Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells19340
Missing cells (%)12.9%
Duplicate rows3
Duplicate rows (%)< 0.1%
Total size in memory1.3 MiB
Average record size in memory133.0 B

Variable types

Categorical4
Text6
Numeric5

Dataset

Description등록신청사업,영업구분,등록증번호,상호,법인여부,사업장 전화번호,소재지,소재지(도로명),우편번호,등록일자,유효기간만료일자,폐쇄일자,지점설립일자,본점여부,최근수정일자
Author광진구
URLhttps://data.seoul.go.kr/dataList/OA-9942/S/1/datasetView.do

Alerts

Dataset has 3 (< 0.1%) duplicate rowsDuplicates
등록일자 is highly overall correlated with 유효기간만료일자 and 2 other fieldsHigh correlation
유효기간만료일자 is highly overall correlated with 등록일자 and 2 other fieldsHigh correlation
폐쇄일자 is highly overall correlated with 등록일자 and 2 other fieldsHigh correlation
최근수정일자 is highly overall correlated with 등록일자 and 2 other fieldsHigh correlation
본점여부 is highly imbalanced (94.3%)Imbalance
등록증번호 has 180 (1.8%) missing valuesMissing
사업장 전화번호 has 3402 (34.0%) missing valuesMissing
소재지 has 269 (2.7%) missing valuesMissing
소재지(도로명) has 4833 (48.3%) missing valuesMissing
우편번호 has 5682 (56.8%) missing valuesMissing
유효기간만료일자 has 2132 (21.3%) missing valuesMissing
폐쇄일자 has 1568 (15.7%) missing valuesMissing
지점설립일자 has 1274 (12.7%) missing valuesMissing

Reproduction

Analysis started2024-05-04 03:32:10.863499
Analysis finished2024-05-04 03:32:24.634839
Duration13.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대부업
6283 
대부중개업
3328 
<NA>
 
389

Length

Max length5
Median length3
Mean length3.7045
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대부업 6283
62.8%
대부중개업 3328
33.3%
<NA> 389
 
3.9%

Length

2024-05-04T03:32:24.883037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T03:32:25.260719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대부업 6283
62.8%
대부중개업 3328
33.3%
na 389
 
3.9%

영업구분
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
3624 
<NA>
2977 
타시군구이관
1256 
영업중
835 
유효기간만료
774 

Length

Max length6
Median length4
Mean length3.5977
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 3624
36.2%
<NA> 2977
29.8%
타시군구이관 1256
 
12.6%
영업중 835
 
8.3%
유효기간만료 774
 
7.7%
직권취소 534
 
5.3%

Length

2024-05-04T03:32:25.609970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T03:32:25.980169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 3624
36.2%
na 2977
29.8%
타시군구이관 1256
 
12.6%
영업중 835
 
8.3%
유효기간만료 774
 
7.7%
직권취소 534
 
5.3%

등록증번호
Text

MISSING 

Distinct9772
Distinct (%)99.5%
Missing180
Missing (%)1.8%
Memory size156.2 KiB
2024-05-04T03:32:26.443069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length26
Mean length19.513544
Min length4

Characters and Unicode

Total characters191623
Distinct characters64
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

Unique9726 ?
Unique (%)99.0%

Sample

1st row2010-서울강북-0019
2nd row2023-서울강서-0033(대부중개업)
3rd row2016-서울도봉-0053(대부중개업)
4th row2017-서울마포-0016(대부업)
5th row2010-서울서초-0043(대부업)
ValueCountFrequency (%)
2011-서울특별시 23
 
0.2%
2010-서울 20
 
0.2%
2013-서울특별시 18
 
0.2%
2012-서울특별시 11
 
0.1%
2014-서울특별시 11
 
0.1%
2016-서울특별시 11
 
0.1%
대부업 9
 
0.1%
성북구-00003 7
 
0.1%
2017-서울특별시 6
 
0.1%
성북구-00006 6
 
0.1%
Other values (9737) 9847
98.8%
2024-05-04T03:32:27.214569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 33939
17.7%
- 19625
 
10.2%
2 15759
 
8.2%
1 11730
 
6.1%
10936
 
5.7%
9789
 
5.1%
8492
 
4.4%
( 8197
 
4.3%
8169
 
4.3%
) 8135
 
4.2%
Other values (54) 56852
29.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 82699
43.2%
Other Letter 72818
38.0%
Dash Punctuation 19625
 
10.2%
Open Punctuation 8197
 
4.3%
Close Punctuation 8135
 
4.2%
Space Separator 149
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10936
15.0%
9789
13.4%
8492
11.7%
8169
11.2%
7925
10.9%
3406
 
4.7%
2825
 
3.9%
2577
 
3.5%
2569
 
3.5%
2569
 
3.5%
Other values (40) 13561
18.6%
Decimal Number
ValueCountFrequency (%)
0 33939
41.0%
2 15759
19.1%
1 11730
 
14.2%
3 3840
 
4.6%
8 3100
 
3.7%
4 3017
 
3.6%
9 2885
 
3.5%
7 2862
 
3.5%
6 2829
 
3.4%
5 2738
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 19625
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8197
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8135
100.0%
Space Separator
ValueCountFrequency (%)
149
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 118805
62.0%
Hangul 72818
38.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10936
15.0%
9789
13.4%
8492
11.7%
8169
11.2%
7925
10.9%
3406
 
4.7%
2825
 
3.9%
2577
 
3.5%
2569
 
3.5%
2569
 
3.5%
Other values (40) 13561
18.6%
Common
ValueCountFrequency (%)
0 33939
28.6%
- 19625
16.5%
2 15759
13.3%
1 11730
 
9.9%
( 8197
 
6.9%
) 8135
 
6.8%
3 3840
 
3.2%
8 3100
 
2.6%
4 3017
 
2.5%
9 2885
 
2.4%
Other values (4) 8578
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118805
62.0%
Hangul 72818
38.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33939
28.6%
- 19625
16.5%
2 15759
13.3%
1 11730
 
