Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells19019
Missing cells (%)12.7%
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
Author구로구
URLhttps://data.seoul.go.kr/dataList/OA-2513/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 (93.6%)Imbalance
등록증번호 has 172 (1.7%) missing valuesMissing
사업장 전화번호 has 3320 (33.2%) missing valuesMissing
소재지 has 312 (3.1%) missing valuesMissing
소재지(도로명) has 4862 (48.6%) missing valuesMissing
우편번호 has 5489 (54.9%) missing valuesMissing
유효기간만료일자 has 1999 (20.0%) missing valuesMissing
폐쇄일자 has 1568 (15.7%) missing valuesMissing
지점설립일자 has 1297 (13.0%) missing valuesMissing

Reproduction

Analysis started2024-05-03 22:33:10.003519
Analysis finished2024-05-03 22:33:29.226065
Duration19.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대부업
6230 
대부중개업
3352 
<NA>
 
418

Length

Max length5
Median length3
Mean length3.7122
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대부업 6230
62.3%
대부중개업 3352
33.5%
<NA> 418
 
4.2%

Length

2024-05-03T22:33:29.449311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T22:33:29.792094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대부업 6230
62.3%
대부중개업 3352
33.5%
na 418
 
4.2%

영업구분
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
3715 
<NA>
2842 
타시군구이관
1241 
유효기간만료
852 
영업중
806 
Other values (3)
544 

Length

Max length6
Median length4
Mean length3.5954
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row폐업
2nd row폐업
3rd row영업중
4th row폐업
5th row유효기간만료

Common Values

ValueCountFrequency (%)
폐업 3715
37.1%
<NA> 2842
28.4%
타시군구이관 1241
 
12.4%
유효기간만료 852
 
8.5%
영업중 806
 
8.1%
직권취소 540
 
5.4%
갱신등록불가 3
 
< 0.1%
휴업 1
 
< 0.1%

Length

2024-05-03T22:33:30.261604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T22:33:30.644899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 3715
37.1%
na 2842
28.4%
타시군구이관 1241
 
12.4%
유효기간만료 852
 
8.5%
영업중 806
 
8.1%
직권취소 540
 
5.4%
갱신등록불가 3
 
< 0.1%
휴업 1
 
< 0.1%

등록증번호
Text

MISSING 

Distinct9777
Distinct (%)99.5%
Missing172
Missing (%)1.7%
Memory size156.2 KiB
2024-05-03T22:33:31.232415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length25
Mean length19.503867
Min length1

Characters and Unicode

Total characters191684
Distinct characters73
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

Unique9727 ?
Unique (%)99.0%

Sample

1st row2018-서울강동-00021
2nd row2014-서울강남-0133(대부업)
3rd row2023-서울동대문-0001
4th row2016-서울노원-00032
5th row2012-서울특별시 성북구-00002
ValueCountFrequency (%)
2011-서울특별시 19
 
0.2%
2012-서울특별시 19
 
0.2%
2010-서울 15
 
0.2%
2013-서울특별시 14
 
0.1%
2015-서울특별시 10
 
0.1%
대부업 9
 
0.1%
2014-서울특별시 9
 
0.1%
2016-서울특별시 8
 
0.1%
대부중개업 8
 
0.1%
성북구-00001 6
 
0.1%
Other values (9740) 9851
98.8%
2024-05-03T22:33:32.593656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 33896
17.7%
- 19642
 
10.2%
2 15610
 
8.1%
1 12061
 
6.3%
10909
 
5.7%
9796
 
5.1%
8493
 
4.4%
( 8204
 
4.3%
8172
 
4.3%
) 8156
 
4.3%
Other values (63) 56745
29.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 82669
43.1%
Other Letter 72873
38.0%
Dash Punctuation 19642
 
10.2%
Open Punctuation 8204
 
4.3%
Close Punctuation 8156
 
4.3%
Space Separator 140
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10909
15.0%
9796
13.4%
8493
11.7%
8172
11.2%
7939
10.9%
3481
 
4.8%
2874
 
3.9%
2482
 
3.4%
2474
 
3.4%
2473
 
3.4%
Other values (49) 13780
18.9%
Decimal Number
ValueCountFrequency (%)
0 33896
41.0%
2 15610
18.9%
1 12061
 
14.6%
3 3743
 
4.5%
8 3180
 
3.8%
4 3054
 
3.7%
6 2833
 
3.4%
9 2816
 
3.4%
5 2744
 
3.3%
7 2732
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 19642
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8204
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8156
100.0%
Space Separator
ValueCountFrequency (%)
140
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 118811
62.0%
Hangul 72873
38.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10909
15.0%
9796
13.4%
8493
11.7%
8172
11.2%
7939
10.9%
3481
 
4.8%
2874
 
3.9%
2482
 
3.4%
2474
 
3.4%
2473
 
3.4%
Other values (49) 13780
18.9%
Common
ValueCountFrequency (%)
0 33896
28.5%
- 19642
16.5%
2 15610
13.1%
1 12061
 
10.2%
( 8204
 
6.9%
) 8156
 
6.9%
3 3743
 
3.2%
8 3180
 
2.7%
4 3054
 
2.6%
6 2833
 
2.4%
Other values (4) 8432
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118811
62.0%
Hangul 72873
38.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33896
28.5%
- 19642
16.5%
2 15610
13.1%
1 12061
 
10.2%
( 8204
 
6.9%
) 8156
 
6.9%
3 3743
 
3.2%
8 3180
 
2.7%
4 3054
 
2.6%
6 2833
 
2.4%
Other values (4) 8432
 
7.1%
Hangul
ValueCountFrequency (%)
10909
15.0%
9796
13.4%
8493
11.7%
8172
11.2%
7939
10.9%
3481
 
