Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells19078
Missing cells (%)12.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory133.0 B

Variable types

Categorical4
Text6
Numeric5

Dataset

Description등록신청사업,영업구분,등록증번호,상호,법인여부,사업장 전화번호,소재지,소재지(도로명),우편번호,등록일자,유효기간만료일자,폐쇄일자,지점설립일자,본점여부,최근수정일자
Author서초구
URLhttps://data.seoul.go.kr/dataList/OA-11097/S/1/datasetView.do

Alerts

등록일자 is highly overall correlated with 유효기간만료일자 and 2 other fieldsHigh correlation
유효기간만료일자 is highly overall correlated with 등록일자 and 2 other fieldsHigh correlation
폐쇄일자 is highly overall correlated with 등록일자 and 2 other fieldsHigh correlation
최근수정일자 is highly overall correlated with 등록일자 and 2 other fieldsHigh correlation
본점여부 is highly imbalanced (94.0%)Imbalance
등록증번호 has 181 (1.8%) missing valuesMissing
사업장 전화번호 has 3394 (33.9%) missing valuesMissing
소재지 has 295 (2.9%) missing valuesMissing
소재지(도로명) has 4809 (48.1%) missing valuesMissing
우편번호 has 5532 (55.3%) missing valuesMissing
유효기간만료일자 has 2091 (20.9%) missing valuesMissing
폐쇄일자 has 1523 (15.2%) missing valuesMissing
지점설립일자 has 1253 (12.5%) missing valuesMissing

Reproduction

Analysis started2024-05-18 02:17:44.433559
Analysis finished2024-05-18 02:18:04.009761
Duration19.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대부업
6261 
대부중개업
3309 
<NA>
 
430

Length

Max length5
Median length3
Mean length3.7048
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대부업 6261
62.6%
대부중개업 3309
33.1%
<NA> 430
 
4.3%

Length

2024-05-18T11:18:04.385535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T11:18:04.758353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대부업 6261
62.6%
대부중개업 3309
33.1%
na 430
 
4.3%

영업구분
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
3717 
<NA>
2934 
타시군구이관
1227 
유효기간만료
799 
영업중
794 
Other values (2)
529 

Length

Max length6
Median length4
Mean length3.5826
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 3717
37.2%
<NA> 2934
29.3%
타시군구이관 1227
 
12.3%
유효기간만료 799
 
8.0%
영업중 794
 
7.9%
직권취소 528
 
5.3%
갱신등록불가 1
 
< 0.1%

Length

2024-05-18T11:18:05.304720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T11:18:05.717242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 3717
37.2%
na 2934
29.3%
타시군구이관 1227
 
12.3%
유효기간만료 799
 
8.0%
영업중 794
 
7.9%
직권취소 528
 
5.3%
갱신등록불가 1
 
< 0.1%

등록증번호
Text

MISSING 

Distinct9750
Distinct (%)99.3%
Missing181
Missing (%)1.8%
Memory size156.2 KiB
2024-05-18T11:18:06.304752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length27
Mean length19.476525
Min length1

Characters and Unicode

Total characters191240
Distinct characters79
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9684 ?
Unique (%)98.6%

Sample

1st row2017-서울관악-0037(대부업)
2nd row2011-서울종로-00044(대부업)
3rd row2014-서울강남-0068
4th row2009-서울특별시-00766(대부중개업)
5th row2013-서울노원-00045
ValueCountFrequency (%)
2011-서울특별시 23
 
0.2%
2010-서울 16
 
0.2%
2014-서울특별시 14
 
0.1%
2012-서울특별시 14
 
0.1%
2013-서울특별시 13
 
0.1%
2015-서울특별시 12
 
0.1%
대부업 9
 
0.1%
대부중개업 8
 
0.1%
2016-서울특별시 7
 
0.1%
성북구-00006 7
 
0.1%
Other values (9720) 9856
98.8%
2024-05-18T11:18:07.484413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 34002
17.8%
- 19622
 
10.3%
2 15646
 
8.2%
1 11850
 
6.2%
10798
 
5.6%
9790
 
5.1%
8414
 
4.4%
( 8132
 
4.3%
8101
 
4.2%
) 8086
 
4.2%
Other values (69) 56799
29.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 82659
43.2%
Other Letter 72581
38.0%
Dash Punctuation 19622
 
10.3%
Open Punctuation 8132
 
4.3%
Close Punctuation 8086
 
4.2%
Space Separator 160
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10798
14.9%
9790
13.5%
8414
11.6%
8101
11.2%
7859
10.8%
3461
 
4.8%
2813
 
3.9%
2515
 
3.5%
2506
 
3.5%
2506
 
3.5%
Other values (55) 13818
19.0%
Decimal Number
ValueCountFrequency (%)
0 34002
41.1%
2 15646
18.9%
1 11850
 
14.3%
3 3767
 
4.6%
8 3096
 
3.7%
4 3078
 
3.7%
7 2826
 
3.4%
6 2820
 
3.4%
5 2807
 
3.4%
9 2767
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 19622
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8132
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8086
100.0%
Space Separator
ValueCountFrequency (%)
160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 118659
62.0%
Hangul 72581
38.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10798
14.9%
9790
13.5%
8414
11.6%
8101
11.2%
7859
10.8%
3461
 
4.8%
2813
 
3.9%
2515
 
3.5%
2506
 
3.5%
2506
 
3.5%
Other values (55) 13818
19.0%
Common
ValueCountFrequency (%)
0 34002
28.7%
- 19622
16.5%
2 15646
13.2%
1 11850
 
10.0%
( 8132
 
6.9%
) 8086
 
6.8%
3 3767
 
3.2%
8 3096
 
2.6%
4 3078
 
2.6%
7 2826
 
2.4%
Other values (4) 8554
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118659
62.0%
Hangul 72581
38.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 34002
28.7%
- 19622
16.5%
2 15646
13.2%
1 11850
 
