Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells19158
Missing cells (%)12.8%
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
Author마포구
URLhttps://data.seoul.go.kr/dataList/OA-11405/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 (93.7%)Imbalance
등록증번호 has 187 (1.9%) missing valuesMissing
사업장 전화번호 has 3353 (33.5%) missing valuesMissing
소재지 has 327 (3.3%) missing valuesMissing
소재지(도로명) has 4789 (47.9%) missing valuesMissing
우편번호 has 5610 (56.1%) missing valuesMissing
유효기간만료일자 has 2075 (20.8%) missing valuesMissing
폐쇄일자 has 1579 (15.8%) missing valuesMissing
지점설립일자 has 1238 (12.4%) missing valuesMissing

Reproduction

Analysis started2024-05-04 04:27:47.447766
Analysis finished2024-05-04 04:28:06.510052
Duration19.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

등록신청사업
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대부업
6141 
대부중개업
3417 
<NA>
 
442

Length

Max length5
Median length3
Mean length3.7276
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대부업 6141
61.4%
대부중개업 3417
34.2%
<NA> 442
 
4.4%

Length

2024-05-04T04:28:06.755723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T04:28:07.238192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대부업 6141
61.4%
대부중개업 3417
34.2%
na 442
 
4.4%

영업구분
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
3752 
<NA>
2865 
타시군구이관
1214 
영업중
862 
유효기간만료
779 
Other values (3)
528 

Length

Max length6
Median length4
Mean length3.5624
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 3752
37.5%
<NA> 2865
28.6%
타시군구이관 1214
 
12.1%
영업중 862
 
8.6%
유효기간만료 779
 
7.8%
직권취소 525
 
5.2%
갱신등록불가 2
 
< 0.1%
영업정지 1
 
< 0.1%

Length

2024-05-04T04:28:07.748035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T04:28:08.239882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 3752
37.5%
na 2865
28.6%
타시군구이관 1214
 
12.1%
영업중 862
 
8.6%
유효기간만료 779
 
7.8%
직권취소 525
 
5.2%
갱신등록불가 2
 
< 0.1%
영업정지 1
 
< 0.1%

등록증번호
Text

MISSING 

Distinct9771
Distinct (%)99.6%
Missing187
Missing (%)1.9%
Memory size156.2 KiB
2024-05-04T04:28:08.935733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length27
Mean length19.516254
Min length1

Characters and Unicode

Total characters191513
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

Unique9730 ?
Unique (%)99.2%

Sample

1st row2015-서울서초-0065(대부업)
2nd row2008-서울특별시-01256(대부업)
3rd row2009-서울특별시-02006(대부중개
4th row2011-서울동작-00043(대부중개업)
5th row2016-서울영등포-0894(대부업)
ValueCountFrequency (%)
2010-서울 19
 
0.2%
2013-서울특별시 16
 
0.2%
2012-서울특별시 16
 
0.2%
2011-서울특별시 11
 
0.1%
대부업 11
 
0.1%
2015-서울특별시 10
 
0.1%
2014-서울특별시 9
 
0.1%
2016-서울특별시 8
 
0.1%
성북구-00002 7
 
0.1%
2018-서울특별시 6
 
0.1%
Other values (9739) 9851
98.9%
2024-05-04T04:28:10.062060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 33806
17.7%
- 19616
 
10.2%
2 15752
 
8.2%
1 11906
 
6.2%
서 10876
 
5.7%
울 9784
 
5.1%
대 8481
 
4.4%
( 8184
 
4.3%
부 8144
 
4.3%
) 8129
 
4.2%
Other values (65) 56835
29.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 82538
43.1%
Other Letter 72894
38.1%
Dash Punctuation 19616
 
10.2%
Open Punctuation 8184
 
4.3%
Close Punctuation 8129
 
4.2%
Space Separator 152
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 10876
14.9%
울 9784
13.4%
대 8481
11.6%
부 8144
11.2%
업 7905
10.8%
중 3572
 
4.9%
개 2930
 
4.0%
시 2522
 
3.5%
별 2515
 
3.5%
특 2515
 
3.5%
Other values (51) 13650
18.7%
Decimal Number
ValueCountFrequency (%)
0 33806
41.0%
2 15752
19.1%
1 11906
 
