Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells19104
Missing cells (%)12.7%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory1.3 MiB
Average record size in memory133.0 B

Variable types

Categorical4
Text6
Numeric5

Dataset

Description등록신청사업,영업구분,등록증번호,상호,법인여부,사업장 전화번호,소재지,소재지(도로명),우편번호,등록일자,유효기간만료일자,폐쇄일자,지점설립일자,본점여부,최근수정일자
Author노원구
URLhttps://data.seoul.go.kr/dataList/OA-11020/S/1/datasetView.do

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
등록일자 is highly overall correlated with 유효기간만료일자 and 2 other fieldsHigh correlation
유효기간만료일자 is highly overall correlated with 등록일자 and 2 other fieldsHigh correlation
폐쇄일자 is highly overall correlated with 등록일자 and 2 other fieldsHigh correlation
최근수정일자 is highly overall correlated with 등록일자 and 2 other fieldsHigh correlation
본점여부 is highly imbalanced (94.1%)Imbalance
등록증번호 has 157 (1.6%) missing valuesMissing
사업장 전화번호 has 3378 (33.8%) missing valuesMissing
소재지 has 291 (2.9%) missing valuesMissing
소재지(도로명) has 4784 (47.8%) missing valuesMissing
우편번호 has 5636 (56.4%) missing valuesMissing
유효기간만료일자 has 2018 (20.2%) missing valuesMissing
폐쇄일자 has 1638 (16.4%) missing valuesMissing
지점설립일자 has 1202 (12.0%) missing valuesMissing

Reproduction

Analysis started2024-05-11 00:46:44.212407
Analysis finished2024-05-11 00:47:01.541407
Duration17.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대부업
6216 
대부중개업
3375 
<NA>
 
409

Length

Max length5
Median length3
Mean length3.7159
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대부업 6216
62.2%
대부중개업 3375
33.8%
<NA> 409
 
4.1%

Length

2024-05-11T00:47:01.786505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T00:47:02.127286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대부업 6216
62.2%
대부중개업 3375
33.8%
na 409
 
4.1%

영업구분
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
3741 
<NA>
2862 
타시군구이관
1186 
유효기간만료
839 
영업중
809 
Other values (2)
563 

Length

Max length6
Median length4
Mean length3.5763
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 3741
37.4%
<NA> 2862
28.6%
타시군구이관 1186
 
11.9%
유효기간만료 839
 
8.4%
영업중 809
 
8.1%
직권취소 561
 
5.6%
갱신등록불가 2
 
< 0.1%

Length

2024-05-11T00:47:02.451123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T00:47:02.802150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 3741
37.4%
na 2862
28.6%
타시군구이관 1186
 
11.9%
유효기간만료 839
 
8.4%
영업중 809
 
8.1%
직권취소 561
 
5.6%
갱신등록불가 2
 
< 0.1%

등록증번호
Text

MISSING 

Distinct9792
Distinct (%)99.5%
Missing157
Missing (%)1.6%
Memory size156.2 KiB
2024-05-11T00:47:03.511082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length26
Mean length19.519862
Min length1

Characters and Unicode

Total characters192134
Distinct characters75
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

Unique9741 ?
Unique (%)99.0%

Sample

1st row2018-서울종로-00040(대부중개업)
2nd row2008-서울특별시-03083
3rd row2016-서울관악-00021(대부중개업)
4th row2008-서울특별시-02402(대부업)
5th row2022-서울강남-0215(대부중개업)
ValueCountFrequency (%)
2011-서울특별시 20
 
0.2%
2013-서울특별시 16
 
0.2%
2010-서울 16
 
0.2%
2012-서울특별시 14
 
0.1%
2016-서울특별시 10
 
0.1%
2015-서울특별시 9
 
0.1%
대부업 8
 
0.1%
성북구-00006 7
 
0.1%
2018-서울특별시 7
 
0.1%
2017-서울특별시 7
 
0.1%
Other values (9757) 9888
98.9%
2024-05-11T00:47:04.580179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 33900
17.6%
- 19670
 
10.2%
2 15752
 
8.2%
1 11935
 
6.2%
10917
 
5.7%
9820
 
5.1%
8516
 
4.4%
( 8247
 
4.3%
8206
 
4.3%
) 8196
 
4.3%
Other values (65) 56975
29.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 82741
43.1%
Other Letter 73121
38.1%
Dash Punctuation 19670
 
10.2%
Open Punctuation 8247
 
4.3%
Close Punctuation 8196
 
4.3%
Space Separator 159
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10917
14.9%
9820
13.4%
8516
11.6%
8206
11.2%
7962
10.9%
3550
 
4.9%
2905
 
4.0%
2490
 
3.4%
2479
 
3.4%
2479
 
3.4%
Other values (51) 13797
18.9%
Decimal Number
ValueCountFrequency (%)
0 33900
41.0%
2 15752
19.0%
1 11935
 
14.4%
3 3730
 
4.5%
8 3134
 
3.8%
4 3026
 
3.7%
9 2865
 
3.5%
6 2834
 
3.4%
5 2801
 
3.4%
7 2764
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 19670
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8247
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8196
100.0%
Space Separator
ValueCountFrequency (%)
159
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 119013
61.9%
Hangul 73121
38.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10917
14.9%
9820
13.4%
8516
11.6%
8206
11.2%
7962
10.9%
3550
 
4.9%
2905
 
4.0%
2490
 
3.4%
2479
 
3.4%
2479
 
3.4%
Other values (51) 13797
18.9%
Common
ValueCountFrequency (%)
0 33900
28.5%
- 19670
16.5%
2 15752
13.2%
1 11935
 
10.0%
( 8247
 
6.9%
) 8196
 
6.9%
3 3730
 
3.1%
8 3134
 
2.6%
4 3026
 
2.5%
9 2865
 
2.4%
Other values (4) 8558
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119013
61.9%
Hangul 73121
38.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33900
28.5%
- 19670
16.5%
2 15752
13.2%
1 11935
 