10.0%
( 8132
 
6.9%
) 8086
 
6.8%
3 3767
 
3.2%
8 3096
 
2.6%
4 3078
 
2.6%
7 2826
 
2.4%
Other values (4) 8554
 
7.2%
Hangul
ValueCountFrequency (%)
10798
14.9%
9790
13.5%
8414
11.6%
8101
11.2%
7859
10.8%
3461
 
4.8%
2813
 
3.9%
2515
 
3.5%
2506
 
3.5%
2506
 
3.5%
Other values (55) 13818
19.0%

상호
Text

Distinct8669
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T11:18:08.448124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length28
Mean length7.6762
Min length1

Characters and Unicode

Total characters76762
Distinct characters771
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

Unique7593 ?
Unique (%)75.9%

Sample

1st row실로암대부
2nd row피아이에이엔피엘3차대부 유한회사
3rd row대산대부업
4th row우정금융
5th row우리대부파트너즈
ValueCountFrequency (%)
주식회사 759
 
6.4%
대부중개 309
 
2.6%
대부 266
 
2.2%
유한회사 51
 
0.4%
대부업 22
 
0.2%
캐피탈 17
 
0.1%
14
 
0.1%
대부중개업 12
 
0.1%
loan 12
 
0.1%
미래 11
 
0.1%
Other values (8680) 10369
87.6%
2024-05-18T11:18:09.733137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8497
 
11.1%
8085
 
10.5%
2635
 
3.4%
2181
 
2.8%
2077
 
2.7%
2066
 
2.7%
) 1902
 
2.5%
( 1895
 
2.5%
1848
 
2.4%
1815
 
2.4%
Other values (761) 43761
57.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 66991
87.3%
Uppercase Letter 2367
 
3.1%
Close Punctuation 1902
 
2.5%
Open Punctuation 1895
 
2.5%
Space Separator 1848
 
2.4%
Lowercase Letter 1219
 
1.6%
Decimal Number 262
 
0.3%
Other Punctuation 233
 
0.3%
Dash Punctuation 28
 
< 0.1%
Other Symbol 10
 
< 0.1%
Other values (2) 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
8497
 
12.7%
8085
 
12.1%
2635
 
3.9%
2181
 
3.3%
2077
 
3.1%
2066
 
3.1%
1815
 
2.7%
1289
 
1.9%
1130
 
1.7%
1012
 
1.5%
Other values (685) 36204
54.0%
Uppercase Letter
ValueCountFrequency (%)
S 301
 
12.7%
K 212
 
9.0%
C 193
 
8.2%
J 183
 
7.7%
M 166
 
7.0%
H 120
 
5.1%
B 107
 
4.5%
G 100
 
4.2%
L 96
 
4.1%
N 94
 
4.0%
Other values (15) 795
33.6%
Lowercase Letter
ValueCountFrequency (%)
e 154
12.6%
n 150
12.3%
o 130
10.7%
a 116
9.5%
i 81
 
6.6%
t 77
 
6.3%
l 61
 
5.0%
s 60
 
4.9%
c 58
 
4.8%
m 48
 
3.9%
Other values (14) 284
23.3%
Decimal Number
ValueCountFrequency (%)
1 90
34.4%
2 45
17.2%
4 33
 
12.6%
9 26
 
9.9%
3 18
 
6.9%
5 14
 
5.3%
6 13
 
5.0%
0 9
 
3.4%
8 8
 
3.1%
7 6
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 142
60.9%
& 77
33.0%
? 5
 
2.1%
, 5
 
2.1%
1
 
0.4%
@ 1
 
0.4%
1
 
0.4%
* 1
 
0.4%
Math Symbol
ValueCountFrequency (%)
~ 4
66.7%
< 1
 
16.7%
> 1
 
16.7%
Close Punctuation
ValueCountFrequency (%)
) 1902
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1895
100.0%
Space Separator
ValueCountFrequency (%)
1848
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 28
100.0%
Other Symbol
ValueCountFrequency (%)
10
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 66983
87.3%
Common 6174
 
8.0%
Latin 3587
 
4.7%
Han 18
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
8497
 
12.7%
8085
 
12.1%
2635
 
3.9%
2181
 
3.3%
2077
 
3.1%
2066
 
3.1%
1815
 
2.7%
1289
 
1.9%
1130
 
1.7%
1012
 
1.5%
Other values (669) 36196
54.0%
Latin
ValueCountFrequency (%)
S 301
 
8.4%
K 212
 
5.9%
C 193
 
5.4%
J 183
 
5.1%
M 166
 
4.6%
e 154
 
4.3%
n 150
 
4.2%
o 130
 
3.6%
H 120
 
3.3%
a 116
 
3.2%
Other values (40) 1862
51.9%
Common
ValueCountFrequency (%)
) 1902
30.8%
( 1895
30.7%
1848
29.9%
. 142
 
2.3%
1 90
 
1.5%
& 77
 
1.2%
2 45
 
0.7%
4 33
 
0.5%
- 28
 
0.5%
9 26
 
0.4%
Other values (15) 88
 
1.4%
Han
ValueCountFrequency (%)
2
 
11.1%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
Other values (7) 7
38.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 66973
87.2%
ASCII 9758
 
12.7%
CJK 18
 
< 0.1%
None 12
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
8497
 
12.7%
8085
 
12.1%
2635
 
3.9%
2181
 
3.3%
2077
 
3.1%
2066
 
3.1%
1815
 
2.7%
1289
 
1.9%
1130
 
1.7%
1012
 
1.5%
Other values (668) 36186
54.0%
ASCII
ValueCountFrequency (%)
) 1902
19.5%
( 1895
19.4%
1848
18.9%
S 301
 
