구 신림동 1474-1. 302<NA>15189420060501<NA>2009050220060420본점20090609
30835<NA><NA>2008-서울특별시-02997세원개인023079050서울특별시 광진구 자양동 24-20<NA>14319020081119<NA>20090525<NA>본점20090622
13825대부업타시군구이관2015-서울서초-0031(대부업)(주)테이팩필림자산관리대부법인02-6140-5753서울특별시 서초구 서초동 1364번지 39호 지훈빌딩 4층서울특별시 서초구 서운로6길 26, 4층 (서초동, 지훈빌딩)13786320130717201607172015102320130717본점20151023
5921대부업폐업2018-서울금천-0011청명대부개인<NA>서울특별시 금천구 가산동 60번지 19호 609 SJ테크노빌-275서울특별시 금천구 벚꽃로 278, SJ테크노빌 609동 275호 (가산동)<NA>20180309202103092021021920180309본점20210219

Duplicate rows

Most frequently occurring

등록신청사업영업구분등록증번호상호법인여부사업장 전화번호소재지소재지(도로명)우편번호등록일자유효기간만료일자폐쇄일자지점설립일자본점여부최근수정일자# duplicates
0대부업타시군구이관2014-서울동작-00014(대부업)캐시오토론대부개인<NA>서울특별시 동작구 흑석동 336번지 105 흑석한강푸르지오-803서울특별시 동작구 흑석한강로 27, 105동 803호 (흑석동, 흑석한강푸르지오)15678420141209201712092016072620120109본점201607262
1대부업<NA>2009-서울특별시-02231(대부업)한빛투자금융대부개인025638488서울특별시 은평구 구산동 177번지 2호 명성골든빌 A-502호<NA><NA>20090918<NA>2010021120090918본점201006042
2대부중개업타시군구이관2013-서울광진-0050(대부중개)ONE PLUS대부중개개인02-2201-8863서울특별시 광진구 자양동 769번지 10호 Y타워-917<NA>14385320130828201608282014032420130828본점201403242