4.8%
2874
 
3.9%
2482
 
3.4%
2474
 
3.4%
2473
 
3.4%
Other values (49) 13780
18.9%

상호
Text

Distinct8700
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-03T22:33:33.429571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length26
Mean length7.7425
Min length1

Characters and Unicode

Total characters77425
Distinct characters779
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

Unique7634 ?
Unique (%)76.3%

Sample

1st row소원대부
2nd row주식회사 조은대부
3rd row주식회사 마케팅의대부
4th rowSD머니대부중개
5th row대양대부
ValueCountFrequency (%)
주식회사 793
 
6.7%
대부중개 304
 
2.6%
대부 279
 
2.3%
유한회사 62
 
0.5%
캐피탈 21
 
0.2%
대부업 19
 
0.2%
15
 
0.1%
대부중개업 11
 
0.1%
loan 10
 
0.1%
money 10
 
0.1%
Other values (8737) 10366
87.2%
2024-05-03T22:33:35.026406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8502
 
11.0%
8136
 
10.5%
2709
 
3.5%
2259
 
2.9%
2083
 
2.7%
2072
 
2.7%
) 1910
 
2.5%
( 1902
 
2.5%
1894
 
2.4%
1892
 
2.4%
Other values (769) 44066
56.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 67813
87.6%
Uppercase Letter 2288
 
3.0%
Close Punctuation 1910
 
2.5%
Open Punctuation 1902
 
2.5%
Space Separator 1894
 
2.4%
Lowercase Letter 1069
 
1.4%
Decimal Number 275
 
0.4%
Other Punctuation 230
 
0.3%
Dash Punctuation 37
 
< 0.1%
Other Symbol 3
 
< 0.1%
Other values (3) 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
8502
 
12.5%
8136
 
12.0%
2709
 
4.0%
2259
 
3.3%
2083
 
3.1%
2072
 
3.1%
1892
 
2.8%
1326
 
2.0%
1102
 
1.6%
1025
 
1.5%
Other values (692) 36707
54.1%
Uppercase Letter
ValueCountFrequency (%)
S 313
13.7%
K 212
 
9.3%
C 177
 
7.7%
M 171
 
7.5%
J 162
 
7.1%
H 126
 
5.5%
B 101
 
4.4%
L 96
 
4.2%
O 91
 
4.0%
A 90
 
3.9%
Other values (16) 749
32.7%
Lowercase Letter
ValueCountFrequency (%)
n 132
12.3%
e 119
11.1%
o 113
10.6%
a 106
9.9%
t 72
 
6.7%
i 69
 
6.5%
l 56
 
5.2%
s 55
 
5.1%
c 54
 
5.1%
r 45
 
4.2%
Other values (15) 248
23.2%
Decimal Number
ValueCountFrequency (%)
1 98
35.6%
2 28
 
10.2%
9 27
 
9.8%
4 27
 
9.8%
5 24
 
8.7%
3 22
 
8.0%
6 17
 
6.2%
0 13
 
4.7%
7 12
 
4.4%
8 7
 
2.5%
Other Punctuation
ValueCountFrequency (%)
. 124
53.9%
& 86
37.4%
? 7
 
3.0%
, 6
 
2.6%
* 3
 
1.3%
' 2
 
0.9%
/ 2
 
0.9%
Math Symbol
ValueCountFrequency (%)
< 1
50.0%
> 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 1910
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1902
100.0%
Space Separator
ValueCountFrequency (%)
1894
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 37
100.0%
Other Symbol
ValueCountFrequency (%)
3
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 1
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 67803
87.6%
Common 6251
 
8.1%
Latin 3358
 
4.3%
Han 13
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
8502
 
12.5%
8136
 
12.0%
2709
 
4.0%
2259
 
3.3%
2083
 
3.1%
2072
 
3.1%
1892
 
2.8%
1326
 
2.0%
1102
 
1.6%
1025
 
1.5%
Other values (681) 36697
54.1%
Latin
ValueCountFrequency (%)
S 313
 
9.3%
K 212
 
6.3%
C 177
 
5.3%
M 171
 
5.1%
J 162
 
4.8%
n 132
 
3.9%
H 126
 
3.8%
e 119
 
3.5%
o 113
 
3.4%
a 106
 
3.2%
Other values (42) 1727
51.4%
Common
ValueCountFrequency (%)
) 1910
30.6%
( 1902
30.4%
1894
30.3%
. 124
 
2.0%
1 98
 
1.6%
& 86
 
1.4%
- 37
 
0.6%
2 28
 
0.4%
9 27
 
0.4%
4 27
 
0.4%
Other values (14) 118
 
1.9%
Han
ValueCountFrequency (%)
2
15.4%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
Other values (2) 2
15.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 67800
87.6%
ASCII 9608
 
12.4%
CJK 13
 
< 0.1%
None 3
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
8502
 
12.5%
8136
 
12.0%
2709
 
4.0%
2259
 
3.3%
2083
 
3.1%
2072
 
3.1%
1892
 
2.8%
1326
 
2.0%
1102
 
1.6%
1025
 
1.5%
Other values (680) 36694
54.1%
ASCII
ValueCountFrequency (%)
) 1910
19.9%
( 1902
19.8%
1894
19.7%
S 313
 
3.3%
K 212
 
2.2%
C 177
 
1.8%
M 171
 
1.8%
J 162
 
1.7%
n 132
 
1.4%
H 126
 
1.3%
Other values (65) 2609
27.2%
None
ValueCountFrequency (%)
3
100.0%
CJK
ValueCountFrequency (%)
2
15.4%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
1
7.7%
Other values (2) 2
15.4%
Number Forms
ValueCountFrequency (%)
1
100.0%

법인여부
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
개인
7187 
법인
2813 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
개인 7187
71.9%
법인 2813
 
28.1%

Length

2024-05-03T22:33:35.612434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T22:33:36.023026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 7187
71.9%
법인 2813
 