3.1%
K 212
 
2.2%
C 193
 
2.0%
J 183
 
1.9%
M 166
 
1.7%
e 154
 
1.6%
n 150
 
1.5%
Other values (62) 2754
28.2%
None
ValueCountFrequency (%)
10
83.3%
1
 
8.3%
1
 
8.3%
CJK
ValueCountFrequency (%)
2
 
11.1%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
Other values (7) 7
38.9%
Number Forms
ValueCountFrequency (%)
1
100.0%

법인여부
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
개인
7249 
법인
2751 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
개인 7249
72.5%
법인 2751
 
27.5%

Length

2024-05-18T11:18:10.155515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T11:18:10.447001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 7249
72.5%
법인 2751
 
27.5%
Distinct5837
Distinct (%)88.4%
Missing3394
Missing (%)33.9%
Memory size156.2 KiB
2024-05-18T11:18:10.948018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length40
Mean length10.661974
Min length1

Characters and Unicode

Total characters70433
Distinct characters28
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5215 ?
Unique (%)78.9%

Sample

1st row02-734-6901
2nd row02-501-5550
3rd row0222444624
4th row02-2064-0472
5th row15991508
ValueCountFrequency (%)
02 302
 
4.0%
64
 
0.9%
070 37
 
0.5%
010 11
 
0.1%
1566 8
 
0.1%
1599 6
 
0.1%
02-737-2882 5
 
0.1%
1688 5
 
0.1%
1644-7694 5
 
0.1%
02-568-2513 5
 
0.1%
Other values (6188) 7040
94.0%
2024-05-18T11:18:12.066398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11307
16.1%
2 10559
15.0%
- 7055
10.0%
5 5940
8.4%
7 5416
7.7%
1 5171
7.3%
6 5084
7.2%
3 4896
7.0%
8 4808
6.8%
4 4780
6.8%
Other values (18) 5417
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 62046
88.1%
Dash Punctuation 7055
 
10.0%
Space Separator 1005
 
1.4%
Other Punctuation 183
 
0.3%
Close Punctuation 78
 
0.1%
Math Symbol 31
 
< 0.1%
Open Punctuation 26
 
< 0.1%
Other Letter 6
 
< 0.1%
Uppercase Letter 2
 
< 0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11307
18.2%
2 10559
17.0%
5 5940
9.6%
7 5416
8.7%
1 5171
8.3%
6 5084
8.2%
3 4896
7.9%
8 4808
7.7%
4 4780
7.7%
9 4085
 
6.6%
Other Letter
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Other Punctuation
ValueCountFrequency (%)
* 106
57.9%
/ 54
29.5%
. 23
 
12.6%
Math Symbol
ValueCountFrequency (%)
~ 30
96.8%
× 1
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
K 1
50.0%
T 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 7055
100.0%
Space Separator
ValueCountFrequency (%)
1005
100.0%
Close Punctuation
ValueCountFrequency (%)
) 78
100.0%
Open Punctuation
ValueCountFrequency (%)
( 26
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70425
> 99.9%
Hangul 6
 
< 0.1%
Latin 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11307
16.1%
2 10559
15.0%
- 7055
10.0%
5 5940
8.4%
7 5416
7.7%
1 5171
7.3%
6 5084
7.2%
3 4896
7.0%
8 4808
6.8%
4 4780
6.8%
Other values (10) 5409
7.7%
Hangul
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Latin
ValueCountFrequency (%)
K 1
50.0%
T 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70426
> 99.9%
Hangul 6
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11307
16.1%
2 10559
15.0%
- 7055
10.0%
5 5940
8.4%
7 5416
7.7%
1 5171
7.3%
6 5084
7.2%
3 4896
7.0%
8 4808
6.8%
4 4780
6.8%
Other values (11) 5410
7.7%
None
ValueCountFrequency (%)
× 1
100.0%
Hangul
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%

소재지
Text

MISSING 

Distinct8701
Distinct (%)89.7%
Missing295
Missing (%)2.9%
Memory size156.2 KiB
2024-05-18T11:18:12.862591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length48
Mean length31.473364
Min length15

Characters and Unicode

Total characters305449
Distinct characters613
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

Unique7948 ?
Unique (%)81.9%

Sample

1st row서울특별시 관악구 신림동 1424번지 19호
2nd row서울특별시 종로구 인사동 194번지 4호 하나로빌딩 5층-513
3rd row서울특별시 강남구 개포동 1234번지 11호 에이스빌라-302
4th row서울특별시 동대문구 답십리동 10-1 동아아파트 109동 703호
5th row서울특별시 노원구 상계동 707번지 1호 명성빌딩-407
ValueCountFrequency (%)
서울특별시 9702
 
17.0%
강남구 1615
 
2.8%
서초구 905
 
1.6%
1호 697
 
1.2%
역삼동 678
 
1.2%
송파구 603
 
1.1%
중구 559
 
1.0%
서초동 557
 
1.0%
영등포구 460
 
0.8%
2호 444
 
0.8%
Other values (9474) 40992
71.6%
2024-05-18T11:18:14.035872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
67785
22.2%
1 13527
 
4.4%
11957
 
3.9%
11135
 
3.6%
10512
 
3.4%
9955
 
3.3%
9758
 
3.2%
9710
 
3.2%
9704
 
3.2%
2 8797
 
2.9%
Other values (603) 142609
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 166964
54.7%
Space Separator 67785
22.2%
Decimal Number 63484
 
20.8%
Dash Punctuation 5497
 
1.8%
Uppercase Letter 1131
 
0.4%
Other Punctuation 231
 
0.1%
Lowercase Letter 128
 
< 0.1%
Close Punctuation 102
 
< 0.1%
Open Punctuation 99
 
< 0.1%
Letter Number 21
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
11957
 
