Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells19373
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
Text5
Numeric6

Dataset

Description등록신청사업,영업구분,등록증번호,상호,법인여부,사업장 전화번호,소재지,소재지(도로명),우편번호,등록일자,유효기간만료일자,폐쇄일자,지점설립일자,본점여부,최근수정일자
Author강남구
URLhttps://data.seoul.go.kr/dataList/OA-11328/S/1/datasetView.do

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
등록일자 is highly overall correlated with 유효기간만료일자 and 3 other fieldsHigh correlation
유효기간만료일자 is highly overall correlated with 등록일자 and 3 other fieldsHigh correlation
폐쇄일자 is highly overall correlated with 등록일자 and 3 other fieldsHigh correlation
지점설립일자 is highly overall correlated with 등록일자 and 3 other fieldsHigh correlation
최근수정일자 is highly overall correlated with 등록일자 and 3 other fieldsHigh correlation
본점여부 is highly imbalanced (94.9%)Imbalance
등록증번호 has 196 (2.0%) missing valuesMissing
사업장 전화번호 has 3379 (33.8%) missing valuesMissing
소재지 has 318 (3.2%) missing valuesMissing
소재지(도로명) has 4834 (48.3%) missing valuesMissing
우편번호 has 5663 (56.6%) missing valuesMissing
유효기간만료일자 has 2082 (20.8%) missing valuesMissing
폐쇄일자 has 1617 (16.2%) missing valuesMissing
지점설립일자 has 1284 (12.8%) missing valuesMissing

Reproduction

Analysis started2024-05-11 05:41:35.684182
Analysis finished2024-05-11 05:42:36.318819
Duration1 minute and 0.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대부업
6270 
대부중개업
3328 
<NA>
 
402

Length

Max length5
Median length3
Mean length3.7058
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대부업 6270
62.7%
대부중개업 3328
33.3%
<NA> 402
 
4.0%

Length

2024-05-11T14:42:36.402796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:42:36.503925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대부업 6270
62.7%
대부중개업 3328
33.3%
na 402
 
4.0%

영업구분
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
3697 
<NA>
2922 
타시군구이관
1190 
영업중
861 
유효기간만료
797 
Other values (2)
533 

Length

Max length6
Median length4
Mean length3.5729
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 3697
37.0%
<NA> 2922
29.2%
타시군구이관 1190
 
11.9%
영업중 861
 
8.6%
유효기간만료 797
 
8.0%
직권취소 528
 
5.3%
갱신등록불가 5
 
0.1%

Length

2024-05-11T14:42:36.649516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:42:36.784094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 3697
37.0%
na 2922
29.2%
타시군구이관 1190
 
11.9%
영업중 861
 
8.6%
유효기간만료 797
 
8.0%
직권취소 528
 
5.3%
갱신등록불가 5
 
< 0.1%

등록증번호
Text

MISSING 

Distinct9756
Distinct (%)99.5%
Missing196
Missing (%)2.0%
Memory size156.2 KiB
2024-05-11T14:42:37.010200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length27
Mean length19.515708
Min length1

Characters and Unicode

Total characters191332
Distinct characters85
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9709 ?
Unique (%)99.0%

Sample

1st row2010-서울노원-00044(대부중개업)
2nd row2020-서울종로-00001(대부업)
3rd row2012-서울마포-0041(대부중개업)
4th row2007-서울특별시-00442(대부업)
5th row2017-서울강북-0062(대부중개업)
ValueCountFrequency (%)
2012-서울특별시 19
 
0.2%
2010-서울 19
 
0.2%
2013-서울특별시 16
 
0.2%
대부업 12
 
0.1%
2011-서울특별시 11
 
0.1%
2017-서울특별시 10
 
0.1%
2014-서울특별시 9
 
0.1%
2015-서울특별시 8
 
0.1%
2016-서울특별시 6
 
0.1%
2018-서울특별시 5
 
0.1%
Other values (9727) 9842
98.8%
2024-05-11T14:42:38.054528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 33935
17.7%
- 19587
 
10.2%
2 15737
 
8.2%
1 11776
 
6.2%
10870
 
5.7%
9776
 
5.1%
8494
 
4.4%
( 8201
 
4.3%
8169
 
4.3%
) 8151
 
4.3%
Other values (75) 56636
29.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 82486
43.1%
Other Letter 72754
38.0%
Dash Punctuation 19587
 
10.2%
Open Punctuation 8201
 
4.3%
Close Punctuation 8151
 
4.3%
Space Separator 153
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10870
14.9%
9776
13.4%
8494
11.7%
8169
11.2%
7927
10.9%
3427
 
4.7%
2840
 
3.9%
2510
 
3.4%
2503
 
3.4%
2502
 
3.4%
Other values (61) 13736
18.9%
Decimal Number
ValueCountFrequency (%)
0 33935
41.1%
2 15737
19.1%
1 11776
 
14.3%
3 3722
 
4.5%
8 3151
 
3.8%
4 3048
 
3.7%
9 2874
 
3.5%
7 2787
 
3.4%
6 2766
 
3.4%
5 2690
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 19587
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8201
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8151
100.0%
Space Separator
ValueCountFrequency (%)
153
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 118578
62.0%
Hangul 72754
38.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10870
14.9%
9776
13.4%
8494
11.7%
8169
11.2%
7927
10.9%
3427
 
4.7%
2840
 
3.9%
2510
 
3.4%
2503
 
3.4%
2502
 
3.4%
Other values (61) 13736
18.9%
Common
ValueCountFrequency (%)
0 33935
28.6%
- 19587
16.5%
2 15737
13.3%
1 11776
 
9.9%
( 8201
 
6.9%
) 8151
 
6.9%
3 3722
 
3.1%
8 3151
 
2.7%
4 3048
 
2.6%
9 2874
 
2.4%
Other values (4) 8396
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118578
62.0%
Hangul 72754
38.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33935
28.6%
- 19587
16.5%
2 15737
13.3%
1 11776
 
