Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells19338
Missing cells (%)12.9%
Duplicate rows1
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
Author성동구
URLhttps://data.seoul.go.kr/dataList/OA-10789/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 (93.5%)Imbalance
등록증번호 has 171 (1.7%) missing valuesMissing
사업장 전화번호 has 3455 (34.5%) missing valuesMissing
소재지 has 299 (3.0%) missing valuesMissing
소재지(도로명) has 4792 (47.9%) missing valuesMissing
우편번호 has 5665 (56.6%) missing valuesMissing
유효기간만료일자 has 2087 (20.9%) missing valuesMissing
폐쇄일자 has 1596 (16.0%) missing valuesMissing
지점설립일자 has 1273 (12.7%) missing valuesMissing

Reproduction

Analysis started2024-05-11 01:57:26.742232
Analysis finished2024-05-11 01:57:43.940534
Duration17.2 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대부업
6211 
대부중개업
3372 
<NA>
 
417

Length

Max length5
Median length3
Mean length3.7161
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대부업 6211
62.1%
대부중개업 3372
33.7%
<NA> 417
 
4.2%

Length

2024-05-11T01:57:44.244205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:57:44.681379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대부업 6211
62.1%
대부중개업 3372
33.7%
na 417
 
4.2%

영업구분
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
3660 
<NA>
2942 
타시군구이관
1251 
영업중
842 
유효기간만료
804 
Other values (2)
501 

Length

Max length6
Median length4
Mean length3.5952
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 3660
36.6%
<NA> 2942
29.4%
타시군구이관 1251
 
12.5%
영업중 842
 
8.4%
유효기간만료 804
 
8.0%
직권취소 499
 
5.0%
갱신등록불가 2
 
< 0.1%

Length

2024-05-11T01:57:45.185797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:57:45.638052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 3660
36.6%
na 2942
29.4%
타시군구이관 1251
 
12.5%
영업중 842
 
8.4%
유효기간만료 804
 
8.0%
직권취소 499
 
5.0%
갱신등록불가 2
 
< 0.1%

등록증번호
Text

MISSING 

Distinct9792
Distinct (%)99.6%
Missing171
Missing (%)1.7%
Memory size156.2 KiB
2024-05-11T01:57:46.348874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length26
Mean length19.515515
Min length9

Characters and Unicode

Total characters191818
Distinct characters79
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

Unique9755 ?
Unique (%)99.2%

Sample

1st row2009-서울특별시-02070(대부업)
2nd row2011-서울강남-0257(대부업)
3rd row2022-서울양천-00001(대부중개업)
4th row2013-서울강남-0042(대부업)
5th row2010-서울관악-00040(대부중개업)
ValueCountFrequency (%)
2010-서울 19
 
0.2%
2014-서울특별시 14
 
0.1%
2013-서울특별시 14
 
0.1%
2016-서울특별시 11
 
0.1%
2012-서울특별시 9
 
0.1%
2011-서울특별시 9
 
0.1%
2015-서울특별시 9
 
0.1%
대부업 8
 
0.1%
성북구-00005 6
 
0.1%
2018-서울특별시 6
 
0.1%
Other values (9767) 9865
98.9%
2024-05-11T01:57:48.030402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 34008
17.7%
- 19642
 
10.2%
2 15771
 
8.2%
1 11742
 
6.1%
10898
 
5.7%
9795
 
5.1%
8480
 
4.4%
( 8208
 
4.3%
8179
 
4.3%
) 8151
 
4.2%
Other values (69) 56944
29.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 82759
43.1%
Other Letter 72917
38.0%
Dash Punctuation 19642
 
10.2%
Open Punctuation 8208
 
4.3%
Close Punctuation 8151
 
4.2%
Space Separator 141
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10898
14.9%
9795
13.4%
8480
11.6%
8179
11.2%
7941
10.9%
3473
 
4.8%
2860
 
3.9%
2571
 
3.5%
2564
 
3.5%
2564
 
3.5%
Other values (55) 13592
18.6%
Decimal Number
ValueCountFrequency (%)
0 34008
41.1%
2 15771
19.1%
1 11742
 
14.2%
3 3782
 
4.6%
8 3192
 
3.9%
4 3089
 
3.7%
6 2838
 
3.4%
9 2807
 
3.4%
7 2786
 
3.4%
5 2744
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 19642
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8208
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8151
100.0%
Space Separator
ValueCountFrequency (%)
141
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 118901
62.0%
Hangul 72917
38.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10898
14.9%
9795
13.4%
8480
11.6%
8179
11.2%
7941
10.9%
3473
 
4.8%
2860
 
3.9%
2571
 
3.5%
2564
 
3.5%
2564
 
3.5%
Other values (55) 13592
18.6%
Common
ValueCountFrequency (%)
0 34008
28.6%
- 19642
16.5%
2 15771
13.3%
1 11742
 
9.9%
( 8208
 
6.9%
) 8151
 
6.9%
3 3782
 
3.2%
8 3192
 
2.7%
4 3089
 
2.6%
6 2838
 
2.4%
Other values (4) 8478
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118901
62.0%
Hangul 72917
38.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 34008
28.6%
- 19642
16.5%
2 15771
13.3%
1 11742
 
9.9%
( 8208
 
6.9%
) 8151
 
6.9%
3 3782
 
3.2%
8 3192
 
2.7%
4 3089
 
2.6%
6 2838
 
2.4%
Other values (4) 8478
 
7.1%
Hangul
ValueCountFrequency (%)
10898
14.9%
9795
13.4%
8480
11.6%
8179
11.2%
7941
10.9%
3473
 
4.8%
2860
 
3.9%
2571
 
3.5%
2564
 
3.5%
2564
 
3.5%
Other values (55) 13592
18.6%

상호
Text

Distinct8682
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T01:57:49.204766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length33
Mean length7.7402
Min length1

Characters and Unicode

Total characters77402
Distinct characters792
Distinct categories13 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7623 ?
Unique (%)76.2%