7.2%
11135
 
6.7%
10512
 
6.3%
9955
 
6.0%
9758
 
5.8%
9710
 
5.8%
9704
 
5.8%
8625
 
5.2%
8441
 
5.1%
7949
 
4.8%
Other values (534) 69218
41.5%
Uppercase Letter
ValueCountFrequency (%)
B 275
24.3%
A 201
17.8%
D 81
 
7.2%
S 77
 
6.8%
T 48
 
4.2%
K 46
 
4.1%
L 45
 
4.0%
C 45
 
4.0%
G 41
 
3.6%
I 40
 
3.5%
Other values (16) 232
20.5%
Lowercase Letter
ValueCountFrequency (%)
e 21
16.4%
n 17
13.3%
i 12
9.4%
r 12
9.4%
w 8
 
6.2%
o 7
 
5.5%
k 7
 
5.5%
a 7
 
5.5%
s 6
 
4.7%
c 6
 
4.7%
Other values (9) 25
19.5%
Decimal Number
ValueCountFrequency (%)
1 13527
21.3%
2 8797
13.9%
0 7989
12.6%
3 7037
11.1%
4 5849
9.2%
5 5003
 
7.9%
6 4468
 
7.0%
7 4050
 
6.4%
9 3395
 
5.3%
8 3369
 
5.3%
Other Punctuation
ValueCountFrequency (%)
/ 83
35.9%
, 78
33.8%
. 66
28.6%
@ 2
 
0.9%
# 1
 
0.4%
* 1
 
0.4%
Letter Number
ValueCountFrequency (%)
15
71.4%
3
 
14.3%
3
 
14.3%
Space Separator
ValueCountFrequency (%)
67785
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5497
100.0%
Close Punctuation
ValueCountFrequency (%)
) 102
100.0%
Open Punctuation
ValueCountFrequency (%)
( 99
100.0%
Math Symbol
ValueCountFrequency (%)
~ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 166961
54.7%
Common 137205
44.9%
Latin 1280
 
0.4%
Han 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
11957
 
7.2%
11135
 
6.7%
10512
 
6.3%
9955
 
6.0%
9758
 
5.8%
9710
 
5.8%
9704
 
5.8%
8625
 
5.2%
8441
 
5.1%
7949
 
4.8%
Other values (531) 69215
41.5%
Latin
ValueCountFrequency (%)
B 275
21.5%
A 201
15.7%
D 81
 
6.3%
S 77
 
6.0%
T 48
 
3.8%
K 46
 
3.6%
L 45
 
3.5%
C 45
 
3.5%
G 41
 
3.2%
I 40
 
3.1%
Other values (38) 381
29.8%
Common
ValueCountFrequency (%)
67785
49.4%
1 13527
 
9.9%
2 8797
 
6.4%
0 7989
 
5.8%
3 7037
 
5.1%
4 5849
 
4.3%
- 5497
 
4.0%
5 5003
 
3.6%
6 4468
 
3.3%
7 4050
 
3.0%
Other values (11) 7203
 
5.2%
Han
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 166961
54.7%
ASCII 138464
45.3%
Number Forms 21
 
< 0.1%
CJK 2
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
67785
49.0%
1 13527
 
9.8%
2 8797
 
6.4%
0 7989
 
5.8%
3 7037
 
5.1%
4 5849
 
4.2%
- 5497
 
4.0%
5 5003
 
3.6%
6 4468
 
3.2%
7 4050
 
2.9%
Other values (56) 8462
 
6.1%
Hangul
ValueCountFrequency (%)
11957
 
7.2%
11135
 
6.7%
10512
 
6.3%
9955
 
6.0%
9758
 
5.8%
9710
 
5.8%
9704
 
5.8%
8625
 
5.2%
8441
 
5.1%
7949
 
4.8%
Other values (531) 69215
41.5%
Number Forms
ValueCountFrequency (%)
15
71.4%
3
 
14.3%
3
 
14.3%
CJK
ValueCountFrequency (%)
1
50.0%
1
50.0%
CJK Compat Ideographs
ValueCountFrequency (%)
1
100.0%

소재지(도로명)
Text

MISSING 

Distinct4734
Distinct (%)91.2%
Missing4809
Missing (%)48.1%
Memory size156.2 KiB
2024-05-18T11:18:14.769404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length52
Mean length37.108264
Min length19

Characters and Unicode

Total characters192629
Distinct characters599
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4322 ?
Unique (%)83.3%

Sample

1st row서울특별시 관악구 신림로 354, 5층 (신림동)
2nd row서울특별시 강남구 논현로18길 14-15, 302호 (개포동, 에이스빌라)
3rd row서울특별시 강서구 방화동로3길 49, 2층 (방화동)
4th row서울특별시 영등포구 양평로 24, 수정빌딩 6층 616호 (당산동6가)
5th row서울특별시 마포구 양화진길 51, B07호 (합정동, 신우빌딩)
ValueCountFrequency (%)
서울특별시 5189
 
14.2%
강남구 944
 
2.6%
서초구 518
 
1.4%
2층 428
 
1.2%
역삼동 383
 
1.0%
서초동 351
 
1.0%
3층 335
 
0.9%
영등포구 331
 
0.9%
송파구 318
 
0.9%
4층 299
 
0.8%
Other values (6549) 27522
75.2%
2024-05-18T11:18:16.165664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31449
 
16.3%
1 7392
 
3.8%
, 7100
 
3.7%
6834
 
3.5%
6763
 
3.5%
5729
 
3.0%
5714
 
3.0%
5395
 
2.8%
2 5278
 
2.7%
5244
 
2.7%
Other values (589) 105731
54.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 107286
55.7%
Decimal Number 34303
 