10.0%
( 8247
 
6.9%
) 8196
 
6.9%
3 3730
 
3.1%
8 3134
 
2.6%
4 3026
 
2.5%
9 2865
 
2.4%
Other values (4) 8558
 
7.2%
Hangul
ValueCountFrequency (%)
10917
14.9%
9820
13.4%
8516
11.6%
8206
11.2%
7962
10.9%
3550
 
4.9%
2905
 
4.0%
2490
 
3.4%
2479
 
3.4%
2479
 
3.4%
Other values (51) 13797
18.9%

상호
Text

Distinct8679
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T00:47:05.374670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length30
Mean length7.7639
Min length1

Characters and Unicode

Total characters77639
Distinct characters773
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

Unique7618 ?
Unique (%)76.2%

Sample

1st row한국대부중개 주식회사
2nd row대한
3rd row대출마트대부중개
4th row(주) 청도그린
5th row대출톡대부중개
ValueCountFrequency (%)
주식회사 820
 
6.9%
대부중개 333
 
2.8%
대부 301
 
2.5%
유한회사 57
 
0.5%
대부업 19
 
0.2%
캐피탈 15
 
0.1%
13
 
0.1%
전당포대부 13
 
0.1%
전당포 12
 
0.1%
money 11
 
0.1%
Other values (8682) 10349
86.7%
2024-05-11T00:47:06.850642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8532
 
11.0%
8168
 
10.5%
2768
 
3.6%
2241
 
2.9%
2080
 
2.7%
2062
 
2.7%
1949
 
2.5%
) 1939
 
2.5%
( 1930
 
2.5%
1905
 
2.5%
Other values (763) 44065
56.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 67943
87.5%
Uppercase Letter 2255
 
2.9%
Space Separator 1949
 
2.5%
Close Punctuation 1939
 
2.5%
Open Punctuation 1930
 
2.5%
Lowercase Letter 1099
 
1.4%
Decimal Number 261
 
0.3%
Other Punctuation 226
 
0.3%
Dash Punctuation 26
 
< 0.1%
Other Symbol 7
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
8532
 
12.6%
8168
 
12.0%
2768
 
4.1%
2241
 
3.3%
2080
 
3.1%
2062
 
3.0%
1905
 
2.8%
1349
 
2.0%
1139
 
1.7%
1033
 
1.5%
Other values (689) 36666
54.0%
Uppercase Letter
ValueCountFrequency (%)
S 280
 
12.4%
K 190
 
8.4%
M 180
 
8.0%
J 179
 
7.9%
C 176
 
7.8%
H 129
 
5.7%
A 100
 
4.4%
B 98
 
4.3%
L 91
 
4.0%
G 89
 
3.9%
Other values (16) 743
32.9%
Lowercase Letter
ValueCountFrequency (%)
e 141
12.8%
n 122
11.1%
o 114
10.4%
a 112
10.2%
t 70
 
6.4%
s 67
 
6.1%
i 62
 
5.6%
r 53
 
4.8%
l 49
 
4.5%
c 48
 
4.4%
Other values (14) 261
23.7%
Decimal Number
ValueCountFrequency (%)
1 84
32.2%
2 47
18.0%
4 41
15.7%
9 21
 
8.0%
3 15
 
5.7%
5 15
 
5.7%
8 12
 
4.6%
6 11
 
4.2%
7 10
 
3.8%
0 5
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 119
52.7%
& 93
41.2%
, 6
 
2.7%
? 5
 
2.2%
* 1
 
0.4%
1
 
0.4%
1
 
0.4%
Space Separator
ValueCountFrequency (%)
1949
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1939
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1930
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%
Other Symbol
ValueCountFrequency (%)
7
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 67940
87.5%
Common 6334
 
8.2%
Latin 3355
 
4.3%
Han 10
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
8532
 
12.6%
8168
 
12.0%
2768
 
4.1%
2241
 
3.3%
2080
 
3.1%
2062
 
3.0%
1905
 
2.8%
1349
 
2.0%
1139
 
1.7%
1033
 
1.5%
Other values (680) 36663
54.0%
Latin
ValueCountFrequency (%)
S 280
 
8.3%
K 190
 
5.7%
M 180
 
5.4%
J 179
 
5.3%
C 176
 
5.2%
e 141
 
4.2%
H 129
 
3.8%
n 122
 
3.6%
o 114
 
3.4%
a 112
 
3.3%
Other values (41) 1732
51.6%
Common
ValueCountFrequency (%)
1949
30.8%
) 1939
30.6%
( 1930
30.5%
. 119
 
1.9%
& 93
 
1.5%
1 84
 
1.3%
2 47
 
0.7%
4 41
 
0.6%
- 26
 
0.4%
9 21
 
0.3%
Other values (12) 85
 
1.3%
Han
ValueCountFrequency (%)
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 67932
87.5%
ASCII 9686
 
12.5%
CJK 10
 
< 0.1%
None 9
 
< 0.1%
Number Forms 1
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
8532
 
12.6%
8168
 
12.0%
2768
 
4.1%
2241
 
3.3%
2080
 
3.1%
2062
 
3.0%
1905
 
2.8%
1349
 
2.0%
1139
 
1.7%
1033
 
1.5%
Other values (678) 36655
54.0%
ASCII
ValueCountFrequency (%)
1949
20.1%
) 1939
20.0%
( 1930
19.9%
S 280
 
2.9%
K 190
 
2.0%
M 180
 
1.9%
J 179
 
1.8%
C 176
 
1.8%
e 141
 
1.5%
H 129
 
1.3%
Other values (60) 2593
26.8%
None
ValueCountFrequency (%)
7
77.8%
1
 
11.1%
1
 
11.1%
CJK
ValueCountFrequency (%)
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
Number Forms
ValueCountFrequency (%)
1
100.0%
Compat Jamo
ValueCountFrequency (%)
1
100.0%

법인여부
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
개인
7125 
법인
2875 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
개인 7125
71.2%
법인 2875
28.7%