28.1%
Distinct5928
Distinct (%)88.7%
Missing3320
Missing (%)33.2%
Memory size156.2 KiB
2024-05-03T22:33:36.646077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length41
Mean length10.628892
Min length1

Characters and Unicode

Total characters71001
Distinct characters27
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5299 ?
Unique (%)79.3%

Sample

1st row1800-7339
2nd row02-960-7776
3rd row1522-3979
4th row02-763-8949
5th row02-2157-2205
ValueCountFrequency (%)
02 323
 
4.3%
60
 
0.8%
070 40
 
0.5%
010 10
 
0.1%
2209 6
 
0.1%
434 5
 
0.1%
2244 5
 
0.1%
6212 5
 
0.1%
1644 5
 
0.1%
1566 5
 
0.1%
Other values (6266) 7116
93.9%
2024-05-03T22:33:38.287604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11483
16.2%
2 10485
14.8%
- 7087
10.0%
5 5942
8.4%
7 5455
7.7%
6 5223
7.4%
1 5192
7.3%
3 5045
7.1%
8 4883
6.9%
4 4837
6.8%
Other values (17) 5369
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 62604
88.2%
Dash Punctuation 7087
 
10.0%
Space Separator 1019
 
1.4%
Other Punctuation 162
 
0.2%
Close Punctuation 64
 
0.1%
Math Symbol 27
 
< 0.1%
Open Punctuation 23
 
< 0.1%
Other Letter 15
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11483
18.3%
2 10485
16.7%
5 5942
9.5%
7 5455
8.7%
6 5223
8.3%
1 5192
8.3%
3 5045
8.1%
8 4883
7.8%
4 4837
7.7%
9 4059
 
6.5%
Other Letter
ValueCountFrequency (%)
3
20.0%
3
20.0%
3
20.0%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
Other Punctuation
ValueCountFrequency (%)
* 99
61.1%
/ 46
28.4%
. 17
 
10.5%
Dash Punctuation
ValueCountFrequency (%)
- 7087
100.0%
Space Separator
ValueCountFrequency (%)
1019
100.0%
Close Punctuation
ValueCountFrequency (%)
) 64
100.0%
Math Symbol
ValueCountFrequency (%)
~ 27
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70986
> 99.9%
Hangul 15
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11483
16.2%
2 10485
14.8%
- 7087
10.0%
5 5942
8.4%
7 5455
7.7%
6 5223
7.4%
1 5192
7.3%
3 5045
7.1%
8 4883
6.9%
4 4837
6.8%
Other values (8) 5354
7.5%
Hangul
ValueCountFrequency (%)
3
20.0%
3
20.0%
3
20.0%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70986
> 99.9%
Hangul 15
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11483
16.2%
2 10485
14.8%
- 7087
10.0%
5 5942
8.4%
7 5455
7.7%
6 5223
7.4%
1 5192
7.3%
3 5045
7.1%
8 4883
6.9%
4 4837
6.8%
Other values (8) 5354
7.5%
Hangul
ValueCountFrequency (%)
3
20.0%
3
20.0%
3
20.0%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%

소재지
Text

MISSING 

Distinct8653
Distinct (%)89.3%
Missing312
Missing (%)3.1%
Memory size156.2 KiB
2024-05-03T22:33:39.334206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length48
Mean length31.510219
Min length15

Characters and Unicode

Total characters305271
Distinct characters624
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

Unique7899 ?
Unique (%)81.5%

Sample

1st row서울특별시 강동구 길동 359번지 52호 1층
2nd row서울특별시 강남구 역삼동 823번지 20호 영진빌딩 2층
3rd row서울특별시 동대문구 이문동 363번지 9호
4th row서울특별시 노원구 상계동 1118번지 34호
5th row서울특별시 성북구 성북동 산 25번지 9호
ValueCountFrequency (%)
서울특별시 9684
 
17.0%
강남구 1648
 
2.9%
서초구 953
 
1.7%
1호 712
 
1.2%
역삼동 705
 
1.2%
송파구 617
 
1.1%
서초동 567
 
1.0%
중구 550
 
1.0%
영등포구 489
 
0.9%
2호 467
 
0.8%
Other values (9484) 40695
71.3%
2024-05-03T22:33:40.998208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
67608
22.1%
1 13564
 
4.4%
12038
 
3.9%
11080
 
3.6%
10464
 
3.4%
9942
 
3.3%
9741
 
3.2%
9697
 
3.2%
9686
 
3.2%
2 8913
 
2.9%
Other values (614) 142538
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 166889
54.7%
Space Separator 67608
22.1%
Decimal Number 63648
 
20.8%
Dash Punctuation 5517
 
1.8%
Uppercase Letter 1053
 
0.3%
Other Punctuation 226
 
0.1%
Close Punctuation 106
 
< 0.1%
Open Punctuation 105
 
< 0.1%
Lowercase Letter 90
 
< 0.1%
Letter Number 23
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
12038
 
7.2%
11080
 
6.6%
10464
 
6.3%
9942
 
6.0%
9741
 
5.8%
9697
 
5.8%
9686
 
5.8%
8662
 
5.2%
8455
 
5.1%
7937
 
4.8%
Other values (541) 69187
41.5%
Uppercase Letter
ValueCountFrequency (%)
B 244
23.2%
A 205
19.5%
D 74
 
7.0%
S 65
 
6.2%
T 50
 
4.7%
K 48
 
4.6%
C 41
 
3.9%
E 36
 
3.4%
L 35
 
3.3%
I 32
 
3.0%
Other values (16) 223
21.2%
Lowercase Letter
ValueCountFrequency (%)
e 18
20.0%
n 10
11.1%
r 8
8.9%
i 8
8.9%
t 7
 