Sample

1st row조흥캐피탈대부
2nd row에스엠씨 대부
3rd row㈜젠유텍 홀딩스 대부중개
4th row(주)한빛투자금융대부
5th row삼성머니대부중개
ValueCountFrequency (%)
주식회사 828
 
6.9%
대부중개 302
 
2.5%
대부 283
 
2.4%
유한회사 60
 
0.5%
대부업 17
 
0.1%
캐피탈 16
 
0.1%
money 16
 
0.1%
전당포 11
 
0.1%
the 10
 
0.1%
10
 
0.1%
Other values (8722) 10383
87.0%
2024-05-11T01:57:50.863222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8444
 
10.9%
8073
 
10.4%
2684
 
3.5%
2240
 
2.9%
2107
 
2.7%
2090
 
2.7%
1940
 
2.5%
1922
 
2.5%
) 1858
 
2.4%
( 1847
 
2.4%
Other values (782) 44197
57.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 67526
87.2%
Uppercase Letter 2389
 
3.1%
Space Separator 1940
 
2.5%
Close Punctuation 1858
 
2.4%
Open Punctuation 1847
 
2.4%
Lowercase Letter 1291
 
1.7%
Decimal Number 262
 
0.3%
Other Punctuation 243
 
0.3%
Dash Punctuation 28
 
< 0.1%
Other Symbol 13
 
< 0.1%
Other values (3) 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
8444
 
12.5%
8073
 
12.0%
2684
 
4.0%
2240
 
3.3%
2107
 
3.1%
2090
 
3.1%
1922
 
2.8%
1362
 
2.0%
1123
 
1.7%
1045
 
1.5%
Other values (703) 36436
54.0%
Uppercase Letter
ValueCountFrequency (%)
S 321
13.4%
K 203
 
8.5%
C 189
 
7.9%
J 179
 
7.5%
M 177
 
7.4%
H 134
 
5.6%
B 107
 
4.5%
N 96
 
4.0%
A 94
 
3.9%
G 89
 
3.7%
Other values (16) 800
33.5%
Lowercase Letter
ValueCountFrequency (%)
e 157
12.2%
n 148
11.5%
o 137
10.6%
a 106
 
8.2%
i 83
 
6.4%
t 81
 
6.3%
s 74
 
5.7%
r 60
 
4.6%
l 55
 
4.3%
c 54
 
4.2%
Other values (15) 336
26.0%
Decimal Number
ValueCountFrequency (%)
1 86
32.8%
2 39
14.9%
4 33
 
12.6%
3 24
 
9.2%
5 23
 
8.8%
9 22
 
8.4%
6 15
 
5.7%
0 9
 
3.4%
8 8
 
3.1%
7 3
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 120
49.4%
& 101
41.6%
? 8
 
3.3%
, 7
 
2.9%
2
 
0.8%
/ 2
 
0.8%
* 2
 
0.8%
@ 1
 
0.4%
Math Symbol
ValueCountFrequency (%)
~ 1
33.3%
< 1
33.3%
> 1
33.3%
Space Separator
ValueCountFrequency (%)
1940
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1858
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1847
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 28
100.0%
Other Symbol
ValueCountFrequency (%)
13
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 67520
87.2%
Common 6182
 
8.0%
Latin 3681
 
4.8%
Han 19
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
8444
 
12.5%
8073
 
12.0%
2684
 
4.0%
2240
 
3.3%
2107
 
3.1%
2090
 
3.1%
1922
 
2.8%
1362
 
2.0%
1123
 
1.7%
1045
 
1.5%
Other values (685) 36430
54.0%
Latin
ValueCountFrequency (%)
S 321
 
8.7%
K 203
 
5.5%
C 189
 
5.1%
J 179
 
4.9%
M 177
 
4.8%
e 157
 
4.3%
n 148
 
4.0%
o 137
 
3.7%
H 134
 
3.6%
B 107
 
2.9%
Other values (42) 1929
52.4%
Common
ValueCountFrequency (%)
1940
31.4%
) 1858
30.1%
( 1847
29.9%
. 120
 
1.9%
& 101
 
1.6%
1 86
 
1.4%
2 39
 
0.6%
4 33
 
0.5%
- 28
 
0.5%
3 24
 
0.4%
Other values (16) 106
 
1.7%
Han
ValueCountFrequency (%)
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
Other values (9) 9
47.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 67507
87.2%
ASCII 9860
 
12.7%
CJK 19
 
< 0.1%
None 15
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
8444
 
12.5%
8073
 
12.0%
2684
 
4.0%
2240
 
3.3%
2107
 
3.1%
2090
 
3.1%
1922
 
2.8%
1362
 
2.0%
1123
 
1.7%
1045
 
1.5%
Other values (684) 36417
53.9%
ASCII
ValueCountFrequency (%)
1940
19.7%
) 1858
18.8%
( 1847
18.7%
S 321
 
3.3%
K 203
 
2.1%
C 189
 
1.9%
J 179
 
1.8%
M 177
 
1.8%
e 157
 
1.6%
n 148
 
1.5%
Other values (66) 2841
28.8%
None
ValueCountFrequency (%)
13
86.7%
2
 
13.3%
CJK
ValueCountFrequency (%)
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
Other values (9) 9
47.4%
Number Forms
ValueCountFrequency (%)
1
100.0%

법인여부
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
개인
7201 
법인
2799 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
개인 7201
72.0%
법인 2799
 
28.0%

Length

2024-05-11T01:57:51.305454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:57:51.652393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 7201
72.0%
법인 2799
 
28.0%
Distinct5788
Distinct (%)88.4%
Missing3455
Missing (%)34.5%
Memory size156.2 KiB
2024-05-11T01:57:52.584406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length39
Mean length10.616654
Min length1

Characters and Unicode

Total characters69486
Distinct characters25
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