Length

2024-05-11T00:47:07.308653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T00:47:07.593278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 7125
71.2%
법인 2875
28.7%
Distinct5819
Distinct (%)87.9%
Missing3378
Missing (%)33.8%
Memory size156.2 KiB
2024-05-11T00:47:08.285641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length38
Mean length10.608879
Min length1

Characters and Unicode

Total characters70252
Distinct characters24
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5174 ?
Unique (%)78.1%

Sample

1st row070-4405-0055
2nd row02-6082-6155
3rd row0222156690
4th row02-540-6268
5th row02-425-3690
ValueCountFrequency (%)
02 286
 
3.8%
56
 
0.8%
070 39
 
0.5%
010 12
 
0.2%
432 7
 
0.1%
1644 7
 
0.1%
1544 6
 
0.1%
1566 6
 
0.1%
02-543-1666 6
 
0.1%
1688 5
 
0.1%
Other values (6129) 7005
94.2%
2024-05-11T00:47:09.729343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11258
16.0%
2 10158
14.5%
- 7263
10.3%
5 6008
8.6%
7 5446
7.8%
1 5225
7.4%
6 5058
7.2%
4 4919
7.0%
3 4883
7.0%
8 4837
6.9%
Other values (14) 5197
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 61800
88.0%
Dash Punctuation 7263
 
10.3%
Space Separator 910
 
1.3%
Other Punctuation 148
 
0.2%
Close Punctuation 64
 
0.1%
Math Symbol 31
 
< 0.1%
Open Punctuation 25
 
< 0.1%
Other Letter 11
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11258
18.2%
2 10158
16.4%
5 6008
9.7%
7 5446
8.8%
1 5225
8.5%
6 5058
8.2%
4 4919
8.0%
3 4883
7.9%
8 4837
7.8%
9 4008
 
6.5%
Other Letter
ValueCountFrequency (%)
3
27.3%
3
27.3%
3
27.3%
1
 
9.1%
1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
* 85
57.4%
/ 43
29.1%
. 20
 
13.5%
Math Symbol
ValueCountFrequency (%)
~ 30
96.8%
× 1
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 7263
100.0%
Space Separator
ValueCountFrequency (%)
910
100.0%
Close Punctuation
ValueCountFrequency (%)
) 64
100.0%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70241
> 99.9%
Hangul 11
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11258
16.0%
2 10158
14.5%
- 7263
10.3%
5 6008
8.6%
7 5446
7.8%
1 5225
7.4%
6 5058
7.2%
4 4919
7.0%
3 4883
7.0%
8 4837
6.9%
Other values (9) 5186
7.4%
Hangul
ValueCountFrequency (%)
3
27.3%
3
27.3%
3
27.3%
1
 
9.1%
1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70240
> 99.9%
Hangul 11
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11258
16.0%
2 10158
14.5%
- 7263
10.3%
5 6008
8.6%
7 5446
7.8%
1 5225
7.4%
6 5058
7.2%
4 4919
7.0%
3 4883
7.0%
8 4837
6.9%
Other values (8) 5185
7.4%
Hangul
ValueCountFrequency (%)
3
27.3%
3
27.3%
3
27.3%
1
 
9.1%
1
 
9.1%
None
ValueCountFrequency (%)
× 1
100.0%

소재지
Text

MISSING 

Distinct8612
Distinct (%)88.7%
Missing291
Missing (%)2.9%
Memory size156.2 KiB
2024-05-11T00:47:10.784281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length49
Mean length31.529715
Min length15

Characters and Unicode

Total characters306122
Distinct characters621
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

Unique7839 ?
Unique (%)80.7%

Sample

1st row서울특별시 종로구 내수동 75번지 용비어천가-1404
2nd row서울특별시 노원구 상계동 751-1 주공아파트 405동 504호
3rd row서울특별시 관악구 신림동 1666번지 56호 -204
4th row서울특별시 동대문구 전농동 652-1 2층
5th row서울특별시 강남구 논현동 187번지 5호
ValueCountFrequency (%)
서울특별시 9705
 
16.9%
강남구 1651
 
2.9%
서초구 952
 
1.7%
1호 753
 
1.3%
역삼동 709
 
1.2%
송파구 605
 
1.1%
서초동 577
 
1.0%
중구 552
 
1.0%
영등포구 461
 
0.8%
2호 453
 
0.8%
Other values (9424) 40939
71.4%
2024-05-11T00:47:12.759312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
67959
22.2%
1 13563
 
4.4%
12080
 
3.9%
11143
 
3.6%
10512
 
3.4%
9958
 
3.3%
9750
 
3.2%
9716
 
3.2%
9706
 
3.2%
2 8871
 
2.9%
Other values (611) 142864
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 167385
54.7%
Space Separator 67959
22.2%
Decimal Number 63545
 
20.8%
Dash Punctuation 5449
 
1.8%
Uppercase Letter 1195
 
0.4%
Other Punctuation 239
 
0.1%
Lowercase Letter 113
 
< 0.1%
Close Punctuation 104
 
< 0.1%
Open Punctuation 101
 
< 0.1%
Letter Number 22
 
< 0.1%
Other values (2) 10
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
12080
 
7.2%
11143
 
6.7%
10512
 
6.3%
9958
 
5.9%
9750
 
5.8%
9716
 
5.8%
9706
 
5.8%
8735
 
5.2%
8469
 
5.1%
7987
 
4.8%
Other values (534) 69329
41.4%
Uppercase Letter
ValueCountFrequency (%)
B 248
20.8%
A 218
18.2%
S 91
 
7.6%
D 73
 
6.1%
K 61
 
5.1%
T 57
 
4.8%
C 52
 
4.4%
L 51
 
4.3%
I 49
 
4.1%
E 41
 
3.4%
Other values (16) 254
21.3%
Lowercase Letter
ValueCountFrequency (%)
e 25
22.1%
r 11
9.7%
n 10
 
8.8%
t 8
 
7.1%
o 8
 
7.1%
i 7
 
6.2%
w 6
 
5.3%
c 6
 
5.3%
k 5
 
4.4%
s 5
 
4.4%
Other values (13) 22
19.5%
Decimal Number
ValueCountFrequency (%)
1 13563
21.3%
2 8871
14.0%
0 7983
12.6%
3 7016
11.0%
4 5802
9.1%
5 4990
 
