Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells19271
Missing cells (%)12.8%
Duplicate rows0
Duplicate rows (%)0.0%
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
Author중구
URLhttps://data.seoul.go.kr/dataList/OA-10250/S/1/datasetView.do

Alerts

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 (93.6%)Imbalance
등록증번호 has 182 (1.8%) missing valuesMissing
사업장 전화번호 has 3397 (34.0%) missing valuesMissing
소재지 has 302 (3.0%) missing valuesMissing
소재지(도로명) has 4856 (48.6%) missing valuesMissing
우편번호 has 5584 (55.8%) missing valuesMissing
유효기간만료일자 has 2106 (21.1%) missing valuesMissing
폐쇄일자 has 1585 (15.8%) missing valuesMissing
지점설립일자 has 1259 (12.6%) missing valuesMissing

Reproduction

Analysis started2024-05-18 06:02:42.743705
Analysis finished2024-05-18 06:03:00.232416
Duration17.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대부업
6179 
대부중개업
3348 
<NA>
 
473

Length

Max length5
Median length3
Mean length3.7169
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대부업 6179
61.8%
대부중개업 3348
33.5%
<NA> 473
 
4.7%

Length

2024-05-18T15:03:00.579283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T15:03:00.990777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대부업 6179
61.8%
대부중개업 3348
33.5%
na 473
 
4.7%

영업구분
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
3666 
<NA>
2956 
타시군구이관
1207 
영업중
840 
유효기간만료
802 
Other values (2)
529 

Length

Max length6
Median length4
Mean length3.5848
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 3666
36.7%
<NA> 2956
29.6%
타시군구이관 1207
 
12.1%
영업중 840
 
8.4%
유효기간만료 802
 
8.0%
직권취소 528
 
5.3%
갱신등록불가 1
 
< 0.1%

Length

2024-05-18T15:03:01.528893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T15:03:01.919148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 3666
36.7%
na 2956
29.6%
타시군구이관 1207
 
12.1%
영업중 840
 
8.4%
유효기간만료 802
 
8.0%
직권취소 528
 
5.3%
갱신등록불가 1
 
< 0.1%

등록증번호
Text

MISSING 

Distinct9769
Distinct (%)99.5%
Missing182
Missing (%)1.8%
Memory size156.2 KiB
2024-05-18T15:03:02.541951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length27
Mean length19.521186
Min length1

Characters and Unicode

Total characters191659
Distinct characters87
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

Unique9720 ?
Unique (%)99.0%

Sample

1st row2007-서울특별시-01020(대부업)
2nd row2008-서울특별시-00385(대부업)
3rd row2021-서울영등포-2122(대부중개업)
4th row2017-서울구로-030(대부중개업)
5th row2013-서울서초-0037(대부업)
ValueCountFrequency (%)
2013-서울특별시 18
 
0.2%
2012-서울특별시 16
 
0.2%
2010-서울 16
 
0.2%
2011-서울특별시 12
 
0.1%
2015-서울특별시 9
 
0.1%
대부업 9
 
0.1%
2014-서울특별시 7
 
0.1%
2016-서울특별시 7
 
0.1%
2018-서울특별시 6
 
0.1%
성북구-00006 6
 
0.1%
Other values (9741) 9859
98.9%
2024-05-18T15:03:03.640921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 33890
17.7%
- 19620
 
10.2%
2 15810
 
8.2%
1 11768
 
6.1%
10863
 
5.7%
9783
 
5.1%
8486
 
4.4%
( 8204
 
4.3%
8167
 
4.3%
) 8132
 
4.2%
Other values (77) 56936
29.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 82628
43.1%
Other Letter 72927
38.1%
Dash Punctuation 19620
 
10.2%
Open Punctuation 8204
 
4.3%
Close Punctuation 8132
 
4.2%
Space Separator 148
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10863
14.9%
9783
13.4%
8486
11.6%
8167
11.2%
7922
10.9%
3473
 
4.8%
2895
 
4.0%
2552
 
3.5%
2547
 
3.5%
2547
 
3.5%
Other values (63) 13692
18.8%
Decimal Number
ValueCountFrequency (%)
0 33890
41.0%
2 15810
19.1%
1 11768
 
14.2%
3 3730
 
4.5%
8 3168
 
3.8%
4 3039
 
3.7%
6 2885
 
3.5%
7 2830
 
3.4%
9 2806
 
3.4%
5 2702
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 19620
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8204
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8132
100.0%
Space Separator
ValueCountFrequency (%)
148
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 118732
61.9%
Hangul 72927
38.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10863
14.9%
9783
13.4%
8486
11.6%
8167
11.2%
7922
10.9%
3473
 
4.8%
2895
 
4.0%
2552
 
3.5%
2547
 
3.5%
2547
 
3.5%
Other values (63) 13692
18.8%
Common
ValueCountFrequency (%)
0 33890
28.5%
- 19620
16.5%
2 15810
13.3%
1 11768
 
9.9%
( 8204
 
6.9%
) 8132
 
6.8%
3 3730
 
3.1%
8 3168
 
2.7%
4 3039
 
2.6%
6 2885
 
2.4%
Other values (4) 8486
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118732
61.9%
Hangul 72927
38.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33890
28.5%
- 19620
16.5%
2 15810
13.3%
1 11768
 
9.9%
( 8204
 
6.9%
) 8132
 
6.8%
3 3730
 
3.1%
8 3168
 
2.7%
4 3039
 
2.6%
6 2885
 
2.4%
Other values (4) 8486
 
7.1%
Hangul
ValueCountFrequency (%)
10863
14.9%
9783
13.4%
8486
11.6%
8167
11.2%
7922
10.9%
3473
 
4.8%
2895
 
4.0%
2552
 
3.5%
2547
 
3.5%
2547
 
3.5%
Other values (63) 13692
18.8%

상호
Text

Distinct8587
Distinct (%)85.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T15:03:04.396816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length28
Mean length7.6868
Min length1