9.9%
( 8201
 
6.9%
) 8151
 
6.9%
3 3722
 
3.1%
8 3151
 
2.7%
4 3048
 
2.6%
9 2874
 
2.4%
Other values (4) 8396
 
7.1%
Hangul
ValueCountFrequency (%)
10870
14.9%
9776
13.4%
8494
11.7%
8169
11.2%
7927
10.9%
3427
 
4.7%
2840
 
3.9%
2510
 
3.4%
2503
 
3.4%
2502
 
3.4%
Other values (61) 13736
18.9%

상호
Text

Distinct8665
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T14:42:38.410996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length32
Mean length7.7112
Min length1

Characters and Unicode

Total characters77112
Distinct characters780
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7587 ?
Unique (%)75.9%

Sample

1st row연앤정대부중개
2nd row등대대부업
3rd row중외대부중개
4th row미래와가치
5th row크레딧 매니져 대부중개
ValueCountFrequency (%)
주식회사 814
 
6.8%
대부중개 320
 
2.7%
대부 272
 
2.3%
유한회사 53
 
0.4%
대부업 25
 
0.2%
캐피탈 21
 
0.2%
대부중개업 13
 
0.1%
money 13
 
0.1%
미래 11
 
0.1%
11
 
0.1%
Other values (8710) 10346
86.9%
2024-05-11T14:42:39.010903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8475
 
11.0%
8096
 
10.5%
2656
 
3.4%
2290
 
3.0%
2142
 
2.8%
2124
 
2.8%
1903
 
2.5%
1868
 
2.4%
) 1835
 
2.4%
( 1827
 
2.4%
Other values (770) 43896
56.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 67628
87.7%
Uppercase Letter 2250
 
2.9%
Space Separator 1903
 
2.5%
Close Punctuation 1835
 
2.4%
Open Punctuation 1827
 
2.4%
Lowercase Letter 1124
 
1.5%
Other Punctuation 248
 
0.3%
Decimal Number 248
 
0.3%
Dash Punctuation 34
 
< 0.1%
Other Symbol 10
 
< 0.1%
Other values (2) 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
8475
 
12.5%
8096
 
12.0%
2656
 
3.9%
2290
 
3.4%
2142
 
3.2%
2124
 
3.1%
1868
 
2.8%
1333
 
2.0%
1166
 
1.7%
1039
 
1.5%
Other values (694) 36439
53.9%
Uppercase Letter
ValueCountFrequency (%)
S 305
13.6%
K 219
 
9.7%
C 178
 
7.9%
J 174
 
7.7%
M 163
 
7.2%
H 123
 
5.5%
G 92
 
4.1%
B 90
 
4.0%
L 89
 
4.0%
O 89
 
4.0%
Other values (16) 728
32.4%
Lowercase Letter
ValueCountFrequency (%)
e 141
12.5%
o 133
11.8%
n 128
11.4%
a 111
9.9%
i 68
 
6.0%
t 65
 
5.8%
s 64
 
5.7%
c 50
 
4.4%
l 47
 
4.2%
m 46
 
4.1%
Other values (14) 271
24.1%
Decimal Number
ValueCountFrequency (%)
1 82
33.1%
2 45
18.1%
4 43
17.3%
9 17
 
6.9%
5 17
 
6.9%
3 15
 
6.0%
6 13
 
5.2%
0 8
 
3.2%
7 5
 
2.0%
8 3
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 132
53.2%
& 102
41.1%
? 7
 
2.8%
, 4
 
1.6%
1
 
0.4%
' 1
 
0.4%
* 1
 
0.4%
Math Symbol
ValueCountFrequency (%)
~ 2
50.0%
> 1
25.0%
< 1
25.0%
Space Separator
ValueCountFrequency (%)
1903
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1835
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1827
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 34
100.0%
Other Symbol
ValueCountFrequency (%)
10
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 67623
87.7%
Common 6099
 
7.9%
Latin 3375
 
4.4%
Han 15
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
8475
 
12.5%
8096
 
12.0%
2656
 
3.9%
2290
 
3.4%
2142
 
3.2%
2124
 
3.1%
1868
 
2.8%
1333
 
2.0%
1166
 
1.7%
1039
 
1.5%
Other values (680) 36434
53.9%
Latin
ValueCountFrequency (%)
S 305
 
9.0%
K 219
 
6.5%
C 178
 
5.3%
J 174
 
5.2%
M 163
 
4.8%
e 141
 
4.2%
o 133
 
3.9%
n 128
 
3.8%
H 123
 
3.6%
a 111
 
3.3%
Other values (41) 1700
50.4%
Common
ValueCountFrequency (%)
1903
31.2%
) 1835
30.1%
( 1827
30.0%
. 132
 
2.2%
& 102
 
1.7%
1 82
 
1.3%
2 45
 
0.7%
4 43
 
0.7%
- 34
 
0.6%
9 17
 
0.3%
Other values (14) 79
 
1.3%
Han
ValueCountFrequency (%)
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
Other values (5) 5
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 67612
87.7%
ASCII 9472
 
12.3%
CJK 15
 
< 0.1%
None 11
 
< 0.1%
Compat Jamo 1
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
8475
 
12.5%
8096
 
12.0%
2656
 
3.9%
2290
 
3.4%
2142
 
3.2%
2124
 
3.1%
1868
 
2.8%
1333
 
2.0%
1166
 
1.7%
1039
 
1.5%
Other values (678) 36423
53.9%
ASCII
ValueCountFrequency (%)
1903
20.1%
) 1835
19.4%
( 1827
19.3%
S 305
 
3.2%
K 219
 
2.3%
C 178
 
1.9%
J 174
 
1.8%
M 163
 
1.7%
e 141
 
1.5%
o 133
 
1.4%
Other values (63) 2594
27.4%
None
ValueCountFrequency (%)
10
90.9%
1
 
9.1%
CJK
ValueCountFrequency (%)
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
Other values (5) 5
33.3%
Compat Jamo
ValueCountFrequency (%)
1
100.0%
Number Forms
ValueCountFrequency (%)
1
100.0%

