Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells19147
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
Text5
Numeric6

Dataset

Description등록신청사업,영업구분,등록증번호,상호,법인여부,사업장 전화번호,소재지,소재지(도로명),우편번호,등록일자,유효기간만료일자,폐쇄일자,지점설립일자,본점여부,최근수정일자
Author관악구
URLhttps://data.seoul.go.kr/dataList/OA-11559/S/1/datasetView.do

Alerts

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 (93.4%)Imbalance
등록증번호 has 158 (1.6%) missing valuesMissing
사업장 전화번호 has 3298 (33.0%) missing valuesMissing
소재지 has 290 (2.9%) missing valuesMissing
소재지(도로명) has 4827 (48.3%) missing valuesMissing
우편번호 has 5597 (56.0%) missing valuesMissing
유효기간만료일자 has 2098 (21.0%) missing valuesMissing
폐쇄일자 has 1609 (16.1%) missing valuesMissing
지점설립일자 has 1270 (12.7%) missing valuesMissing

Reproduction

Analysis started2024-05-18 02:38:00.312148
Analysis finished2024-05-18 02:39:45.790223
Duration1 minute and 45.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

등록신청사업
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대부업
6215 
대부중개업
3361 
<NA>
 
424

Length

Max length5
Median length3
Mean length3.7146
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대부업 6215
62.2%
대부중개업 3361
33.6%
<NA> 424
 
4.2%

Length

2024-05-18T11:39:46.106646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T11:39:46.326540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대부업 6215
62.2%
대부중개업 3361
33.6%
na 424
 
4.2%

영업구분
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
3672 
<NA>
2942 
타시군구이관
1230 
영업중
844 
유효기간만료
787 
Other values (2)
525 

Length

Max length6
Median length4
Mean length3.585
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 3672
36.7%
<NA> 2942
29.4%
타시군구이관 1230
 
12.3%
영업중 844
 
8.4%
유효기간만료 787
 
7.9%
직권취소 523
 
5.2%
갱신등록불가 2
 
< 0.1%

Length

2024-05-18T11:39:46.641361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T11:39:47.008601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 3672
36.7%
na 2942
29.4%
타시군구이관 1230
 
12.3%
영업중 844
 
8.4%
유효기간만료 787
 
7.9%
직권취소 523
 
5.2%
갱신등록불가 2
 
< 0.1%

등록증번호
Text

MISSING 

Distinct9792
Distinct (%)99.5%
Missing158
Missing (%)1.6%
Memory size156.2 KiB
2024-05-18T11:39:47.431962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length27
Mean length19.513615
Min length1

Characters and Unicode

Total characters192053
Distinct characters81
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

Unique9743 ?
Unique (%)99.0%

Sample

1st row2009-서울특별시-01389(대부중개업)
2nd row2019-서울중구-0076(대부중개업)
3rd row2008-서울특별시-01840(대부업)
4th row2019-서울구로015(대부업)
5th row2017-서울강남-0079(대부업)
ValueCountFrequency (%)
2010-서울 19
 
0.2%
2013-서울특별시 17
 
0.2%
2012-서울특별시 15
 
0.1%
2011-서울특별시 15
 
0.1%
2016-서울특별시 13
 
0.1%
대부중개업 12
 
0.1%
대부업 10
 
0.1%
2014-서울특별시 10
 
0.1%
2017-서울특별시 8
 
0.1%
2015-서울특별시 8
 
0.1%
Other values (9751) 9886
98.7%
2024-05-18T11:39:48.293346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 33957
17.7%
- 19663
 
10.2%
2 15768
 
8.2%
1 11886
 
6.2%
서 10907
 
5.7%
울 9814
 
5.1%
대 8492
 
4.4%
( 8203
 
4.3%
부 8167
 
4.3%
) 8151
 
4.2%
Other values (71) 57045
29.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 82786
43.1%
Other Letter 73079
38.1%
Dash Punctuation 19663
 
10.2%
Open Punctuation 8203
 
4.3%
Close Punctuation 8151
 
4.2%
Space Separator 171
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 10907
14.9%
울 9814
13.4%
대 8492
11.6%
부 8167
11.2%
업 7943
10.9%
중 3526
 
4.8%
개 2887
 
4.0%
시 2559
 
3.5%
특 2554
 
3.5%
별 2554
 
3.5%
Other values (57) 13676
18.7%
Decimal Number
ValueCountFrequency (%)
0 33957
41.0%
2 15768
19.0%
1 11886
 
14.4%
3 3738
 
4.5%
8 3123
 
3.8%
4 3084
 
3.7%
7 2858
 
3.5%
6 2813
 
3.4%
9 2809
 
3.4%
5 2750
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 19663
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8203
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8151
100.0%
Space Separator
ValueCountFrequency (%)
171
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 118974
61.9%
Hangul 73079
38.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 10907
14.9%
울 9814
13.4%
대 8492
11.6%
부 8167
11.2%
업 7943
10.9%
중 3526
 
4.8%
개 2887
 
4.0%
시 2559
 
3.5%
특 2554
 
3.5%
별 2554
 
3.5%
Other values (57) 13676
18.7%
Common
ValueCountFrequency (%)
0 33957
28.5%
- 19663
16.5%
2 15768
13.3%
1 11886
 
10.0%
( 8203
 
6.9%
) 8151
 
6.9%
3 3738
 
3.1%
8 3123
 
2.6%
4 3084
 
2.6%
7 2858
 
2.4%
Other values (4) 8543
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118974
61.9%
Hangul 73079
38.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33957
28.5%
- 19663
16.5%
2 15768
13.3%
1 11886
 