Characters and Unicode

Total characters76868
Distinct characters755
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

Unique7463 ?
Unique (%)74.6%

Sample

1st row이정두
2nd row성진실업
3rd row주식회사 아임머니대부
4th row씨제이엠대부중개
5th row이티원강남대부
ValueCountFrequency (%)
주식회사 813
 
6.8%
대부중개 310
 
2.6%
대부 278
 
2.3%
유한회사 54
 
0.5%
캐피탈 20
 
0.2%
대부업 17
 
0.1%
14
 
0.1%
대부중개업 13
 
0.1%
미래 10
 
0.1%
미래대부 9
 
0.1%
Other values (8633) 10342
87.1%
2024-05-18T15:03:05.730371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8444
 
11.0%
8089
 
10.5%
2680
 
3.5%
2231
 
2.9%
2071
 
2.7%
2059
 
2.7%
1940
 
2.5%
1881
 
2.4%
) 1869
 
2.4%
( 1857
 
2.4%
Other values (745) 43747
56.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 67505
87.8%
Uppercase Letter 2282
 
3.0%
Space Separator 1881
 
2.4%
Close Punctuation 1869
 
2.4%
Open Punctuation 1857
 
2.4%
Lowercase Letter 961
 
1.3%
Other Punctuation 244
 
0.3%
Decimal Number 241
 
0.3%
Dash Punctuation 21
 
< 0.1%
Other Symbol 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
8444
 
12.5%
8089
 
12.0%
2680
 
4.0%
2231
 
3.3%
2071
 
3.1%
2059
 
3.1%
1940
 
2.9%
1342
 
2.0%
1118
 
1.7%
1037
 
1.5%
Other values (670) 36494
54.1%
Uppercase Letter
ValueCountFrequency (%)
S 286
 
12.5%
K 214
 
9.4%
C 190
 
8.3%
J 174
 
7.6%
M 166
 
7.3%
H 120
 
5.3%
B 113
 
5.0%
L 93
 
4.1%
A 88
 
3.9%
N 82
 
3.6%
Other values (16) 756
33.1%
Lowercase Letter
ValueCountFrequency (%)
e 117
12.2%
n 111
11.6%
o 93
 
9.7%
a 92
 
9.6%
i 61
 
6.3%
s 51
 
5.3%
t 51
 
5.3%
c 47
 
4.9%
l 44
 
4.6%
r 43
 
4.5%
Other values (15) 251
26.1%
Decimal Number
ValueCountFrequency (%)
1 79
32.8%
2 40
16.6%
4 29
 
12.0%
9 22
 
9.1%
5 17
 
7.1%
3 15
 
6.2%
0 13
 
5.4%
6 11
 
4.6%
8 8
 
3.3%
7 7
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 135
55.3%
& 92
37.7%
? 7
 
2.9%
, 6
 
2.5%
* 1
 
0.4%
@ 1
 
0.4%
' 1
 
0.4%
1
 
0.4%
Space Separator
ValueCountFrequency (%)
1881
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1869
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1857
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%
Other Symbol
ValueCountFrequency (%)
6
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 67503
87.8%
Common 6114
 
8.0%
Latin 3243
 
4.2%
Han 8
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
8444
 
12.5%
8089
 
12.0%
2680
 
4.0%
2231
 
3.3%
2071
 
3.1%
2059
 
3.1%
1940
 
2.9%
1342
 
2.0%
1118
 
1.7%
1037
 
1.5%
Other values (665) 36492
54.1%
Latin
ValueCountFrequency (%)
S 286
 
8.8%
K 214
 
6.6%
C 190
 
5.9%
J 174
 
5.4%
M 166
 
5.1%
H 120
 
3.7%
e 117
 
3.6%
B 113
 
3.5%
n 111
 
3.4%
o 93
 
2.9%
Other values (41) 1659
51.2%
Common
ValueCountFrequency (%)
1881
30.8%
) 1869
30.6%
( 1857
30.4%
. 135
 
2.2%
& 92
 
1.5%
1 79
 
1.3%
2 40
 
0.7%
4 29
 
0.5%
9 22
 
0.4%
- 21
 
0.3%
Other values (13) 89
 
1.5%
Han
ValueCountFrequency (%)
2
25.0%
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 67497
87.8%
ASCII 9356
 
12.2%
CJK 8
 
< 0.1%
None 7
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
8444
 
12.5%
8089
 
12.0%
2680
 
4.0%
2231
 
3.3%
2071
 
3.1%
2059
 
3.1%
1940
 
2.9%
1342
 
2.0%
1118
 
1.7%
1037
 
1.5%
Other values (664) 36486
54.1%
ASCII
ValueCountFrequency (%)
1881
20.1%
) 1869
20.0%
( 1857
19.8%
S 286
 
3.1%
K 214
 
2.3%
C 190
 
2.0%
J 174
 
1.9%
M 166
 
1.8%
. 135
 
1.4%
H 120
 
1.3%
Other values (63) 2464
26.3%
None
ValueCountFrequency (%)
6
85.7%
1
 
14.3%
CJK
ValueCountFrequency (%)
2
25.0%
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%

법인여부
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
개인
7222 
법인
2778 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
개인 7222
72.2%
법인 2778
 
27.8%

Length

2024-05-18T15:03:06.099631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T15:03:06.354764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 7222
72.2%
법인 2778
 
27.8%
Distinct5805
Distinct (%)87.9%
Missing3397
Missing (%)34.0%
Memory size156.2 KiB
2024-05-18T15:03:06.907594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length41
Mean length10.594427
Min length1

Characters and Unicode

Total characters69955
Distinct characters22
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

Unique5159 ?
Unique (%)78.1%

Sample

1st row025438199
2nd row023320464
3rd row0262151725
4th row1544-3087
5th row02-6408-5414
ValueCountFrequency (%)
02 289
 