14.4%
3 3712
 
4.5%
8 3148
 
3.8%
4 3013
 
3.7%
7 2861
 
3.5%
9 2861
 
3.5%
6 2835
 
3.4%
5 2644
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 19616
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8184
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8129
100.0%
Space Separator
ValueCountFrequency (%)
152
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 118619
61.9%
Hangul 72894
38.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 10876
14.9%
울 9784
13.4%
대 8481
11.6%
부 8144
11.2%
업 7905
10.8%
중 3572
 
4.9%
개 2930
 
4.0%
시 2522
 
3.5%
별 2515
 
3.5%
특 2515
 
3.5%
Other values (51) 13650
18.7%
Common
ValueCountFrequency (%)
0 33806
28.5%
- 19616
16.5%
2 15752
13.3%
1 11906
 
10.0%
( 8184
 
6.9%
) 8129
 
6.9%
3 3712
 
3.1%
8 3148
 
2.7%
4 3013
 
2.5%
7 2861
 
2.4%
Other values (4) 8492
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118619
61.9%
Hangul 72894
38.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33806
28.5%
- 19616
16.5%
2 15752
13.3%
1 11906
 
10.0%
( 8184
 
6.9%
) 8129
 
6.9%
3 3712
 
3.1%
8 3148
 
2.7%
4 3013
 
2.5%
7 2861
 
2.4%
Other values (4) 8492
 
7.2%
Hangul
ValueCountFrequency (%)
서 10876
14.9%
울 9784
13.4%
대 8481
11.6%
부 8144
11.2%
업 7905
10.8%
중 3572
 
4.9%
개 2930
 
4.0%
시 2522
 
3.5%
별 2515
 
3.5%
특 2515
 
3.5%
Other values (51) 13650
18.7%

상호
Text

Distinct8683
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-04T04:28:10.803215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length26
Mean length7.7421
Min length1

Characters and Unicode

Total characters77421
Distinct characters774
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

Unique7620 ?
Unique (%)76.2%

Sample

1st row천사펀딩대부
2nd row대원
3rd row성일금융대부
4th rowMS credit 대부
5th row나이스제7차대부(유)
ValueCountFrequency (%)
주식회사 800
 
6.7%
대부중개 310
 
2.6%
대부 296
 
2.5%
유한회사 58
 
0.5%
대부업 18
 
0.2%
캐피탈 17
 
0.1%
대부중개업 14
 
0.1%
전당포 12
 
0.1%
미래대부 11
 
0.1%
money 10
 
0.1%
Other values (8721) 10354
87.0%
2024-05-04T04:28:12.217253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
대 8442
 
10.9%
부 8107
 
10.5%
주 2672
 
3.5%
이 2281
 
2.9%
개 2147
 
2.8%
중 2126
 
2.7%
스 1959
 
2.5%
1904
 
2.5%
) 1875
 
2.4%
( 1866
 
2.4%
Other values (764) 44042
56.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 67868
87.7%
Uppercase Letter 2234
 
2.9%
Space Separator 1904
 
2.5%
Close Punctuation 1875
 
2.4%
Open Punctuation 1866
 
2.4%
Lowercase Letter 1165
 
1.5%
Decimal Number 242
 
0.3%
Other Punctuation 227
 
0.3%
Dash Punctuation 31
 
< 0.1%
Other Symbol 7
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 8442
 
12.4%
부 8107
 
11.9%
주 2672
 
3.9%
이 2281
 
3.4%
개 2147
 
3.2%
중 2126
 
3.1%
스 1959
 
2.9%
사 1323
 
1.9%
피 1117
 
1.6%
캐 1019
 
1.5%
Other values (691) 36675
54.0%
Uppercase Letter
ValueCountFrequency (%)
S 285
 
12.8%
K 196
 
8.8%
J 173
 
7.7%
C 164
 
7.3%
M 148
 
6.6%
H 134
 
6.0%
B 105
 
4.7%
A 95
 
4.3%
N 93
 
4.2%
T 81
 
3.6%
Other values (15) 760
34.0%
Lowercase Letter
ValueCountFrequency (%)
n 141
12.1%
e 134
11.5%
o 125
10.7%
a 119
10.2%
i 78
 