Unique5173 ?
Unique (%)79.0%

Sample

1st row025832525
2nd row02-6091-1010
3rd row02-563-8488
4th row028788520
5th row02-888-8770
ValueCountFrequency (%)
02 289
 
3.9%
51
 
0.7%
070 41
 
0.6%
010 11
 
0.1%
1688 8
 
0.1%
2209 7
 
0.1%
495 6
 
0.1%
1599 6
 
0.1%
434 6
 
0.1%
070-7617-0528 5
 
0.1%
Other values (6083) 6938
94.2%
2024-05-11T01:57:53.913090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11246
16.2%
2 10287
14.8%
- 7015
10.1%
5 5735
8.3%
7 5413
7.8%
1 5048
7.3%
6 5041
7.3%
3 4916
7.1%
4 4879
7.0%
8 4826
6.9%
Other values (15) 5080
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 61330
88.3%
Dash Punctuation 7015
 
10.1%
Space Separator 910
 
1.3%
Other Punctuation 121
 
0.2%
Close Punctuation 62
 
0.1%
Math Symbol 22
 
< 0.1%
Open Punctuation 16
 
< 0.1%
Other Letter 8
 
< 0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11246
18.3%
2 10287
16.8%
5 5735
9.4%
7 5413
8.8%
1 5048
8.2%
6 5041
8.2%
3 4916
8.0%
4 4879
8.0%
8 4826
7.9%
9 3939
 
6.4%
Other Letter
ValueCountFrequency (%)
2
25.0%
2
25.0%
2
25.0%
1
12.5%
1
12.5%
Other Punctuation
ValueCountFrequency (%)
/ 50
41.3%
* 47
38.8%
. 24
19.8%
Uppercase Letter
ValueCountFrequency (%)
S 1
50.0%
K 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 7015
100.0%
Space Separator
ValueCountFrequency (%)
910
100.0%
Close Punctuation
ValueCountFrequency (%)
) 62
100.0%
Math Symbol
ValueCountFrequency (%)
~ 22
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69476
> 99.9%
Hangul 8
 
< 0.1%
Latin 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11246
16.2%
2 10287
14.8%
- 7015
10.1%
5 5735
8.3%
7 5413
7.8%
1 5048
7.3%
6 5041
7.3%
3 4916
7.1%
4 4879
7.0%
8 4826
6.9%
Other values (8) 5070
7.3%
Hangul
ValueCountFrequency (%)
2
25.0%
2
25.0%
2
25.0%
1
12.5%
1
12.5%
Latin
ValueCountFrequency (%)
S 1
50.0%
K 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69478
> 99.9%
Hangul 8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11246
16.2%
2 10287
14.8%
- 7015
10.1%
5 5735
8.3%
7 5413
7.8%
1 5048
7.3%
6 5041
7.3%
3 4916
7.1%
4 4879
7.0%
8 4826
6.9%
Other values (10) 5072
7.3%
Hangul
ValueCountFrequency (%)
2
25.0%
2
25.0%
2
25.0%
1
12.5%
1
12.5%

소재지
Text

MISSING 

Distinct8633
Distinct (%)89.0%
Missing299
Missing (%)3.0%
Memory size156.2 KiB
2024-05-11T01:57:54.859290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length65
Median length47
Mean length31.503866
Min length15

Characters and Unicode

Total characters305619
Distinct characters629
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

Unique7865 ?
Unique (%)81.1%

Sample

1st row서울특별시 서초구 방배동 450번지 25호 309호
2nd row서울특별시 강남구 역삼동 742번지 21호 -202
3rd row서울특별시 양천구 신월동 412번지 7호
4th row서울특별시 강남구 역삼동 642번지 19호 역삼하이츠빌딩-1401
5th row서울특별시 관악구 신림동 1421번지 45호 -302
ValueCountFrequency (%)
서울특별시 9698
 
17.0%
강남구 1576
 
2.8%
서초구 967
 
1.7%
1호 674
 
1.2%
역삼동 656
 
1.1%
서초동 576
 
1.0%
송파구 565
 
1.0%
중구 529
 
0.9%
2호 484
 
0.8%
영등포구 453
 
0.8%
Other values (9497) 40983
71.7%
2024-05-11T01:57:56.587051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
67736
22.2%
1 13421
 
4.4%
12094
 
4.0%
11152
 
3.6%
10520
 
3.4%
9967
 
3.3%
9756
 
3.2%
9715
 
3.2%
9700
 
3.2%
2 8870
 
2.9%
Other values (619) 142688
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 167220
54.7%
Space Separator 67736
22.2%
Decimal Number 63490
 
20.8%
Dash Punctuation 5460
 
1.8%
Uppercase Letter 1118
 
0.4%
Other Punctuation 246
 
0.1%
Lowercase Letter 134
 
< 0.1%
Close Punctuation 92
 
< 0.1%
Open Punctuation 90
 
< 0.1%
Letter Number 26
 
< 0.1%
Other values (2) 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
12094
 
7.2%
11152
 
6.7%
10520
 
6.3%
9967
 
6.0%
9756
 
5.8%
9715
 
5.8%
9700
 
5.8%
8597
 
5.1%
8488
 
5.1%
7931
 
4.7%
Other values (540) 69300
41.4%
Uppercase Letter
ValueCountFrequency (%)
B 275
24.6%
A 214
19.1%
D 76
 
6.8%
S 73
 
6.5%
C 58
 
5.2%
T 47
 
4.2%
K 45
 
4.0%
I 43
 
3.8%
L 41
 
3.7%
G 36
 
3.2%
Other values (15) 210
18.8%
Lowercase Letter
ValueCountFrequency (%)
e 23
17.2%
i 14
10.4%
t 12
 
9.0%
n 12
 
9.0%
c 9
 
6.7%
r 8
 
6.0%
l 8
 
6.0%
w 6
 
4.5%
o 6
 
4.5%
y 6
 
4.5%
Other values (12) 30
22.4%
Decimal Number
ValueCountFrequency (%)
1 13421
21.1%
2 8870
14.0%
0 7984
12.6%
3 6983
11.0%
4 5863
9.2%
5 4996
 