3.9%
79
 
1.1%
070 36
 
0.5%
010 14
 
0.2%
1688 7
 
0.1%
0 6
 
0.1%
1599 6
 
0.1%
024693344 5
 
0.1%
02-525-3469 4
 
0.1%
496 4
 
0.1%
Other values (6118) 6982
93.9%
2024-05-18T15:03:08.286147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11285
16.1%
2 10252
14.7%
- 7030
10.0%
5 5868
8.4%
7 5436
7.8%
6 5135
7.3%
1 5117
7.3%
3 4882
7.0%
4 4841
6.9%
8 4794
6.9%
Other values (12) 5315
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 61660
88.1%
Dash Punctuation 7030
 
10.0%
Space Separator 941
 
1.3%
Other Punctuation 192
 
0.3%
Close Punctuation 73
 
0.1%
Math Symbol 25
 
< 0.1%
Open Punctuation 24
 
< 0.1%
Uppercase Letter 6
 
< 0.1%
Other Letter 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11285
18.3%
2 10252
16.6%
5 5868
9.5%
7 5436
8.8%
6 5135
8.3%
1 5117
8.3%
3 4882
7.9%
4 4841
7.9%
8 4794
7.8%
9 4050
 
6.6%
Other Punctuation
ValueCountFrequency (%)
* 118
61.5%
/ 46
 
24.0%
. 28
 
14.6%
Uppercase Letter
ValueCountFrequency (%)
K 3
50.0%
T 3
50.0%
Other Letter
ValueCountFrequency (%)
2
50.0%
2
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 7030
100.0%
Space Separator
ValueCountFrequency (%)
941
100.0%
Close Punctuation
ValueCountFrequency (%)
) 73
100.0%
Math Symbol
ValueCountFrequency (%)
~ 25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69945
> 99.9%
Latin 6
 
< 0.1%
Hangul 4
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11285
16.1%
2 10252
14.7%
- 7030
10.1%
5 5868
8.4%
7 5436
7.8%
6 5135
7.3%
1 5117
7.3%
3 4882
7.0%
4 4841
6.9%
8 4794
6.9%
Other values (8) 5305
7.6%
Latin
ValueCountFrequency (%)
K 3
50.0%
T 3
50.0%
Hangul
ValueCountFrequency (%)
2
50.0%
2
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69951
> 99.9%
Hangul 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11285
16.1%
2 10252
14.7%
- 7030
10.0%
5 5868
8.4%
7 5436
7.8%
6 5135
7.3%
1 5117
7.3%
3 4882
7.0%
4 4841
6.9%
8 4794
6.9%
Other values (10) 5311
7.6%
Hangul
ValueCountFrequency (%)
2
50.0%
2
50.0%

소재지
Text

MISSING 

Distinct8612
Distinct (%)88.8%
Missing302
Missing (%)3.0%
Memory size156.2 KiB
2024-05-18T15:03:09.370058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length65
Median length49
Mean length31.474634
Min length15

Characters and Unicode

Total characters305241
Distinct characters625
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

Unique7840 ?
Unique (%)80.8%

Sample

1st row서울특별시 서초구 잠원동 51번지 잠원훼미리A 1동 1205호
2nd row서울특별시 마포구 합정동 373-4 성지빌딩 611호
3rd row서울특별시 영등포구 여의도동 36번지 2호 맨하탄빌딩 919(A867)호
4th row서울특별시 구로구 구로동 197번지 10호 -1002
5th row서울특별시 서초구 서초동 1307번지 7호 센터프라자-409
ValueCountFrequency (%)
서울특별시 9697
 
17.0%
강남구 1598
 
2.8%
서초구 967
 
1.7%
1호 744
 
1.3%
역삼동 708
 
1.2%
송파구 625
 
1.1%
서초동 595
 
1.0%
중구 520
 
0.9%
2호 477
 
0.8%
영등포구 469
 
0.8%
Other values (9472) 40787
71.3%
2024-05-18T15:03:11.163425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
67777
22.2%
1 13468
 
4.4%
12096
 
4.0%
11129
 
3.6%
10490
 
3.4%
9941
 
3.3%
9744
 
3.2%
9714
 
3.2%
9698
 
3.2%
2 8738
 
2.9%
Other values (615) 142446
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 166966
54.7%
Space Separator 67777
22.2%
Decimal Number 63285
 
20.7%
Dash Punctuation 5402
 
1.8%
Uppercase Letter 1174
 
0.4%
Other Punctuation 272
 
0.1%
Lowercase Letter 120
 
< 0.1%
Close Punctuation 112
 
< 0.1%
Open Punctuation 109
 
< 0.1%
Letter Number 18
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
12096
 
7.2%
11129
 
6.7%
10490
 
6.3%
9941
 
6.0%
9744
 
5.8%
9714
 
5.8%
9698
 
5.8%
8624
 
5.2%
8390
 
5.0%
7943
 
4.8%
Other values (539) 69197
41.4%
Uppercase Letter
ValueCountFrequency (%)
B 261
22.2%
A 236
20.1%
S 96
 
8.2%
D 76
 
6.5%
T 61
 
5.2%
K 58
 
4.9%
C 44
 
3.7%
I 37
 
3.2%
L 34
 
2.9%
E 34
 
2.9%
Other values (16) 237
20.2%
Lowercase Letter
ValueCountFrequency (%)
e 18
15.0%
i 17
14.2%
n 10
 
8.3%
r 9
 
7.5%
w 8
 
6.7%
t 8
 
6.7%
o 7
 
5.8%
s 6
 
5.0%
y 6
 
5.0%
l 5
 
4.2%
Other values (11) 26
21.7%
Decimal Number
ValueCountFrequency (%)
1 13468
21.3%
2 8738
13.8%
0 8024
12.7%
3 6931
11.0%
4 5706
9.0%
5 4988
 