10.0%
( 8203
 
6.9%
) 8151
 
6.9%
3 3738
 
3.1%
8 3123
 
2.6%
4 3084
 
2.6%
7 2858
 
2.4%
Other values (4) 8543
 
7.2%
Hangul
ValueCountFrequency (%)
서 10907
14.9%
울 9814
13.4%
대 8492
11.6%
부 8167
11.2%
업 7943
10.9%
중 3526
 
4.8%
개 2887
 
4.0%
시 2559
 
3.5%
특 2554
 
3.5%
별 2554
 
3.5%
Other values (57) 13676
18.7%

상호
Text

Distinct8701
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T11:39:48.743898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length26
Mean length7.7627
Min length1

Characters and Unicode

Total characters77627
Distinct characters775
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7676 ?
Unique (%)76.8%

Sample

1st row비엘대부중개투자금융
2nd rowSJ1대부
3rd row최종해
4th row주식회사 나라자산관리대부
5th row랑대부주식회사
ValueCountFrequency (%)
주식회사 842
 
7.1%
대부중개 311
 
2.6%
대부 290
 
2.4%
유한회사 65
 
0.5%
캐피탈 27
 
0.2%
대부업 22
 
0.2%
loan 12
 
0.1%
전당포 11
 
0.1%
대부중개업 10
 
0.1%
전당포대부 10
 
0.1%
Other values (8707) 10341
86.6%
2024-05-18T11:39:49.819481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
대 8477
 
10.9%
부 8105
 
10.4%
주 2774
 
3.6%
이 2245
 
2.9%
개 2082
 
2.7%
중 2057
 
2.6%
1946
 
2.5%
스 1911
 
2.5%
) 1910
 
2.5%
( 1900
 
2.4%
Other values (765) 44220
57.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 67992
87.6%
Uppercase Letter 2291
 
3.0%
Space Separator 1946
 
2.5%
Close Punctuation 1910
 
2.5%
Open Punctuation 1900
 
2.4%
Lowercase Letter 1068
 
1.4%
Other Punctuation 249
 
0.3%
Decimal Number 231
 
0.3%
Dash Punctuation 27
 
< 0.1%
Other Symbol 11
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 8477
 
12.5%
부 8105
 
11.9%
주 2774
 
4.1%
이 2245
 
3.3%
개 2082
 
3.1%
중 2057
 
3.0%
스 1911
 
2.8%
사 1390
 
2.0%
피 1121
 
1.6%
회 1077
 
1.6%
Other values (690) 36753
54.1%
Uppercase Letter
ValueCountFrequency (%)
S 313
13.7%
K 202
 
8.8%
C 190
 
8.3%
M 175
 
7.6%
J 161
 
7.0%
H 140
 
6.1%
B 112
 
4.9%
N 94
 
4.1%
L 88
 
3.8%
D 87
 
3.8%
Other values (16) 729
31.8%
Lowercase Letter
ValueCountFrequency (%)
o 130
12.2%
n 121
11.3%
e 115
10.8%
a 103
 
9.6%
i 65
 
6.1%
t 64
 
6.0%
s 58
 
5.4%
r 54
 
5.1%
l 52
 
4.9%
m 46
 
4.3%
Other values (15) 260
24.3%
Decimal Number
ValueCountFrequency (%)
1 77
33.3%
2 37
16.0%
4 32
13.9%
9 18
 
7.8%
5 17
 
7.4%
3 13
 
5.6%
0 12
 
5.2%
6 11
 
4.8%
7 8
 
3.5%
8 6
 
2.6%
Other Punctuation
ValueCountFrequency (%)
. 138
55.4%
& 96
38.6%
? 7
 
2.8%
, 4
 
1.6%
＆ 2
 
0.8%
' 1
 
0.4%
* 1
 
0.4%
Space Separator
ValueCountFrequency (%)
1946
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1910
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1900
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 27
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 11
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 67992
87.6%
Common 6265
 
8.1%
Latin 3359
 
4.3%
Han 11
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 8477
 
12.5%
부 8105
 
11.9%
주 2774
 
4.1%
이 2245
 
3.3%
개 2082
 
3.1%
중 2057
 
3.0%
스 1911
 
2.8%
사 1390
 
2.0%
피 1121
 
1.6%
회 1077
 
1.6%
Other values (680) 36753
54.1%
Latin
ValueCountFrequency (%)
S 313
 
9.3%
K 202
 
6.0%
C 190
 
5.7%
M 175
 
5.2%
J 161
 
4.8%
H 140
 
4.2%
o 130
 
3.9%
n 121
 
3.6%
e 115
 
3.4%
B 112
 
3.3%
Other values (41) 1700
50.6%
Common
ValueCountFrequency (%)
1946
31.1%
) 1910
30.5%
( 1900
30.3%
. 138
 
2.2%
& 96
 
1.5%
1 77
 
1.2%
2 37
 
0.6%
4 32
 
0.5%
- 27
 
0.4%
9 18
 
0.3%
Other values (13) 84
 
1.3%
Han
ValueCountFrequency (%)
錫 1
9.1%
正 1
9.1%
信 1
9.1%
田 1
9.1%
福 1
9.1%
雲 1
9.1%
靑 1
9.1%
昇 1
9.1%
利 1
9.1%
百 1
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 67980
87.6%
ASCII 9622
 
12.4%
None 13
 
< 0.1%
CJK 11
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
대 8477
 
12.5%
부 8105
 
11.9%
주 2774
 
4.1%
이 2245
 
3.3%
개 2082
 
3.1%
중 2057
 
3.0%
스 1911
 
2.8%
사 1390
 
2.0%
피 1121
 
1.6%
회 1077
 
1.6%
Other values (678) 36741
54.0%
ASCII
ValueCountFrequency (%)
1946
20.2%
) 1910
19.9%
( 1900
19.7%
S 313
 