17.8%
Space Separator 31449
 
16.3%
Other Punctuation 7112
 
3.7%
Close Punctuation 5229
 
2.7%
Open Punctuation 5228
 
2.7%
Dash Punctuation 1043
 
0.5%
Uppercase Letter 828
 
0.4%
Lowercase Letter 118
 
0.1%
Letter Number 20
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6834
 
6.4%
6763
 
6.3%
5729
 
5.3%
5714
 
5.3%
5395
 
5.0%
5244
 
4.9%
5194
 
4.8%
5191
 
4.8%
4228
 
3.9%
2801
 
2.6%
Other values (522) 54193
50.5%
Uppercase Letter
ValueCountFrequency (%)
B 165
19.9%
A 116
14.0%
S 75
9.1%
T 51
 
6.2%
G 43
 
5.2%
L 43
 
5.2%
E 42
 
5.1%
C 41
 
5.0%
I 35
 
4.2%
K 33
 
4.0%
Other values (15) 184
22.2%
Lowercase Letter
ValueCountFrequency (%)
n 17
14.4%
e 15
12.7%
r 12
10.2%
i 10
8.5%
w 9
7.6%
o 9
7.6%
s 7
 
5.9%
c 6
 
5.1%
t 6
 
5.1%
b 5
 
4.2%
Other values (9) 22
18.6%
Decimal Number
ValueCountFrequency (%)
1 7392
21.5%
2 5278
15.4%
0 4507
13.1%
3 4062
11.8%
4 2917
 
8.5%
5 2689
 
7.8%
6 2290
 
6.7%
7 1910
 
5.6%
8 1744
 
5.1%
9 1514
 
4.4%
Other Punctuation
ValueCountFrequency (%)
, 7100
99.8%
. 9
 
0.1%
/ 3
 
< 0.1%
Letter Number
ValueCountFrequency (%)
14
70.0%
3
 
15.0%
3
 
15.0%
Math Symbol
ValueCountFrequency (%)
~ 9
69.2%
> 2
 
15.4%
< 2
 
15.4%
Space Separator
ValueCountFrequency (%)
31449
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5229
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5228
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1043
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 107286
55.7%
Common 84377
43.8%
Latin 966
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
6834
 
6.4%
6763
 
6.3%
5729
 
5.3%
5714
 
5.3%
5395
 
5.0%
5244
 
4.9%
5194
 
4.8%
5191
 
4.8%
4228
 
3.9%
2801
 
2.6%
Other values (522) 54193
50.5%
Latin
ValueCountFrequency (%)
B 165
17.1%
A 116
 
12.0%
S 75
 
7.8%
T 51
 
5.3%
G 43
 
4.5%
L 43
 
4.5%
E 42
 
4.3%
C 41
 
4.2%
I 35
 
3.6%
K 33
 
3.4%
Other values (37) 322
33.3%
Common
ValueCountFrequency (%)
31449
37.3%
1 7392
 
8.8%
, 7100
 
8.4%
2 5278
 
6.3%
) 5229
 
6.2%
( 5228
 
6.2%
0 4507
 
5.3%
3 4062
 
4.8%
4 2917
 
3.5%
5 2689
 
3.2%
Other values (10) 8526
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 107286
55.7%
ASCII 85323
44.3%
Number Forms 20
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31449
36.9%
1 7392
 
8.7%
, 7100
 
8.3%
2 5278
 
6.2%
) 5229
 
6.1%
( 5228
 
6.1%
0 4507
 
5.3%
3 4062
 
4.8%
4 2917
 
3.4%
5 2689
 
3.2%
Other values (54) 9472
 
11.1%
Hangul
ValueCountFrequency (%)
6834
 
6.4%
6763
 
6.3%
5729
 
5.3%
5714
 
5.3%
5395
 
5.0%
5244
 
4.9%
5194
 
4.8%
5191
 
4.8%
4228
 
3.9%
2801
 
2.6%
Other values (522) 54193
50.5%
Number Forms
ValueCountFrequency (%)
14
70.0%
3
 
15.0%
3
 
15.0%

우편번호
Real number (ℝ)

MISSING 

Distinct1390
Distinct (%)31.1%
Missing5532
Missing (%)55.3%
Infinite0
Infinite (%)0.0%
Mean135959.62
Minimum2519
Maximum429842
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T11:18:16.651220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2519
5-th percentile100845
Q1131880.25
median136060
Q3143190
95-th percentile157030
Maximum429842
Range427323
Interquartile range (IQR)11309.75

Descriptive statistics

Standard deviation16440.564
Coefficient of variation (CV)0.1209224
Kurtosis49.810222
Mean135959.62
Median Absolute Deviation (MAD)5248
Skewness0.58217953
Sum6.0746758 × 108
Variance2.7029213 × 108
MonotonicityNot monotonic
2024-05-18T11:18:17.138202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135080 168
 
1.7%
137070 140
 
1.4%
157010 62
 
0.6%
135010 54
 
0.5%
158070 54
 
0.5%
142070 52
 
0.5%
151015 49
 
0.5%
152050 45
 
0.4%
151050 42
 
0.4%
138160 41
 
0.4%
Other values (1380) 3761
37.6%
(Missing) 5532
55.3%
ValueCountFrequency (%)
2519 1
< 0.1%
3163 1
< 0.1%
4526 1
< 0.1%
4534 1
< 0.1%
4536 1
< 0.1%
4537 1
< 0.1%
4554 1
< 0.1%
4801 1
< 0.1%
5510 1
< 0.1%
7238 1
< 0.1%
ValueCountFrequency (%)
429842 1
 