7.9%
6 4604
 
7.3%
7 4012
 
6.3%
8 3409
 
5.4%
9 3405
 
5.4%
Other Punctuation
ValueCountFrequency (%)
, 93
34.2%
/ 92
33.8%
. 77
28.3%
@ 3
 
1.1%
3
 
1.1%
& 2
 
0.7%
* 1
 
0.4%
; 1
 
0.4%
Letter Number
ValueCountFrequency (%)
14
77.8%
2
 
11.1%
2
 
11.1%
Close Punctuation
ValueCountFrequency (%)
) 111
99.1%
] 1
 
0.9%
Open Punctuation
ValueCountFrequency (%)
( 108
99.1%
[ 1
 
0.9%
Space Separator
ValueCountFrequency (%)
67777
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5402
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 166967
54.7%
Common 136962
44.9%
Latin 1312
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
12096
 
7.2%
11129
 
6.7%
10490
 
6.3%
9941
 
6.0%
9744
 
5.8%
9714
 
5.8%
9698
 
5.8%
8624
 
5.2%
8390
 
5.0%
7943
 
4.8%
Other values (540) 69198
41.4%
Latin
ValueCountFrequency (%)
B 261
19.9%
A 236
18.0%
S 96
 
7.3%
D 76
 
5.8%
T 61
 
4.6%
K 58
 
4.4%
C 44
 
3.4%
I 37
 
2.8%
L 34
 
2.6%
E 34
 
2.6%
Other values (40) 375
28.6%
Common
ValueCountFrequency (%)
67777
49.5%
1 13468
 
9.8%
2 8738
 
6.4%
0 8024
 
5.9%
3 6931
 
5.1%
4 5706
 
4.2%
- 5402
 
3.9%
5 4988
 
3.6%
6 4604
 
3.4%
7 4012
 
2.9%
Other values (15) 7312
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 166966
54.7%
ASCII 138253
45.3%
Number Forms 18
 
< 0.1%
None 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
67777
49.0%
1 13468
 
9.7%
2 8738
 
6.3%
0 8024
 
5.8%
3 6931
 
5.0%
4 5706
 
4.1%
- 5402
 
3.9%
5 4988
 
3.6%
6 4604
 
3.3%
7 4012
 
2.9%
Other values (61) 8603
 
6.2%
Hangul
ValueCountFrequency (%)
12096
 
7.2%
11129
 
6.7%
10490
 
6.3%
9941
 
6.0%
9744
 
5.8%
9714
 
5.8%
9698
 
5.8%
8624
 
5.2%
8390
 
5.0%
7943
 
4.8%
Other values (539) 69197
41.4%
Number Forms
ValueCountFrequency (%)
14
77.8%
2
 
11.1%
2
 
11.1%
None
ValueCountFrequency (%)
3
75.0%
1
 
25.0%

소재지(도로명)
Text

MISSING 

Distinct4639
Distinct (%)90.2%
Missing4856
Missing (%)48.6%
Memory size156.2 KiB
2024-05-18T15:03:12.055549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length67
Median length55
Mean length37.153383
Min length21

Characters and Unicode

Total characters191117
Distinct characters608
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

Unique4189 ?
Unique (%)81.4%

Sample

1st row서울특별시 영등포구 국제금융로6길 33, 맨하탄빌딩 9층 919(A867)호 (여의도동)
2nd row서울특별시 구로구 디지털로33길 55, 1002호 (구로동, 이앤씨벤처드림타워2차)
3rd row서울특별시 성동구 자동차시장1길 51, 502호 (용답동, 나성빌딩)
4th row서울특별시 강남구 개포로82길 13-17, 501호 (개포동, 대화빌딩)
5th row서울특별시 송파구 충민로 66, 엘-8145호 (문정동, 가든파이브라이프)
ValueCountFrequency (%)
서울특별시 5144
 
14.1%
강남구 947
 
2.6%
서초구 565
 
1.6%
2층 466
 
1.3%
역삼동 409
 
1.1%
3층 374
 
1.0%
서초동 370
 
1.0%
송파구 334
 
0.9%
영등포구 322
 
0.9%
4층 300
 
0.8%
Other values (6490) 27185
74.7%
2024-05-18T15:03:13.533825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31296
 
16.4%
1 7280
 
3.8%
, 7111
 
3.7%
6780
 
3.5%
6700
 
3.5%
5693
 
3.0%
5658
 
3.0%
5334
 
2.8%
2 5306
 
2.8%
) 5189
 
2.7%
Other values (598) 104770
54.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 106476
55.7%
Decimal Number 33828
 
17.7%
Space Separator 31296
 
16.4%
Other Punctuation 7132
 
3.7%
Close Punctuation 5190
 
2.7%
Open Punctuation 5190
 
2.7%
Dash Punctuation 1015
 
0.5%
Uppercase Letter 846
 
0.4%
Lowercase Letter 113
 
0.1%
Letter Number 20
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6780
 
6.4%
6700
 
6.3%
5693
 
5.3%
5658
 
5.3%
5334
 
5.0%
5186
 
4.9%
5154
 
4.8%
5145
 
4.8%
4153
 
3.9%
2706
 
2.5%
Other values (524) 53967
50.7%
Uppercase Letter
ValueCountFrequency (%)
B 164
19.4%
A 125
14.8%
S 79
9.3%
C 50
 
5.9%
T 47
 
5.6%
E 42
 
5.0%
K 39
 
4.6%
G 38
 
4.5%
L 33
 
3.9%
I 32
 
3.8%
Other values (15) 197
23.3%
Lowercase Letter
ValueCountFrequency (%)
e 15
13.3%
i 13
11.5%
w 10
8.8%
n 10
8.8%
r 10
8.8%
o 9
 