법인여부
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
개인
7248 
법인
2752 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
개인 7248
72.5%
법인 2752
 
27.5%

Length

2024-05-11T14:42:39.180461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:42:39.294525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 7248
72.5%
법인 2752
 
27.5%
Distinct5877
Distinct (%)88.8%
Missing3379
Missing (%)33.8%
Memory size156.2 KiB
2024-05-11T14:42:39.621807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length40
Mean length10.62921
Min length1

Characters and Unicode

Total characters70376
Distinct characters28
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

Unique5273 ?
Unique (%)79.6%

Sample

1st row02 909 5001
2nd row02-2274-8885
3rd row1544-8137
4th row7458572
5th row1599-0675
ValueCountFrequency (%)
02 317
 
4.2%
72
 
1.0%
070 39
 
0.5%
2212 7
 
0.1%
010 7
 
0.1%
025117185 7
 
0.1%
1544 6
 
0.1%
63880505 6
 
0.1%
02-737-2882 5
 
0.1%
1577 5
 
0.1%
Other values (6205) 7036
93.7%
2024-05-11T14:42:40.267428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11405
16.2%
2 10286
14.6%
- 7056
10.0%
5 5874
8.3%
7 5474
7.8%
6 5117
7.3%
1 5078
7.2%
3 4943
7.0%
8 4903
7.0%
4 4813
6.8%
Other values (18) 5427
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 62003
88.1%
Dash Punctuation 7056
 
10.0%
Space Separator 998
 
1.4%
Other Punctuation 180
 
0.3%
Close Punctuation 76
 
0.1%
Math Symbol 29
 
< 0.1%
Open Punctuation 24
 
< 0.1%
Other Letter 8
 
< 0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11405
18.4%
2 10286
16.6%
5 5874
9.5%
7 5474
8.8%
6 5117
8.3%
1 5078
8.2%
3 4943
8.0%
8 4903
7.9%
4 4813
7.8%
9 4110
 
6.6%
Other Letter
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Other Punctuation
ValueCountFrequency (%)
* 104
57.8%
/ 52
28.9%
. 24
 
13.3%
Uppercase Letter
ValueCountFrequency (%)
K 1
50.0%
T 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 7056
100.0%
Space Separator
ValueCountFrequency (%)
998
100.0%
Close Punctuation
ValueCountFrequency (%)
) 76
100.0%
Math Symbol
ValueCountFrequency (%)
~ 29
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70366
> 99.9%
Hangul 8
 
< 0.1%
Latin 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11405
16.2%
2 10286
14.6%
- 7056
10.0%
5 5874
8.3%
7 5474
7.8%
6 5117
7.3%
1 5078
7.2%
3 4943
7.0%
8 4903
7.0%
4 4813
6.8%
Other values (8) 5417
7.7%
Hangul
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Latin
ValueCountFrequency (%)
K 1
50.0%
T 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70368
> 99.9%
Hangul 8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11405
16.2%
2 10286
14.6%
- 7056
10.0%
5 5874
8.3%
7 5474
7.8%
6 5117
7.3%
1 5078
7.2%
3 4943
7.0%
8 4903
7.0%
4 4813
6.8%
Other values (10) 5419
7.7%
Hangul
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%

소재지
Text

MISSING 

Distinct8603
Distinct (%)88.9%
Missing318
Missing (%)3.2%
Memory size156.2 KiB
2024-05-11T14:42:40.783343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length49
Mean length31.45001
Min length15

Characters and Unicode

Total characters304499
Distinct characters616
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7847 ?
Unique (%)81.0%

Sample

1st row서울특별시 노원구 공릉동 670번지 11호 로우폴리스-507
2nd row서울특별시 종로구 종로5가 265번지 1호
3rd row서울특별시 종로구 운니동 98-78 가든타워 2층 203호
4th row서울특별시 강북구 미아동 159번지 1호
5th row서울특별시 송파구 오금동 43번지 지하 현대아파트-24
ValueCountFrequency (%)
서울특별시 9679
 
17.0%
강남구 1608
 
2.8%
서초구 944
 
1.7%
1호 707
 
1.2%
역삼동 688
 
1.2%
송파구 602
 
1.1%
서초동 567
 
1.0%
중구 495
 
0.9%
영등포구 467
 
0.8%
2호 427
 
0.7%
Other values (9462) 40885
71.6%
2024-05-11T14:42:41.425498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
67605
22.2%
1 13412
 
4.4%
12055
 
4.0%
11135
 
3.7%
10493
 
3.4%
9930
 
3.3%
9731
 
3.2%
9691
 
3.2%
9679
 
3.2%
2 8864
 
2.9%
Other values (606) 141904
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 166536
54.7%
Space Separator 67605
22.2%
Decimal Number 63292
 
20.8%
Dash Punctuation 5399
 
1.8%
Uppercase Letter 1118
 
0.4%
Other Punctuation 232
 
0.1%
Close Punctuation 104
 
< 0.1%
Open Punctuation 100
 
< 0.1%
Lowercase Letter 82
 
< 0.1%
Letter Number 25
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
12055
 
7.2%
11135
 
6.7%
10493
 
6.3%
9930
 
6.0%
9731
 
5.8%
9691
 
5.8%
9679
 
5.8%
8633
 
5.2%
8399
 
5.0%
7899
 
4.7%
Other values (534) 68891
41.4%
Uppercase Letter
ValueCountFrequency (%)
B 262
23.4%
A 207
18.5%
S 76
 
6.8%
D 72
 
6.4%
I 54
 
4.8%
T 49
 
4.4%
K 49
 
4.4%
C 48
 
4.3%
L 36
 
3.2%
E 36
 
3.2%
Other values (16) 229
20.5%
Lowercase Letter
ValueCountFrequency (%)
e 16
19.5%
t 7
8.5%
i 7
8.5%
n 6
 