7.9%
6 4511
 
7.1%
7 4040
 
6.4%
9 3444
 
5.4%
8 3378
 
5.3%
Other Punctuation
ValueCountFrequency (%)
, 86
35.0%
/ 81
32.9%
. 70
28.5%
3
 
1.2%
@ 2
 
0.8%
* 1
 
0.4%
; 1
 
0.4%
# 1
 
0.4%
& 1
 
0.4%
Letter Number
ValueCountFrequency (%)
19
73.1%
6
 
23.1%
1
 
3.8%
Math Symbol
ValueCountFrequency (%)
~ 4
66.7%
> 1
 
16.7%
< 1
 
16.7%
Close Punctuation
ValueCountFrequency (%)
) 91
98.9%
] 1
 
1.1%
Open Punctuation
ValueCountFrequency (%)
( 89
98.9%
[ 1
 
1.1%
Space Separator
ValueCountFrequency (%)
67736
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5460
100.0%
Other Number
ValueCountFrequency (%)
½ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 167220
54.7%
Common 137121
44.9%
Latin 1278
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
12094
 
7.2%
11152
 
6.7%
10520
 
6.3%
9967
 
6.0%
9756
 
5.8%
9715
 
5.8%
9700
 
5.8%
8597
 
5.1%
8488
 
5.1%
7931
 
4.7%
Other values (540) 69300
41.4%
Latin
ValueCountFrequency (%)
B 275
21.5%
A 214
16.7%
D 76
 
5.9%
S 73
 
5.7%
C 58
 
4.5%
T 47
 
3.7%
K 45
 
3.5%
I 43
 
3.4%
L 41
 
3.2%
G 36
 
2.8%
Other values (40) 370
29.0%
Common
ValueCountFrequency (%)
67736
49.4%
1 13421
 
9.8%
2 8870
 
6.5%
0 7984
 
5.8%
3 6983
 
5.1%
4 5863
 
4.3%
- 5460
 
4.0%
5 4996
 
3.6%
6 4511
 
3.3%
7 4040
 
2.9%
Other values (19) 7257
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 167220
54.7%
ASCII 138369
45.3%
Number Forms 26
 
< 0.1%
None 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
67736
49.0%
1 13421
 
9.7%
2 8870
 
6.4%
0 7984
 
5.8%
3 6983
 
5.0%
4 5863
 
4.2%
- 5460
 
3.9%
5 4996
 
3.6%
6 4511
 
3.3%
7 4040
 
2.9%
Other values (64) 8505
 
6.1%
Hangul
ValueCountFrequency (%)
12094
 
7.2%
11152
 
6.7%
10520
 
6.3%
9967
 
6.0%
9756
 
5.8%
9715
 
5.8%
9700
 
5.8%
8597
 
5.1%
8488
 
5.1%
7931
 
4.7%
Other values (540) 69300
41.4%
Number Forms
ValueCountFrequency (%)
19
73.1%
6
 
23.1%
1
 
3.8%
None
ValueCountFrequency (%)
3
75.0%
½ 1
 
25.0%

소재지(도로명)
Text

MISSING 

Distinct4733
Distinct (%)90.9%
Missing4792
Missing (%)47.9%
Memory size156.2 KiB
2024-05-11T01:57:57.854873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length67
Median length52
Mean length37.267473
Min length19

Characters and Unicode

Total characters194089
Distinct characters611
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

Unique4305 ?
Unique (%)82.7%

Sample

1st row서울특별시 양천구 국회대로 50, 지층 (신월동)
2nd row서울특별시 강남구 테헤란로 151, 1401호 (역삼동, 역삼하이츠빌딩)
3rd row서울특별시 금천구 가마산로 96, 306호 (가산동, 대륭테크노타운8차)
4th row서울특별시 강남구 논현로 542, 금석빌딩 201호 (역삼동)
5th row서울특별시 송파구 백제고분로22길 9, 303호 (삼전동)
ValueCountFrequency (%)
서울특별시 5205
 
14.1%
강남구 956
 
2.6%
서초구 585
 
1.6%
2층 469
 
1.3%
역삼동 409
 
1.1%
서초동 379
 
1.0%
3층 365
 
1.0%
4층 309
 
0.8%
영등포구 301
 
0.8%
송파구 284
 
0.8%
Other values (6588) 27641
74.9%
2024-05-11T01:57:59.581088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31719
 
16.3%
1 7422
 
3.8%
, 7127
 
3.7%
6897
 
3.6%
6796
 
3.5%
5822
 
3.0%
5752
 
3.0%
5412
 
2.8%
2 5318
 
2.7%
5252
 
2.7%
Other values (601) 106572
54.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 108061
55.7%
Decimal Number 34673
 
17.9%
Space Separator 31719
 
16.3%
Other Punctuation 7142
 
3.7%
Close Punctuation 5250
 
2.7%
Open Punctuation 5250
 
2.7%
Dash Punctuation 1025
 
0.5%
Uppercase Letter 820
 
0.4%
Lowercase Letter 111
 
0.1%
Letter Number 29
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6897
 
6.4%
6796
 
6.3%
5822
 
5.4%
5752
 
5.3%
5412
 
5.0%
5252
 
4.9%
5218
 
4.8%
5206
 
4.8%
4267
 
3.9%
2736
 
2.5%
Other values (527) 54703
50.6%
Uppercase Letter
ValueCountFrequency (%)
B 169
20.6%
A 127
15.5%
S 72
8.8%
C 60
 
7.3%
G 42
 
5.1%
T 39
 
4.8%
L 36
 
4.4%
K 36
 
4.4%
I 34
 
4.1%
E 33
 
4.0%
Other values (15) 172
21.0%
Lowercase Letter
ValueCountFrequency (%)
e 16
14.4%
t 10
 