8.0%
t 8
 
7.1%
s 5
 
4.4%
c 5
 
4.4%
b 5
 
4.4%
Other values (11) 23
20.4%
Decimal Number
ValueCountFrequency (%)
1 7280
21.5%
2 5306
15.7%
0 4393
13.0%
3 4027
11.9%
4 2882
 
8.5%
5 2644
 
7.8%
6 2187
 
6.5%
7 1857
 
5.5%
8 1724
 
5.1%
9 1528
 
4.5%
Other Punctuation
ValueCountFrequency (%)
, 7111
99.7%
. 10
 
0.1%
@ 4
 
0.1%
/ 3
 
< 0.1%
& 2
 
< 0.1%
2
 
< 0.1%
Letter Number
ValueCountFrequency (%)
14
70.0%
4
 
20.0%
2
 
10.0%
Math Symbol
ValueCountFrequency (%)
~ 9
81.8%
> 1
 
9.1%
< 1
 
9.1%
Close Punctuation
ValueCountFrequency (%)
) 5189
> 99.9%
] 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 5189
> 99.9%
[ 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
31296
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1015
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 106476
55.7%
Common 83662
43.8%
Latin 979
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
6780
 
6.4%
6700
 
6.3%
5693
 
5.3%
5658
 
5.3%
5334
 
5.0%
5186
 
4.9%
5154
 
4.8%
5145
 
4.8%
4153
 
3.9%
2706
 
2.5%
Other values (524) 53967
50.7%
Latin
ValueCountFrequency (%)
B 164
16.8%
A 125
 
12.8%
S 79
 
8.1%
C 50
 
5.1%
T 47
 
4.8%
E 42
 
4.3%
K 39
 
4.0%
G 38
 
3.9%
L 33
 
3.4%
I 32
 
3.3%
Other values (39) 330
33.7%
Common
ValueCountFrequency (%)
31296
37.4%
1 7280
 
8.7%
, 7111
 
8.5%
2 5306
 
6.3%
) 5189
 
6.2%
( 5189
 
6.2%
0 4393
 
5.3%
3 4027
 
4.8%
4 2882
 
3.4%
5 2644
 
3.2%
Other values (15) 8345
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 106476
55.7%
ASCII 84619
44.3%
Number Forms 20
 
< 0.1%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31296
37.0%
1 7280
 
8.6%
, 7111
 
8.4%
2 5306
 
6.3%
) 5189
 
6.1%
( 5189
 
6.1%
0 4393
 
5.2%
3 4027
 
4.8%
4 2882
 
3.4%
5 2644
 
3.1%
Other values (60) 9302
 
11.0%
Hangul
ValueCountFrequency (%)
6780
 
6.4%
6700
 
6.3%
5693
 
5.3%
5658
 
5.3%
5334
 
5.0%
5186
 
4.9%
5154
 
4.8%
5145
 
4.8%
4153
 
3.9%
2706
 
2.5%
Other values (524) 53967
50.7%
Number Forms
ValueCountFrequency (%)
14
70.0%
4
 
20.0%
2
 
10.0%
None
ValueCountFrequency (%)
2
100.0%

우편번호
Real number (ℝ)

MISSING 

Distinct1375
Distinct (%)31.1%
Missing5584
Missing (%)55.8%
Infinite0
Infinite (%)0.0%
Mean136789.83
Minimum3163
Maximum158877
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T15:03:14.087864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3163
5-th percentile110086.75
Q1132821
median137050.5
Q3143825.25
95-th percentile157220
Maximum158877
Range155714
Interquartile range (IQR)11004.25

Descriptive statistics

Standard deviation14347.709
Coefficient of variation (CV)0.10488871
Kurtosis9.6028996
Mean136789.83
Median Absolute Deviation (MAD)5813.5
Skewness-1.6404183
Sum6.0406389 × 108
Variance2.0585674 × 108
MonotonicityNot monotonic
2024-05-18T15:03:14.725213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135080 173
 
1.7%
137070 147
 
1.5%
157010 72
 
0.7%
152050 60
 
0.6%
135010 55
 
0.5%
158070 50
 
0.5%
151050 50
 
0.5%
151015 47
 
0.5%
158050 41
 
0.4%
142070 41
 
0.4%
Other values (1365) 3680
36.8%
(Missing) 5584
55.8%
ValueCountFrequency (%)
3163 1
 
< 0.1%
4534 1
 
< 0.1%
4536 1
 
< 0.1%
4538 1
 
< 0.1%
7220 1
 
< 0.1%
7327 1
 
< 0.1%
100011 9
 
0.1%
100014 1
 
< 0.1%
100015 3
 
< 0.1%
100021 34
0.3%
ValueCountFrequency (%)
158877 1
 
< 0.1%
158871 1
 
< 0.1%
158864 3
< 0.1%
158863 1
 
< 0.1%
158860 7
0.1%
158859 2
 
< 0.1%
158857 1
 
< 0.1%
158856 1
 
< 0.1%
158846 1
 
< 0.1%
158841 1
 
< 0.1%

등록일자
Real number (ℝ)

HIGH CORRELATION 

Distinct3492
Distinct (%)34.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20136200
Minimum20051216
Maximum20240516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T15:03:15.347988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20051216
5-th percentile20070821
Q120091103
median20130131
Q320170616
95-th percentile20230206
Maximum20240516
Range189300
Interquartile range (IQR)79513.25

Descriptive statistics

Standard deviation49008.257
Coefficient of variation (CV)0.0024338384
Kurtosis-0.89510934
Mean20136200
Median Absolute Deviation (MAD)39429
Skewness0.4716277
Sum2.01362 × 1011
Variance2.4018093 × 109
MonotonicityNot monotonic
2024-05-18T15:03:16.118814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20080814 36
 