7.8%
l 5
 
5.6%
c 5
 
5.6%
o 5
 
5.6%
a 4
 
4.4%
y 4
 
4.4%
Other values (10) 16
17.8%
Decimal Number
ValueCountFrequency (%)
1 13564
21.3%
2 8913
14.0%
0 8042
12.6%
3 7025
11.0%
4 5812
9.1%
5 5020
 
7.9%
6 4557
 
7.2%
7 4029
 
6.3%
9 3367
 
5.3%
8 3319
 
5.2%
Other Punctuation
ValueCountFrequency (%)
, 88
38.9%
/ 73
32.3%
. 58
25.7%
4
 
1.8%
& 1
 
0.4%
; 1
 
0.4%
@ 1
 
0.4%
Letter Number
ValueCountFrequency (%)
14
60.9%
8
34.8%
1
 
4.3%
Math Symbol
ValueCountFrequency (%)
~ 4
66.7%
< 1
 
16.7%
> 1
 
16.7%
Space Separator
ValueCountFrequency (%)
67608
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5517
100.0%
Close Punctuation
ValueCountFrequency (%)
) 106
100.0%
Open Punctuation
ValueCountFrequency (%)
( 105
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 166889
54.7%
Common 137216
44.9%
Latin 1166
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
12038
 
7.2%
11080
 
6.6%
10464
 
6.3%
9942
 
6.0%
9741
 
5.8%
9697
 
5.8%
9686
 
5.8%
8662
 
5.2%
8455
 
5.1%
7937
 
4.8%
Other values (541) 69187
41.5%
Latin
ValueCountFrequency (%)
B 244
20.9%
A 205
17.6%
D 74
 
6.3%
S 65
 
5.6%
T 50
 
4.3%
K 48
 
4.1%
C 41
 
3.5%
E 36
 
3.1%
L 35
 
3.0%
I 32
 
2.7%
Other values (39) 336
28.8%
Common
ValueCountFrequency (%)
67608
49.3%
1 13564
 
9.9%
2 8913
 
6.5%
0 8042
 
5.9%
3 7025
 
5.1%
4 5812
 
4.2%
- 5517
 
4.0%
5 5020
 
3.7%
6 4557
 
3.3%
7 4029
 
2.9%
Other values (14) 7129
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 166889
54.7%
ASCII 138355
45.3%
Number Forms 23
 
< 0.1%
None 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
67608
48.9%
1 13564
 
9.8%
2 8913
 
6.4%
0 8042
 
5.8%
3 7025
 
5.1%
4 5812
 
4.2%
- 5517
 
4.0%
5 5020
 
3.6%
6 4557
 
3.3%
7 4029
 
2.9%
Other values (59) 8268
 
6.0%
Hangul
ValueCountFrequency (%)
12038
 
7.2%
11080
 
6.6%
10464
 
6.3%
9942
 
6.0%
9741
 
5.8%
9697
 
5.8%
9686
 
5.8%
8662
 
5.2%
8455
 
5.1%
7937
 
4.8%
Other values (541) 69187
41.5%
Number Forms
ValueCountFrequency (%)
14
60.9%
8
34.8%
1
 
4.3%
None
ValueCountFrequency (%)
4
100.0%

소재지(도로명)
Text

MISSING 

Distinct4700
Distinct (%)91.5%
Missing4862
Missing (%)48.6%
Memory size156.2 KiB
2024-05-03T22:33:42.039380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length66
Median length53
Mean length37.076878
Min length19

Characters and Unicode

Total characters190501
Distinct characters615
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

Unique4304 ?
Unique (%)83.8%

Sample

1st row서울특별시 강동구 천중로50길 35-6, 1층 (길동)
2nd row서울특별시 강남구 테헤란로10길 16, 2층 (역삼동, 영진빌딩)
3rd row서울특별시 동대문구 휘경로3길 6, 2층 (이문동)
4th row서울특별시 노원구 동일로241가길 5, 2층 (상계동)
5th row서울특별시 중랑구 동일로 859, 2층 16호 (묵동)
ValueCountFrequency (%)
서울특별시 5136
 
14.2%
강남구 948
 
2.6%
서초구 556
 
1.5%
2층 456
 
1.3%
역삼동 400
 
1.1%
3층 359
 
1.0%
서초동 348
 
1.0%
영등포구 335
 
0.9%
송파구 308
 
0.8%
4층 296
 
0.8%
Other values (6495) 27109
74.8%
2024-05-03T22:33:44.049120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31125
 
16.3%
1 7423
 
3.9%
, 7043
 
3.7%
6775
 
3.6%
6713
 
3.5%
5699
 
3.0%
5676
 
3.0%
5322
 
2.8%
2 5257
 
2.8%
5198
 
2.7%
Other values (605) 104270
54.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 106070
55.7%
Decimal Number 33973
 
17.8%
Space Separator 31125
 
16.3%
Other Punctuation 7058
 
3.7%
Close Punctuation 5181
 
2.7%
Open Punctuation 5181
 
2.7%
Dash Punctuation 982
 
0.5%
Uppercase Letter 786
 
0.4%
Lowercase Letter 108
 
0.1%
Letter Number 23
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6775
 
6.4%
6713
 
6.3%
5699
 
5.4%
5676
 
5.4%
5322
 
5.0%
5198
 
4.9%
5149
 
4.9%
5138
 
4.8%
4192
 
4.0%
2675
 
2.5%
Other values (532) 53533
50.5%
Uppercase Letter
ValueCountFrequency (%)
B 154
19.6%
A 111
14.1%
S 57
 
7.3%
C 53
 
6.7%
T 48
 
6.1%
E 46
 
5.9%
K 33
 
4.2%
L 31
 
3.9%
I 31
 
3.9%
G 28
 
3.6%
Other values (16) 194
24.7%
Lowercase Letter
ValueCountFrequency (%)
e 15
13.9%
n 10
9.3%
r 10
9.3%
i 8
 