< 0.1%
410762 1
 
< 0.1%
158877 1
 
< 0.1%
158871 2
< 0.1%
158865 1
 
< 0.1%
158864 3
< 0.1%
158863 1
 
< 0.1%
158860 3
< 0.1%
158859 3
< 0.1%
158857 2
< 0.1%

등록일자
Real number (ℝ)

HIGH CORRELATION 

Distinct3530
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20135851
Minimum20051216
Maximum20240516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T11:18:17.598309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20051216
5-th percentile20070810
Q120091120
median20130220
Q320170522
95-th percentile20230202
Maximum20240516
Range189300
Interquartile range (IQR)79402

Descriptive statistics

Standard deviation48510.686
Coefficient of variation (CV)0.00240917
Kurtosis-0.84497221
Mean20135851
Median Absolute Deviation (MAD)39393
Skewness0.48765458
Sum2.0135851 × 1011
Variance2.3532867 × 109
MonotonicityNot monotonic
2024-05-18T11:18:18.163815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20080814 30
 
0.3%
20080731 23
 
0.2%
20081222 22
 
0.2%
20090611 17
 
0.2%
20080718 16
 
0.2%
20080926 15
 
0.1%
20090520 15
 
0.1%
20081229 14
 
0.1%
20080806 14
 
0.1%
20091008 14
 
0.1%
Other values (3520) 9820
98.2%
ValueCountFrequency (%)
20051216 1
 
< 0.1%
20060124 1
 
< 0.1%
20060306 1
 
< 0.1%
20060310 2
< 0.1%
20060320 4
< 0.1%
20060323 1
 
< 0.1%
20060324 2
< 0.1%
20060329 4
< 0.1%
20060331 1
 
< 0.1%
20060407 3
< 0.1%
ValueCountFrequency (%)
20240516 3
< 0.1%
20240514 1
 
< 0.1%
20240510 1
 
< 0.1%
20240509 1
 
< 0.1%
20240507 3
< 0.1%
20240503 2
< 0.1%
20240502 2
< 0.1%
20240429 2
< 0.1%
20240426 1
 
< 0.1%
20240425 3
< 0.1%

유효기간만료일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3297
Distinct (%)41.7%
Missing2091
Missing (%)20.9%
Infinite0
Infinite (%)0.0%
Mean20180526
Minimum20090310
Maximum20270517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T11:18:18.722732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20090310
5-th percentile20120316
Q120141020
median20171120
Q320211010
95-th percentile20260503
Maximum20270517
Range180207
Interquartile range (IQR)69990

Descriptive statistics

Standard deviation44197.748
Coefficient of variation (CV)0.0021901188
Kurtosis-0.92211854
Mean20180526
Median Absolute Deviation (MAD)30409
Skewness0.35794319
Sum1.5960778 × 1011
Variance1.9534409 × 109
MonotonicityNot monotonic
2024-05-18T11:18:19.195798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20140831 20
 
0.2%
20110831 16
 
0.2%
20190720 12
 
0.1%
20190216 12
 
0.1%
20180914 11
 
0.1%
20140711 11
 
0.1%
20140701 11
 
0.1%
20150531 10
 
0.1%
20120520 10
 
0.1%
20140811 10
 
0.1%
Other values (3287) 7786
77.9%
(Missing) 2091
 
20.9%
ValueCountFrequency (%)
20090310 1
< 0.1%
20090907 1
< 0.1%
20091220 1
< 0.1%
20100112 1
< 0.1%
20100117 1
< 0.1%
20100308 1
< 0.1%
20100323 1
< 0.1%
20100326 1
< 0.1%
20100405 1
< 0.1%
20100418 2
< 0.1%
ValueCountFrequency (%)
20270517 1
< 0.1%
20270516 2
< 0.1%
20270514 1
< 0.1%
20270510 1
< 0.1%
20270509 1
< 0.1%
20270507 2
< 0.1%
20270506 1
< 0.1%
20270503 2
< 0.1%
20270502 2
< 0.1%
20270429 2
< 0.1%

폐쇄일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3096
Distinct (%)36.5%
Missing1523
Missing (%)15.2%
Infinite0
Infinite (%)0.0%
Mean20141653
Minimum20071115
Maximum20240516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T11:18:19.617627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20071115
5-th percentile20090914
Q120110402
median20130731
Q320170223
95-th percentile20220906
Maximum20240516
Range169401
Interquartile range (IQR)59821

Descriptive statistics

Standard deviation40501.913
Coefficient of variation (CV)0.0020108535
Kurtosis-0.51937851
Mean20141653
Median Absolute Deviation (MAD)29802
Skewness0.69385123
Sum1.7074079 × 1011
Variance1.6404049 × 109
MonotonicityNot monotonic
2024-05-18T11:18:20.514642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20091116 226
 
2.3%
20100927 69
 
0.7%
20101213 24
 
0.2%
20110420 24
 
0.2%
20170124 23
 
0.2%
20160725 20
 
0.2%
20110901 19
 
0.2%
20111108 17
 
0.2%
20110425 17
 
0.2%
20110125 15
 
0.1%
Other values (3086) 8023
80.2%
(Missing) 1523
 
15.2%
ValueCountFrequency (%)
20071115 1
 
< 0.1%
20080730 1
 
< 0.1%
20081217 1
 
< 0.1%
20090125 1
 
< 0.1%
20090305 2
 
< 0.1%
20090306 1
 
< 0.1%
20090307 1
 
< 0.1%
20090309 2
 
< 0.1%
20090311 6
0.1%
20090312 4
< 0.1%
ValueCountFrequency (%)
20240516 1
 
< 0.1%
20240513 3
< 0.1%
20240510 2
< 0.1%
20240509 1
 
< 0.1%
20240507 1
 
< 0.1%
20240503 3
< 0.1%
20240502 2
< 0.1%
20240501 1
 
< 0.1%
20240430 2
< 0.1%
20240426 1
 
< 0.1%

지점설립일자
Text

MISSING 

Distinct3553
Distinct (%)40.6%
Missing1253
Missing (%)12.5%
Memory size156.2 KiB
2024-05-18T11:18:21.487831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters69976
Distinct characters17
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