9.9%
( 8197
 
6.9%
) 8135
 
6.8%
3 3840
 
3.2%
8 3100
 
2.6%
4 3017
 
2.5%
9 2885
 
2.4%
Other values (4) 8578
 
7.2%
Hangul
ValueCountFrequency (%)
10936
15.0%
9789
13.4%
8492
11.7%
8169
11.2%
7925
10.9%
3406
 
4.7%
2825
 
3.9%
2577
 
3.5%
2569
 
3.5%
2569
 
3.5%
Other values (40) 13561
18.6%

상호
Text

Distinct8626
Distinct (%)86.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-04T03:32:27.869556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length28
Mean length7.7272
Min length1

Characters and Unicode

Total characters77272
Distinct characters777
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

Unique7532 ?
Unique (%)75.3%

Sample

1st row조은대부
2nd row365일24시오케이대부중개
3rd rowOK론대부중개
4th row리셋대부
5th row우리건설캐피탈대부
ValueCountFrequency (%)
주식회사 798
 
6.7%
대부중개 293
 
2.5%
대부 276
 
2.3%
유한회사 54
 
0.5%
캐피탈 21
 
0.2%
16
 
0.1%
대부업 13
 
0.1%
loan 11
 
0.1%
money 11
 
0.1%
미래대부 10
 
0.1%
Other values (8656) 10374
87.3%
2024-05-04T03:32:28.969971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8428
 
10.9%
8081
 
10.5%
2694
 
3.5%
2263
 
2.9%
2048
 
2.7%
2035
 
2.6%
1913
 
2.5%
) 1900
 
2.5%
( 1893
 
2.4%
1883
 
2.4%
Other values (767) 44134
57.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 67355
87.2%
Uppercase Letter 2393
 
3.1%
Close Punctuation 1900
 
2.5%
Open Punctuation 1893
 
2.4%
Space Separator 1883
 
2.4%
Lowercase Letter 1295
 
1.7%
Decimal Number 259
 
0.3%
Other Punctuation 246
 
0.3%
Dash Punctuation 39
 
0.1%
Other Symbol 6
 
< 0.1%
Other values (2) 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
8428
 
12.5%
8081
 
12.0%
2694
 
4.0%
2263
 
3.4%
2048
 
3.0%
2035
 
3.0%
1913
 
2.8%
1319
 
2.0%
1140
 
1.7%
1017
 
1.5%
Other values (692) 36417
54.1%
Uppercase Letter
ValueCountFrequency (%)
S 312
 
13.0%
K 216
 
9.0%
J 174
 
7.3%
M 171
 
7.1%
C 167
 
7.0%
H 142
 
5.9%
T 105
 
4.4%
B 101
 
4.2%
G 95
 
4.0%
A 91
 
3.8%
Other values (16) 819
34.2%
Lowercase Letter
ValueCountFrequency (%)
e 150
11.6%
n 136
10.5%
o 132
10.2%
a 124
 
9.6%
i 81
 
6.3%
s 78
 
6.0%
l 77
 
5.9%
t 74
 
5.7%
c 66
 
5.1%
r 55
 
4.2%
Other values (15) 322
24.9%
Decimal Number
ValueCountFrequency (%)
1 92
35.5%
2 36
 
13.9%
9 31
 
12.0%
4 27
 
10.4%
3 20
 
7.7%
5 19
 
7.3%
6 14
 
5.4%
8 9
 
3.5%
0 6
 
2.3%
7 5
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 139
56.5%
& 90
36.6%
, 6
 
2.4%
? 6
 
2.4%
* 2
 
0.8%
2
 
0.8%
/ 1
 
0.4%
Close Punctuation
ValueCountFrequency (%)
) 1900
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1893
100.0%
Space Separator
ValueCountFrequency (%)
1883
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 39
100.0%
Other Symbol
ValueCountFrequency (%)
6
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 67353
87.2%
Common 6222
 
8.1%
Latin 3689
 
4.8%
Han 8
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
8428
 
12.5%
8081
 
12.0%
2694
 
4.0%
2263
 
3.4%
2048
 
3.0%
2035
 
3.0%
1913
 
2.8%
1319
 
2.0%
1140
 
1.7%
1017
 
1.5%
Other values (686) 36415
54.1%
Latin
ValueCountFrequency (%)
S 312
 
8.5%
K 216
 
5.9%
J 174
 
4.7%
M 171
 
4.6%
C 167
 
4.5%
e 150
 
4.1%
H 142
 
3.8%
n 136
 
3.7%
o 132
 
3.6%
a 124
 
3.4%
Other values (42) 1965
53.3%
Common
ValueCountFrequency (%)
) 1900
30.5%
( 1893
30.4%
1883
30.3%
. 139
 
2.2%
1 92
 
1.5%
& 90
 
1.4%
- 39
 
0.6%
2 36
 
0.6%
9 31
 
0.5%
4 27
 
0.4%
Other values (12) 92
 
1.5%
Han
ValueCountFrequency (%)
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 67346
87.2%
ASCII 9908
 
12.8%
None 8
 
< 0.1%
CJK 8
 
< 0.1%
Compat Jamo 1
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
8428
 
12.5%
8081
 
12.0%
2694
 
4.0%
2263
 
3.4%
2048
 
3.0%
2035
 
3.0%
1913
 
2.8%
1319
 
2.0%
1140
 
1.7%
1017
 
1.5%
Other values (684) 36408
54.1%
ASCII
ValueCountFrequency (%)
) 1900
19.2%
( 1893
19.1%
1883
19.0%
S 312
 
3.1%
K 216
 
2.2%
J 174
 
1.8%
M 171
 
1.7%
C 167
 
1.7%
e 150
 
1.5%
H 142
 
1.4%
Other values (62) 2900
29.3%
None
ValueCountFrequency (%)
6
75.0%
2
 
25.0%
CJK
ValueCountFrequency (%)
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
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
7212 
법인
2788 

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 7212
72.1%
법인 2788
 
27.9%

Length

2024-05-04T03:32:29.315468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T03:32:29.618233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 7212
72.1%
법인 2788
 