0.4%
20080731 28
 
0.3%
20080818 22
 
0.2%
20081222 16
 
0.2%
20090303 15
 
0.1%
20080926 15
 
0.1%
20090611 15
 
0.1%
20080806 14
 
0.1%
20080822 14
 
0.1%
20080708 13
 
0.1%
Other values (3482) 9812
98.1%
ValueCountFrequency (%)
20051216 1
 
< 0.1%
20060124 1
 
< 0.1%
20060306 3
< 0.1%
20060308 1
 
< 0.1%
20060310 2
 
< 0.1%
20060320 5
0.1%
20060321 1
 
< 0.1%
20060323 1
 
< 0.1%
20060324 3
< 0.1%
20060327 1
 
< 0.1%
ValueCountFrequency (%)
20240516 4
< 0.1%
20240514 2
< 0.1%
20240510 1
 
< 0.1%
20240509 1
 
< 0.1%
20240508 1
 
< 0.1%
20240507 1
 
< 0.1%
20240503 2
< 0.1%
20240502 2
< 0.1%
20240430 3
< 0.1%
20240429 1
 
< 0.1%

유효기간만료일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3270
Distinct (%)41.4%
Missing2106
Missing (%)21.1%
Infinite0
Infinite (%)0.0%
Mean20181132
Minimum20090310
Maximum20270517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T15:03:16.658151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20090310
5-th percentile20120224
Q120141019
median20180105
Q320211220
95-th percentile20260508
Maximum20270517
Range180207
Interquartile range (IQR)70200.75

Descriptive statistics

Standard deviation44648.476
Coefficient of variation (CV)0.0022123871
Kurtosis-0.97401854
Mean20181132
Median Absolute Deviation (MAD)39085.5
Skewness0.32542578
Sum1.5930985 × 1011
Variance1.9934864 × 109
MonotonicityNot monotonic
2024-05-18T15:03:17.275771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20110831 21
 
0.2%
20140831 12
 
0.1%
20190613 12
 
0.1%
20140711 12
 
0.1%
20170602 11
 
0.1%
20190718 11
 
0.1%
20110814 10
 
0.1%
20190720 10
 
0.1%
20180914 10
 
0.1%
20140111 10
 
0.1%
Other values (3260) 7775
77.8%
(Missing) 2106
 
21.1%
ValueCountFrequency (%)
20090310 1
< 0.1%
20091116 1
< 0.1%
20100112 1
< 0.1%
20100308 1
< 0.1%
20100326 1
< 0.1%
20100410 1
< 0.1%
20100411 1
< 0.1%
20100418 1
< 0.1%
20100419 1
< 0.1%
20100427 1
< 0.1%
ValueCountFrequency (%)
20270517 1
 
< 0.1%
20270516 3
< 0.1%
20270514 2
< 0.1%
20270510 1
 
< 0.1%
20270509 1
 
< 0.1%
20270508 1
 
< 0.1%
20270506 1
 
< 0.1%
20270503 2
< 0.1%
20270502 2
< 0.1%
20270430 3
< 0.1%

폐쇄일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3098
Distinct (%)36.8%
Missing1585
Missing (%)15.8%
Infinite0
Infinite (%)0.0%
Mean20141247
Minimum20060920
Maximum20240516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T15:03:17.694494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20060920
5-th percentile20090813
Q120110317
median20130708
Q320170328
95-th percentile20220914
Maximum20240516
Range179596
Interquartile range (IQR)60010.5

Descriptive statistics

Standard deviation40674.289
Coefficient of variation (CV)0.0020194524
Kurtosis-0.52495965
Mean20141247
Median Absolute Deviation (MAD)29781
Skewness0.70488896
Sum1.6948859 × 1011
Variance1.6543978 × 109
MonotonicityNot monotonic
2024-05-18T15:03:18.120676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20091116 216
 
2.2%
20100927 65
 
0.7%
20101213 25
 
0.2%
20160725 19
 
0.2%
20110914 18
 
0.2%
20110425 17
 
0.2%
20110412 17
 
0.2%
20110901 16
 
0.2%
20170124 16
 
0.2%
20110407 15
 
0.1%
Other values (3088) 7991
79.9%
(Missing) 1585
 
15.8%
ValueCountFrequency (%)
20060920 1
 
< 0.1%
20080730 1
 
< 0.1%
20081217 1
 
< 0.1%
20090125 1
 
< 0.1%
20090307 3
< 0.1%
20090309 4
< 0.1%
20090311 5
0.1%
20090312 3
< 0.1%
20090313 3
< 0.1%
20090316 4
< 0.1%
ValueCountFrequency (%)
20240516 1
 
< 0.1%
20240514 3
< 0.1%
20240513 3
< 0.1%
20240510 1
 
< 0.1%
20240507 1
 
< 0.1%
20240501 2
< 0.1%
20240430 1
 
< 0.1%
20240426 1
 
< 0.1%
20240423 1
 
< 0.1%
20240422 3
< 0.1%

지점설립일자
Text

MISSING 

Distinct3521
Distinct (%)40.3%
Missing1259
Missing (%)12.6%
Memory size156.2 KiB
2024-05-18T15:03:18.899292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters69928
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

Unique1331 ?
Unique (%)15.2%

Sample

1st row20210507
2nd row20170808
3rd row20130228
4th row20140212
5th row20140501
ValueCountFrequency (%)
20090611 21
 
0.2%
20090820 21
 
0.2%
20090514 19
 
0.2%
20090511 16
 
0.2%
20090528 15
 
0.2%
20090520 15
 
0.2%
20090512 14
 
0.2%
20090612 13
 
0.1%
20090529 13
 
0.1%
20160215 12
 
0.1%
Other values (3511) 8582
98.2%
2024-05-18T15:03:19.800424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 22739
32.5%
2 15914
22.8%
1 13884
19.9%
3 2806
 
4.0%
7 2642
 
3.8%
9 2616
 
3.7%
6 2495
 
3.6%
5 2406
 
3.4%
8 2214
 
3.2%
4 2206
 
3.2%
Other values (4) 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69922
> 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 22739
32.5%
2 15914
22.8%
1 13884
19.9%
3 2806
 