Unique1370 ?
Unique (%)15.7%

Sample

1st row20171215
2nd row20110602
3rd row20140428
4th row20090518
5th row20130903
ValueCountFrequency (%)
20090611 20
 
0.2%
20090520 20
 
0.2%
20090514 18
 
0.2%
20090528 17
 
0.2%
20090821 17
 
0.2%
20090820 17
 
0.2%
20090512 15
 
0.2%
20090605 14
 
0.2%
20090511 14
 
0.2%
20091008 13
 
0.1%
Other values (3543) 8582
98.1%
2024-05-18T11:18:23.090049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 22621
32.3%
2 15820
22.6%
1 14174
20.3%
3 2872
 
4.1%
9 2593
 
3.7%
7 2499
 
3.6%
6 2498
 
3.6%
5 2411
 
3.4%
8 2264
 
3.2%
4 2206
 
3.2%
Other values (7) 18
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69958
> 99.9%
Space Separator 9
 
< 0.1%
Lowercase Letter 6
 
< 0.1%
Uppercase Letter 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22621
32.3%
2 15820
22.6%
1 14174
20.3%
3 2872
 
4.1%
9 2593
 
3.7%
7 2499
 
3.6%
6 2498
 
3.6%
5 2411
 
3.4%
8 2264
 
3.2%
4 2206
 
3.2%
Lowercase Letter
ValueCountFrequency (%)
a 2
33.3%
r 2
33.3%
y 1
16.7%
p 1
16.7%
Uppercase Letter
ValueCountFrequency (%)
M 2
66.7%
A 1
33.3%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69967
> 99.9%
Latin 9
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22621
32.3%
2 15820
22.6%
1 14174
20.3%
3 2872
 
4.1%
9 2593
 
3.7%
7 2499
 
3.6%
6 2498
 
3.6%
5 2411
 
3.4%
8 2264
 
3.2%
4 2206
 
3.2%
Latin
ValueCountFrequency (%)
M 2
22.2%
a 2
22.2%
r 2
22.2%
y 1
11.1%
A 1
11.1%
p 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22621
32.3%
2 15820
22.6%
1 14174
20.3%
3 2872
 
4.1%
9 2593
 
3.7%
7 2499
 
3.6%
6 2498
 
3.6%
5 2411
 
3.4%
8 2264
 
3.2%
4 2206
 
3.2%
Other values (7) 18
 
< 0.1%

본점여부
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
본점
9930 
지점
 
70

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 9930
99.3%
지점 70
 
0.7%

Length

2024-05-18T11:18:23.529630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T11:18:23.877926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
본점 9930
99.3%
지점 70
 
0.7%

최근수정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct3173
Distinct (%)31.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20152177
Minimum20090518
Maximum20240517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T11:18:24.253351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20090518
5-th percentile20091117
Q120110930
median20140806
Q320190110
95-th percentile20231013
Maximum20240517
Range149999
Interquartile range (IQR)79180.5

Descriptive statistics

Standard deviation45484.751
Coefficient of variation (CV)0.0022570638
Kurtosis-1.003767
Mean20152177
Median Absolute Deviation (MAD)30401
Skewness0.47388623
Sum2.0152177 × 1011
Variance2.0688625 × 109
MonotonicityNot monotonic
2024-05-18T11:18:24.738942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20091117 87
 
0.9%
20091116 50
 
0.5%
20091118 46
 
0.5%
20100330 46
 
0.5%
20100927 45
 
0.4%
20090609 45
 
0.4%
20110425 36
 
0.4%
20090622 33
 
0.3%
20130621 32
 
0.3%
20100517 29
 
0.3%
Other values (3163) 9551
95.5%
ValueCountFrequency (%)
20090518 1
 
< 0.1%
20090521 4
 
< 0.1%
20090601 5
 
0.1%
20090602 1
 
< 0.1%
20090603 13
 
0.1%
20090604 18
 
0.2%
20090605 2
 
< 0.1%
20090608 4
 
< 0.1%
20090609 45
0.4%
20090610 20
0.2%
ValueCountFrequency (%)
20240517 4
< 0.1%
20240516 2
 
< 0.1%
20240514 3
 
< 0.1%
20240513 5
0.1%
20240510 4
< 0.1%
20240509 4
< 0.1%
20240508 4
< 0.1%
20240507 3
 