27.9%
Distinct5820
Distinct (%)88.2%
Missing3402
Missing (%)34.0%
Memory size156.2 KiB
2024-05-04T03:32:30.085859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length41
Mean length10.575174
Min length1

Characters and Unicode

Total characters69775
Distinct characters35
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5194 ?
Unique (%)78.7%

Sample

1st row64049136 01039939136
2nd row02-338-0789
3rd row025986988
4th row02 358 6073
5th row0236633340
ValueCountFrequency (%)
02 262
 
3.6%
55
 
0.7%
070 35
 
0.5%
2209 8
 
0.1%
02-591-0880 7
 
0.1%
1661-1973 6
 
0.1%
0236745525 6
 
0.1%
010 6
 
0.1%
025117185 5
 
0.1%
02-734-6901 5
 
0.1%
Other values (6109) 6948
94.6%
2024-05-04T03:32:31.027128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11326
16.2%
2 10324
14.8%
- 7063
10.1%
5 5914
8.5%
7 5503
7.9%
1 5183
7.4%
6 5043
7.2%
3 4873
7.0%
8 4695
6.7%
4 4655
6.7%
Other values (25) 5196
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 61603
88.3%
Dash Punctuation 7063
 
10.1%
Space Separator 827
 
1.2%
Other Punctuation 125
 
0.2%
Close Punctuation 71
 
0.1%
Open Punctuation 31
 
< 0.1%
Math Symbol 28
 
< 0.1%
Other Letter 19
 
< 0.1%
Uppercase Letter 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3
15.8%
3
15.8%
3
15.8%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
Other values (3) 3
15.8%
Decimal Number
ValueCountFrequency (%)
0 11326
18.4%
2 10324
16.8%
5 5914
9.6%
7 5503
8.9%
1 5183
8.4%
6 5043
8.2%
3 4873
7.9%
8 4695
7.6%
4 4655
7.6%
9 4087
 
6.6%
Other Punctuation
ValueCountFrequency (%)
* 64
51.2%
/ 40
32.0%
. 21
 
16.8%
Uppercase Letter
ValueCountFrequency (%)
K 4
50.0%
T 3
37.5%
S 1
 
12.5%
Math Symbol
ValueCountFrequency (%)
~ 27
96.4%
× 1
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 7063
100.0%
Space Separator
ValueCountFrequency (%)
827
100.0%
Close Punctuation
ValueCountFrequency (%)
) 71
100.0%
Open Punctuation
ValueCountFrequency (%)
( 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69748
> 99.9%
Hangul 19
 
< 0.1%
Latin 8
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11326
16.2%
2 10324
14.8%
- 7063
10.1%
5 5914
8.5%
7 5503
7.9%
1 5183
7.4%
6 5043
7.2%
3 4873
7.0%
8 4695
6.7%
4 4655
6.7%
Other values (9) 5169
7.4%
Hangul
ValueCountFrequency (%)
3
15.8%
3
15.8%
3
15.8%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
Other values (3) 3
15.8%
Latin
ValueCountFrequency (%)
K 4
50.0%
T 3
37.5%
S 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69755
> 99.9%
Hangul 19
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11326
16.2%
2 10324
14.8%
- 7063
10.1%
5 5914
8.5%
7 5503
7.9%
1 5183
7.4%
6 5043
7.2%
3 4873
7.0%
8 4695
6.7%
4 4655
6.7%
Other values (11) 5176
7.4%
Hangul
ValueCountFrequency (%)
3
15.8%
3
15.8%
3
15.8%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
Other values (3) 3
15.8%
None
ValueCountFrequency (%)
× 1
100.0%

소재지
Text

MISSING 

Distinct8694
Distinct (%)89.3%
Missing269
Missing (%)2.7%
Memory size156.2 KiB
2024-05-04T03:32:31.771904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length48
Mean length31.434488
Min length15

Characters and Unicode

Total characters305889
Distinct characters628
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

Unique7941 ?
Unique (%)81.6%

Sample

1st row서울특별시 강북구 수유동 2-2 202호
2nd row서울특별시 강서구 공항동 60번지 118호 대성빌딩
3rd row서울특별시 도봉구 창동 650번지 58호 -201
4th row서울특별시 마포구 도화동 251번지 1호 근신빌딩 별관-304
5th row서울특별시 서초구 서초동 1355번지 8호 중앙로얄오피스텔 810호
ValueCountFrequency (%)
서울특별시 9727
 
17.0%
강남구 1633
 
2.8%
서초구 996
 
1.7%
1호 720
 
1.3%
역삼동 710
 
1.2%
송파구 611
 
1.1%
서초동 589
 
1.0%
중구 538
 
0.9%
영등포구 452
 
0.8%
2호 449
 
0.8%
Other values (9500) 40892
71.3%
2024-05-04T03:32:33.083375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
67936
22.2%
1 13611
 
4.4%
12170
 
4.0%
11085
 
3.6%
10503
 
3.4%
9994
 
3.3%
9781
 
3.2%
9742
 
3.2%
9728
 
3.2%
2 8899
 
2.9%
Other values (618) 142440
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 166976
54.6%
Space Separator 67936
22.2%
Decimal Number 63668
 
20.8%
Dash Punctuation 5507
 
1.8%
Uppercase Letter 1159
 
0.4%
Other Punctuation 264
 
0.1%
Lowercase Letter 128
 
< 0.1%
Close Punctuation 110
 
< 0.1%
Open Punctuation 106
 
< 0.1%
Letter Number 27
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
12170
 
7.3%
11085
 
6.6%
10503
 
6.3%
9994
 
6.0%
9781
 
5.9%
9742
 
5.8%
9728
 
5.8%
8607
 
5.2%
8430
 
5.0%
7915
 
4.7%
Other values (548) 69021
41.3%
Uppercase Letter
ValueCountFrequency (%)
B 263
22.7%
A 212
18.3%
S 80
 