7.3%
r 6
 
7.3%
s 5
 
6.1%
w 5
 
6.1%
o 5
 
6.1%
c 5
 
6.1%
y 4
 
4.9%
Other values (8) 16
19.5%
Decimal Number
ValueCountFrequency (%)
1 13412
21.2%
2 8864
14.0%
0 8015
12.7%
3 6997
11.1%
4 5777
9.1%
5 4943
 
7.8%
6 4483
 
7.1%
7 4120
 
6.5%
9 3384
 
5.3%
8 3297
 
5.2%
Other Punctuation
ValueCountFrequency (%)
/ 83
35.8%
, 80
34.5%
. 64
27.6%
@ 1
 
0.4%
1
 
0.4%
; 1
 
0.4%
* 1
 
0.4%
& 1
 
0.4%
Letter Number
ValueCountFrequency (%)
16
64.0%
5
 
20.0%
4
 
16.0%
Close Punctuation
ValueCountFrequency (%)
) 103
99.0%
] 1
 
1.0%
Open Punctuation
ValueCountFrequency (%)
( 99
99.0%
[ 1
 
1.0%
Space Separator
ValueCountFrequency (%)
67605
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5399
100.0%
Math Symbol
ValueCountFrequency (%)
~ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 166534
54.7%
Common 136738
44.9%
Latin 1225
 
0.4%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
12055
 
7.2%
11135
 
6.7%
10493
 
6.3%
9930
 
6.0%
9731
 
5.8%
9691
 
5.8%
9679
 
5.8%
8633
 
5.2%
8399
 
5.0%
7899
 
4.7%
Other values (532) 68889
41.4%
Latin
ValueCountFrequency (%)
B 262
21.4%
A 207
16.9%
S 76
 
6.2%
D 72
 
5.9%
I 54
 
4.4%
T 49
 
4.0%
K 49
 
4.0%
C 48
 
3.9%
L 36
 
2.9%
E 36
 
2.9%
Other values (37) 336
27.4%
Common
ValueCountFrequency (%)
67605
49.4%
1 13412
 
9.8%
2 8864
 
6.5%
0 8015
 
5.9%
3 6997
 
5.1%
4 5777
 
4.2%
- 5399
 
3.9%
5 4943
 
3.6%
6 4483
 
3.3%
7 4120
 
3.0%
Other values (15) 7123
 
5.2%
Han
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 166534
54.7%
ASCII 137937
45.3%
Number Forms 25
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%
CJK 1
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
67605
49.0%
1 13412
 
9.7%
2 8864
 
6.4%
0 8015
 
5.8%
3 6997
 
5.1%
4 5777
 
4.2%
- 5399
 
3.9%
5 4943
 
3.6%
6 4483
 
3.3%
7 4120
 
3.0%
Other values (58) 8322
 
6.0%
Hangul
ValueCountFrequency (%)
12055
 
7.2%
11135
 
6.7%
10493
 
6.3%
9930
 
6.0%
9731
 
5.8%
9691
 
5.8%
9679
 
5.8%
8633
 
5.2%
8399
 
5.0%
7899
 
4.7%
Other values (532) 68889
41.4%
Number Forms
ValueCountFrequency (%)
16
64.0%
5
 
20.0%
4
 
16.0%
CJK Compat Ideographs
ValueCountFrequency (%)
1
100.0%
CJK
ValueCountFrequency (%)
1
100.0%
None
ValueCountFrequency (%)
1
100.0%

소재지(도로명)
Text

MISSING 

Distinct4718
Distinct (%)91.3%
Missing4834
Missing (%)48.3%
Memory size156.2 KiB
2024-05-11T14:42:41.912190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length55
Mean length37.187379
Min length19

Characters and Unicode

Total characters192110
Distinct characters605
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4317 ?
Unique (%)83.6%

Sample

1st row서울특별시 종로구 종로 252-7, 300호 (종로5가)
2nd row서울특별시 마포구 마포대로 15, 1010호 (마포동, 현대빌딩)
3rd row서울특별시 강북구 덕릉로 104, 202호 (미아동, 뉴그린오피스텔)
4th row서울특별시 송파구 오금로35길 17, 지하층 24호 (오금동, 현대아파트)
5th row서울특별시 강서구 양천로 551-24, 305-19호 (가양동, 한화비즈메트로2차)
ValueCountFrequency (%)
서울특별시 5165
 
14.1%
강남구 953
 
2.6%
서초구 548
 
1.5%
2층 478
 
1.3%
역삼동 409
 
1.1%
3층 364
 
1.0%
서초동 362
 
1.0%
영등포구 331
 
0.9%
송파구 317
 
0.9%
4층 302
 
0.8%
Other values (6574) 27332
74.8%
2024-05-11T14:42:42.606844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31423
 
16.4%
1 7407
 
3.9%
, 7016
 
3.7%
6769
 
3.5%
6738
 
3.5%
5734
 
3.0%
5716
 
3.0%
2 5393
 
2.8%
5386
 
2.8%
5212
 
2.7%
Other values (595) 105316
54.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 106947
55.7%
Decimal Number 34304
 
17.9%
Space Separator 31423
 
16.4%
Other Punctuation 7034
 
3.7%
Close Punctuation 5202
 
2.7%
Open Punctuation 5200
 
2.7%
Dash Punctuation 1083
 
0.6%
Uppercase Letter 803
 
0.4%
Lowercase Letter 78
 
< 0.1%
Letter Number 27
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6769
 
6.3%
6738
 
6.3%
5734
 
5.4%
5716
 
5.3%
5386
 
5.0%
5212
 
4.9%
5172
 
4.8%
5165
 
4.8%
4224
 
3.9%
2692
 
2.5%
Other values (523) 54139
50.6%
Uppercase Letter
ValueCountFrequency (%)
B 169
21.0%
A 108
13.4%
S 64
 
8.0%
C 49
 
6.1%
T 49
 
6.1%
E 42
 
5.2%
I 36
 
4.5%
K 32
 
4.0%
R 29
 
3.6%
G 28
 
3.5%
Other values (15) 197
24.5%
Lowercase Letter
ValueCountFrequency (%)
e 15
19.2%
i 8
10.3%
r 8
10.3%
t 7
9.0%
w 6
 