9.0%
i 9
 
8.1%
w 8
 
7.2%
n 8
 
7.2%
o 8
 
7.2%
r 8
 
7.2%
l 7
 
6.3%
c 5
 
4.5%
b 5
 
4.5%
Other values (10) 27
24.3%
Decimal Number
ValueCountFrequency (%)
1 7422
21.4%
2 5318
15.3%
0 4499
13.0%
3 4135
11.9%
4 3017
8.7%
5 2757
 
8.0%
6 2190
 
6.3%
7 1965
 
5.7%
8 1821
 
5.3%
9 1549
 
4.5%
Other Punctuation
ValueCountFrequency (%)
, 7127
99.8%
. 8
 
0.1%
2
 
< 0.1%
@ 2
 
< 0.1%
# 1
 
< 0.1%
& 1
 
< 0.1%
/ 1
 
< 0.1%
Letter Number
ValueCountFrequency (%)
21
72.4%
6
 
20.7%
2
 
6.9%
Math Symbol
ValueCountFrequency (%)
~ 7
77.8%
> 1
 
11.1%
< 1
 
11.1%
Close Punctuation
ValueCountFrequency (%)
) 5249
> 99.9%
] 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 5249
> 99.9%
[ 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
31719
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1025
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 108061
55.7%
Common 85068
43.8%
Latin 960
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
6897
 
6.4%
6796
 
6.3%
5822
 
5.4%
5752
 
5.3%
5412
 
5.0%
5252
 
4.9%
5218
 
4.8%
5206
 
4.8%
4267
 
3.9%
2736
 
2.5%
Other values (527) 54703
50.6%
Latin
ValueCountFrequency (%)
B 169
17.6%
A 127
13.2%
S 72
 
7.5%
C 60
 
6.2%
G 42
 
4.4%
T 39
 
4.1%
L 36
 
3.8%
K 36
 
3.8%
I 34
 
3.5%
E 33
 
3.4%
Other values (38) 312
32.5%
Common
ValueCountFrequency (%)
31719
37.3%
1 7422
 
8.7%
, 7127
 
8.4%
2 5318
 
6.3%
) 5249
 
6.2%
( 5249
 
6.2%
0 4499
 
5.3%
3 4135
 
4.9%
4 3017
 
3.5%
5 2757
 
3.2%
Other values (16) 8576
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 108061
55.7%
ASCII 85997
44.3%
Number Forms 29
 
< 0.1%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31719
36.9%
1 7422
 
8.6%
, 7127
 
8.3%
2 5318
 
6.2%
) 5249
 
6.1%
( 5249
 
6.1%
0 4499
 
5.2%
3 4135
 
4.8%
4 3017
 
3.5%
5 2757
 
3.2%
Other values (60) 9505
 
11.1%
Hangul
ValueCountFrequency (%)
6897
 
6.4%
6796
 
6.3%
5822
 
5.4%
5752
 
5.3%
5412
 
5.0%
5252
 
4.9%
5218
 
4.8%
5206
 
4.8%
4267
 
3.9%
2736
 
2.5%
Other values (527) 54703
50.6%
Number Forms
ValueCountFrequency (%)
21
72.4%
6
 
20.7%
2
 
6.9%
None
ValueCountFrequency (%)
2
100.0%

우편번호
Real number (ℝ)

MISSING 

Distinct1384
Distinct (%)31.9%
Missing5665
Missing (%)56.6%
Infinite0
Infinite (%)0.0%
Mean136440.78
Minimum4526
Maximum429842
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T01:58:00.208433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4526
5-th percentile110043.7
Q1132020
median136130
Q3143200
95-th percentile157030
Maximum429842
Range425316
Interquartile range (IQR)11180

Descriptive statistics

Standard deviation15849.502
Coefficient of variation (CV)0.11616397
Kurtosis55.49792
Mean136440.78
Median Absolute Deviation (MAD)5295
Skewness1.1700573
Sum5.9147076 × 108
Variance2.5120671 × 108
MonotonicityNot monotonic
2024-05-11T01:58:00.844219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
137070 138
 
1.4%
135080 129
 
1.3%
157010 67
 
0.7%
135010 60
 
0.6%
151015 49
 
0.5%
152050 48
 
0.5%
151050 47
 
0.5%
158070 42
 
0.4%
142070 41
 
0.4%
158050 40
 
0.4%
Other values (1374) 3674
36.7%
(Missing) 5665
56.6%
ValueCountFrequency (%)
4526 1
 
< 0.1%
4534 1
 
< 0.1%
4537 1
 
< 0.1%
4538 2
 
< 0.1%
4550 1
 
< 0.1%
4554 1
 
< 0.1%
7327 1
 
< 0.1%
100011 5
0.1%
100012 4
< 0.1%
100013 1
 
< 0.1%
ValueCountFrequency (%)
429842 1
 
< 0.1%
410380 1
 
< 0.1%
158881 1
 
< 0.1%
158877 1
 
< 0.1%
158871 1
 
< 0.1%
158864 4
< 0.1%
158863 2
 
< 0.1%
158860 5
0.1%
158859 2
 
< 0.1%
158857 1
 
< 0.1%

등록일자
Real number (ℝ)

HIGH CORRELATION 

Distinct3551
Distinct (%)35.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20136911
Minimum20051216
Maximum20240510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T01:58:01.442021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20051216
5-th percentile20070830
Q120091110
median20130309
Q320170905
95-th percentile20230221
Maximum20240510
Range189294
Interquartile range (IQR)79795

Descriptive statistics

Standard deviation49179.279
Coefficient of variation (CV)0.0024422454
Kurtosis-0.93520241
Mean20136911
Median Absolute Deviation (MAD)39691
Skewness0.45200657
Sum2.0136911 × 1011
Variance2.4186015 × 109
MonotonicityNot monotonic
2024-05-11T01:58:02.046031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20080814 28
 