< 0.1%
20240503 8
0.1%
20240502 6
0.1%

Interactions

2024-05-18T11:17:58.969399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:17:50.621459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:17:52.548945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:17:54.207613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:17:56.519083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:17:59.380402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:17:50.975431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:17:52.832894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:17:54.613809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:17:56.996793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:17:59.859449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:17:51.257854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:17:53.123522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:17:54.979530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:17:57.504561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:18:00.522860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:17:51.619340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:17:53.440369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:17:55.535370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:17:58.243819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:18:01.008031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:17:52.075605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:17:53.881743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:17:56.047787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:17:58.604462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T11:18:25.105728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록신청사업영업구분법인여부우편번호등록일자유효기간만료일자폐쇄일자본점여부최근수정일자
등록신청사업1.0000.1240.0000.0000.2240.1440.2020.0000.168
영업구분0.1241.0000.2910.0930.6110.6240.2030.0290.540
법인여부0.0000.2911.0000.0540.3270.2750.2470.2020.336
우편번호0.0000.0930.0541.0000.3240.3240.2400.0000.244
등록일자0.2240.6110.3270.3241.0000.9890.9350.0730.937
유효기간만료일자0.1440.6240.2750.3240.9891.0000.8540.0680.925
폐쇄일자0.2020.2030.2470.2400.9350.8541.0000.0710.986
본점여부0.0000.0290.2020.0000.0730.0680.0711.0000.106
최근수정일자0.1680.5400.3360.2440.9370.9250.9860.1061.000
2024-05-18T11:18:25.532362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록신청사업영업구분본점여부법인여부
등록신청사업1.0000.0890.0000.000
영업구분0.0891.0000.0210.209
본점여부0.0000.0211.0000.129
법인여부0.0000.2090.1291.000
2024-05-18T11:18:25.799880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호등록일자유효기간만료일자폐쇄일자최근수정일자등록신청사업영업구분법인여부본점여부
우편번호1.0000.011-0.0020.0270.0100.0000.0720.0310.000
등록일자0.0111.0000.9960.9610.9650.1720.3790.2510.056
유효기간만료일자-0.0020.9961.0000.9640.9660.1100.3900.2110.052
폐쇄일자0.0270.9610.9641.0000.9910.1550.1180.1890.054
최근수정일자0.0100.9650.9660.9911.0000.1290.3030.2580.081
등록신청사업0.0000.1720.1100.1550.1291.0000.0890.0000.000
영업구분0.0720.3790.3900.1180.3030.0891.0000.2090.021
법인여부0.0310.2510.2110.1890.2580.0000.2091.0000.129
본점여부0.0000.0560.0520.0540.0810.0000.0210.1291.000

Missing values

2024-05-18T11:18:01.874322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T11:18:02.936360image/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.
2024-05-18T11:18:03.524637image/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
6744대부업폐업2017-서울관악-0037(대부업)실로암대부개인<NA>서울특별시 관악구 신림동 1424번지 19호서울특별시 관악구 신림로 354, 5층 (신림동)<NA>20171215202012152020042320171215본점20200423
20223대부업타시군구이관2011-서울종로-00044(대부업)피아이에이엔피엘3차대부 유한회사법인02-734-6901서울특별시 종로구 인사동 194번지 4호 하나로빌딩 5층-513<NA>11079420110602201406022012120620110602본점20121206
13716대부업직권취소2014-서울강남-0068대산대부업개인02-501-5550서울특별시 강남구 개포동 1234번지 11호 에이스빌라-302서울특별시 강남구 논현로18길 14-15, 302호 (개포동, 에이스빌라)13596420140428201704282015111320140428본점20151118
28326대부중개업<NA>2009-서울특별시-00766(대부중개업)우정금융개인0222444624서울특별시 동대문구 답십리동 10-1 동아아파트 109동 703호<NA><NA>20090309<NA>2010050420090518본점20100506
16433대부업폐업2013-서울노원-00045우리대부파트너즈개인<NA>서울특별시 노원구 상계동 707번지 1호 명성빌딩-407<NA>13994220130903201609032014040920130903본점20140410
8579대부업폐업2017-서울강서-00041(대부업)가연대부개인02-2064-0472서울특별시 강서구 방화동 616번지 27호서울특별시 강서구 방화동로3길 49, 2층 (방화동)<NA>20171221202012212018082920171221본점20180829
22750대부중개업직권취소2010-서울종로구-00024(대부중개업)캘리포니아론대부개인15991508서울특별시 종로구 예지동 269번지 27호 2층 202호<NA>11083320100507201305072012010420100507본점20120104
2032대부중개업직권취소2021-서울영등포-2146(대부중개업)유니솔루션 대부개인<NA>서울특별시 영등포구 당산동6가 217번지 4호 수정빌딩서울특별시 영등포구 양평로 24, 수정빌딩 6층 616호 (당산동6가)<NA>20210825202408252023081420210825본점20230814
23447대부중개업<NA>2008-서울특별시-03639(대부중개업)A one 캐피탈개인024551918서울특별시 강동구 성내동 203-1<NA><NA>200810222011102220111013<NA>본점20111013
28906대부중개업<NA>2007-서울특별시-01658(대부중개케이제이아이파이낸스인터내셔널 유한회사법인0222635005서울특별시 중구 을지로6가 18번지 130호 기승빌딩 7층(동대문지점)<NA>10085120070226<NA>2010022720070226본점20100302
등록신청사업영업구분등록증번호상호법인여부사업장 전화번호소재지소재지(도로명)우편번호등록일자유효기간만료일자폐쇄일자지점설립일자본점여부최근수정일자
26926대부업<NA>2009-서울특별시-00205(대부업)필(feel)개인<NA>서울특별시 강남구 역삼동 706-20 한화진넥스빌 2005호<NA><NA>20090122<NA>20100927<NA>본점20100927
22847대부업타시군구이관2011-서울특별시 성북구-00023은성대부사개인<NA>서울특별시 성북구 정릉동 903번지 13호 보광빌라 3층 1호<NA>13610320110715201407152011102120110715본점20111223
29786대부업<NA>2007-서울특별시-01317(대부업)월드개인0222422506서울특별시 동대문구 장안동 458-1 동진빌딩 405호<NA><NA>20071019<NA>2009111620071015본점20091118
1813대부업폐업2022-서울구로-00015(대부업)동부대부개인02-6015-9840서울특별시 구로구 구로동 612번지 11호 -304-1서울특별시 구로구 구로중앙로 211, 304-1호 (구로동)<NA>20220622202506232023091520220622본점20230915
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29581<NA><NA>2008-서울특별시-02816상록유통개인<NA>서울특별시 성북구 동소문동4가 75-2. 922호<NA>13603420080910<NA>20090414<NA>본점20091120
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