7.4%
o 8
 
7.4%
s 8
 
7.4%
t 7
 
6.5%
w 7
 
6.5%
a 6
 
5.6%
c 5
 
4.6%
Other values (10) 24
22.2%
Decimal Number
ValueCountFrequency (%)
1 7423
21.8%
2 5257
15.5%
0 4374
12.9%
3 4005
11.8%
4 2936
 
8.6%
5 2685
 
7.9%
6 2219
 
6.5%
7 1901
 
5.6%
8 1694
 
5.0%
9 1479
 
4.4%
Other Punctuation
ValueCountFrequency (%)
, 7043
99.8%
. 7
 
0.1%
3
 
< 0.1%
@ 2
 
< 0.1%
/ 1
 
< 0.1%
& 1
 
< 0.1%
? 1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
~ 12
85.7%
> 1
 
7.1%
< 1
 
7.1%
Letter Number
ValueCountFrequency (%)
12
52.2%
9
39.1%
2
 
8.7%
Space Separator
ValueCountFrequency (%)
31125
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5181
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5181
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 982
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 106070
55.7%
Common 83514
43.8%
Latin 917
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
6775
 
6.4%
6713
 
6.3%
5699
 
5.4%
5676
 
5.4%
5322
 
5.0%
5198
 
4.9%
5149
 
4.9%
5138
 
4.8%
4192
 
4.0%
2675
 
2.5%
Other values (532) 53533
50.5%
Latin
ValueCountFrequency (%)
B 154
16.8%
A 111
 
12.1%
S 57
 
6.2%
C 53
 
5.8%
T 48
 
5.2%
E 46
 
5.0%
K 33
 
3.6%
L 31
 
3.4%
I 31
 
3.4%
G 28
 
3.1%
Other values (39) 325
35.4%
Common
ValueCountFrequency (%)
31125
37.3%
1 7423
 
8.9%
, 7043
 
8.4%
2 5257
 
6.3%
) 5181
 
6.2%
( 5181
 
6.2%
0 4374
 
5.2%
3 4005
 
4.8%
4 2936
 
3.5%
5 2685
 
3.2%
Other values (14) 8304
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 106070
55.7%
ASCII 84405
44.3%
Number Forms 23
 
< 0.1%
None 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31125
36.9%
1 7423
 
8.8%
, 7043
 
8.3%
2 5257
 
6.2%
) 5181
 
6.1%
( 5181
 
6.1%
0 4374
 
5.2%
3 4005
 
4.7%
4 2936
 
3.5%
5 2685
 
3.2%
Other values (59) 9195
 
10.9%
Hangul
ValueCountFrequency (%)
6775
 
6.4%
6713
 
6.3%
5699
 
5.4%
5676
 
5.4%
5322
 
5.0%
5198
 
4.9%
5149
 
4.9%
5138
 
4.8%
4192
 
4.0%
2675
 
2.5%
Other values (532) 53533
50.5%
Number Forms
ValueCountFrequency (%)
12
52.2%
9
39.1%
2
 
8.7%
None
ValueCountFrequency (%)
3
100.0%

우편번호
Real number (ℝ)

MISSING 

Distinct1389
Distinct (%)30.8%
Missing5489
Missing (%)54.9%
Infinite0
Infinite (%)0.0%
Mean136212.84
Minimum3163
Maximum429842
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T22:33:44.605914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3163
5-th percentile100845
Q1132040
median136130
Q3142876.5
95-th percentile157030
Maximum429842
Range426679
Interquartile range (IQR)10836.5

Descriptive statistics

Standard deviation15068.45
Coefficient of variation (CV)0.1106243
Kurtosis39.220626
Mean136212.84
Median Absolute Deviation (MAD)5010
Skewness0.21020716
Sum6.1445613 × 108
Variance2.2705818 × 108
MonotonicityNot monotonic
2024-05-03T22:33:45.177748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135080 183
 
1.8%
137070 150
 
1.5%
157010 61
 
0.6%
151050 53
 
0.5%
135010 52
 
0.5%
142070 48
 
0.5%
151015 48
 
0.5%
158070 42
 
0.4%
152050 42
 
0.4%
142100 39
 
0.4%
Other values (1379) 3793
37.9%
(Missing) 5489
54.9%
ValueCountFrequency (%)
3163 1
 
< 0.1%
3182 1
 
< 0.1%
4534 1
 
< 0.1%
4538 1
 
< 0.1%
7326 1
 
< 0.1%
7327 1
 
< 0.1%
100011 3
< 0.1%
100012 2
< 0.1%
100013 2
< 0.1%
100014 2
< 0.1%
ValueCountFrequency (%)
429842 1
 
< 0.1%
158881 1
 
< 0.1%
158871 2
< 0.1%
158865 1
 
< 0.1%
158864 4
< 0.1%
158860 4
< 0.1%
158859 3
< 0.1%
158858 1
 
< 0.1%
158857 1
 
< 0.1%
158846 1
 
< 0.1%

등록일자
Real number (ℝ)

HIGH CORRELATION 

Distinct3540
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20135948
Minimum20030519
Maximum20240503
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T22:33:45.577949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20030519
5-th percentile20070907
Q120091207
median20130122
Q320170523
95-th percentile20230119
Maximum20240503
Range209984
Interquartile range (IQR)79316.25

Descriptive statistics

Standard deviation48318.41
Coefficient of variation (CV)0.0023996095
Kurtosis-0.84877821
Mean20135948
Median Absolute Deviation (MAD)39213
Skewness0.48903718
Sum2.0135948 × 1011
Variance2.3346688 × 109
MonotonicityNot monotonic
2024-05-03T22:33:46.168588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20080814 36
 