7.9%
6 4460
 
7.0%
7 4097
 
6.4%
8 3396
 
5.3%
9 3367
 
5.3%
Other Punctuation
ValueCountFrequency (%)
, 94
39.3%
/ 74
31.0%
. 63
26.4%
2
 
0.8%
; 2
 
0.8%
* 1
 
0.4%
@ 1
 
0.4%
# 1
 
0.4%
& 1
 
0.4%
Letter Number
ValueCountFrequency (%)
16
72.7%
4
 
18.2%
2
 
9.1%
Space Separator
ValueCountFrequency (%)
67959
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5449
100.0%
Close Punctuation
ValueCountFrequency (%)
) 104
100.0%
Open Punctuation
ValueCountFrequency (%)
( 101
100.0%
Math Symbol
ValueCountFrequency (%)
~ 9
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 167385
54.7%
Common 137406
44.9%
Latin 1330
 
0.4%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
12080
 
7.2%
11143
 
6.7%
10512
 
6.3%
9958
 
5.9%
9750
 
5.8%
9716
 
5.8%
9706
 
5.8%
8735
 
5.2%
8469
 
5.1%
7987
 
4.8%
Other values (534) 69329
41.4%
Latin
ValueCountFrequency (%)
B 248
18.6%
A 218
16.4%
S 91
 
6.8%
D 73
 
5.5%
K 61
 
4.6%
T 57
 
4.3%
C 52
 
3.9%
L 51
 
3.8%
I 49
 
3.7%
E 41
 
3.1%
Other values (42) 389
29.2%
Common
ValueCountFrequency (%)
67959
49.5%
1 13563
 
9.9%
2 8871
 
6.5%
0 7983
 
5.8%
3 7016
 
5.1%
4 5802
 
4.2%
- 5449
 
4.0%
5 4990
 
3.6%
6 4460
 
3.2%
7 4097
 
3.0%
Other values (14) 7216
 
5.3%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 167384
54.7%
ASCII 138712
45.3%
Number Forms 22
 
< 0.1%
None 3
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
67959
49.0%
1 13563
 
9.8%
2 8871
 
6.4%
0 7983
 
5.8%
3 7016
 
5.1%
4 5802
 
4.2%
- 5449
 
3.9%
5 4990
 
3.6%
6 4460
 
3.2%
7 4097
 
3.0%
Other values (62) 8522
 
6.1%
Hangul
ValueCountFrequency (%)
12080
 
7.2%
11143
 
6.7%
10512
 
6.3%
9958
 
5.9%
9750
 
5.8%
9716
 
5.8%
9706
 
5.8%
8735
 
5.2%
8469
 
5.1%
7987
 
4.8%
Other values (533) 69328
41.4%
Number Forms
ValueCountFrequency (%)
16
72.7%
4
 
18.2%
2
 
9.1%
None
ValueCountFrequency (%)
2
66.7%
1
33.3%
CJK
ValueCountFrequency (%)
1
100.0%

소재지(도로명)
Text

MISSING 

Distinct4738
Distinct (%)90.8%
Missing4784
Missing (%)47.8%
Memory size156.2 KiB
2024-05-11T00:47:14.011102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length66
Median length54
Mean length37.193827
Min length19

Characters and Unicode

Total characters194003
Distinct characters602
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

Unique4320 ?
Unique (%)82.8%

Sample

1st row서울특별시 종로구 새문안로3길 36, 14층 1404호 (내수동, 용비어천가)
2nd row서울특별시 관악구 시흥대로 536-1, 204호 (신림동, 대동빌딩)
3rd row서울특별시 강남구 강남대로118길 64, 201호 (논현동)
4th row서울특별시 송파구 오금로11길 55, 2동 216호 (방이동, 현대주상복합빌딩)
5th row서울특별시 서초구 반포대로20길 61, 지하1층 (서초동, 동성빌딩)
ValueCountFrequency (%)
서울특별시 5214
 
14.1%
강남구 966
 
2.6%
서초구 587
 
1.6%
2층 455
 
1.2%
역삼동 409
 
1.1%
서초동 387
 
1.0%
3층 387
 
1.0%
송파구 339
 
0.9%
4층 316
 
0.9%
영등포구 310
 
0.8%
Other values (6492) 27579
74.6%
2024-05-11T00:47:15.788402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31759
 
16.4%
1 7399
 
3.8%
, 7157
 
3.7%
6936
 
3.6%
6814
 
3.5%
5764
 
3.0%
5739
 
3.0%
2 5451
 
2.8%
5431
 
2.8%
5266
 
2.7%
Other values (592) 106287
54.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 107998
55.7%
Decimal Number 34471
 
17.8%
Space Separator 31759
 
16.4%
Other Punctuation 7174
 
3.7%
Close Punctuation 5252
 
2.7%
Open Punctuation 5251
 
2.7%
Dash Punctuation 1022
 
0.5%
Uppercase Letter 939
 
0.5%
Lowercase Letter 108
 
0.1%
Letter Number 22
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6936
 
6.4%
6814
 
6.3%
5764
 
5.3%
5739
 
5.3%
5431
 
5.0%
5266
 
4.9%
5222
 
4.8%
5215
 
4.8%
4230
 
3.9%
2689
 
2.5%
Other values (520) 54692
50.6%
Uppercase Letter
ValueCountFrequency (%)
B 168
17.9%
A 125
13.3%
S 79
 
8.4%
T 56
 
6.0%
L 53
 
5.6%
C 50
 
5.3%
E 50
 
5.3%
K 47
 
5.0%
I 42
 
4.5%
G 36
 
3.8%
Other values (16) 233
24.8%
Lowercase Letter
ValueCountFrequency (%)
e 20
18.5%
r 13
12.0%
n 10
9.3%
t 9
8.3%
o 8
 