3.3%
K 202
 
2.1%
C 190
 
2.0%
M 175
 
1.8%
J 161
 
1.7%
H 140
 
1.5%
. 138
 
1.4%
Other values (63) 2547
26.5%
None
ValueCountFrequency (%)
㈜ 11
84.6%
＆ 2
 
15.4%
CJK
ValueCountFrequency (%)
錫 1
9.1%
正 1
9.1%
信 1
9.1%
田 1
9.1%
福 1
9.1%
雲 1
9.1%
靑 1
9.1%
昇 1
9.1%
利 1
9.1%
百 1
9.1%
Compat Jamo
ValueCountFrequency (%)
ㆍ 1
100.0%

법인여부
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
개인
7119 
법인
2881 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
개인 7119
71.2%
법인 2881
28.8%

Length

2024-05-18T11:39:50.221356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T11:39:50.516817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 7119
71.2%
법인 2881
28.8%

사업장 전화번호
Text

MISSING 

Distinct5905
Distinct (%)88.1%
Missing3298
Missing (%)33.0%
Memory size156.2 KiB
2024-05-18T11:39:50.964367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length40
Mean length10.596091
Min length1

Characters and Unicode

Total characters71015
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

Unique5257 ?
Unique (%)78.4%

Sample

1st row07082710798
2nd row02-2272-5804
3rd row02-6219-0325
4th row1566-0630
5th row02-6494-0136
ValueCountFrequency (%)
02 298
 
3.9%
60
 
0.8%
070 47
 
0.6%
010 10
 
0.1%
1688 9
 
0.1%
2212 7
 
0.1%
1566 6
 
0.1%
432 6
 
0.1%
1544 6
 
0.1%
02-1644-6517 6
 
0.1%
Other values (6241) 7123
94.0%
2024-05-18T11:39:51.937804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11513
16.2%
2 10408
14.7%
- 7052
9.9%
5 5978
8.4%
7 5583
7.9%
1 5279
7.4%
6 5177
7.3%
3 4907
6.9%
8 4847
6.8%
4 4831
6.8%
Other values (18) 5440
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 62685
88.3%
Dash Punctuation 7052
 
9.9%
Space Separator 979
 
1.4%
Other Punctuation 188
 
0.3%
Close Punctuation 52
 
0.1%
Math Symbol 26
 
< 0.1%
Open Punctuation 17
 
< 0.1%
Other Letter 14
 
< 0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11513
18.4%
2 10408
16.6%
5 5978
9.5%
7 5583
8.9%
1 5279
8.4%
6 5177
8.3%
3 4907
7.8%
8 4847
7.7%
4 4831
7.7%
9 4162
 
6.6%
Other Letter
ValueCountFrequency (%)
미 3
21.4%
록 3
21.4%
등 3
21.4%
무 1
 
7.1%
번 1
 
7.1%
호 1
 
7.1%
바 1
 
7.1%
뀜 1
 
7.1%
Other Punctuation
ValueCountFrequency (%)
* 118
62.8%
/ 52
27.7%
. 18
 
9.6%
Uppercase Letter
ValueCountFrequency (%)
K 1
50.0%
T 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 7052
100.0%
Space Separator
ValueCountFrequency (%)
979
100.0%
Close Punctuation
ValueCountFrequency (%)
) 52
100.0%
Math Symbol
ValueCountFrequency (%)
~ 26
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70999
> 99.9%
Hangul 14
 
< 0.1%
Latin 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11513
16.2%
2 10408
14.7%
- 7052
9.9%
5 5978
8.4%
7 5583
7.9%
1 5279
7.4%
6 5177
7.3%
3 4907
6.9%
8 4847
6.8%
4 4831
6.8%
Other values (8) 5424
7.6%
Hangul
ValueCountFrequency (%)
미 3
21.4%
록 3
21.4%
등 3
21.4%
무 1
 
7.1%
번 1
 
7.1%
호 1
 
7.1%
바 1
 
7.1%
뀜 1
 
7.1%
Latin
ValueCountFrequency (%)
K 1
50.0%
T 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71001
> 99.9%
Hangul 14
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11513
16.2%
2 10408
14.7%
- 7052
9.9%
5 5978
8.4%
7 5583
7.9%
1 5279
7.4%
6 5177
7.3%
3 4907
6.9%
8 4847
6.8%
4 4831
6.8%
Other values (10) 5426
7.6%
Hangul
ValueCountFrequency (%)
미 3
21.4%
록 3
21.4%
등 3
21.4%
무 1
 
7.1%
번 1
 
7.1%
호 1
 
7.1%
바 1
 
7.1%
뀜 1
 
7.1%

소재지
Text

MISSING 

Distinct8606
Distinct (%)88.6%
Missing290
Missing (%)2.9%
Memory size156.2 KiB
2024-05-18T11:39:52.632568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length47
Mean length31.490113
Min length15

Characters and Unicode

Total characters305769
Distinct characters629
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

Unique7833 ?
Unique (%)80.7%

Sample

1st row서울특별시 종로구 숭인동 1401번지 스타일빌딩 501호
2nd row서울특별시 중구 을지로6가 23번지 타임캐슬 오피스텔
3rd row서울특별시 구로구 구로5동 42번지 LG자이아파트 102동 704호
4th row서울특별시 구로구 구로동 197번지 28호 이앤씨벤처드림타워6차-707
5th row서울특별시 강남구 신사동 563번지 17호 2층
ValueCountFrequency (%)
서울특별시 9708
 
16.9%
강남구 1620
 
2.8%
서초구 942
 
1.6%
1호 709
 
1.2%
역삼동 685
 
1.2%
서초동 585
 
1.0%
송파구 572
 
1.0%
중구 552
 
1.0%
2호 483
 
0.8%
영등포구 471
 
0.8%
Other values (9470) 40970
71.5%
2024-05-18T11:39:53.559416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
67899
22.2%
1 13371
 