6.7%
s 68
 
5.8%
t 61
 
5.2%
c 60
 
5.2%
l 57
 
4.9%
r 49
 
4.2%
Other values (15) 273
23.4%
Decimal Number
ValueCountFrequency (%)
1 83
34.3%
4 30
 
12.4%
2 29
 
12.0%
3 25
 
10.3%
5 22
 
9.1%
9 21
 
8.7%
6 14
 
5.8%
0 11
 
4.5%
8 4
 
1.7%
7 3
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 124
54.6%
& 88
38.8%
? 6
 
2.6%
, 5
 
2.2%
' 2
 
0.9%
* 2
 
0.9%
Space Separator
ValueCountFrequency (%)
1904
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1875
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1866
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 31
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 7
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 67851
87.6%
Common 6146
 
7.9%
Latin 3400
 
4.4%
Han 24
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 8442
 
12.4%
부 8107
 
11.9%
주 2672
 
3.9%
이 2281
 
3.4%
개 2147
 
3.2%
중 2126
 
3.1%
스 1959
 
2.9%
사 1323
 
1.9%
피 1117
 
1.6%
캐 1019
 
1.5%
Other values (673) 36658
54.0%
Latin
ValueCountFrequency (%)
S 285
 
8.4%
K 196
 
5.8%
J 173
 
5.1%
C 164
 
4.8%
M 148
 
4.4%
n 141
 
4.1%
H 134
 
3.9%
e 134
 
3.9%
o 125
 
3.7%
a 119
 
3.5%
Other values (41) 1781
52.4%
Common
ValueCountFrequency (%)
1904
31.0%
) 1875
30.5%
( 1866
30.4%
. 124
 
2.0%
& 88
 
1.4%
1 83
 
1.4%
- 31
 
0.5%
4 30
 
0.5%
2 29
 
0.5%
3 25
 
0.4%
Other values (11) 91
 
1.5%
Han
ValueCountFrequency (%)
大 4
16.7%
福 2
 
8.3%
田 2
 
8.3%
勳 1
 
4.2%
道 1
 
4.2%
五 1
 
4.2%
花 1
 
4.2%
多 1
 
4.2%
趙 1
 
4.2%
鄭 1
 
4.2%
Other values (9) 9
37.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 67844
87.6%
ASCII 9545
 
12.3%
CJK 24
 
< 0.1%
None 7
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
대 8442
 
12.4%
부 8107
 
11.9%
주 2672
 
3.9%
이 2281
 
3.4%
개 2147
 
3.2%
중 2126
 
3.1%
스 1959
 
2.9%
사 1323
 
2.0%
피 1117
 
1.6%
캐 1019
 
1.5%
Other values (672) 36651
54.0%
ASCII
ValueCountFrequency (%)
1904
19.9%
) 1875
19.6%
( 1866
19.5%
S 285
 
3.0%
K 196
 
2.1%
J 173
 
1.8%
C 164
 
1.7%
M 148
 
1.6%
n 141
 
1.5%
H 134
 
1.4%
Other values (61) 2659
27.9%
None
ValueCountFrequency (%)
㈜ 7
100.0%
CJK
ValueCountFrequency (%)
大 4
16.7%
福 2
 
8.3%
田 2
 
8.3%
勳 1
 
4.2%
道 1
 
4.2%
五 1
 
4.2%
花 1
 
4.2%
多 1
 
4.2%
趙 1
 
4.2%
鄭 1
 
4.2%
Other values (9) 9
37.5%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

법인여부
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
개인
7259 
법인
2741 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
개인 7259
72.6%
법인 2741
 
27.4%

Length

2024-05-04T04:28:12.727901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T04:28:13.105049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 7259
72.6%
법인 2741
 
27.4%

사업장 전화번호
Text

MISSING 

Distinct5872
Distinct (%)88.3%
Missing3353
Missing (%)33.5%
Memory size156.2 KiB
2024-05-04T04:28:13.736783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length38
Mean length10.57289
Min length1

Characters and Unicode

Total characters70278
Distinct characters30
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

Unique5253 ?
Unique (%)79.0%

Sample

1st row070-4262-1763
2nd row024763368
3rd row027717870
4th row02-761-6875
5th row02-553-7772
ValueCountFrequency (%)
02 269
 