7.4%
c 7
 
6.5%
w 7
 
6.5%
i 7
 
6.5%
b 5
 
4.6%
s 4
 
3.7%
Other values (10) 18
16.7%
Decimal Number
ValueCountFrequency (%)
1 7399
21.5%
2 5451
15.8%
0 4406
12.8%
3 4125
12.0%
4 2903
 
8.4%
5 2700
 
7.8%
6 2223
 
6.4%
7 1874
 
5.4%
8 1784
 
5.2%
9 1606
 
4.7%
Other Punctuation
ValueCountFrequency (%)
, 7157
99.8%
. 6
 
0.1%
/ 4
 
0.1%
# 2
 
< 0.1%
@ 2
 
< 0.1%
& 1
 
< 0.1%
1
 
< 0.1%
? 1
 
< 0.1%
Letter Number
ValueCountFrequency (%)
15
68.2%
4
 
18.2%
3
 
13.6%
Space Separator
ValueCountFrequency (%)
31759
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5252
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5251
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1022
100.0%
Math Symbol
ValueCountFrequency (%)
~ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 107997
55.7%
Common 84936
43.8%
Latin 1069
 
0.6%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
6936
 
6.4%
6814
 
6.3%
5764
 
5.3%
5739
 
5.3%
5431
 
5.0%
5266
 
4.9%
5222
 
4.8%
5215
 
4.8%
4230
 
3.9%
2689
 
2.5%
Other values (519) 54691
50.6%
Latin
ValueCountFrequency (%)
B 168
15.7%
A 125
 
11.7%
S 79
 
7.4%
T 56
 
5.2%
L 53
 
5.0%
C 50
 
4.7%
E 50
 
4.7%
K 47
 
4.4%
I 42
 
3.9%
G 36
 
3.4%
Other values (39) 363
34.0%
Common
ValueCountFrequency (%)
31759
37.4%
1 7399
 
8.7%
, 7157
 
8.4%
2 5451
 
6.4%
) 5252
 
6.2%
( 5251
 
6.2%
0 4406
 
5.2%
3 4125
 
4.9%
4 2903
 
3.4%
5 2700
 
3.2%
Other values (13) 8533
 
10.0%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 107997
55.7%
ASCII 85982
44.3%
Number Forms 22
 
< 0.1%
None 1
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31759
36.9%
1 7399
 
8.6%
, 7157
 
8.3%
2 5451
 
6.3%
) 5252
 
6.1%
( 5251
 
6.1%
0 4406
 
5.1%
3 4125
 
4.8%
4 2903
 
3.4%
5 2700
 
3.1%
Other values (58) 9579
 
11.1%
Hangul
ValueCountFrequency (%)
6936
 
6.4%
6814
 
6.3%
5764
 
5.3%
5739
 
5.3%
5431
 
5.0%
5266
 
4.9%
5222
 
4.8%
5215
 
4.8%
4230
 
3.9%
2689
 
2.5%
Other values (519) 54691
50.6%
Number Forms
ValueCountFrequency (%)
15
68.2%
4
 
18.2%
3
 
13.6%
None
ValueCountFrequency (%)
1
100.0%
CJK
ValueCountFrequency (%)
1
100.0%

우편번호
Real number (ℝ)

MISSING 

Distinct1383
Distinct (%)31.7%
Missing5636
Missing (%)56.4%
Infinite0
Infinite (%)0.0%
Mean136324.59
Minimum2519
Maximum429842
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T00:47:16.325192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2519
5-th percentile100862.3
Q1132020
median136033
Q3143190
95-th percentile157030
Maximum429842
Range427323
Interquartile range (IQR)11170

Descriptive statistics

Standard deviation15568.67
Coefficient of variation (CV)0.11420294
Kurtosis54.186164
Mean136324.59
Median Absolute Deviation (MAD)5191
Skewness1.5063622
Sum5.9492053 × 108
Variance2.4238349 × 108
MonotonicityNot monotonic
2024-05-11T00:47:16.933446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135080 171
 
1.7%
137070 129
 
1.3%
157010 77
 
0.8%
135010 71
 
0.7%
151015 54
 
0.5%
158070 48
 
0.5%
152050 47
 
0.5%
142070 45
 
0.4%
151050 41
 
0.4%
135090 39
 
0.4%
Other values (1373) 3642
36.4%
(Missing) 5636
56.4%
ValueCountFrequency (%)
2519 1
 
< 0.1%
4526 1
 
< 0.1%
4534 1
 
< 0.1%
4550 1
 
< 0.1%
4554 1
 
< 0.1%
100011 5
0.1%
100012 2
 
< 0.1%
100013 3
< 0.1%
100014 1
 
< 0.1%
100015 1
 
< 0.1%
ValueCountFrequency (%)
429842 1
 
< 0.1%
403866 1
 
< 0.1%
158877 1
 
< 0.1%
158871 1
 
< 0.1%
158865 1
 
< 0.1%
158864 2
< 0.1%
158863 1
 
< 0.1%
158860 3
< 0.1%
158859 2
< 0.1%
158858 1
 
< 0.1%

등록일자
Real number (ℝ)

HIGH CORRELATION 

Distinct3522
Distinct (%)35.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20136638
Minimum20051216
Maximum20240510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T00:47:17.669552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20051216
5-th percentile20070810
Q120091210
median20130314
Q320170704
95-th percentile20230130
Maximum20240510
Range189294
Interquartile range (IQR)79494.25

Descriptive statistics

Standard deviation48590.432
Coefficient of variation (CV)0.0024130359
Kurtosis-0.88504303
Mean20136638
Median Absolute Deviation (MAD)39486.5
Skewness0.46399443
Sum2.0136638 × 1011
Variance2.36103 × 109
MonotonicityNot monotonic
2024-05-11T00:47:18.151742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20080731 25
 