4.4%
서 12074
 
3.9%
동 11107
 
3.6%
구 10512
 
3.4%
시 9950
 
3.3%
울 9764
 
3.2%
별 9716
 
3.2%
특 9708
 
3.2%
2 8995
 
2.9%
Other values (619) 142673
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 167363
54.7%
Space Separator 67899
22.2%
Decimal Number 63332
 
20.7%
Dash Punctuation 5397
 
1.8%
Uppercase Letter 1184
 
0.4%
Other Punctuation 237
 
0.1%
Lowercase Letter 119
 
< 0.1%
Close Punctuation 106
 
< 0.1%
Open Punctuation 102
 
< 0.1%
Letter Number 24
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 12074
 
7.2%
동 11107
 
6.6%
구 10512
 
6.3%
시 9950
 
5.9%
울 9764
 
5.8%
별 9716
 
5.8%
특 9708
 
5.8%
호 8644
 
5.2%
지 8449
 
5.0%
번 7954
 
4.8%
Other values (547) 69485
41.5%
Uppercase Letter
ValueCountFrequency (%)
B 257
21.7%
A 206
17.4%
S 94
 
7.9%
D 80
 
6.8%
L 56
 
4.7%
T 56
 
4.7%
K 55
 
4.6%
E 45
 
3.8%
I 44
 
3.7%
C 39
 
3.3%
Other values (16) 252
21.3%
Lowercase Letter
ValueCountFrequency (%)
e 26
21.8%
r 12
10.1%
i 10
 
8.4%
n 10
 
8.4%
o 9
 
7.6%
t 8
 
6.7%
l 8
 
6.7%
w 7
 
5.9%
u 5
 
4.2%
c 4
 
3.4%
Other values (10) 20
16.8%
Decimal Number
ValueCountFrequency (%)
1 13371
21.1%
2 8995
14.2%
0 8012
12.7%
3 6987
11.0%
4 5730
9.0%
5 5076
 
8.0%
6 4535
 
7.2%
7 3987
 
6.3%
8 3328
 
5.3%
9 3311
 
5.2%
Other Punctuation
ValueCountFrequency (%)
, 82
34.6%
. 76
32.1%
/ 74
31.2%
& 2
 
0.8%
@ 1
 
0.4%
* 1
 
0.4%
; 1
 
0.4%
Math Symbol
ValueCountFrequency (%)
~ 4
66.7%
> 1
 
16.7%
< 1
 
16.7%
Letter Number
ValueCountFrequency (%)
Ⅱ 19
79.2%
Ⅰ 5
 
20.8%
Space Separator
ValueCountFrequency (%)
67899
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5397
100.0%
Close Punctuation
ValueCountFrequency (%)
) 106
100.0%
Open Punctuation
ValueCountFrequency (%)
( 102
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 167359
54.7%
Common 137079
44.8%
Latin 1327
 
0.4%
Han 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 12074
 
7.2%
동 11107
 
6.6%
구 10512
 
6.3%
시 9950
 
5.9%
울 9764
 
5.8%
별 9716
 
5.8%
특 9708
 
5.8%
호 8644
 
5.2%
지 8449
 
5.0%
번 7954
 
4.8%
Other values (545) 69481
41.5%
Latin
ValueCountFrequency (%)
B 257
19.4%
A 206
15.5%
S 94
 
7.1%
D 80
 
6.0%
L 56
 
4.2%
T 56
 
4.2%
K 55
 
4.1%
E 45
 
3.4%
I 44
 
3.3%
C 39
 
2.9%
Other values (38) 395
29.8%
Common
ValueCountFrequency (%)
67899
49.5%
1 13371
 
9.8%
2 8995
 
6.6%
0 8012
 
5.8%
3 6987
 
5.1%
4 5730
 
4.2%
- 5397
 
3.9%
5 5076
 
3.7%
6 4535
 
3.3%
7 3987
 
2.9%
Other values (14) 7090
 
5.2%
Han
ValueCountFrequency (%)
塘 2
50.0%
金 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 167359
54.7%
ASCII 138382
45.3%
Number Forms 24
 
< 0.1%
CJK 2
 
< 0.1%
CJK Compat Ideographs 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
67899
49.1%
1 13371
 
9.7%
2 8995
 
6.5%
0 8012
 
5.8%
3 6987
 
5.0%
4 5730
 
4.1%
- 5397
 
3.9%
5 5076
 
3.7%
6 4535
 
3.3%
7 3987
 
2.9%
Other values (60) 8393
 
6.1%
Hangul
ValueCountFrequency (%)
서 12074
 
7.2%
동 11107
 
6.6%
구 10512
 
6.3%
시 9950
 
5.9%
울 9764
 
5.8%
별 9716
 
5.8%
특 9708
 
5.8%
호 8644
 
5.2%
지 8449
 
5.0%
번 7954
 
4.8%
Other values (545) 69481
41.5%
Number Forms
ValueCountFrequency (%)
Ⅱ 19
79.2%
Ⅰ 5
 
20.8%
CJK
ValueCountFrequency (%)
塘 2
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
金 2
100.0%

소재지(도로명)
Text

MISSING 

Distinct4694
Distinct (%)90.7%
Missing4827
Missing (%)48.3%
Memory size156.2 KiB
2024-05-18T11:39:54.227221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length52
Mean length37.066306
Min length20

Characters and Unicode

Total characters191744
Distinct characters617
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