4.0%
7 2642
 
3.8%
9 2616
 
3.7%
6 2495
 
3.6%
5 2406
 
3.4%
8 2214
 
3.2%
4 2206
 
3.2%
Lowercase Letter
ValueCountFrequency (%)
a 1
50.0%
y 1
50.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Uppercase Letter
ValueCountFrequency (%)
M 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69925
> 99.9%
Latin 3
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22739
32.5%
2 15914
22.8%
1 13884
19.9%
3 2806
 
4.0%
7 2642
 
3.8%
9 2616
 
3.7%
6 2495
 
3.6%
5 2406
 
3.4%
8 2214
 
3.2%
4 2206
 
3.2%
Latin
ValueCountFrequency (%)
M 1
33.3%
a 1
33.3%
y 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69928
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22739
32.5%
2 15914
22.8%
1 13884
19.9%
3 2806
 
4.0%
7 2642
 
3.8%
9 2616
 
3.7%
6 2495
 
3.6%
5 2406
 
3.4%
8 2214
 
3.2%
4 2206
 
3.2%
Other values (4) 6
 
< 0.1%

본점여부
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
본점
9924 
지점
 
76

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 9924
99.2%
지점 76
 
0.8%

Length

2024-05-18T15:03:20.248384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T15:03:20.428329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
본점 9924
99.2%
지점 76
 
0.8%

최근수정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct3173
Distinct (%)31.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20152292
Minimum20090518
Maximum20240517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T15:03:20.648081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20090518
5-th percentile20091117
Q120110915
median20140728
Q320190212
95-th percentile20231016
Maximum20240517
Range149999
Interquartile range (IQR)79297.25

Descriptive statistics

Standard deviation45859.536
Coefficient of variation (CV)0.0022756486
Kurtosis-1.036225
Mean20152292
Median Absolute Deviation (MAD)30426
Skewness0.46376198
Sum2.0152292 × 1011
Variance2.1030971 × 109
MonotonicityNot monotonic
2024-05-18T15:03:21.096312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20091117 88
 
0.9%
20090609 61
 
0.6%
20091118 52
 
0.5%
20100330 42
 
0.4%
20091116 42
 
0.4%
20100927 41
 
0.4%
20090622 40
 
0.4%
20130621 35
 
0.4%
20091119 34
 
0.3%
20130401 30
 
0.3%
Other values (3163) 9535
95.3%
ValueCountFrequency (%)
20090518 3
 
< 0.1%
20090519 2
 
< 0.1%
20090521 3
 
< 0.1%
20090601 4
 
< 0.1%
20090602 1
 
< 0.1%
20090603 8
 
0.1%
20090604 19
 
0.2%
20090605 2
 
< 0.1%
20090608 5
 
0.1%
20090609 61
0.6%
ValueCountFrequency (%)
20240517 4
 
< 0.1%
20240516 11
0.1%
20240514 5
0.1%
20240513 3
 
< 0.1%
20240510 3
 
< 0.1%
20240509 2
 
< 0.1%
20240508 6
0.1%
20240507 5
0.1%
20240503 7
0.1%
20240502 4
 
< 0.1%

Interactions

2024-05-18T15:02:55.978017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:48.513344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:50.920318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:52.718386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:54.276475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:56.271093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:48.897213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:51.330431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:53.146133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:54.622592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:56.535660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:49.339412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:51.687860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:53.429165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:54.958970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:56.953799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:49.758011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:52.049350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:53.754869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:55.253811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:57.336768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:50.468212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:52.357959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:54.026916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:02:55.666499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T15:03:21.385432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록신청사업영업구분법인여부우편번호등록일자유효기간만료일자폐쇄일자본점여부최근수정일자
등록신청사업1.0000.1170.0170.0120.2240.1590.1670.0210.160
영업구분0.1171.0000.2840.0940.6140.6270.2000.0670.547
법인여부0.0170.2841.0000.0630.3530.2810.2030.2010.360
우편번호0.0120.0940.0631.0000.1870.2000.1960.0300.235
등록일자0.2240.6140.3530.1871.0000.9890.8590.0970.938
유효기간만료일자0.1590.6270.2810.2000.9891.0000.8330.1130.926
폐쇄일자0.1670.2000.2030.1960.8590.8331.0000.0610.961
본점여부0.0210.0670.2010.0300.0970.1130.0611.0000.125
최근수정일자0.1600.5470.3600.2350.9380.9260.9610.1251.000
2024-05-18T15:03:21.681730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
본점여부법인여부등록신청사업영업구분
본점여부1.0000.1290.0130.048
법인여부0.1291.0000.0110.204
등록신청사업0.0130.0111.0000.084
영업구분0.0480.2040.0841.000
2024-05-18T15:03:21.896750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호등록일자유효기간만료일자폐쇄일자최근수정일자등록신청사업영업구분법인여부본점여부
우편번호1.000-0.005-0.0050.015-0.0030.0190.0360.0730.037
등록일자-0.0051.0000.9960.9610.9650.1720.3810.2710.075
유효기간만료일자-0.0050.9961.0000.9640.9670.1210.3930.2160.087
폐쇄일자0.0150.9610.9641.0000.9920.1660.1160.2030.060
최근수정일자-0.0030.9650.9670.9921.0000.1230.3080.2760.096
등록신청사업0.0190.1720.1210.1660.1231.0000.0840.0110.013
영업구분0.0360.3810.3930.1160.3080.0841.0000.2040.048
법인여부0.0730.2710.2160.2030.2760.0110.2041.0000.129
본점여부0.0370.0750.0870.0600.0960.0130.0480.1291.000