6.9%
D 76
 
6.6%
T 70
 
6.0%
K 55
 
4.7%
I 45
 
3.9%
L 38
 
3.3%
C 38
 
3.3%
E 37
 
3.2%
Other values (16) 245
21.1%
Lowercase Letter
ValueCountFrequency (%)
e 26
20.3%
i 14
10.9%
n 13
10.2%
r 13
10.2%
o 11
8.6%
w 10
 
7.8%
t 6
 
4.7%
c 5
 
3.9%
s 4
 
3.1%
k 4
 
3.1%
Other values (11) 22
17.2%
Decimal Number
ValueCountFrequency (%)
1 13611
21.4%
2 8899
14.0%
0 8091
12.7%
3 7030
11.0%
4 5753
9.0%
5 4897
 
7.7%
6 4582
 
7.2%
7 4073
 
6.4%
8 3392
 
5.3%
9 3340
 
5.2%
Other Punctuation
ValueCountFrequency (%)
, 105
39.8%
/ 91
34.5%
. 65
24.6%
2
 
0.8%
* 1
 
0.4%
Letter Number
ValueCountFrequency (%)
19
70.4%
5
 
18.5%
3
 
11.1%
Space Separator
ValueCountFrequency (%)
67936
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5507
100.0%
Close Punctuation
ValueCountFrequency (%)
) 110
100.0%
Open Punctuation
ValueCountFrequency (%)
( 106
100.0%
Math Symbol
ValueCountFrequency (%)
~ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 166975
54.6%
Common 137599
45.0%
Latin 1314
 
0.4%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
12170
 
7.3%
11085
 
6.6%
10503
 
6.3%
9994
 
6.0%
9781
 
5.9%
9742
 
5.8%
9728
 
5.8%
8607
 
5.2%
8430
 
5.0%
7915
 
4.7%
Other values (547) 69020
41.3%
Latin
ValueCountFrequency (%)
B 263
20.0%
A 212
16.1%
S 80
 
6.1%
D 76
 
5.8%
T 70
 
5.3%
K 55
 
4.2%
I 45
 
3.4%
L 38
 
2.9%
C 38
 
2.9%
E 37
 
2.8%
Other values (40) 400
30.4%
Common
ValueCountFrequency (%)
67936
49.4%
1 13611
 
9.9%
2 8899
 
6.5%
0 8091
 
5.9%
3 7030
 
5.1%
4 5753
 
4.2%
- 5507
 
4.0%
5 4897
 
3.6%
6 4582
 
3.3%
7 4073
 
3.0%
Other values (10) 7220
 
5.2%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 166975
54.6%
ASCII 138884
45.4%
Number Forms 27
 
< 0.1%
None 2
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
67936
48.9%
1 13611
 
9.8%
2 8899
 
6.4%
0 8091
 
5.8%
3 7030
 
5.1%
4 5753
 
4.1%
- 5507
 
4.0%
5 4897
 
3.5%
6 4582
 
3.3%
7 4073
 
2.9%
Other values (56) 8505
 
6.1%
Hangul
ValueCountFrequency (%)
12170
 
7.3%
11085
 
6.6%
10503
 
6.3%
9994
 
6.0%
9781
 
5.9%
9742
 
5.8%
9728
 
5.8%
8607
 
5.2%
8430
 
5.0%
7915
 
4.7%
Other values (547) 69020
41.3%
Number Forms
ValueCountFrequency (%)
19
70.4%
5
 
18.5%
3
 
11.1%
None
ValueCountFrequency (%)
2
100.0%
CJK
ValueCountFrequency (%)
1
100.0%

소재지(도로명)
Text

MISSING 

Distinct4724
Distinct (%)91.4%
Missing4833
Missing (%)48.3%
Memory size156.2 KiB
2024-05-04T03:32:33.968283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length66
Median length56
Mean length37.13083
Min length22

Characters and Unicode

Total characters191855
Distinct characters609
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4330 ?
Unique (%)83.8%

Sample

1st row서울특별시 강서구 송정로 51, 대성빌딩 402-26호 (공항동)
2nd row서울특별시 도봉구 도봉로 452, 201호 (창동)
3rd row서울특별시 마포구 삼개로 20, 근신빌딩 별관 304호 (도화동)
4th row서울특별시 서대문구 응암로 63 (북가좌동)
5th row서울특별시 종로구 대학로2길 30 (종로5가)
ValueCountFrequency (%)
서울특별시 5166
 
14.1%
강남구 951
 
2.6%
서초구 593
 
1.6%
2층 473
 
1.3%
역삼동 392
 
1.1%
서초동 376
 
1.0%
3층 361
 
1.0%
송파구 325
 
0.9%
4층 317
 
0.9%
영등포구 300
 
0.8%
Other values (6549) 27318
74.7%
2024-05-04T03:32:35.352680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31431
 
16.4%
1 7450
 
3.9%
, 7114
 
3.7%
6868
 
3.6%
6692
 
3.5%
5728
 
3.0%
5693
 
3.0%
5371
 
2.8%
2 5328
 
2.8%
5218
 
2.7%
Other values (599) 104962
54.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 106552
55.5%
Decimal Number 34166
 
17.8%
Space Separator 31431
 
16.4%
Other Punctuation 7127
 
3.7%
Open Punctuation 5211
 
2.7%
Close Punctuation 5211
 
2.7%
Dash Punctuation 1032
 
0.5%
Uppercase Letter 951
 
0.5%
Lowercase Letter 133
 
0.1%
Letter Number 28
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6868
 
6.4%
6692
 
6.3%
5728
 
5.4%
5693
 
5.3%
5371
 
5.0%
5218
 
4.9%
5179
 
4.9%
5167
 
4.8%
4141
 
3.9%
2697
 
2.5%
Other values (531) 53798
50.5%
Uppercase Letter
ValueCountFrequency (%)
B 168
17.7%
A 137
14.4%
S 81
 