Unique4266 ?
Unique (%)82.5%

Sample

1st row서울특별시 중구 을지로 254, 타임캐슬 오피스텔 401-2호 (을지로6가)
2nd row서울특별시 구로구 디지털로31길 41, 이앤씨벤처드림타워6차 707호 C-7호호 (구로동)
3rd row서울특별시 강남구 도산대로 165, 2층 (신사동, 신사힐)
4th row서울특별시 금천구 벚꽃로 278, SJ테크노빌 609동 118호 (가산동)
5th row서울특별시 은평구 통일로 712-1, 4층 412호 (불광동, 진우빌딩)
ValueCountFrequency (%)
서울특별시 5171
 
14.1%
강남구 945
 
2.6%
서초구 551
 
1.5%
2층 449
 
1.2%
역삼동 393
 
1.1%
3층 386
 
1.1%
서초동 364
 
1.0%
영등포구 314
 
0.9%
4층 313
 
0.9%
송파구 304
 
0.8%
Other values (6568) 27368
74.9%
2024-05-18T11:39:55.423342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31412
 
16.4%
1 7318
 
3.8%
, 7027
 
3.7%
서 6759
 
3.5%
동 6682
 
3.5%
로 5727
 
3.0%
구 5698
 
3.0%
시 5337
 
2.8%
울 5219
 
2.7%
) 5215
 
2.7%
Other values (607) 105350
54.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 106639
55.6%
Decimal Number 34055
 
17.8%
Space Separator 31412
 
16.4%
Other Punctuation 7047
 
3.7%
Close Punctuation 5215
 
2.7%
Open Punctuation 5214
 
2.7%
Dash Punctuation 1064
 
0.6%
Uppercase Letter 923
 
0.5%
Lowercase Letter 145
 
0.1%
Letter Number 22
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 6759
 
6.3%
동 6682
 
6.3%
로 5727
 
5.4%
구 5698
 
5.3%
시 5337
 
5.0%
울 5219
 
4.9%
별 5180
 
4.9%
특 5171
 
4.8%
호 4166
 
3.9%
길 2663
 
2.5%
Other values (535) 54037
50.7%
Uppercase Letter
ValueCountFrequency (%)
B 164
17.8%
A 116
12.6%
S 89
 
9.6%
T 57
 
6.2%
E 53
 
5.7%
L 47
 
5.1%
C 41
 
4.4%
K 37
 
4.0%
I 37
 
4.0%
R 33
 
3.6%
Other values (16) 249
27.0%
Lowercase Letter
ValueCountFrequency (%)
e 27
18.6%
r 16
11.0%
n 13
9.0%
o 12
8.3%
t 12
8.3%
w 11
7.6%
i 10
 
6.9%
c 9
 
6.2%
l 8
 
5.5%
u 5
 
3.4%
Other values (11) 22
15.2%
Decimal Number
ValueCountFrequency (%)
1 7318
21.5%
2 5176
15.2%
0 4391
12.9%
3 4083
12.0%
4 2889
 
8.5%
5 2701
 
7.9%
6 2277
 
6.7%
7 1906
 
5.6%
8 1758
 
5.2%
9 1556
 
4.6%
Other Punctuation
ValueCountFrequency (%)
, 7027
99.7%
. 12
 
0.2%
/ 3
 
< 0.1%
# 2
 
< 0.1%
& 2
 
< 0.1%
? 1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
~ 6
75.0%
> 1
 
12.5%
< 1
 
12.5%
Letter Number
ValueCountFrequency (%)
Ⅱ 17
77.3%
Ⅰ 5
 
22.7%
Space Separator
ValueCountFrequency (%)
31412
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5215
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5214
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1064
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 106636
55.6%
Common 84015
43.8%
Latin 1090
 
0.6%
Han 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 6759
 
6.3%
동 6682
 
6.3%
로 5727
 
5.4%
구 5698
 
5.3%
시 5337
 
5.0%
울 5219
 
4.9%
별 5180
 
4.9%
특 5171
 
4.8%
호 4166
 
3.9%
길 2663
 
2.5%
Other values (532) 54034
50.7%
Latin
ValueCountFrequency (%)
B 164
 
15.0%
A 116
 
10.6%
S 89
 
8.2%
T 57
 
5.2%
E 53
 
4.9%
L 47
 
4.3%
C 41
 
3.8%
K 37
 
3.4%
I 37
 
3.4%
R 33
 
3.0%
Other values (39) 416
38.2%
Common
ValueCountFrequency (%)
31412
37.4%
1 7318
 
8.7%
, 7027
 
8.4%
) 5215
 
6.2%
( 5214
 
6.2%
2 5176
 
6.2%
0 4391
 
5.2%
3 4083
 
4.9%
4 2889
 
3.4%
5 2701
 
3.2%
Other values (13) 8589
 
10.2%
Han
ValueCountFrequency (%)
家 1
33.3%
塘 1
33.3%
金 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 106636
55.6%
ASCII 85083
44.4%
Number Forms 22
 
< 0.1%
CJK 2
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31412
36.9%
1 7318
 
8.6%
, 7027
 
8.3%
) 5215
 
6.1%
( 5214
 
6.1%
2 5176
 
6.1%
0 4391
 
5.2%
3 4083
 
4.8%
4 2889
 
3.4%
5 2701
 
3.2%
Other values (60) 9657
 
11.4%
Hangul
ValueCountFrequency (%)
서 6759
 
6.3%
동 6682
 
6.3%
로 5727
 
5.4%
구 5698
 
5.3%
시 5337
 
5.0%
울 5219
 
4.9%
별 5180
 
4.9%
특 5171
 
4.8%
호 4166
 
3.9%
길 2663
 
2.5%
Other values (532) 54034
50.7%
Number Forms
ValueCountFrequency (%)
Ⅱ 17
77.3%
Ⅰ 5
 