0.4%
20080731 24
 
0.2%
20080818 21
 
0.2%
20090611 16
 
0.2%
20080926 16
 
0.2%
20110822 16
 
0.2%
20081222 16
 
0.2%
20090511 15
 
0.1%
20090325 15
 
0.1%
20080806 14
 
0.1%
Other values (3530) 9811
98.1%
ValueCountFrequency (%)
20030519 1
< 0.1%
20060306 2
< 0.1%
20060310 1
< 0.1%
20060321 1
< 0.1%
20060323 2
< 0.1%
20060324 2
< 0.1%
20060327 1
< 0.1%
20060329 1
< 0.1%
20060405 1
< 0.1%
20060407 1
< 0.1%
ValueCountFrequency (%)
20240503 1
 
< 0.1%
20240502 1
 
< 0.1%
20240430 3
< 0.1%
20240425 4
< 0.1%
20240424 5
0.1%
20240423 1
 
< 0.1%
20240422 2
 
< 0.1%
20240419 1
 
< 0.1%
20240418 1
 
< 0.1%
20240416 1
 
< 0.1%

유효기간만료일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3308
Distinct (%)41.3%
Missing1999
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean20179839
Minimum20080922
Maximum20270503
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T22:33:46.733144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20080922
5-th percentile20120224
Q120140909
median20171016
Q320211015
95-th percentile20260420
Maximum20270503
Range189581
Interquartile range (IQR)70106

Descriptive statistics

Standard deviation44316.419
Coefficient of variation (CV)0.002196074
Kurtosis-0.93874985
Mean20179839
Median Absolute Deviation (MAD)30395
Skewness0.36340527
Sum1.6145889 × 1011
Variance1.963945 × 109
MonotonicityNot monotonic
2024-05-03T22:33:47.431790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20110831 16
 
0.2%
20140822 13
 
0.1%
20190705 11
 
0.1%
20190718 11
 
0.1%
20190613 11
 
0.1%
20170331 11
 
0.1%
20240728 10
 
0.1%
20131116 10
 
0.1%
20150420 10
 
0.1%
20140711 10
 
0.1%
Other values (3298) 7888
78.9%
(Missing) 1999
 
20.0%
ValueCountFrequency (%)
20080922 1
< 0.1%
20091220 1
< 0.1%
20100117 1
< 0.1%
20100216 1
< 0.1%
20100219 1
< 0.1%
20100323 2
< 0.1%
20100405 1
< 0.1%
20100418 1
< 0.1%
20100427 1
< 0.1%
20100511 1
< 0.1%
ValueCountFrequency (%)
20270503 1
 
< 0.1%
20270501 1
 
< 0.1%
20270430 3
< 0.1%
20270425 4
< 0.1%
20270424 5
0.1%
20270423 1
 
< 0.1%
20270422 1
 
< 0.1%
20270421 1
 
< 0.1%
20270419 1
 
< 0.1%
20270418 1
 
< 0.1%

폐쇄일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3088
Distinct (%)36.6%
Missing1568
Missing (%)15.7%
Infinite0
Infinite (%)0.0%
Mean20141552
Minimum20050517
Maximum20240503
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T22:33:48.084132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20050517
5-th percentile20090909
Q120110420
median20130718
Q320170222
95-th percentile20220914
Maximum20240503
Range189986
Interquartile range (IQR)59802

Descriptive statistics

Standard deviation40404.197
Coefficient of variation (CV)0.0020060121
Kurtosis-0.4766633
Mean20141552
Median Absolute Deviation (MAD)29693
Skewness0.71757292
Sum1.6983357 × 1011
Variance1.6324991 × 109
MonotonicityNot monotonic
2024-05-03T22:33:48.878920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20091116 199
 
2.0%
20100927 73
 
0.7%
20101213 27
 
0.3%
20110420 25
 
0.2%
20100913 17
 
0.2%
20160725 17
 
0.2%
20110125 16
 
0.2%
20110425 16
 
0.2%
20111108 15
 
0.1%
20220412 14
 
0.1%
Other values (3078) 8013
80.1%
(Missing) 1568
 
15.7%
ValueCountFrequency (%)
20050517 1
 
< 0.1%
20060920 1
 
< 0.1%
20071030 1
 
< 0.1%
20071115 1
 
< 0.1%
20090305 2
< 0.1%
20090307 2
< 0.1%
20090309 2
< 0.1%
20090311 4
< 0.1%
20090312 4
< 0.1%
20090316 1
 
< 0.1%
ValueCountFrequency (%)
20240503 4
< 0.1%
20240501 2
< 0.1%
20240429 1
 
< 0.1%
20240426 1
 
< 0.1%
20240425 2
< 0.1%
20240423 2
< 0.1%
20240422 2
< 0.1%
20240419 2
< 0.1%
20240418 3
< 0.1%
20240416 2
< 0.1%

지점설립일자
Text

MISSING 

Distinct3544
Distinct (%)40.7%
Missing1297
Missing (%)13.0%
Memory size156.2 KiB
2024-05-03T22:33:49.917165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters69624
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

Unique1327 ?
Unique (%)15.2%

Sample

1st row20180622
2nd row20140721
3rd row20230102
4th row20160718
5th row20090203
ValueCountFrequency (%)
20090820 25
 
0.3%
20090511 21
 
0.2%
20090611 20
 
0.2%
20090514 19
 
0.2%
20090512 15
 
0.2%
20090722 14
 
0.2%
20090528 14
 
0.2%
20090529 14
 
0.2%
20091207 13
 
0.1%
20090520 13
 
0.1%
Other values (3534) 8535
98.1%
2024-05-03T22:33:51.483705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 22552
32.4%
2 15812
22.7%
1 14068
20.2%
3 2839
 