0.2%
20080814 25
 
0.2%
20080822 21
 
0.2%
20080818 18
 
0.2%
20081222 17
 
0.2%
20090325 17
 
0.2%
20080926 15
 
0.1%
20160720 15
 
0.1%
20090611 15
 
0.1%
20160718 14
 
0.1%
Other values (3512) 9818
98.2%
ValueCountFrequency (%)
20051216 1
 
< 0.1%
20060124 1
 
< 0.1%
20060127 1
 
< 0.1%
20060306 1
 
< 0.1%
20060320 1
 
< 0.1%
20060323 2
< 0.1%
20060324 2
< 0.1%
20060329 2
< 0.1%
20060405 2
< 0.1%
20060407 3
< 0.1%
ValueCountFrequency (%)
20240510 1
 
< 0.1%
20240507 3
< 0.1%
20240503 3
< 0.1%
20240502 1
 
< 0.1%
20240430 1
 
< 0.1%
20240426 1
 
< 0.1%
20240425 2
 
< 0.1%
20240424 5
0.1%
20240422 2
 
< 0.1%
20240419 2
 
< 0.1%

유효기간만료일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3311
Distinct (%)41.5%
Missing2018
Missing (%)20.2%
Infinite0
Infinite (%)0.0%
Mean20181145
Minimum20080922
Maximum22180428
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T00:47:18.796418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20080922
5-th percentile20120406
Q120141013
median20171222
Q320211203
95-th percentile20260425
Maximum22180428
Range2099506
Interquartile range (IQR)70189.75

Descriptive statistics

Standard deviation49572.026
Coefficient of variation (CV)0.0024563535
Kurtosis330.04728
Mean20181145
Median Absolute Deviation (MAD)30599.5
Skewness8.4519338
Sum1.610859 × 1011
Variance2.4573858 × 109
MonotonicityNot monotonic
2024-05-11T00:47:19.335261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20110831 15
 
0.1%
20190720 15
 
0.1%
20190718 14
 
0.1%
20140721 13
 
0.1%
20140816 13
 
0.1%
20190613 12
 
0.1%
20140720 12
 
0.1%
20140822 11
 
0.1%
20150420 10
 
0.1%
20180105 10
 
0.1%
Other values (3301) 7857
78.6%
(Missing) 2018
 
20.2%
ValueCountFrequency (%)
20080922 1
< 0.1%
20090907 1
< 0.1%
20091116 1
< 0.1%
20100125 1
< 0.1%
20100216 1
< 0.1%
20100308 1
< 0.1%
20100411 1
< 0.1%
20100418 2
< 0.1%
20100419 1
< 0.1%
20100426 1
< 0.1%
ValueCountFrequency (%)
22180428 1
 
< 0.1%
20270510 1
 
< 0.1%
20270507 2
 
< 0.1%
20270506 1
 
< 0.1%
20270503 3
< 0.1%
20270501 1
 
< 0.1%
20270430 1
 
< 0.1%
20270426 1
 
< 0.1%
20270425 2
 
< 0.1%
20270424 5
0.1%

폐쇄일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3107
Distinct (%)37.2%
Missing1638
Missing (%)16.4%
Infinite0
Infinite (%)0.0%
Mean20142060
Minimum20060920
Maximum20240509
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T00:47:19.847779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20060920
5-th percentile20090922
Q120110415
median20130766
Q320170403
95-th percentile20221004
Maximum20240509
Range179589
Interquartile range (IQR)59988

Descriptive statistics

Standard deviation40761.783
Coefficient of variation (CV)0.0020237146
Kurtosis-0.53163997
Mean20142060
Median Absolute Deviation (MAD)29836
Skewness0.69620469
Sum1.6842791 × 1011
Variance1.6615229 × 109
MonotonicityNot monotonic
2024-05-11T00:47:20.588497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20091116 201
 
2.0%
20100927 83
 
0.8%
20101213 24
 
0.2%
20170124 19
 
0.2%
20160725 17
 
0.2%
20110914 17
 
0.2%
20110420 17
 
0.2%
20120420 17
 
0.2%
20110823 16
 
0.2%
20110125 14
 
0.1%
Other values (3097) 7937
79.4%
(Missing) 1638
 
16.4%
ValueCountFrequency (%)
20060920 1
< 0.1%
20071030 1
< 0.1%
20071115 1
< 0.1%
20081217 1
< 0.1%
20090125 1
< 0.1%
20090128 1
< 0.1%
20090211 1
< 0.1%
20090305 2
< 0.1%
20090306 1
< 0.1%
20090307 1
< 0.1%
ValueCountFrequency (%)
20240509 1
 
< 0.1%
20240507 1
 
< 0.1%
20240503 4
< 0.1%
20240502 1
 
< 0.1%
20240501 2
< 0.1%
20240430 1
 
< 0.1%
20240424 2
< 0.1%
20240423 2
< 0.1%
20240422 3
< 0.1%
20240419 3
< 0.1%

지점설립일자
Text

MISSING 

Distinct3558
Distinct (%)40.4%
Missing1202
Missing (%)12.0%
Memory size156.2 KiB
2024-05-11T00:47:21.680667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters70384
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

Unique1377 ?
Unique (%)15.7%

Sample

1st row20181031
2nd row20081128
3rd row20160512
4th row20210823
5th row20230130
ValueCountFrequency (%)
20090520 21
 
0.2%
20090820 19
 
0.2%
20090611 19
 
0.2%
20090528 18
 
0.2%
20160720 18
 
0.2%
20090514 18
 
0.2%
20090507 16
 
0.2%
20090511 13
 
0.1%
20091128 13
 
0.1%
20160718 13
 
0.1%
Other values (3548) 8630
98.1%
2024-05-11T00:47:22.955527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 22833
32.4%
2 15934
22.6%
1 14236
20.2%
3 2798
 
4.0%
9 2687
 
3.8%
7 2615
 
3.7%
6 2446
 
3.5%
5 2387
 
3.4%
8 2245
 
3.2%
4 2197
 
3.1%
Other values (4) 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70378
> 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 22833
32.4%
2 15934
22.6%
1 14236
20.2%
3 2798
 
4.0%
9 2687
 
3.8%
7 2615
 
3.7%
6 2446
 
3.5%
5 2387
 
3.4%
8 2245
 
3.2%
4 2197
 
3.1%
Lowercase Letter
ValueCountFrequency (%)
a 1
50.0%
y 1
50.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Uppercase Letter
ValueCountFrequency (%)
M 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70381
> 99.9%
Latin 3
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22833
32.4%
2 15934
22.6%
1 14236
20.2%
3 2798
 