7.7%
n 6
 
7.7%
o 5
 
6.4%
l 4
 
5.1%
c 4
 
5.1%
s 4
 
5.1%
Other values (9) 11
14.1%
Decimal Number
ValueCountFrequency (%)
1 7407
21.6%
2 5393
15.7%
0 4442
12.9%
3 4061
11.8%
4 2866
 
8.4%
5 2638
 
7.7%
6 2286
 
6.7%
7 1886
 
5.5%
8 1770
 
5.2%
9 1555
 
4.5%
Other Punctuation
ValueCountFrequency (%)
, 7016
99.7%
. 10
 
0.1%
@ 4
 
0.1%
/ 2
 
< 0.1%
1
 
< 0.1%
& 1
 
< 0.1%
Letter Number
ValueCountFrequency (%)
15
55.6%
7
25.9%
5
 
18.5%
Math Symbol
ValueCountFrequency (%)
~ 7
77.8%
> 1
 
11.1%
< 1
 
11.1%
Close Punctuation
ValueCountFrequency (%)
) 5201
> 99.9%
] 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 5199
> 99.9%
[ 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
31423
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1083
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 106946
55.7%
Common 84255
43.9%
Latin 908
 
0.5%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
6769
 
6.3%
6738
 
6.3%
5734
 
5.4%
5716
 
5.3%
5386
 
5.0%
5212
 
4.9%
5172
 
4.8%
5165
 
4.8%
4224
 
3.9%
2692
 
2.5%
Other values (522) 54138
50.6%
Latin
ValueCountFrequency (%)
B 169
18.6%
A 108
 
11.9%
S 64
 
7.0%
C 49
 
5.4%
T 49
 
5.4%
E 42
 
4.6%
I 36
 
4.0%
K 32
 
3.5%
R 29
 
3.2%
G 28
 
3.1%
Other values (37) 302
33.3%
Common
ValueCountFrequency (%)
31423
37.3%
1 7407
 
8.8%
, 7016
 
8.3%
2 5393
 
6.4%
) 5201
 
6.2%
( 5199
 
6.2%
0 4442
 
5.3%
3 4061
 
4.8%
4 2866
 
3.4%
5 2638
 
3.1%
Other values (15) 8609
 
10.2%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 106946
55.7%
ASCII 85135
44.3%
Number Forms 27
 
< 0.1%
CJK 1
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31423
36.9%
1 7407
 
8.7%
, 7016
 
8.2%
2 5393
 
6.3%
) 5201
 
6.1%
( 5199
 
6.1%
0 4442
 
5.2%
3 4061
 
4.8%
4 2866
 
3.4%
5 2638
 
3.1%
Other values (58) 9489
 
11.1%
Hangul
ValueCountFrequency (%)
6769
 
6.3%
6738
 
6.3%
5734
 
5.4%
5716
 
5.3%
5386
 
5.0%
5212
 
4.9%
5172
 
4.8%
5165
 
4.8%
4224
 
3.9%
2692
 
2.5%
Other values (522) 54138
50.6%
Number Forms
ValueCountFrequency (%)
15
55.6%
7
25.9%
5
 
18.5%
CJK
ValueCountFrequency (%)
1
100.0%
None
ValueCountFrequency (%)
1
100.0%

우편번호
Real number (ℝ)

MISSING 

Distinct1343
Distinct (%)31.0%
Missing5663
Missing (%)56.6%
Infinite0
Infinite (%)0.0%
Mean136558.12
Minimum2519
Maximum429842
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T14:42:42.808403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2519
5-th percentile100897
Q1132030
median136110
Q3143220
95-th percentile157031
Maximum429842
Range427323
Interquartile range (IQR)11190

Descriptive statistics

Standard deviation16106.606
Coefficient of variation (CV)0.11794689
Kurtosis67.11027
Mean136558.12
Median Absolute Deviation (MAD)5287
Skewness2.4216114
Sum5.9225255 × 108
Variance2.5942275 × 108
MonotonicityNot monotonic
2024-05-11T14:42:43.028439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135080 153
 
1.5%
137070 122
 
1.2%
151015 65
 
0.7%
135010 62
 
0.6%
157010 61
 
0.6%
152050 55
 
0.5%
158070 45
 
0.4%
132040 44
 
0.4%
130100 42
 
0.4%
151050 40
 
0.4%
Other values (1333) 3648
36.5%
(Missing) 5663
56.6%
ValueCountFrequency (%)
2519 1
 
< 0.1%
4534 1
 
< 0.1%
4538 1
 
< 0.1%
5510 1
 
< 0.1%
7220 1
 
< 0.1%
14538 1
 
< 0.1%
100011 6
0.1%
100012 3
< 0.1%
100013 1
 
< 0.1%
100014 1
 
< 0.1%
ValueCountFrequency (%)
429842 1
< 0.1%
410762 1
< 0.1%
403866 1
< 0.1%
158877 2
< 0.1%
158860 1
< 0.1%
158856 1
< 0.1%
158846 1
< 0.1%
158845 1
< 0.1%
158842 1
< 0.1%
158841 2
< 0.1%

등록일자
Real number (ℝ)

HIGH CORRELATION 

Distinct3549
Distinct (%)35.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20136616
Minimum20060124
Maximum20240510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T14:42:43.188042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20060124
5-th percentile20070905
Q120091119
median20130215
Q320170819
95-th percentile20230221
Maximum20240510
Range180386
Interquartile range (IQR)79699.75

Descriptive statistics

Standard deviation48977.802
Coefficient of variation (CV)0.0024322757
Kurtosis-0.9157657
Mean20136616
Median Absolute Deviation (MAD)39592
Skewness0.46557504
Sum2.0136616 × 1011
Variance2.398825 × 109
MonotonicityNot monotonic
2024-05-11T14:42:43.375666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20080814 36
 