8.5%
T 68
 
7.2%
E 52
 
5.5%
K 46
 
4.8%
I 43
 
4.5%
C 40
 
4.2%
G 38
 
4.0%
L 37
 
3.9%
Other values (16) 241
25.3%
Lowercase Letter
ValueCountFrequency (%)
e 26
19.5%
r 16
12.0%
w 14
10.5%
o 14
10.5%
i 14
10.5%
n 12
9.0%
b 6
 
4.5%
t 6
 
4.5%
c 4
 
3.0%
s 4
 
3.0%
Other values (8) 17
12.8%
Decimal Number
ValueCountFrequency (%)
1 7450
21.8%
2 5328
15.6%
0 4413
12.9%
3 4022
11.8%
4 2904
 
8.5%
5 2659
 
7.8%
6 2223
 
6.5%
7 1901
 
5.6%
8 1679
 
4.9%
9 1587
 
4.6%
Other Punctuation
ValueCountFrequency (%)
, 7114
99.8%
. 8
 
0.1%
/ 3
 
< 0.1%
@ 2
 
< 0.1%
Letter Number
ValueCountFrequency (%)
18
64.3%
6
 
21.4%
4
 
14.3%
Math Symbol
ValueCountFrequency (%)
~ 11
84.6%
< 1
 
7.7%
> 1
 
7.7%
Space Separator
ValueCountFrequency (%)
31431
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5211
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5211
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 106550
55.5%
Common 84191
43.9%
Latin 1112
 
0.6%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
6868
 
6.4%
6692
 
6.3%
5728
 
5.4%
5693
 
5.3%
5371
 
5.0%
5218
 
4.9%
5179
 
4.9%
5167
 
4.8%
4141
 
3.9%
2697
 
2.5%
Other values (530) 53796
50.5%
Latin
ValueCountFrequency (%)
B 168
15.1%
A 137
 
12.3%
S 81
 
7.3%
T 68
 
6.1%
E 52
 
4.7%
K 46
 
4.1%
I 43
 
3.9%
C 40
 
3.6%
G 38
 
3.4%
L 37
 
3.3%
Other values (37) 402
36.2%
Common
ValueCountFrequency (%)
31431
37.3%
1 7450
 
8.8%
, 7114
 
8.4%
2 5328
 
6.3%
( 5211
 
6.2%
) 5211
 
6.2%
0 4413
 
5.2%
3 4022
 
4.8%
4 2904
 
3.4%
5 2659
 
3.2%
Other values (11) 8448
 
10.0%
Han
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 106550
55.5%
ASCII 85275
44.4%
Number Forms 28
 
< 0.1%
CJK 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31431
36.9%
1 7450
 
8.7%
, 7114
 
8.3%
2 5328
 
6.2%
( 5211
 
6.1%
) 5211
 
6.1%
0 4413
 
5.2%
3 4022
 
4.7%
4 2904
 
3.4%
5 2659
 
3.1%
Other values (55) 9532
 
11.2%
Hangul
ValueCountFrequency (%)
6868
 
6.4%
6692
 
6.3%
5728
 
5.4%
5693
 
5.3%
5371
 
5.0%
5218
 
4.9%
5179
 
4.9%
5167
 
4.8%
4141
 
3.9%
2697
 
2.5%
Other values (530) 53796
50.5%
Number Forms
ValueCountFrequency (%)
18
64.3%
6
 
21.4%
4
 
14.3%
CJK
ValueCountFrequency (%)
2
100.0%

우편번호
Real number (ℝ)

MISSING 

Distinct1343
Distinct (%)31.1%
Missing5682
Missing (%)56.8%
Infinite0
Infinite (%)0.0%
Mean136479.68
Minimum4526
Maximum410380
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T03:32:35.804255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4526
5-th percentile110098.8
Q1132040
median136140
Q3143190
95-th percentile157031
Maximum410380
Range405854
Interquartile range (IQR)11150

Descriptive statistics

Standard deviation14691.935
Coefficient of variation (CV)0.10764925
Kurtosis34.926186
Mean136479.68
Median Absolute Deviation (MAD)5000
Skewness0.15508872
Sum5.8931927 × 108
Variance2.1585296 × 108
MonotonicityNot monotonic
2024-05-04T03:32:36.306849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135080 180
 
1.8%
137070 149
 
1.5%
157010 66
 
0.7%
135010 49
 
0.5%
151015 46
 
0.5%
152050 45
 
0.4%
151050 45
 
0.4%
158070 43
 
0.4%
139200 42
 
0.4%
142100 40
 
0.4%
Other values (1333) 3613
36.1%
(Missing) 5682
56.8%
ValueCountFrequency (%)
4526 1
 
< 0.1%
4536 1
 
< 0.1%
4801 1
 
< 0.1%
7220 1
 
< 0.1%
7238 1
 
< 0.1%
100011 7
 
0.1%
100012 3
 
< 0.1%
100015 3
 
< 0.1%
100021 30
0.3%
100022 5
 
0.1%
ValueCountFrequency (%)
410380 1
 
< 0.1%
158877 1
 
< 0.1%
158864 3
< 0.1%
158863 1
 
< 0.1%
158860 7
0.1%
158859 2
 
< 0.1%
158857 2
 
< 0.1%
158846 1
 
< 0.1%
158845 1
 
< 0.1%
158841 1
 
< 0.1%

등록일자
Real number (ℝ)

HIGH CORRELATION 

Distinct3485
Distinct (%)34.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20136026
Minimum20051216
Maximum20240503
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T03:32:36.785539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20051216
5-th percentile20070827
Q120091029
median20130214
Q320170614
95-th percentile20230118
Maximum20240503
Range189287
Interquartile range (IQR)79585

Descriptive statistics

Standard deviation48791.541
Coefficient of variation (CV)0.0024230969
Kurtosis-0.88090083
Mean20136026
Median Absolute Deviation (MAD)39491.5
Skewness0.48026275
Sum2.0136026 × 1011
Variance2.3806145 × 109
MonotonicityNot monotonic
2024-05-04T03:32:37.295736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20080814 28
 