4.0%
9 2687
 
3.8%
7 2615
 
3.7%
6 2446
 
3.5%
5 2387
 
3.4%
8 2245
 
3.2%
4 2197
 
3.1%
Latin
ValueCountFrequency (%)
M 1
33.3%
a 1
33.3%
y 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70384
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22833
32.4%
2 15934
22.6%
1 14236
20.2%
3 2798
 
4.0%
9 2687
 
3.8%
7 2615
 
3.7%
6 2446
 
3.5%
5 2387
 
3.4%
8 2245
 
3.2%
4 2197
 
3.1%
Other values (4) 6
 
< 0.1%

본점여부
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
본점
9932 
지점
 
68

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 9932
99.3%
지점 68
 
0.7%

Length

2024-05-11T00:47:23.438012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T00:47:23.838371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
본점 9932
99.3%
지점 68
 
0.7%

최근수정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct3169
Distinct (%)31.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20152970
Minimum20090518
Maximum20240510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T00:47:24.235075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20090518
5-th percentile20091118
Q120111024
median20140912
Q320190323
95-th percentile20231013
Maximum20240510
Range149992
Interquartile range (IQR)79299

Descriptive statistics

Standard deviation45590.425
Coefficient of variation (CV)0.0022622186
Kurtosis-1.0458702
Mean20152970
Median Absolute Deviation (MAD)30498.5
Skewness0.44854212
Sum2.015297 × 1011
Variance2.0784869 × 109
MonotonicityNot monotonic
2024-05-11T00:47:24.751432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20091117 76
 
0.8%
20090609 56
 
0.6%
20100927 47
 
0.5%
20091118 45
 
0.4%
20091116 43
 
0.4%
20100330 40
 
0.4%
20130621 33
 
0.3%
20160812 32
 
0.3%
20110425 32
 
0.3%
20100928 30
 
0.3%
Other values (3159) 9566
95.7%
ValueCountFrequency (%)
20090518 2
 
< 0.1%
20090519 1
 
< 0.1%
20090521 4
 
< 0.1%
20090601 3
 
< 0.1%
20090602 2
 
< 0.1%
20090603 10
 
0.1%
20090604 22
 
0.2%
20090605 1
 
< 0.1%
20090608 6
 
0.1%
20090609 56
0.6%
ValueCountFrequency (%)
20240510 5
0.1%
20240509 3
< 0.1%
20240507 6
0.1%
20240503 7
0.1%
20240502 6
0.1%
20240501 6
0.1%
20240430 4
< 0.1%
20240429 2
 
< 0.1%
20240426 1
 
< 0.1%
20240425 5
0.1%

Interactions

2024-05-11T00:46:58.229055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:46:51.942482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:46:53.846542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:46:55.315525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:46:56.913885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:46:58.506613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:46:52.259810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:46:54.116183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:46:55.760173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:46:57.148856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:46:58.827456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:46:52.586760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:46:54.413355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:46:56.050055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:46:57.374681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:46:59.084943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:46:53.091446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:46:54.692050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:46:56.305907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:46:57.647367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:46:59.384123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:46:53.485879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:46:55.004784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:46:56.595812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:46:57.936417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T00:47:25.310932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록신청사업영업구분법인여부우편번호등록일자유효기간만료일자폐쇄일자본점여부최근수정일자
등록신청사업1.0000.1140.0000.0250.2080.0000.2140.0000.159
영업구분0.1141.0000.2820.0710.6370.0000.2180.0560.541
법인여부0.0000.2821.0000.0000.3440.0000.2620.1920.345
우편번호0.0250.0710.0001.0000.246NaN0.3080.0000.254
등록일자0.2080.6370.3440.2461.0000.0000.9390.0860.938
유효기간만료일자0.0000.0000.000NaN0.0001.0000.0000.0000.000
폐쇄일자0.2140.2180.2620.3080.9390.0001.0000.0780.989
본점여부0.0000.0560.1920.0000.0860.0000.0781.0000.100
최근수정일자0.1590.5410.3450.2540.9380.0000.9890.1001.000
2024-05-11T00:47:25.922238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법인여부본점여부등록신청사업영업구분
법인여부1.0000.1230.0000.203
본점여부0.1231.0000.0000.040
등록신청사업0.0000.0001.0000.082
영업구분0.2030.0400.0821.000
2024-05-11T00:47:26.304561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호등록일자유효기간만료일자폐쇄일자최근수정일자등록신청사업영업구분법인여부본점여부
우편번호1.0000.0080.0290.0220.0100.0190.0500.0000.000
등록일자0.0081.0000.9960.9620.9650.1600.3790.2640.066
유효기간만료일자0.0290.9961.0000.9640.9650.0000.0000.0000.000
폐쇄일자0.0220.9620.9641.0000.9910.1640.1400.2010.060
최근수정일자0.0100.9650.9650.9911.0000.1220.3040.2650.076
등록신청사업0.0190.1600.0000.1640.1221.0000.0820.0000.000
영업구분0.0500.3790.0000.1400.3040.0821.0000.2030.040
법인여부0.0000.2640.0000.2010.2650.0000.2031.0000.123
본점여부0.0000.0660.0000.0600.0760.0000.0400.1231.000