0.3%
20080731 27
 
0.3%
20080818 26
 
0.3%
20081222 23
 
0.2%
20080806 19
 
0.2%
20090520 18
 
0.2%
20080926 17
 
0.2%
20090611 16
 
0.2%
20090325 15
 
0.1%
20080718 15
 
0.1%
Other values (3541) 9796
98.0%
ValueCountFrequency (%)
20051216 1
 
< 0.1%
20060320 1
 
< 0.1%
20060324 3
< 0.1%
20060329 2
< 0.1%
20060407 3
< 0.1%
20060412 1
 
< 0.1%
20060417 1
 
< 0.1%
20060418 3
< 0.1%
20060425 1
 
< 0.1%
20060501 4
< 0.1%
ValueCountFrequency (%)
20240510 2
< 0.1%
20240508 1
 
< 0.1%
20240507 3
< 0.1%
20240503 3
< 0.1%
20240430 3
< 0.1%
20240425 3
< 0.1%
20240424 2
< 0.1%
20240423 1
 
< 0.1%
20240422 2
< 0.1%
20240419 1
 
< 0.1%

유효기간만료일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3315
Distinct (%)41.9%
Missing2087
Missing (%)20.9%
Infinite0
Infinite (%)0.0%
Mean20182033
Minimum20080922
Maximum22180428
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T01:58:02.750731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20080922
5-th percentile20120219
Q120141031
median20180120
Q320220323
95-th percentile20260508
Maximum22180428
Range2099506
Interquartile range (IQR)79292

Descriptive statistics

Standard deviation50161.133
Coefficient of variation (CV)0.0024854351
Kurtosis316.9078
Mean20182033
Median Absolute Deviation (MAD)39113
Skewness8.1951637
Sum1.5970043 × 1011
Variance2.5161393 × 109
MonotonicityNot monotonic
2024-05-11T01:58:03.290218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20140831 18
 
0.2%
20110831 15
 
0.1%
20120520 13
 
0.1%
20170701 11
 
0.1%
20180428 11
 
0.1%
20140721 10
 
0.1%
20161018 10
 
0.1%
20150515 10
 
0.1%
20140905 10
 
0.1%
20110731 10
 
0.1%
Other values (3305) 7795
78.0%
(Missing) 2087
 
20.9%
ValueCountFrequency (%)
20080922 1
< 0.1%
20090907 1
< 0.1%
20091116 1
< 0.1%
20100112 1
< 0.1%
20100117 1
< 0.1%
20100219 1
< 0.1%
20100321 1
< 0.1%
20100405 1
< 0.1%
20100411 2
< 0.1%
20100418 2
< 0.1%
ValueCountFrequency (%)
22180428 1
 
< 0.1%
20270510 2
< 0.1%
20270508 1
 
< 0.1%
20270507 3
< 0.1%
20270503 3
< 0.1%
20270430 3
< 0.1%
20270425 3
< 0.1%
20270424 2
< 0.1%
20270423 1
 
< 0.1%
20270422 1
 
< 0.1%

폐쇄일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3088
Distinct (%)36.7%
Missing1596
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean20141970
Minimum20060920
Maximum20240510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T01:58:03.836824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20060920
5-th percentile20090921
Q120110322
median20130716
Q320170508
95-th percentile20221110
Maximum20240510
Range179590
Interquartile range (IQR)60186

Descriptive statistics

Standard deviation41193.089
Coefficient of variation (CV)0.0020451371
Kurtosis-0.58796567
Mean20141970
Median Absolute Deviation (MAD)29789.5
Skewness0.69151964
Sum1.6927311 × 1011
Variance1.6968706 × 109
MonotonicityNot monotonic
2024-05-11T01:58:04.400847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20091116 210
 
2.1%
20100927 79
 
0.8%
20160725 28
 
0.3%
20101213 21
 
0.2%
20110420 19
 
0.2%
20110412 17
 
0.2%
20110914 17
 
0.2%
20170124 16
 
0.2%
20101126 16
 
0.2%
20110627 15
 
0.1%
Other values (3078) 7966
79.7%
(Missing) 1596
 
16.0%
ValueCountFrequency (%)
20060920 1
 
< 0.1%
20081023 1
 
< 0.1%
20081212 1
 
< 0.1%
20081217 1
 
< 0.1%
20090219 1
 
< 0.1%
20090305 1
 
< 0.1%
20090309 1
 
< 0.1%
20090311 1
 
< 0.1%
20090312 3
< 0.1%
20090313 3
< 0.1%
ValueCountFrequency (%)
20240510 1
 
< 0.1%
20240508 1
 
< 0.1%
20240507 3
< 0.1%
20240503 1
 
< 0.1%
20240502 1
 
< 0.1%
20240501 3
< 0.1%
20240430 1
 
< 0.1%
20240429 1
 
< 0.1%
20240426 2
< 0.1%
20240423 2
< 0.1%

지점설립일자
Text

MISSING 

Distinct3588
Distinct (%)41.1%
Missing1273
Missing (%)12.7%
Memory size156.2 KiB
2024-05-11T01:58:05.371027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters69816
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

Unique1410 ?
Unique (%)16.2%

Sample

1st row20090821
2nd row20110728
3rd row20220106
4th row20130212
5th row20100503
ValueCountFrequency (%)
20090520 25
 
0.3%
20090820 23
 
0.3%
20090514 18
 
0.2%
20090512 17
 
0.2%
20090611 17
 
0.2%
20090507 16
 
0.2%
20090529 16
 
0.2%
20090821 15
 
0.2%
20090511 15
 
0.2%
20090722 13
 
0.1%
Other values (3578) 8552
98.0%
2024-05-11T01:58:06.440103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 22627
32.4%
2 15946
22.8%
1 14029
20.1%
3 2841
 
4.1%
7 2608
 
3.7%
9 2579
 
3.7%
6 2388
 
3.4%
4 2286
 
3.3%
5 2276
 
3.3%
8 2230
 
3.2%
Other values (4) 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69810
> 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 22627
32.4%
2 15946
22.8%
1 14029
20.1%
3 2841
 