0.3%
20080818 26
 
0.3%
20080926 21
 
0.2%
20080731 17
 
0.2%
20080806 17
 
0.2%
20090514 16
 
0.2%
20090402 16
 
0.2%
20090520 16
 
0.2%
20090213 16
 
0.2%
20090511 15
 
0.1%
Other values (3475) 9812
98.1%
ValueCountFrequency (%)
20051216 1
< 0.1%
20060124 1
< 0.1%
20060306 2
< 0.1%
20060308 1
< 0.1%
20060310 1
< 0.1%
20060320 1
< 0.1%
20060321 1
< 0.1%
20060323 2
< 0.1%
20060324 2
< 0.1%
20060329 1
< 0.1%
ValueCountFrequency (%)
20240503 2
 
< 0.1%
20240502 1
 
< 0.1%
20240425 5
0.1%
20240424 3
< 0.1%
20240423 1
 
< 0.1%
20240422 4
< 0.1%
20240419 1
 
< 0.1%
20240416 1
 
< 0.1%
20240415 1
 
< 0.1%
20240411 2
 
< 0.1%

유효기간만료일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3283
Distinct (%)41.7%
Missing2132
Missing (%)21.3%
Infinite0
Infinite (%)0.0%
Mean20181050
Minimum20091116
Maximum20270503
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T03:32:37.967134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20091116
5-th percentile20120310
Q120141026
median20171210
Q320220102
95-th percentile20260508
Maximum20270503
Range179387
Interquartile range (IQR)79076.25

Descriptive statistics

Standard deviation44502.815
Coefficient of variation (CV)0.0022051784
Kurtosis-0.96588371
Mean20181050
Median Absolute Deviation (MAD)30582.5
Skewness0.32902559
Sum1.587845 × 1011
Variance1.9805005 × 109
MonotonicityNot monotonic
2024-05-04T03:32:38.529981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20110831 15
 
0.1%
20140711 12
 
0.1%
20140721 12
 
0.1%
20190718 11
 
0.1%
20170922 11
 
0.1%
20170306 11
 
0.1%
20120514 10
 
0.1%
20190722 10
 
0.1%
20180914 10
 
0.1%
20131102 10
 
0.1%
Other values (3273) 7756
77.6%
(Missing) 2132
 
21.3%
ValueCountFrequency (%)
20091116 1
< 0.1%
20100122 1
< 0.1%
20100308 1
< 0.1%
20100405 1
< 0.1%
20100410 1
< 0.1%
20100418 2
< 0.1%
20100419 1
< 0.1%
20100511 1
< 0.1%
20100522 1
< 0.1%
20100528 1
< 0.1%
ValueCountFrequency (%)
20270503 2
 
< 0.1%
20270502 1
 
< 0.1%
20270425 5
0.1%
20270424 3
< 0.1%
20270423 1
 
< 0.1%
20270422 1
 
< 0.1%
20270421 3
< 0.1%
20270419 1
 
< 0.1%
20270416 1
 
< 0.1%
20270415 1
 
< 0.1%

폐쇄일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3119
Distinct (%)37.0%
Missing1568
Missing (%)15.7%
Infinite0
Infinite (%)0.0%
Mean20141411
Minimum20060920
Maximum20240503
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T03:32:38.983025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20060920
5-th percentile20090916
Q120110317
median20130714
Q320170224
95-th percentile20220921
Maximum20240503
Range179583
Interquartile range (IQR)59907

Descriptive statistics

Standard deviation40647.928
Coefficient of variation (CV)0.0020181271
Kurtosis-0.52395393
Mean20141411
Median Absolute Deviation (MAD)29786.5
Skewness0.70459366
Sum1.6983238 × 1011
Variance1.652254 × 109
MonotonicityNot monotonic
2024-05-04T03:32:39.956652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20091116 214
 
2.1%
20100927 78
 
0.8%
20170124 25
 
0.2%
20101213 24
 
0.2%
20160725 19
 
0.2%
20110425 18
 
0.2%
20110420 17
 
0.2%
20110729 16
 
0.2%
20100913 16
 
0.2%
20120420 15
 
0.1%
Other values (3109) 7990
79.9%
(Missing) 1568
 
15.7%
ValueCountFrequency (%)
20060920 2
 
< 0.1%
20081217 1
 
< 0.1%
20090125 1
 
< 0.1%
20090305 1
 
< 0.1%
20090306 1
 
< 0.1%
20090307 2
 
< 0.1%
20090309 2
 
< 0.1%
20090311 4
< 0.1%
20090312 7
0.1%
20090313 2
 
< 0.1%
ValueCountFrequency (%)
20240503 4
< 0.1%
20240502 1
 
< 0.1%
20240501 2
< 0.1%
20240430 3
< 0.1%
20240429 1
 
< 0.1%
20240425 2
< 0.1%
20240423 3
< 0.1%
20240419 1
 
< 0.1%
20240417 3
< 0.1%
20240416 1
 
< 0.1%

지점설립일자
Text

MISSING 

Distinct3545
Distinct (%)40.6%
Missing1274
Missing (%)12.7%
Memory size156.2 KiB
2024-05-04T03:32:40.982058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters69808
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1347 ?
Unique (%)15.4%

Sample

1st row20100802
2nd row20230804
3rd row20160711
4th row20170320
5th row20100322
ValueCountFrequency (%)
20090820 26
 
0.3%
20090514 21
 
0.2%
20090528 20
 
0.2%
20090511 19
 
0.2%
20090512 18
 
0.2%
20090611 17
 
0.2%
20090520 17
 
0.2%
20140306 17
 
0.2%
20090821 15
 
0.2%
20100329 14
 
0.2%
Other values (3535) 8542
97.9%
2024-05-04T03:32:42.602159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 22628
32.4%
2 15889
22.8%
1 13932
20.0%
3 2831
 
4.1%
9 2728
 
3.9%
7 2617
 
3.7%
6 2411
 
3.5%
5 2334
 
3.3%
4 2238
 
3.2%
8 2194
 
3.1%
Other values (4) 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69802
> 99.9%
Space Separator 3
 
< 0.1%
Lowercase Letter 2
 
< 0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22628
32.4%
2 15889
22.8%
1 13932
20.0%
3 2831
 