Missing values

2024-05-11T00:46:59.847138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T00:47:00.551766image/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.
2024-05-11T00:47:01.177913image/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
7872대부중개업폐업2018-서울종로-00040(대부중개업)한국대부중개 주식회사법인070-4405-0055서울특별시 종로구 내수동 75번지 용비어천가-1404서울특별시 종로구 새문안로3길 36, 14층 1404호 (내수동, 용비어천가)<NA>20181031202110312019041020181031본점20190410
31326<NA><NA>2008-서울특별시-03083대한개인<NA>서울특별시 노원구 상계동 751-1 주공아파트 405동 504호<NA>13920020081128<NA>2009041620081128본점20090604
9952대부중개업폐업2016-서울관악-00021(대부중개업)대출마트대부중개개인02-6082-6155서울특별시 관악구 신림동 1666번지 56호 -204서울특별시 관악구 시흥대로 536-1, 204호 (신림동, 대동빌딩)<NA>20160512201905122017110620160512본점20171106
26426대부업<NA>2008-서울특별시-02402(대부업)(주) 청도그린법인0222156690서울특별시 동대문구 전농동 652-1 2층<NA><NA>20080910<NA>20101115<NA>본점20101115
2703대부중개업폐업2022-서울강남-0215(대부중개업)대출톡대부중개개인02-540-6268서울특별시 강남구 논현동 187번지 5호서울특별시 강남구 강남대로118길 64, 201호 (논현동)<NA>20210823202408232023050820210823본점20230508
989대부업폐업2023-서울송파-0005(대부업)값진 대부개인02-425-3690서울특별시 송파구 방이동 48번지 5호 현대주상복합빌딩서울특별시 송파구 오금로11길 55, 2동 216호 (방이동, 현대주상복합빌딩)<NA>20230130202601302024011620230130본점20240116
15050대부업폐업2013-서울서초-0068(대부업)(주)씨엠코퍼레이션대부법인1599-0767서울특별시 서초구 서초동 1564번지 11호 동성빌딩 지하1층서울특별시 서초구 반포대로20길 61, 지하1층 (서초동, 동성빌딩)13787420130521201605212015011920130521본점20150119
10967대부업폐업2015-서울영등포-0759(대부업)해피플래이스대부개인<NA><NA>서울특별시 영등포구 버드나루로12가길 13, 1703호 (영등포동7가, 브라운스톤 영등포아파트)<NA>20151113201811132017022720151113본점20170227
14663대부업폐업2013-서울영등포-0486(대부업)DHK인터내셔널대부 양평점개인02-2678-3562서울특별시 영등포구 양평동4가 319번지 6호서울특별시 영등포구 양평로20길 20 (양평동4가)15086720130717201607172015041420130717본점20150414
6051대부업타시군구이관2020-서울구로-0059(대부업)체인지캐피탈대부개인<NA>서울특별시 구로구 구로동 197번지 10호 이앤씨벤처드림타워2차서울특별시 구로구 디지털로33길 55, 이앤씨벤처드림타워2차 702(방11호)호 (구로동)<NA>20201222202312222021010820201222본점20210108
등록신청사업영업구분등록증번호상호법인여부사업장 전화번호소재지소재지(도로명)우편번호등록일자유효기간만료일자폐쇄일자지점설립일자본점여부최근수정일자
11726대부업<NA>2012-서울용산-00034(대부업)(주)한국퀵대부법인02-715-0067~8서울특별시 용산구 서계동 82번지서울특별시 용산구 청파로 359 (서계동)1408272012122620181226<NA>20121226본점20161128
4971대부중개업영업중2021-서울서초-0115(대부중개업)월드론대부 주식회사법인<NA>서울특별시 서초구 서초동 1656번지 2호 일광빌딩-202서울특별시 서초구 서초중앙로 114, 일광빌딩 202호 (서초동)<NA>2021120620241206<NA>20211206본점20211207
25177대부업<NA>2007-서울관악-01064(대부업)CH상사대부개인<NA>서울특별시 관악구 남현동 1061번지 18호 르메이에르2차 1009호<NA><NA>201008162013081620110406<NA>본점20110406
9784대부업폐업2012-서울서초-0224(대부업)(주)석세스게이트대부법인548-3066서울특별시 서초구 반포동 20번지 45호 반포자이주구중심상가 1동 206호서울특별시 서초구 잠원로 24, 1동 206호 (반포동, 반포자이주구중심상가)13704020151211201812112017120620121224본점20171206
24636대부업<NA>2009-서울은평-02272(대부업)ACE대부개인02 356 1250 031 246 1250서울특별시 은평구 대조동 185번지 41호 203호<NA><NA>20090922201209212011060320090922본점20110603
17787대부업폐업2012-서울양천-00078아미파이넨셜대부(주)법인02-2699-9026서울특별시 양천구 신정동 991번지 7호 -201<NA>15886020121204201512042013082820121204본점20130828
2817대부업영업중2011-서울은평-00165(대부업)현대사대부개인02-359-1256서울특별시 은평구 불광동 274번지 11호 -2층 15서울특별시 은평구 통일로 738, 2층 15호 (불광동)1220412023041720260417<NA>20080917본점20230417
17200대부중개업폐업2011-서울영등포-0270(대부중개업)(주)투엔비즈니스대부중개법인02-6968-1203서울특별시 영등포구 양평동5가 1번지 1호 아이에스비즈타워-602<NA>15010520111125201411252013112920111125본점20131129
7972대부업유효기간만료2015-서울동대문-00459(대부업)cm캐피탈대부개인02-929-7989서울특별시 동대문구 제기동 714번지 18호서울특별시 동대문구 고산자로 511-1 (제기동)<NA>2015102320181023<NA>20151023본점20190305
9658대부중개업직권취소2016-서울종로-00023(대부중개업)엠펀드대부유한회사법인070-4327-4516서울특별시 종로구 종로1가 1번지 24호 르메이에르종로타운-비119-서울특별시 종로구 종로 19, 비119-2호 (종로1가, 르메이에르종로타운)<NA>20160607201906072017122720160607본점20171227

Duplicate rows

Most frequently occurring

등록신청사업영업구분등록증번호상호법인여부사업장 전화번호소재지소재지(도로명)우편번호등록일자유효기간만료일자폐쇄일자지점설립일자본점여부최근수정일자# duplicates
0대부업타시군구이관2020-서울마포-0027(대부업)브릿지오토론대부개인1544-5919서울특별시 마포구 공덕동 462번지 마포공덕파크팰리스 Ⅱ서울특별시 마포구 마포대로 143, 마포공덕파크팰리스 Ⅱ 804호 (공덕동)<NA>20200602202306022022012820200602본점202201282