4.1%
7 2608
 
3.7%
9 2579
 
3.7%
6 2388
 
3.4%
4 2286
 
3.3%
5 2276
 
3.3%
8 2230
 
3.2%
Lowercase Letter
ValueCountFrequency (%)
p 1
50.0%
r 1
50.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69813
> 99.9%
Latin 3
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22627
32.4%
2 15946
22.8%
1 14029
20.1%
3 2841
 
4.1%
7 2608
 
3.7%
9 2579
 
3.7%
6 2388
 
3.4%
4 2286
 
3.3%
5 2276
 
3.3%
8 2230
 
3.2%
Latin
ValueCountFrequency (%)
A 1
33.3%
p 1
33.3%
r 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22627
32.4%
2 15946
22.8%
1 14029
20.1%
3 2841
 
4.1%
7 2608
 
3.7%
9 2579
 
3.7%
6 2388
 
3.4%
4 2286
 
3.3%
5 2276
 
3.3%
8 2230
 
3.2%
Other values (4) 6
 
< 0.1%

본점여부
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
본점
9923 
지점
 
77

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 9923
99.2%
지점 77
 
0.8%

Length

2024-05-11T01:58:06.876838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:58:07.276508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
본점 9923
99.2%
지점 77
 
0.8%

최근수정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct3133
Distinct (%)31.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20152963
Minimum20090518
Maximum20240510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T01:58:07.585233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20090518
5-th percentile20091118
Q120110916
median20140904
Q320190522
95-th percentile20231016
Maximum20240510
Range149992
Interquartile range (IQR)79606.25

Descriptive statistics

Standard deviation46247.572
Coefficient of variation (CV)0.0022948274
Kurtosis-1.0901873
Mean20152963
Median Absolute Deviation (MAD)30687.5
Skewness0.44142145
Sum2.0152963 × 1011
Variance2.1388379 × 109
MonotonicityNot monotonic
2024-05-11T01:58:08.027526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20091117 85
 
0.9%
20091118 49
 
0.5%
20090609 48
 
0.5%
20091116 47
 
0.5%
20100927 44
 
0.4%
20090622 39
 
0.4%
20100330 39
 
0.4%
20091119 38
 
0.4%
20110425 32
 
0.3%
20130621 29
 
0.3%
Other values (3123) 9550
95.5%
ValueCountFrequency (%)
20090518 1
 
< 0.1%
20090519 1
 
< 0.1%
20090521 4
 
< 0.1%
20090601 1
 
< 0.1%
20090602 3
 
< 0.1%
20090603 10
 
0.1%
20090604 12
 
0.1%
20090605 4
 
< 0.1%
20090608 3
 
< 0.1%
20090609 48
0.5%
ValueCountFrequency (%)
20240510 5
0.1%
20240509 2
 
< 0.1%
20240508 5
0.1%
20240507 6
0.1%
20240503 5
0.1%
20240502 5
0.1%
20240501 7
0.1%
20240430 3
< 0.1%
20240429 5
0.1%
20240426 2
 
< 0.1%

Interactions

2024-05-11T01:57:39.464192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:57:32.507212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:57:33.936256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:57:35.556102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:57:37.359104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:57:39.812625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:57:32.770868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:57:34.203895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:57:35.917042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:57:37.655666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:57:40.281982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:57:33.050684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:57:34.508212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:57:36.297388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:57:38.031577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:57:40.714561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:57:33.357109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:57:34.788452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:57:36.764410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:57:38.494827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:57:41.040231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:57:33.650140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:57:35.138169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:57:37.064775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:57:38.896862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T01:58:08.352604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록신청사업영업구분법인여부우편번호등록일자유효기간만료일자폐쇄일자본점여부최근수정일자
등록신청사업1.0000.0970.0200.0000.2300.0000.1700.0000.172
영업구분0.0971.0000.2930.0060.6090.0000.1770.0300.538
법인여부0.0200.2931.0000.1260.3560.0000.2060.2100.354
우편번호0.0000.0060.1261.0000.270NaN0.3240.0000.348
등록일자0.2300.6090.3560.2701.0000.0000.8620.0750.939
유효기간만료일자0.0000.0000.000NaN0.0001.0000.0000.0000.000
폐쇄일자0.1700.1770.2060.3240.8620.0001.0000.0320.960
본점여부0.0000.0300.2100.0000.0750.0000.0321.0000.083
최근수정일자0.1720.5380.3540.3480.9390.0000.9600.0831.000
2024-05-11T01:58:08.797434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법인여부등록신청사업영업구분본점여부
법인여부1.0000.0130.2110.135
등록신청사업0.0131.0000.0700.000
영업구분0.2110.0701.0000.022
본점여부0.1350.0000.0221.000
2024-05-11T01:58:09.003530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호등록일자유효기간만료일자폐쇄일자최근수정일자등록신청사업영업구분법인여부본점여부
우편번호1.0000.0230.0300.0440.0310.0000.0430.0790.000
등록일자0.0231.0000.9960.9610.9660.1760.3770.2730.057
유효기간만료일자0.0300.9961.0000.9650.9680.0000.0000.0000.000
폐쇄일자0.0440.9610.9651.0000.9920.1700.1020.2060.031
최근수정일자0.0310.9660.9680.9921.0000.1320.3010.2710.063
등록신청사업0.0000.1760.0000.1700.1321.0000.0700.0130.000
영업구분0.0430.3770.0000.1020.3010.0701.0000.2110.022
법인여부0.0790.2730.0000.2060.2710.0130.2111.0000.135
본점여부0.0000.0570.0000.0310.0630.0000.0220.1351.000