4.1%
9 2728
 
3.9%
7 2617
 
3.7%
6 2411
 
3.5%
5 2334
 
3.3%
4 2238
 
3.2%
8 2194
 
3.1%
Lowercase Letter
ValueCountFrequency (%)
a 1
50.0%
r 1
50.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Uppercase Letter
ValueCountFrequency (%)
M 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69805
> 99.9%
Latin 3
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22628
32.4%
2 15889
22.8%
1 13932
20.0%
3 2831
 
4.1%
9 2728
 
3.9%
7 2617
 
3.7%
6 2411
 
3.5%
5 2334
 
3.3%
4 2238
 
3.2%
8 2194
 
3.1%
Latin
ValueCountFrequency (%)
M 1
33.3%
a 1
33.3%
r 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22628
32.4%
2 15889
22.8%
1 13932
20.0%
3 2831
 
4.1%
9 2728
 
3.9%
7 2617
 
3.7%
6 2411
 
3.5%
5 2334
 
3.3%
4 2238
 
3.2%
8 2194
 
3.1%
Other values (4) 6
 
< 0.1%

본점여부
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
본점
9935 
지점
 
65

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 9935
99.4%
지점 65
 
0.7%

Length

2024-05-04T03:32:43.145106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T03:32:43.481011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
본점 9935
99.4%
지점 65
 
0.7%

최근수정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct3145
Distinct (%)31.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20152284
Minimum20090518
Maximum20240503
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T03:32:44.099926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20090518
5-th percentile20091118
Q120110908
median20140730
Q320190218
95-th percentile20231005
Maximum20240503
Range149985
Interquartile range (IQR)79310.25

Descriptive statistics

Standard deviation45762.578
Coefficient of variation (CV)0.0022708383
Kurtosis-1.037607
Mean20152284
Median Absolute Deviation (MAD)30422
Skewness0.46710466
Sum2.0152284 × 1011
Variance2.0942135 × 109
MonotonicityNot monotonic
2024-05-04T03:32:44.725564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20091117 79
 
0.8%
20090609 57
 
0.6%
20091118 55
 
0.5%
20100927 52
 
0.5%
20100330 48
 
0.5%
20130621 44
 
0.4%
20091116 40
 
0.4%
20110425 37
 
0.4%
20091119 34
 
0.3%
20100517 32
 
0.3%
Other values (3135) 9522
95.2%
ValueCountFrequency (%)
20090518 1
 
< 0.1%
20090521 3
 
< 0.1%
20090601 1
 
< 0.1%
20090603 7
 
0.1%
20090604 21
 
0.2%
20090605 4
 
< 0.1%
20090608 4
 
< 0.1%
20090609 57
0.6%
20090610 16
 
0.2%
20090611 25
0.2%
ValueCountFrequency (%)
20240503 8
0.1%
20240502 10
0.1%
20240501 3
 
< 0.1%
20240430 3
 
< 0.1%
20240429 3
 
< 0.1%
20240425 8
0.1%
20240424 5
0.1%
20240423 9
0.1%
20240422 4
 
< 0.1%
20240419 4
 
< 0.1%

Interactions

2024-05-04T03:32:21.689715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:32:16.025276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:32:17.553986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:32:18.898432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:32:20.260186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:32:21.980555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:32:16.325912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:32:17.866314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:32:19.106204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:32:20.553003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:32:22.283059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:32:16.653611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:32:18.111720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:32:19.389871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:32:20.834246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:32:22.572649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:32:16.959872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:32:18.307864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:32:19.675489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:32:21.128479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:32:23.022749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:32:17.251749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:32:18.582755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:32:19.969252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:32:21.402984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T03:32:45.051444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록신청사업영업구분법인여부우편번호등록일자유효기간만료일자폐쇄일자본점여부최근수정일자
등록신청사업1.0000.0700.0290.0000.2160.1510.1660.0000.158
영업구분0.0701.0000.1580.0700.6260.7840.2010.0420.536
법인여부0.0290.1581.0000.0540.3510.2790.2100.2000.357
우편번호0.0000.0700.0541.0000.1790.1820.3770.0420.274
등록일자0.2160.6260.3510.1791.0000.9870.8600.0920.937
유효기간만료일자0.1510.7840.2790.1820.9871.0000.8320.0790.926
폐쇄일자0.1660.2010.2100.3770.8600.8321.0000.0490.960
본점여부0.0000.0420.2000.0420.0920.0790.0491.0000.100
최근수정일자0.1580.5360.3570.2740.9370.9260.9600.1001.000
2024-05-04T03:32:45.503750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록신청사업법인여부영업구분본점여부
등록신청사업1.0000.0180.0860.000
법인여부0.0181.0000.1930.128
영업구분0.0860.1931.0000.051
본점여부0.0000.1280.0511.000
2024-05-04T03:32:45.941222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호등록일자유효기간만료일자폐쇄일자최근수정일자등록신청사업영업구분법인여부본점여부
우편번호1.0000.0340.0340.0590.0450.0000.1000.0280.028
등록일자0.0341.0000.9950.9600.9640.1650.4250.2690.070
유효기간만료일자0.0340.9951.0000.9650.9660.1160.4400.2140.060
폐쇄일자0.0590.9600.9651.0000.9900.1660.1160.2100.048
최근수정일자0.0450.9640.9660.9901.0000.1210.3460.2740.077
등록신청사업0.0000.1650.1160.1660.1211.0000.0860.0180.000
영업구분0.1000.4250.4400.1160.3460.0861.0000.1930.051
법인여부0.0280.2690.2140.2100.2740.0180.1931.0000.128
본점여부0.0280.0700.0600.0480.0770.0000.0510.1281.000