4.1%
9 2572
 
3.7%
7 2570
 
3.7%
6 2414
 
3.5%
5 2306
 
3.3%
4 2252
 
3.2%
8 2233
 
3.2%
Other values (4) 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69618
> 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 22552
32.4%
2 15812
22.7%
1 14068
20.2%
3 2839
 
4.1%
9 2572
 
3.7%
7 2570
 
3.7%
6 2414
 
3.5%
5 2306
 
3.3%
4 2252
 
3.2%
8 2233
 
3.2%
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 69621
> 99.9%
Latin 3
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22552
32.4%
2 15812
22.7%
1 14068
20.2%
3 2839
 
4.1%
9 2572
 
3.7%
7 2570
 
3.7%
6 2414
 
3.5%
5 2306
 
3.3%
4 2252
 
3.2%
8 2233
 
3.2%
Latin
ValueCountFrequency (%)
M 1
33.3%
a 1
33.3%
r 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22552
32.4%
2 15812
22.7%
1 14068
20.2%
3 2839
 
4.1%
9 2572
 
3.7%
7 2570
 
3.7%
6 2414
 
3.5%
5 2306
 
3.3%
4 2252
 
3.2%
8 2233
 
3.2%
Other values (4) 6
 
< 0.1%

본점여부
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
본점
9925 
지점
 
75

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 9925
99.2%
지점 75
 
0.8%

Length

2024-05-03T22:33:51.960238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T22:33:52.256750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
본점 9925
99.2%
지점 75
 
0.8%

최근수정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct3171
Distinct (%)31.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20152334
Minimum20090518
Maximum20240503
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T22:33:52.589734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20090518
5-th percentile20091117
Q120111021
median20140721
Q320190207
95-th percentile20231005
Maximum20240503
Range149985
Interquartile range (IQR)79186

Descriptive statistics

Standard deviation45464.565
Coefficient of variation (CV)0.0022560446
Kurtosis-1.0111022
Mean20152334
Median Absolute Deviation (MAD)30313.5
Skewness0.47743513
Sum2.0152334 × 1011
Variance2.0670267 × 109
MonotonicityNot monotonic
2024-05-03T22:33:53.030349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20091117 71
 
0.7%
20090609 59
 
0.6%
20100927 55
 
0.5%
20091118 49
 
0.5%
20091116 45
 
0.4%
20110425 41
 
0.4%
20100330 38
 
0.4%
20090622 36
 
0.4%
20091119 35
 
0.4%
20130621 31
 
0.3%
Other values (3161) 9540
95.4%
ValueCountFrequency (%)
20090518 1
 
< 0.1%
20090519 3
 
< 0.1%
20090521 5
 
0.1%
20090601 4
 
< 0.1%
20090602 2
 
< 0.1%
20090603 8
 
0.1%
20090604 14
 
0.1%
20090605 2
 
< 0.1%
20090608 3
 
< 0.1%
20090609 59
0.6%
ValueCountFrequency (%)
20240503 11
0.1%
20240502 4
 
< 0.1%
20240501 6
0.1%
20240430 4
 
< 0.1%
20240429 6
0.1%
20240426 1
 
< 0.1%
20240425 9
0.1%
20240424 5
0.1%
20240423 4
 
< 0.1%
20240422 8
0.1%

Interactions

2024-05-03T22:33:25.606825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:33:16.837052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:33:19.215149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:33:21.701444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:33:23.336509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:33:25.881635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:33:17.263642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:33:19.557847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:33:21.992596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:33:23.672871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:33:26.272662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:33:17.630279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:33:19.990587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:33:22.320896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:33:24.372109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:33:26.694450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:33:18.182973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:33:20.918169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:33:22.664963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:33:24.826539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:33:27.124494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:33:18.709642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:33:21.359067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:33:22.982723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:33:25.176328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-03T22:33:53.495180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록신청사업영업구분법인여부우편번호등록일자유효기간만료일자폐쇄일자본점여부최근수정일자
등록신청사업1.0000.0480.0130.0310.1770.1560.2020.0000.176
영업구분0.0481.0000.1690.0150.5670.5790.2190.0320.478
법인여부0.0130.1691.0000.0890.2490.2550.2490.2020.325
우편번호0.0310.0150.0891.0000.1910.1230.0850.0000.130
등록일자0.1770.5670.2490.1911.0000.8810.8590.0710.854
유효기간만료일자0.1560.5790.2550.1230.8811.0000.8220.0920.924
폐쇄일자0.2020.2190.2490.0850.8590.8221.0000.0650.984
본점여부0.0000.0320.2020.0000.0710.0920.0651.0000.121
최근수정일자0.1760.4780.3250.1300.8540.9240.9840.1211.000
2024-05-03T22:33:53.985132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영업구분본점여부등록신청사업법인여부
영업구분1.0000.0340.0520.181
본점여부0.0341.0000.0000.130
등록신청사업0.0520.0001.0000.008
법인여부0.1810.1300.0081.000
2024-05-03T22:33:54.368874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호등록일자유효기간만료일자폐쇄일자최근수정일자등록신청사업영업구분법인여부본점여부
우편번호1.0000.0450.0510.0490.0330.0240.0150.0530.000
등록일자0.0451.0000.9960.9600.9650.1770.3430.2490.070
유효기간만료일자0.0510.9961.0000.9620.9650.1200.3540.1950.070
폐쇄일자0.0490.9600.9621.0000.9910.1550.1280.1910.050
최근수정일자0.0330.9650.9650.9911.0000.1350.2770.2490.092
등록신청사업0.0240.1770.1200.1550.1351.0000.0520.0080.000
영업구분0.0150.3430.3540.1280.2770.0521.0000.1810.034
법인여부0.0530.2490.1950.1910.2490.0080.1811.0000.130
본점여부0.0000.0700.0700.0500.0920.0000.0340.1301.000