Missing values

2024-05-11T01:57:41.709414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T01:57:42.663002image/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.
2024-05-11T01:57:43.486829image/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
20081대부업타시군구이관2009-서울특별시-02070(대부업)조흥캐피탈대부개인025832525서울특별시 서초구 방배동 450번지 25호 309호<NA>13706020120712201507122012122120090821본점20121221
18292대부업폐업2011-서울강남-0257(대부업)에스엠씨 대부개인02-6091-1010서울특별시 강남구 역삼동 742번지 21호 -202<NA>13508020110728201407282013070220110728본점20130702
1918대부중개업영업중2022-서울양천-00001(대부중개업)㈜젠유텍 홀딩스 대부중개법인<NA>서울특별시 양천구 신월동 412번지 7호서울특별시 양천구 국회대로 50, 지층 (신월동)<NA>2022010720250107<NA>20220106본점20230822
3407대부업폐업2013-서울강남-0042(대부업)(주)한빛투자금융대부법인02-563-8488서울특별시 강남구 역삼동 642번지 19호 역삼하이츠빌딩-1401서울특별시 강남구 테헤란로 151, 1401호 (역삼동, 역삼하이츠빌딩)13598120211129202411292023010220130212본점20230102
24997대부중개업<NA>2010-서울관악-00040(대부중개업)삼성머니대부중개개인028788520서울특별시 관악구 신림동 1421번지 45호 -302<NA>15101520100503201305032011042520100503본점20110425
9277대부업폐업2015-서울금천-00014주식회사 대부리더스법인02-888-8770서울특별시 금천구 가산동 481번지 11호서울특별시 금천구 가마산로 96, 306호 (가산동, 대륭테크노타운8차)15377520150407201804072018030220150407본점20180302
5974대부업폐업2019-서울강남-0135(대부업)(주)선해대부법인02-544-0555서울특별시 강남구 역삼동 667번지 금석빌딩서울특별시 강남구 논현로 542, 금석빌딩 201호 (역삼동)<NA>20190821202208212021012920190820본점20210129
21415대부업폐업2009-서울특별시-01636(대부업)(주)이즈에스원대부법인024693344서울특별시 성동구 성수동2가 277-155 3층<NA>13312020090717201207172012062020090717본점20120620
15912대부업폐업2013-서울송파-0079(대부업)에이치엘대부업개인02-419-0487서울특별시 송파구 삼전동 46번지 -303서울특별시 송파구 백제고분로22길 9, 303호 (삼전동)13883820130819201608192014072120130819본점20140721
23565대부업<NA>2008-서울특별시-00872(대부업)개인025340988서울특별시 서초구 잠원동 58번지 16호 신반포10차아파트 318-806호<NA>13703020080903201110052011100620050921본점20111005
등록신청사업영업구분등록증번호상호법인여부사업장 전화번호소재지소재지(도로명)우편번호등록일자유효기간만료일자폐쇄일자지점설립일자본점여부최근수정일자
11351대부업폐업2016-서울특별시 성북구-00023대부대자기획개인02-914-7265서울특별시 성북구 정릉동 815번지 16호 가 -302서울특별시 성북구 보국문로36길 34, 가동 302호 (정릉동, 태청그린하우스)<NA>20141205201712052017011620060405본점20170119
3875대부업영업중2022-서울서초-0060(대부업)주식회사 베스트이너스대부법인<NA>서울특별시 서초구 방배동 449번지 6호 4층 119호서울특별시 서초구 방배천로2길 21, 4층 119호 (방배동)<NA>2022091420250914<NA>20220914본점20220914
22834대부업폐업2009-서울특별시-00211(대부업)원일개인024772776서울특별시 강동구 길동 106-5<NA><NA>20090122<NA>20111223<NA>본점20111223
29876<NA><NA>2006-서울특별시-00419JR캐피탈개인0237896096/서울특별시 중구 을지로1가 37 서광빌딩 1102호<NA>10019120060928<NA>2009111620060918본점20091117
6118대부업폐업2019-서울금천-0046금보대부개인<NA>서울특별시 금천구 시흥동 1013번지 7호 벽산중심상가서울특별시 금천구 금하로 763, 벽산중심상가 205-5호 (시흥동)<NA>20191023202210232020122220191014본점20201222
21466대부중개업타시군구이관2011-서울서대문-00039(대부중개)(주)프리즈비대부중개법인<NA>서울특별시 서대문구 북가좌동 392번지 35호 -212<NA>12081720111108201411082012061220111108본점20120612
12945대부업타시군구이관2014-서울강북-0052밀레니엄대부개인<NA>서울특별시 강북구 수유동 45번지 1호 쏠라리움타워-606서울특별시 강북구 노해로 3, 606호 (수유동, 쏠라리움타워)14207120141201201712012016053120141201본점20160531
1627대부업직권취소2019-서울강남-0152(대부업)(주)태양대부홀딩스법인1544-6661서울특별시 강남구 역삼동 837번지 18호 서희스타힐스오피스텔-719서울특별시 강남구 도곡로3길 19, 서희스타힐스오피스텔 719호 (역삼동)<NA>20191002202210022022081620191002본점20231005
1596대부업폐업2020-서울구로-0061(대부업)케이에이치행복준대부개인<NA>서울특별시 구로구 구로동 222번지 3호 제이앤케이디지털타워서울특별시 구로구 디지털로26길 111, 제이앤케이디지털타워 1707(방16번)호 (구로동)<NA>20201222202312222023101220201222본점20231012
13350대부중개업폐업2015-서울노원-00004파트너스대부중개개인02-6314-5934서울특별시 노원구 공릉동 81번지 1020 태강아파트-902서울특별시 노원구 공릉로34길 62, 1020동 902호 (공릉동, 태강아파트 )13977320150329201803292016020120120329본점20160202

Duplicate rows

Most frequently occurring

등록신청사업영업구분등록증번호상호법인여부사업장 전화번호소재지소재지(도로명)우편번호등록일자유효기간만료일자폐쇄일자지점설립일자본점여부최근수정일자# duplicates
0대부중개업타시군구이관2013-서울광진-0050(대부중개)ONE PLUS대부중개개인02-2201-8863서울특별시 광진구 자양동 769번지 10호 Y타워-917<NA>14385320130828201608282014032420130828본점201403242