0.4%
20080926 21
 
0.2%
20081222 20
 
0.2%
20080818 19
 
0.2%
20080731 18
 
0.2%
20090507 18
 
0.2%
20090108 15
 
0.1%
20100407 14
 
0.1%
20080806 14
 
0.1%
20090514 14
 
0.1%
Other values (3539) 9811
98.1%
ValueCountFrequency (%)
20060124 1
 
< 0.1%
20060306 1
 
< 0.1%
20060310 2
 
< 0.1%
20060323 2
 
< 0.1%
20060324 1
 
< 0.1%
20060329 1
 
< 0.1%
20060331 1
 
< 0.1%
20060405 1
 
< 0.1%
20060407 6
0.1%
20060411 2
 
< 0.1%
ValueCountFrequency (%)
20240510 1
 
< 0.1%
20240507 2
< 0.1%
20240503 2
< 0.1%
20240502 3
< 0.1%
20240430 1
 
< 0.1%
20240425 2
< 0.1%
20240424 2
< 0.1%
20240423 1
 
< 0.1%
20240422 2
< 0.1%
20240418 2
< 0.1%

유효기간만료일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3327
Distinct (%)42.0%
Missing2082
Missing (%)20.8%
Infinite0
Infinite (%)0.0%
Mean20181283
Minimum20090310
Maximum20270509
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T14:42:43.581515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20090310
5-th percentile20120311
Q120141004
median20171222
Q320220108
95-th percentile20260510
Maximum20270509
Range180199
Interquartile range (IQR)79104

Descriptive statistics

Standard deviation44727.144
Coefficient of variation (CV)0.0022162687
Kurtosis-0.99534266
Mean20181283
Median Absolute Deviation (MAD)30707
Skewness0.31529544
Sum1.5979539 × 1011
Variance2.0005174 × 109
MonotonicityNot monotonic
2024-05-11T14:42:43.794598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20110831 20
 
0.2%
20140831 12
 
0.1%
20140711 12
 
0.1%
20150531 12
 
0.1%
20180914 12
 
0.1%
20140622 11
 
0.1%
20140608 11
 
0.1%
20190722 11
 
0.1%
20140330 10
 
0.1%
20110814 10
 
0.1%
Other values (3317) 7797
78.0%
(Missing) 2082
 
20.8%
ValueCountFrequency (%)
20090310 1
< 0.1%
20090514 1
< 0.1%
20091220 1
< 0.1%
20100117 1
< 0.1%
20100122 1
< 0.1%
20100208 1
< 0.1%
20100321 1
< 0.1%
20100323 1
< 0.1%
20100326 1
< 0.1%
20100405 1
< 0.1%
ValueCountFrequency (%)
20270509 1
< 0.1%
20270507 1
< 0.1%
20270506 1
< 0.1%
20270503 2
< 0.1%
20270502 2
< 0.1%
20270501 1
< 0.1%
20270430 1
< 0.1%
20270425 2
< 0.1%
20270424 2
< 0.1%
20270423 1
< 0.1%

폐쇄일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3087
Distinct (%)36.8%
Missing1617
Missing (%)16.2%
Infinite0
Infinite (%)0.0%
Mean20141443
Minimum20060920
Maximum20240509
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T14:42:44.048160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20060920
5-th percentile20090924
Q120110329
median20130625
Q320170328
95-th percentile20220830
Maximum20240509
Range179589
Interquartile range (IQR)59999

Descriptive statistics

Standard deviation40617.749
Coefficient of variation (CV)0.0020166256
Kurtosis-0.53946399
Mean20141443
Median Absolute Deviation (MAD)29698
Skewness0.70602288
Sum1.6884571 × 1011
Variance1.6498015 × 109
MonotonicityNot monotonic
2024-05-11T14:42:44.246720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20091116 205
 
2.1%
20100927 77
 
0.8%
20101213 33
 
0.3%
20160725 20
 
0.2%
20110823 19
 
0.2%
20110914 18
 
0.2%
20110901 17
 
0.2%
20110420 17
 
0.2%
20101126 16
 
0.2%
20110125 16
 
0.2%
Other values (3077) 7945
79.5%
(Missing) 1617
 
16.2%
ValueCountFrequency (%)
20060920 1
 
< 0.1%
20071030 1
 
< 0.1%
20090125 1
 
< 0.1%
20090220 1
 
< 0.1%
20090305 1
 
< 0.1%
20090307 1
 
< 0.1%
20090309 2
 
< 0.1%
20090311 7
0.1%
20090312 4
< 0.1%
20090313 2
 
< 0.1%
ValueCountFrequency (%)
20240509 1
< 0.1%
20240503 1
< 0.1%
20240502 2
< 0.1%
20240501 2
< 0.1%
20240430 1
< 0.1%
20240429 1
< 0.1%
20240426 1
< 0.1%
20240424 1
< 0.1%
20240423 1
< 0.1%
20240422 2
< 0.1%

지점설립일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3579
Distinct (%)41.1%
Missing1284
Missing (%)12.8%
Infinite0
Infinite (%)0.0%
Mean20135594
Minimum19050627
Maximum20240507
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T14:42:44.449118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19050627
5-th percentile20070116
Q120100427
median20130502
Q320170612
95-th percentile20220620
Maximum20240507
Range1189880
Interquartile range (IQR)70185

Descriptive statistics

Standard deviation50381.956
Coefficient of variation (CV)0.002502134
Kurtosis49.209443
Mean20135594
Median Absolute Deviation (MAD)30619
Skewness-2.1844615
Sum1.7550184 × 1011
Variance2.5383415 × 109
MonotonicityNot monotonic
2024-05-11T14:42:44.715525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20090507 20
 