Missing values

2024-05-03T22:33:27.500645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T22:33:28.383397image/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.
2024-05-03T22:33:28.944591image/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
5979대부업폐업2018-서울강동-00021소원대부개인<NA>서울특별시 강동구 길동 359번지 52호 1층서울특별시 강동구 천중로50길 35-6, 1층 (길동)<NA>20180622202106222021012620180622본점20210127
13465대부업폐업2014-서울강남-0133(대부업)주식회사 조은대부법인1800-7339서울특별시 강남구 역삼동 823번지 20호 영진빌딩 2층서울특별시 강남구 테헤란로10길 16, 2층 (역삼동, 영진빌딩)13508120140721201707212016011420140721본점20160114
1996대부업영업중2023-서울동대문-0001주식회사 마케팅의대부법인02-960-7776서울특별시 동대문구 이문동 363번지 9호서울특별시 동대문구 휘경로3길 6, 2층 (이문동)<NA>2023010220260102<NA>20230102본점20230809
11617대부중개업폐업2016-서울노원-00032SD머니대부중개개인1522-3979서울특별시 노원구 상계동 1118번지 34호서울특별시 노원구 동일로241가길 5, 2층 (상계동)<NA>20160719201907192016121420160718본점20161215
9384대부업유효기간만료2012-서울특별시 성북구-00002대양대부개인02-763-8949서울특별시 성북구 성북동 산 25번지 9호<NA>1360202015011220180112<NA>20090203본점20180212
2718대부중개업영업중2023-서울중랑-0006(대부중개업)레이지대부중개주식회사법인<NA>서울특별시 중랑구 묵동 245번지 6호 2층-16서울특별시 중랑구 동일로 859, 2층 16호 (묵동)<NA>2023042720260426<NA>20230427본점20230428
15406대부업타시군구이관2013-서울송파-0112(대부업)앰엔앤파트너스대부개인02-2157-2205서울특별시 송파구 문정동 634번지 가든파이브라이프 T-7128서울특별시 송파구 충민로 66 (문정동, 가든파이브라이프 T-7128)13896020130827201608272014110320130827본점20141103
4165대부중개업영업중2021-서울강북-0021(대부중개업)MK 파이낸셜 대부중개개인1877-9435서울특별시 강북구 수유동 92번지 8호 어반빌리움 수유서울특별시 강북구 도봉로 277, 어반빌리움 수유 1018호 (수유동)<NA>2021051320240513<NA>20210513본점20220704
14780대부중개업폐업2014-서울송파-0078(대부중개업)오선대부중개개인1566-0650서울특별시 송파구 방이동 67번지 오선빌딩 2층서울특별시 송파구 오금로13길 6, 2층 (방이동, 오선빌딩)13882920140929201709292015031620140929본점20150317
17819대부중개업폐업2012-서울종로-00023(대부중개업)우성투자대부금융 대부중개개인027411075서울특별시 종로구 낙원동 212번지 나스빌오피스텔-702<NA>11032020120412201504122013082320090514본점20130823
등록신청사업영업구분등록증번호상호법인여부사업장 전화번호소재지소재지(도로명)우편번호등록일자유효기간만료일자폐쇄일자지점설립일자본점여부최근수정일자
19951대부업타시군구이관2012-서울성동-0029천호기획 대부업개인02-466-2049서울특별시 성동구 마장동 381번지 4호 -101<NA>13381120120806201508062013010720120806본점20130107
22946대부중개업직권취소2011-서울은평-00093EX대부개인070 7675 3734서울특별시 은평구 응암동 197번지 36호 -303<NA>12290820110228201402282011121220110228본점20111212
13652대부중개업폐업2014-서울강북-0044이지어스캐피탈대부중개개인02-1544-7516서울특별시 강북구 미아동 189번지 37호 4층-401서울특별시 강북구 덕릉로28길 72, 4층 401호 (미아동)14280320141112201711122015120120141112본점20151201
26858대부업<NA>2009-서울특별시-02737(대부업)태동개인0260526141서울특별시 강서구 화곡동 896-16 3층<NA><NA>20070927<NA>2010092820070927본점20100928
2412대부업타시군구이관2021-서울서초-0018(대부업)주식회사 원큐파트너스대부법인02-571-1118서울특별시 서초구 양재동 275번지 1호 에이 삼호물산빌딩-1804서울특별시 서초구 논현로 83, 삼호물산빌딩 에이동 1804호 (양재동)<NA>20210209202402092023061320210209본점20230613
7159대부업<NA>2019-서울서초-0089(대부업)공유지분부동산대부 주식회사법인02-533-6003서울특별시 서초구 서초동 1714번지 35호 -105서울특별시 서초구 서초중앙로 153, 서울빌딩 105호 (서초동)<NA>20190910202209102019111920190910본점20191120
23490대부업<NA>2008-서울특별시-03230(대부업)부모캐피탈개인023746655서울특별시 서대문구 홍은동 454 극동아파트상가 216호<NA><NA>200812192011121920100402<NA>본점20111012
20703대부업폐업2012-서울성동-0002비엠파트너스대부(주)법인02-2212-2398서울특별시 성동구 용답동 105번지 1호 이화연립 지층-301<NA>13384920120104201501042012092020120104지점20120920
11898대부중개업폐업2014-서울송파-0018(대부중개업)(주)프란대부중개법인070-7465-5484서울특별시 송파구 가락동 93번지 금강빌딩 6,7층 아크로피스 송파가락센터-608서울특별시 송파구 양재대로62길 8, 608호 (가락동, 금강빌딩)<NA>20131126201611262016102620110217본점20161027
29162대부업<NA>2007-서울특별시-01146(대부업)유한캐피탈개인024655341서울특별시 광진구 중곡동 161번지 27호 경남빌딩 309호<NA>14322020070906<NA>2010011120070815본점20100121

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