22.7%
CJK
ValueCountFrequency (%)
家 1
50.0%
塘 1
50.0%
CJK Compat Ideographs
ValueCountFrequency (%)
金 1
100.0%

우편번호
Real number (ℝ)

MISSING 

Distinct1372
Distinct (%)31.2%
Missing5597
Missing (%)56.0%
Infinite0
Infinite (%)0.0%
Mean136825.06
Minimum3182
Maximum410380
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T11:39:55.856045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3182
5-th percentile100871.2
Q1132745
median137030
Q3143504.5
95-th percentile157218.2
Maximum410380
Range407198
Interquartile range (IQR)10759.5

Descriptive statistics

Standard deviation14546.713
Coefficient of variation (CV)0.10631614
Kurtosis32.667572
Mean136825.06
Median Absolute Deviation (MAD)5775
Skewness0.42070749
Sum6.0244076 × 108
Variance2.1160686 × 108
MonotonicityNot monotonic
2024-05-18T11:39:56.309267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135080 175
 
1.8%
137070 144
 
1.4%
157010 70
 
0.7%
135010 69
 
0.7%
151015 51
 
0.5%
158070 50
 
0.5%
142070 45
 
0.4%
151050 45
 
0.4%
152050 44
 
0.4%
142100 42
 
0.4%
Other values (1362) 3668
36.7%
(Missing) 5597
56.0%
ValueCountFrequency (%)
3182 1
 
< 0.1%
4550 1
 
< 0.1%
14538 1
 
< 0.1%
100011 6
 
0.1%
100012 3
 
< 0.1%
100013 1
 
< 0.1%
100014 1
 
< 0.1%
100015 4
 
< 0.1%
100021 32
0.3%
100022 7
 
0.1%
ValueCountFrequency (%)
410380 1
 
< 0.1%
158877 1
 
< 0.1%
158871 1
 
< 0.1%
158864 4
< 0.1%
158863 2
 
< 0.1%
158860 9
0.1%
158859 1
 
< 0.1%
158858 1
 
< 0.1%
158845 2
 
< 0.1%
158840 1
 
< 0.1%

등록일자
Real number (ℝ)

HIGH CORRELATION 

Distinct3531
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20136392
Minimum20060306
Maximum20240516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T11:39:56.699720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20060306
5-th percentile20070824
Q120091110
median20130225
Q320170724
95-th percentile20230307
Maximum20240516
Range180210
Interquartile range (IQR)79614

Descriptive statistics

Standard deviation49066.625
Coefficient of variation (CV)0.0024367138
Kurtosis-0.89271266
Mean20136392
Median Absolute Deviation (MAD)39524
Skewness0.47430054
Sum2.0136392 × 1011
Variance2.4075337 × 109
MonotonicityNot monotonic
2024-05-18T11:39:57.140772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20080814 34
 
0.3%
20080818 26
 
0.3%
20080731 23
 
0.2%
20081222 20
 
0.2%
20080926 18
 
0.2%
20080724 17
 
0.2%
20090514 15
 
0.1%
20090520 15
 
0.1%
20110711 15
 
0.1%
20090213 15
 
0.1%
Other values (3521) 9802
98.0%
ValueCountFrequency (%)
20060306 3
< 0.1%
20060308 1
 
< 0.1%
20060310 2
< 0.1%
20060320 3
< 0.1%
20060324 3
< 0.1%
20060329 1
 
< 0.1%
20060331 1
 
< 0.1%
20060407 4
< 0.1%
20060410 1
 
< 0.1%
20060418 4
< 0.1%
ValueCountFrequency (%)
20240516 4
< 0.1%
20240513 1
 
< 0.1%
20240507 2
< 0.1%
20240503 1
 
< 0.1%
20240502 1
 
< 0.1%
20240430 3
< 0.1%
20240429 1
 
< 0.1%
20240425 2
< 0.1%
20240424 4
< 0.1%
20240423 1
 
< 0.1%

유효기간만료일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3318
Distinct (%)42.0%
Missing2098
Missing (%)21.0%
Infinite0
Infinite (%)0.0%
Mean20181272
Minimum20090310
Maximum20270516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T11:39:57.568795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20090310
5-th percentile20120319
Q120141015
median20171201
Q320211224
95-th percentile20260523
Maximum20270516
Range180206
Interquartile range (IQR)70209.25

Descriptive statistics

Standard deviation44643.421
Coefficient of variation (CV)0.0022121213
Kurtosis-0.97590351
Mean20181272
Median Absolute Deviation (MAD)30591.5
Skewness0.33362223
Sum1.5947241 × 1011
Variance1.993035 × 109
MonotonicityNot monotonic
2024-05-18T11:39:58.031993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20110831 18
 
0.2%
20140711 15
 
0.1%
20190720 14
 
0.1%
20140601 11
 
0.1%
20140816 11
 
0.1%
20120514 10
 
0.1%
20140901 10
 
0.1%
20140609 10
 
0.1%
20180428 10
 
0.1%
20140721 10
 
0.1%
Other values (3308) 7783
77.8%
(Missing) 2098
 
21.0%
ValueCountFrequency (%)
20090310 1
 
< 0.1%
20091116 1
 
< 0.1%
20100125 1
 
< 0.1%
20100323 2
< 0.1%
20100326 1
 
< 0.1%
20100411 1
 
< 0.1%
20100418 3
< 0.1%
20100419 2
< 0.1%
20100501 1
 
< 0.1%
20100511 1
 
< 0.1%
ValueCountFrequency (%)
20270516 4
< 0.1%
20270513 1
 
< 0.1%
20270507 2
< 0.1%
20270503 1
 
< 0.1%
20270502 1
 
< 0.1%
20270430 3
< 0.1%
20270429 1
 
< 0.1%
20270425 2
< 0.1%
20270424 4
< 0.1%
20270423 1
 
< 0.1%

폐쇄일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3072
Distinct (%)36.6%
Missing1609
Missing (%)16.1%
Infinite0
Infinite (%)0.0%
Mean20141413
Minimum20071115
Maximum20240516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T11:39:58.456630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20071115
5-th percentile20090907
Q120110320
median20130709
Q320170323
95-th percentile20220901
Maximum20240516
Range169401
Interquartile range (IQR)60003.5