0.2%
20090514 19
 
0.2%
20090611 18
 
0.2%
20090528 17
 
0.2%
20090511 16
 
0.2%
20090520 16
 
0.2%
20090820 16
 
0.2%
20100201 14
 
0.1%
20090722 14
 
0.1%
20100407 13
 
0.1%
Other values (3569) 8553
85.5%
(Missing) 1284
 
12.8%
ValueCountFrequency (%)
19050627 2
< 0.1%
19670425 1
< 0.1%
19840618 1
< 0.1%
19930107 1
< 0.1%
19940223 1
< 0.1%
19950103 1
< 0.1%
19950711 1
< 0.1%
19960712 1
< 0.1%
19970410 1
< 0.1%
19981009 1
< 0.1%
ValueCountFrequency (%)
20240507 1
< 0.1%
20240502 1
< 0.1%
20240429 1
< 0.1%
20240425 2
< 0.1%
20240423 1
< 0.1%
20240422 1
< 0.1%
20240419 1
< 0.1%
20240418 1
< 0.1%
20240415 2
< 0.1%
20240411 2
< 0.1%

본점여부
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
본점
9943 
지점
 
57

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 9943
99.4%
지점 57
 
0.6%

Length

2024-05-11T14:42:44.857650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:42:44.975192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
본점 9943
99.4%
지점 57
 
0.6%

최근수정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct3166
Distinct (%)31.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20152766
Minimum20090519
Maximum20240510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T14:42:45.117115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20090519
5-th percentile20091118
Q120110920
median20140806
Q320190509
95-th percentile20231017
Maximum20240510
Range149991
Interquartile range (IQR)79589.5

Descriptive statistics

Standard deviation46073.993
Coefficient of variation (CV)0.0022862366
Kurtosis-1.0753814
Mean20152766
Median Absolute Deviation (MAD)30501.5
Skewness0.4541081
Sum2.0152766 × 1011
Variance2.1228128 × 109
MonotonicityNot monotonic
2024-05-11T14:42:45.324855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20091117 84
 
0.8%
20091118 55
 
0.5%
20090609 51
 
0.5%
20100927 49
 
0.5%
20100330 42
 
0.4%
20091116 40
 
0.4%
20091119 34
 
0.3%
20130621 33
 
0.3%
20090611 31
 
0.3%
20090622 31
 
0.3%
Other values (3156) 9550
95.5%
ValueCountFrequency (%)
20090519 1
 
< 0.1%
20090521 4
 
< 0.1%
20090601 2
 
< 0.1%
20090602 1
 
< 0.1%
20090603 9
 
0.1%
20090604 14
 
0.1%
20090605 5
 
0.1%
20090608 3
 
< 0.1%
20090609 51
0.5%
20090610 19
 
0.2%
ValueCountFrequency (%)
20240510 4
< 0.1%
20240509 4
< 0.1%
20240508 7
0.1%
20240507 3
 
< 0.1%
20240503 8
0.1%
20240502 8
0.1%
20240501 2
 
< 0.1%
20240430 1
 
< 0.1%
20240429 2
 
< 0.1%
20240426 1
 
< 0.1%

Interactions

2024-05-11T14:42:29.583945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:41:39.733452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:41:43.446154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:41:49.975779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:41:56.237180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:42:02.327991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:42:29.710713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:41:39.891128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:41:43.594357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:41:50.096270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:41:56.393137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:42:04.032875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:42:29.911619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:41:40.101362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:41:43.801730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:41:50.228292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:41:56.567823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:42:08.617102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:42:30.091850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:41:40.300434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:41:44.019900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:41:50.367125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:41:56.713287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:42:12.360032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:42:30.270425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:41:40.466923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:41:44.233029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:41:50.548201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:41:56.860744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:42:16.242307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:42:35.468041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:41:43.282057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:41:49.824301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:41:56.047974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:42:02.151798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:42:24.911334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T14:42:45.476708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록신청사업영업구분법인여부우편번호등록일자유효기간만료일자폐쇄일자지점설립일자본점여부최근수정일자
등록신청사업1.0000.0870.0240.0000.2330.1700.1750.0960.0260.180
영업구분0.0871.0000.2870.0290.6170.6280.1800.1890.0580.549
법인여부0.0240.2871.0000.0310.3480.2940.2600.1600.1800.347
우편번호0.0000.0290.0311.0000.1590.1580.1720.1100.0000.179
등록일자0.2330.6170.3480.1591.0001.0000.9350.7750.0800.939
유효기간만료일자0.1700.6280.2940.1581.0001.0000.8360.7530.0860.840
폐쇄일자0.1750.1800.2600.1720.9350.8361.0000.6670.0610.989
지점설립일자0.0960.1890.1600.1100.7750.7530.6671.0000.0000.771
본점여부0.0260.0580.1800.0000.0800.0860.0610.0001.0000.097
최근수정일자0.1800.5490.3470.1790.9390.8400.9890.7710.0971.000
2024-05-11T14:42:45.641503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록신청사업본점여부영업구분법인여부
등록신청사업1.0000.0160.0620.015
본점여부0.0161.0000.0420.115
영업구분0.0620.0421.0000.206
법인여부0.0150.1150.2061.000
2024-05-11T14:42:46.089187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호등록일자유효기간만료일자폐쇄일자지점설립일자최근수정일자등록신청사업영업구분법인여부본점여부
우편번호1.0000.0090.0200.0210.0450.0120.0000.0220.0080.000
등록일자0.0091.0000.9970.9610.9330.9660.1790.3840.2670.061
유효기간만료일자0.0200.9971.0000.9640.9100.9670.1300.3940.2250.066
폐쇄일자0.0210.9610.9641.0000.9100.9910.1740.1040.1990.047
지점설립일자0.0450.9330.9100.9101.0000.9040.1360.1990.3430.120
최근수정일자0.0120.9660.9670.9910.9041.0000.1380.3090.2660.074
등록신청사업0.0000.1790.1300.1740.1360.1381.0000.0620.0150.016
영업구분0.0220.3840.3940.1040.1990.3090.0621.0000.2060.042
법인여부0.0080.2670.2250.1990.3430.2660.0150.2061.0000.115
본점여부0.0000.0610.0660.0470.1200.0740.0160.0420.1151.000