Missing values

2024-05-04T03:32:23.385542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T03:32:23.777235image/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.
2024-05-04T03:32:24.251702image/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
21413대부업폐업2010-서울강북-0019조은대부개인64049136 01039939136서울특별시 강북구 수유동 2-2 202호<NA>14207020100802<NA>2012061920100802본점20120619
2048대부중개업영업중2023-서울강서-0033(대부중개업)365일24시오케이대부중개개인<NA>서울특별시 강서구 공항동 60번지 118호 대성빌딩서울특별시 강서구 송정로 51, 대성빌딩 402-26호 (공항동)<NA>2023080420260804<NA>20230804본점20230804
11033대부중개업폐업2016-서울도봉-0053(대부중개업)OK론대부중개개인02-338-0789서울특별시 도봉구 창동 650번지 58호 -201서울특별시 도봉구 도봉로 452, 201호 (창동)<NA>20160711201907112017020920160711본점20170209
6950대부업폐업2017-서울마포-0016(대부업)리셋대부개인<NA>서울특별시 마포구 도화동 251번지 1호 근신빌딩 별관-304서울특별시 마포구 삼개로 20, 근신빌딩 별관 304호 (도화동)<NA>20170320202003202020012320170320본점20200202
19303대부업유효기간만료2010-서울서초-0043(대부업)우리건설캐피탈대부개인025986988서울특별시 서초구 서초동 1355번지 8호 중앙로얄오피스텔 810호<NA>13707020100323201303232013032420100322본점20130326
27207대부중개업<NA><NA>K파트너스 대부중개개인<NA>서울특별시 도봉구 방학동 674번지 16호 2층<NA><NA>20091013<NA>2010082720091013본점20100827
22802대부중개업폐업2011-서울은평-00114(대부중개업)그루스대부중개개인02 358 6073서울특별시 은평구 대조동 15번지 91호<NA>12203020110509201405092011122820110509본점20111228
19392대부업폐업2012-서울은평-00228(대부업)든든대부개인<NA>서울특별시 은평구 갈현동 400번지 20호 -302<NA>12280920120723201507232013031520120723본점20130315
28137대부업<NA>2009-서울특별시-01529(대부업)현주대부개인0236633340서울특별시 강서구 등촌동 640번지 보람아파트 101-607<NA><NA>20090708<NA>2010012120090708본점20100517
15914대부업타시군구이관2014-서울서대문-00003(대부업)한필상사대부개인<NA>서울특별시 서대문구 북가좌동 307번지 4호서울특별시 서대문구 응암로 63 (북가좌동)12081420140410201704102014072120140410본점20140721
등록신청사업영업구분등록증번호상호법인여부사업장 전화번호소재지소재지(도로명)우편번호등록일자유효기간만료일자폐쇄일자지점설립일자본점여부최근수정일자
11072대부중개업폐업2015-서울강동-00016대한투자대부중개개인02-3436-8144서울특별시 강동구 성내동 460번지 1호 현대제이드-407서울특별시 강동구 강동대로 155, 407호 (성내동, 현대제이드)13403120150323201803232017020320150323본점20170203
10657대부업유효기간만료2014-서울양천-00009(대부업)나라기획대부개인<NA>서울특별시 양천구 신정동 1012번지 1호 진성주택-201서울특별시 양천구 오목로42길 29, 201호 (신정동, 진성주택)15886020140314201703142017031520140314본점20170511
29804대부업<NA>2007-서울특별시-01522(대부업)명성기획개인028932074서울특별시 금천구 시흥동 824-22<NA><NA>20071211<NA>2009111620071204본점20091118
25572대부업<NA>2008-서울특별시-01141(대부업)태화대부업개인028381284/0서울특별시 구로구 신도림동 642 대림2차 403-2202호<NA><NA>20080130<NA>2011013120080123본점20110216
30339<NA><NA>2008-서울특별시-01553우리대부업개인<NA>서울특별시 마포구 동교동 113-7 미래오피스텔 112호<NA>12120020080417<NA>20090910<NA>본점20090911
27297대부업<NA>2008-서울특별시-00124(대부업)뉴-미래기획개인027775277서울특별시 중구 다동 117 라전빌딩 801호<NA><NA>20080814<NA>20100813<NA>본점20100813
3276대부중개업폐업2022-서울금천-0027(대부중개업)원터치대부중개개인1533-9815서울특별시 금천구 가산동 470번지 5호 에이스테크노타워10차서울특별시 금천구 가산디지털1로 196, 에이스테크노타워10차 608호 (가산동)<NA>20220704202507032023012720220704본점20230127
16164대부업유효기간만료2010-서울양천-00078(대부업)나누미대부개인02 2643 1351서울특별시 양천구 신정동 900번지 1호 3층서울특별시 양천구 목동로25길 1 (신정동,3층)15807020101224201312242013122520101224본점20140527
7107대부업유효기간만료2013-서울광진-0053(대부업)예스금융대부(주)법인070-7522-6455서울특별시 광진구 구의동 220번지 30호서울특별시 광진구 구의로 18, 201호 (구의동)<NA>20161122201911222019120520131223본점20191205
25448대부중개업<NA>2011-서울동대문-00090(대부중개업)119머니캐피탈대부중개개인1644-5059서울특별시 동대문구 장안동 418번지 6호<NA>13010020110222201402222011030220110222본점20110302

Duplicate rows

Most frequently occurring

등록신청사업영업구분등록증번호상호법인여부사업장 전화번호소재지소재지(도로명)우편번호등록일자유효기간만료일자폐쇄일자지점설립일자본점여부최근수정일자# duplicates
0대부업폐업2006-서울특별시-00394(대부업)JSM캐피탈개인0222348157서울특별시 중구 신당동 236번지 89호<NA><NA>20090818201208182006092020060907본점201201172
1대부중개업타시군구이관2013-서울광진-0050(대부중개)ONE PLUS대부중개개인02-2201-8863서울특별시 광진구 자양동 769번지 10호 Y타워-917<NA>14385320130828201608282014032420130828본점201403242
2대부중개업<NA>2009-서울특별시-02581(대부중개업)브이지에프대부중개개인<NA>서울특별시 마포구 도화동 250번지 4호 근신빌딩 본관 5층 505호<NA>12170220091111<NA>2010040520091111본점201007012