Missing values

2024-05-18T15:02:58.306338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T15:02:58.988622image/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.
2024-05-18T15:02:59.649681image/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
28634대부업<NA>2007-서울특별시-01020(대부업)이정두개인025438199서울특별시 서초구 잠원동 51번지 잠원훼미리A 1동 1205호<NA><NA>20070731<NA>20091116<NA>본점20100330
26289대부업<NA>2008-서울특별시-00385(대부업)성진실업개인023320464서울특별시 마포구 합정동 373-4 성지빌딩 611호<NA><NA>20080731<NA>20101130<NA>본점20101130
1802대부중개업폐업2021-서울영등포-2122(대부중개업)주식회사 아임머니대부법인<NA>서울특별시 영등포구 여의도동 36번지 2호 맨하탄빌딩 919(A867)호서울특별시 영등포구 국제금융로6길 33, 맨하탄빌딩 9층 919(A867)호 (여의도동)<NA>20210513202405132023091920210507본점20230919
7493대부중개업타시군구이관2017-서울구로-030(대부중개업)씨제이엠대부중개개인<NA>서울특별시 구로구 구로동 197번지 10호 -1002서울특별시 구로구 디지털로33길 55, 1002호 (구로동, 이앤씨벤처드림타워2차)<NA>20170808202008082019072320170808본점20190723
16616대부업폐업2013-서울서초-0037(대부업)이티원강남대부개인0262151725서울특별시 서초구 서초동 1307번지 7호 센터프라자-409<NA>13785620130228201602282014030620130228본점20140306
10334대부중개업유효기간만료2014-서울성동-0001세기정보통신대부중개개인1544-3087서울특별시 성동구 용답동 232번지 13호 -502서울특별시 성동구 자동차시장1길 51, 502호 (용답동, 나성빌딩)1331702014021220170212<NA>20140212본점20170801
12728대부업폐업2014-서울강남-0076엔젤머니강남대부개인02-6408-5414서울특별시 강남구 개포동 186번지 7호 대화빌딩-501서울특별시 강남구 개포로82길 13-17, 501호 (개포동, 대화빌딩)13599420140501201705012016071920140501본점20160719
11154대부업<NA>2016-서울송파-0055(대부업)호야에셋대부 주식회사법인02-2068-1680서울특별시 송파구 문정동 634번지 가든파이브라이프 엘--8145서울특별시 송파구 충민로 66, 엘-8145호 (문정동, 가든파이브라이프)<NA>2014060220170602<NA>20110720본점20170131
3578대부업유효기간만료2019-서울서초-0117(대부업)소상공인사업자대부개인<NA>서울특별시 서초구 방배동 449번지 6호 -441서울특별시 서초구 방배천로2길 21, 4층 441호 (방배동)<NA>2019112620221126<NA>20191126본점20221129
10310대부중개업폐업2015-서울강남-0152(대부중개업)바로OK론대부중개개인1800-6331서울특별시 강남구 역삼동 831번지 18호 역삼빌딩-329-7서울특별시 강남구 강남대로 342, 329-7호 (역삼동, 역삼빌딩)13593620150612201806122017080720150612본점20170807
등록신청사업영업구분등록증번호상호법인여부사업장 전화번호소재지소재지(도로명)우편번호등록일자유효기간만료일자폐쇄일자지점설립일자본점여부최근수정일자
8810대부업폐업2016-서울양천-00010(대부업)형제대부개인02-855-8528서울특별시 양천구 신정동 914번지 11호서울특별시 양천구 중앙로52길 53, 1층 (신정동)<NA>20150709201807092018070920150709본점20180709
29761대부업<NA>2007-서울특별시-01471(대부업)SD파이낸셜개인0226288870서울특별시 영등포구 영등포동5가 44번지 신한빌딩 3층<NA><NA>20071122<NA>20091116<NA>본점20091118
7748대부업폐업2016-서울서초-0075(대부업)성현대부개인031-202-2072서울특별시 서초구 잠원동 55번지 1호서울특별시 서초구 잠원로 189, 207호 (잠원동, 재규어 랜드로버 서비스센터)<NA>20160613201906132019052220160609본점20190522
27855대부업<NA>2009-서울특별시-02425(대부업)현운대부개인<NA>서울특별시 성동구 성수동1가 656번지 673호<NA><NA>20091020<NA>2010030320091020본점20100611
10892대부중개업직권취소2015-서울중랑-0036(대부중개업)에프디크레딧 대부중개개인1544-2253서울특별시 중랑구 신내동 648번지 -259서울특별시 중랑구 신내로 225, 259호 (신내동, 디아뜨갤러리)<NA>20150810201808102017031320150810본점20170313
25550대부업<NA>2008-서울특별시-00994(대부업)금호개인029399890서울특별시 노원구 상계동 134-40<NA><NA>20081229<NA>2011022220060112본점20110222
6279대부업유효기간만료2014-서울강서-00049(대부업)두드림대부개인<NA>서울특별시 강서구 화곡동 924번지 2호 신성월드오피스텔 2층-208서울특별시 강서구 월정로20길 62-8, 208호 (화곡동, 신성월드오피스텔)<NA>2017092120200921<NA>20141016본점20201022
7071대부업타시군구이관2019-서울종로-00003(대부업)대민대부개인<NA>서울특별시 종로구 효제동 248번지 1호서울특별시 종로구 대학로 4-1, 2층 208호 (효제동)<NA>20190129202201292019122620130328본점20191226
2479대부업영업중2014-서울중구-0114(대부업)한국투자대부개인02-777-7601서울특별시 중구 명동1가 7번지 1호 태흥빌딩-804서울특별시 중구 명동길 55, 태흥빌딩 804호 (명동1가)<NA>2023061320260613<NA>20111004본점20230613
25428대부업<NA>2008-서울특별시-01295(대부업)원진사개인027023443서울특별시 용산구 원효로2가 94-2 원효아파트 205호<NA><NA>20080307201103072011030820080307본점20110308