Descriptive statistics

Standard deviation40852.649
Coefficient of variation (CV)0.0020282911
Kurtosis-0.52582644
Mean20141413
Median Absolute Deviation (MAD)29782
Skewness0.71067877
Sum1.690066 × 1011
Variance1.6689389 × 109
MonotonicityNot monotonic
2024-05-18T11:39:58.895484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20091116 230
 
2.3%
20100927 77
 
0.8%
20101213 25
 
0.2%
20101126 20
 
0.2%
20160725 20
 
0.2%
20110420 19
 
0.2%
20110901 18
 
0.2%
20170125 16
 
0.2%
20110425 16
 
0.2%
20101228 14
 
0.1%
Other values (3062) 7936
79.4%
(Missing) 1609
 
16.1%
ValueCountFrequency (%)
20071115 1
 
< 0.1%
20081023 1
 
< 0.1%
20090211 1
 
< 0.1%
20090306 1
 
< 0.1%
20090307 3
< 0.1%
20090309 6
0.1%
20090311 3
< 0.1%
20090312 1
 
< 0.1%
20090313 2
 
< 0.1%
20090316 2
 
< 0.1%
ValueCountFrequency (%)
20240516 2
< 0.1%
20240514 1
 
< 0.1%
20240513 1
 
< 0.1%
20240510 1
 
< 0.1%
20240507 2
< 0.1%
20240503 1
 
< 0.1%
20240501 2
< 0.1%
20240430 3
< 0.1%
20240429 1
 
< 0.1%
20240426 1
 
< 0.1%

지점설립일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3563
Distinct (%)40.8%
Missing1270
Missing (%)12.7%
Infinite0
Infinite (%)0.0%
Mean20135161
Minimum19050627
Maximum20240516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T11:39:59.339115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19050627
5-th percentile20070102
Q120100422
median20130507
Q320170519
95-th percentile20220802
Maximum20240516
Range1189889
Interquartile range (IQR)70096.75

Descriptive statistics

Standard deviation50977.099
Coefficient of variation (CV)0.0025317453
Kurtosis48.325113
Mean20135161
Median Absolute Deviation (MAD)30615
Skewness-2.2432074
Sum1.7577996 × 1011
Variance2.5986646 × 109
MonotonicityNot monotonic
2024-05-18T11:39:59.784552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20090820 23
 
0.2%
20090520 21
 
0.2%
20090528 19
 
0.2%
20090511 17
 
0.2%
20090514 17
 
0.2%
20090611 16
 
0.2%
20090529 16
 
0.2%
20160720 16
 
0.2%
20090512 14
 
0.1%
20090507 13
 
0.1%
Other values (3553) 8558
85.6%
(Missing) 1270
 
12.7%
ValueCountFrequency (%)
19050627 1
< 0.1%
19050628 1
< 0.1%
19560711 1
< 0.1%
19770919 2
< 0.1%
19900420 1
< 0.1%
19930128 1
< 0.1%
19950501 1
< 0.1%
19950711 1
< 0.1%
19951229 1
< 0.1%
19960327 1
< 0.1%
ValueCountFrequency (%)
20240516 2
< 0.1%
20240513 1
 
< 0.1%
20240430 3
< 0.1%
20240429 1
 
< 0.1%
20240425 2
< 0.1%
20240423 1
 
< 0.1%
20240422 2
< 0.1%
20240419 3
< 0.1%
20240418 1
 
< 0.1%
20240411 3
< 0.1%

본점여부
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
본점
9922 
지점
 
78

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 9922
99.2%
지점 78
 
0.8%

Length

2024-05-18T11:40:00.124891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T11:40:00.418994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
본점 9922
99.2%
지점 78
 
0.8%

최근수정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct3154
Distinct (%)31.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20152582
Minimum20090518
Maximum20240517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T11:40:00.748588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20090518
5-th percentile20091117
Q120110920
median20140729
Q320190417
95-th percentile20231024
Maximum20240517
Range149999
Interquartile range (IQR)79497.5

Descriptive statistics

Standard deviation46113.053
Coefficient of variation (CV)0.0022881957
Kurtosis-1.0557072
Mean20152582
Median Absolute Deviation (MAD)30492.5
Skewness0.45912369
Sum2.0152582 × 1011
Variance2.1264137 × 109
MonotonicityNot monotonic
2024-05-18T11:40:01.166976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20091117 87
 
0.9%
20091118 63
 
0.6%
20090609 60
 
0.6%
20100927 55
 
0.5%
20091116 50
 
0.5%
20100330 40
 
0.4%
20110425 36
 
0.4%
20090622 36
 
0.4%
20130621 35
 
0.4%
20091119 34
 
0.3%
Other values (3144) 9504
95.0%
ValueCountFrequency (%)
20090518 2
 
< 0.1%
20090521 4
 
< 0.1%
20090601 3
 
< 0.1%
20090602 1
 
< 0.1%
20090603 11
 
0.1%
20090604 15
 
0.1%
20090605 8
 
0.1%
20090608 2
 
< 0.1%
20090609 60
0.6%
20090610 16
 
0.2%
ValueCountFrequency (%)
20240517 4
< 0.1%
20240516 7
0.1%
20240514 3
< 0.1%
20240513 4
< 0.1%
20240510 3
< 0.1%
20240509 3
< 0.1%
20240508 2
 