Missing values

2024-05-11T14:42:35.730837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T14:42:35.957032image/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.
2024-05-11T14:42:36.161580image/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
18380대부중개업유효기간만료2010-서울노원-00044(대부중개업)연앤정대부중개개인02 909 5001서울특별시 노원구 공릉동 670번지 11호 로우폴리스-507<NA>13924020100608201306082013061120100608본점20130624
4077대부업폐업2020-서울종로-00001(대부업)등대대부업개인02-2274-8885서울특별시 종로구 종로5가 265번지 1호서울특별시 종로구 종로 252-7, 300호 (종로5가)<NA>20200224202302242022072720200224본점20220727
8964대부중개업폐업2012-서울마포-0041(대부중개업)중외대부중개개인1544-8137<NA>서울특별시 마포구 마포대로 15, 1010호 (마포동, 현대빌딩)<NA>20150526201805262018052320120616본점20180524
29896대부업<NA>2007-서울특별시-00442(대부업)미래와가치개인7458572서울특별시 종로구 운니동 98-78 가든타워 2층 203호<NA><NA>20070328<NA>2009111620070304본점20091117
9474대부중개업폐업2017-서울강북-0062(대부중개업)크레딧 매니져 대부중개개인1599-0675서울특별시 강북구 미아동 159번지 1호서울특별시 강북구 덕릉로 104, 202호 (미아동, 뉴그린오피스텔)<NA>20170925202009252018013020170920본점20180130
2975대부업영업중2012-서울송파-0132(대부업)마중물금융대부개인<NA>서울특별시 송파구 오금동 43번지 지하 현대아파트-24서울특별시 송파구 오금로35길 17, 지하층 24호 (오금동, 현대아파트)<NA>2012121420240906<NA>20121214본점20230321
2536대부업타시군구이관2023-서울강서-0005(대부업)아레스대부개인<NA>서울특별시 강서구 가양동 449번지 21호 한화비즈메트로2차서울특별시 강서구 양천로 551-24, 305-19호 (가양동, 한화비즈메트로2차)<NA>20230322202603222023053120230320본점20230531
6446대부업폐업2018-서울광진-0033(대부업)에이치에스대부개인<NA>서울특별시 광진구 구의동 244번지 51호서울특별시 광진구 구의로 8, 2층 (구의동)<NA>20171208202012082020073120171208본점20200803
8674대부업폐업2018-서울강서-00008(대부업)빠르미 대부개인<NA>서울특별시 강서구 화곡동 350번지 29호 은하아트빌서울특별시 강서구 강서로17가길 7-2, 은하아트빌 B102호 (화곡동)<NA>20180212202102122018073120180212본점20180801
7518대부업폐업2015-서울중구-0016(대부업)웅진(대부)개인02-778-6564서울특별시 중구 명동1가 1번지 1호 YWCA연합회-412서울특별시 중구 명동길 73, 412호 (명동1가, YWCA연합회)<NA>20180221202102212019071520090624본점20190716
등록신청사업영업구분등록증번호상호법인여부사업장 전화번호소재지소재지(도로명)우편번호등록일자유효기간만료일자폐쇄일자지점설립일자본점여부최근수정일자
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5960대부중개업타시군구이관2020-서울영등포-2033(대부중개업)해피안대부개인02-832-0045서울특별시 영등포구 대림동 689번지 4호서울특별시 영등포구 대림로 194, 2층 (대림동)<NA>20200203202302032021020220200203본점20210202
26839대부중개업<NA>2009-서울특별시-02276(대부중개업)진영대부중개개인<NA>서울특별시 강북구 수유동 519번지 54호 극동빌라 102호<NA><NA>20090922<NA>2010092720090922본점20100928
25819대부업<NA>2010-서울구로-00037(대부업)(주)어필자산대부법인0226764955서울특별시 구로구 신도림동 637번지 우성 3차상가-302<NA>15207020100611<NA>2011012120100611본점20110124
9281대부중개업<NA>2017-서울강남-0193(대부중개업)주식회사 펀드1번가대부법인02-557-1721서울특별시 강남구 역삼동 723번지 30호 송정빌딩 2층서울특별시 강남구 테헤란로34길 21-10, 2층 (역삼동, 송정빌딩)<NA>2017071320200713<NA>20170713본점20180228
12112대부업타시군구이관2015-서울강남-0277(대부업)론 컨설팅대부개인<NA>서울특별시 강남구 역삼동 605번지 12호 -301서울특별시 강남구 봉은사로24길 9, 301호 (역삼동)<NA>20151116201811162016091220151116본점20160912
24398대부업<NA>2008-서울특별시-00015(대부업)신우사개인023754864서울특별시 서대문구 북가좌1동 431 한양아파트 상가 106호<NA><NA>200808052011080520110630<NA>본점20110701
8453대부업폐업2016-서울관악-00006(대부업)미래지앤티대부개인<NA>서울특별시 관악구 신림동 503번지 21호서울특별시 관악구 조원로 131-1, 1층 (신림동)<NA>20160120201901202018101620160119본점20181016
20122대부중개업폐업2012-서울구로-034(대부중개업)머니브레인대부중개개인1566-3744서울특별시 구로구 구로동 236번지 한신IT타워-1303<NA>15205020110316201403162012121720110316본점20121217
21649대부업타시군구이관2011-서울용산-00013(대부업)에스비엔터프라이즈대부개인<NA>서울특별시 용산구 보광동 65<NA><NA>20110322201403222012051620080414본점20120516

Duplicate rows

Most frequently occurring

등록신청사업영업구분등록증번호상호법인여부사업장 전화번호소재지소재지(도로명)우편번호등록일자유효기간만료일자폐쇄일자지점설립일자본점여부최근수정일자# duplicates
0대부업<NA>2009-서울특별시-00046(대부업)오리엔트론개인0260817960서울특별시 서초구 방배동 852-21 학촌빌딩 3층<NA><NA>20090211<NA>2010121320060313본점201101122