< 0.1%
20240507 7
0.1%
20240503 5
0.1%
20240502 6
0.1%

Interactions

2024-05-18T11:39:35.335997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:38:08.738894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:38:16.919426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:38:31.375101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:38:40.748229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:38:51.626953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:39:35.680065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:38:09.158132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:38:17.271010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:38:31.761186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:38:41.045414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:38:55.454523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:39:35.979915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:38:09.533802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:38:17.635816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:38:32.095794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:38:41.338734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:39:04.910913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:39:36.291513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:38:09.877981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:38:18.090141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:38:32.515404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:38:41.639935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:39:11.615393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:39:36.569718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:38:10.290186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:38:18.559491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:38:32.910491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:38:41.939194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:39:16.713528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:39:43.815565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:38:16.500572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:38:31.060964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:38:40.456280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:38:51.308714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:39:28.461483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T11:40:01.413360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록신청사업영업구분법인여부우편번호등록일자유효기간만료일자폐쇄일자지점설립일자본점여부최근수정일자
등록신청사업1.0000.0910.0180.0000.2410.1550.2330.1010.0000.200
영업구분0.0911.0000.2660.0540.6190.6290.2040.2220.0640.550
법인여부0.0180.2661.0000.0000.3560.2820.2770.2860.1950.361
우편번호0.0000.0540.0001.0000.2350.2340.3600.0530.0000.292
등록일자0.2410.6190.3560.2351.0001.0000.9360.6180.1170.939
유효기간만료일자0.1550.6290.2820.2341.0001.0000.8500.6700.1030.924
폐쇄일자0.2330.2040.2770.3600.9360.8501.0000.6140.0820.985
지점설립일자0.1010.2220.2860.0530.6180.6700.6141.0000.2440.613
본점여부0.0000.0640.1950.0000.1170.1030.0820.2441.0000.126
최근수정일자0.2000.5500.3610.2920.9390.9240.9850.6130.1261.000
2024-05-18T11:40:01.649409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법인여부영업구분본점여부등록신청사업
법인여부1.0000.1910.1250.011
영업구분0.1911.0000.0460.065
본점여부0.1250.0461.0000.000
등록신청사업0.0110.0650.0001.000
2024-05-18T11:40:01.830219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호등록일자유효기간만료일자폐쇄일자지점설립일자최근수정일자등록신청사업영업구분법인여부본점여부
우편번호1.000-0.0060.0200.0060.048-0.0020.0000.0440.0140.000
등록일자-0.0061.0000.9960.9600.9300.9660.1840.3860.2730.090
유효기간만료일자0.0200.9961.0000.9630.9040.9670.1190.3950.2160.079
폐쇄일자0.0060.9600.9631.0000.9090.9910.1790.1190.2130.063
지점설립일자0.0480.9300.9040.9091.0000.9000.1450.3180.3390.224
최근수정일자-0.0020.9660.9670.9910.9001.0000.1530.3110.2770.096
등록신청사업0.0000.1840.1190.1790.1450.1531.0000.0650.0110.000
영업구분0.0440.3860.3950.1190.3180.3110.0651.0000.1910.046
법인여부0.0140.2730.2160.2130.3390.2770.0110.1911.0000.125
본점여부0.0000.0900.0790.0630.2240.0960.0000.0460.1251.000

Missing values

2024-05-18T11:39:44.243531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T11:39:44.964163image/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.
2024-05-18T11:39:45.477094image/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
23327대부중개업<NA>2009-서울특별시-01389(대부중개업)비엘대부중개투자금융개인07082710798서울특별시 종로구 숭인동 1401번지 스타일빌딩 501호<NA><NA>20090625<NA>2011102520090625본점20111025
3745대부중개업영업중2019-서울중구-0076(대부중개업)SJ1대부개인02-2272-5804서울특별시 중구 을지로6가 23번지 타임캐슬 오피스텔서울특별시 중구 을지로 254, 타임캐슬 오피스텔 401-2호 (을지로6가)<NA>2022092920250929<NA>20191028본점20221024
29693대부업<NA>2008-서울특별시-01840(대부업)최종해개인<NA>서울특별시 구로구 구로5동 42번지 LG자이아파트 102동 704호<NA><NA>20080619<NA>20091116<NA>본점20091119
4618대부업영업중2019-서울구로015(대부업)주식회사 나라자산관리대부법인<NA>서울특별시 구로구 구로동 197번지 28호 이앤씨벤처드림타워6차-707서울특별시 구로구 디지털로31길 41, 이앤씨벤처드림타워6차 707호 C-7호호 (구로동)<NA>2022040420250403<NA>20190328본점20220318
9524대부업<NA>2017-서울강남-0079(대부업)랑대부주식회사법인02-6219-0325서울특별시 강남구 신사동 563번지 17호 2층서울특별시 강남구 도산대로 165, 2층 (신사동, 신사힐)<NA>2017040320200403<NA>20170403본점20180124
24646대부업<NA>2008-서울특별시-01760(대부업)동서울개인<NA>서울특별시 광진구 중곡동 114-33 신한연립 A동 가호<NA><NA>20080603201106032011060420080526본점20110603
20193대부중개업타시군구이관2011-서울강북-0052(대부중개업)써니론 대부중개개인1566-0630서울특별시 강북구 번동 446번지 13호 가든타워-1802<NA>14286720111118201411182012121020111118본점20121210
7610대부중개업유효기간만료2018-서울금천-0020(주)금요일대부중개법인02-6494-0136서울특별시 금천구 가산동 60번지 19호 609 SJ테크노빌-118서울특별시 금천구 벚꽃로 278, SJ테크노빌 609동 118호 (가산동)<NA>2016061320190613<NA>20160613본점20190627
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