3.6%
63
 
0.9%
070 34
 
0.5%
0 6
 
0.1%
432 5
 
0.1%
2244 5
 
0.1%
1688 5
 
0.1%
02-6215-8647 5
 
0.1%
1661-1547 5
 
0.1%
02-763-8949 5
 
0.1%
Other values (6162) 7005
94.6%
2024-05-04T04:28:15.153435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11512
16.4%
2 10335
14.7%
- 7185
10.2%
5 5918
8.4%
7 5526
7.9%
6 5022
7.1%
1 5011
7.1%
3 4982
7.1%
8 4909
7.0%
4 4843
6.9%
Other values (20) 5035
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 61999
88.2%
Dash Punctuation 7185
 
10.2%
Space Separator 846
 
1.2%
Other Punctuation 127
 
0.2%
Close Punctuation 59
 
0.1%
Open Punctuation 23
 
< 0.1%
Math Symbol 23
 
< 0.1%
Uppercase Letter 8
 
< 0.1%
Other Letter 8
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11512
18.6%
2 10335
16.7%
5 5918
9.5%
7 5526
8.9%
6 5022
8.1%
1 5011
8.1%
3 4982
8.0%
8 4909
7.9%
4 4843
7.8%
9 3941
 
6.4%
Other Letter
ValueCountFrequency (%)
미 1
12.5%
등 1
12.5%
록 1
12.5%
번 1
12.5%
호 1
12.5%
바 1
12.5%
뀜 1
12.5%
무 1
12.5%
Other Punctuation
ValueCountFrequency (%)
* 61
48.0%
/ 48
37.8%
. 18
 
14.2%
Uppercase Letter
ValueCountFrequency (%)
K 4
50.0%
T 3
37.5%
S 1
 
12.5%
Math Symbol
ValueCountFrequency (%)
~ 22
95.7%
× 1
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 7185
100.0%
Space Separator
ValueCountFrequency (%)
846
100.0%
Close Punctuation
ValueCountFrequency (%)
) 59
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70262
> 99.9%
Latin 8
 
< 0.1%
Hangul 8
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11512
16.4%
2 10335
14.7%
- 7185
10.2%
5 5918
8.4%
7 5526
7.9%
6 5022
7.1%
1 5011
7.1%
3 4982
7.1%
8 4909
7.0%
4 4843
6.9%
Other values (9) 5019
7.1%
Hangul
ValueCountFrequency (%)
미 1
12.5%
등 1
12.5%
록 1
12.5%
번 1
12.5%
호 1
12.5%
바 1
12.5%
뀜 1
12.5%
무 1
12.5%
Latin
ValueCountFrequency (%)
K 4
50.0%
T 3
37.5%
S 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70269
> 99.9%
Hangul 8
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11512
16.4%
2 10335
14.7%
- 7185
10.2%
5 5918
8.4%
7 5526
7.9%
6 5022
7.1%
1 5011
7.1%
3 4982
7.1%
8 4909
7.0%
4 4843
6.9%
Other values (11) 5026
7.2%
Hangul
ValueCountFrequency (%)
미 1
12.5%
등 1
12.5%
록 1
12.5%
번 1
12.5%
호 1
12.5%
바 1
12.5%
뀜 1
12.5%
무 1
12.5%
None
ValueCountFrequency (%)
× 1
100.0%

소재지
Text

MISSING 

Distinct8643
Distinct (%)89.4%
Missing327
Missing (%)3.3%
Memory size156.2 KiB
2024-05-04T04:28:16.331444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length65
Median length48
Mean length31.434095
Min length15

Characters and Unicode

Total characters304062
Distinct characters622
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7888 ?
Unique (%)81.5%

Sample

1st row서울특별시 서초구 서초동 1338번지 21호 현대렉시온-1117
2nd row서울특별시 강동구 천호동 221-39
3rd row서울특별시 중구 무교동 25번지 1호 원창빌딩 304호
4th row서울특별시 동작구 상도동 285번지 9호 401
5th row서울특별시 영등포구 여의도동 14번지 24호 삼보호정빌딩
ValueCountFrequency (%)
서울특별시 9671
 
17.0%
강남구 1629
 
2.9%
서초구 945
 
1.7%
1호 733
 
1.3%
역삼동 728
 
1.3%
서초동 571
 
1.0%
중구 568
 
1.0%
송파구 564
 
1.0%
2호 459
 
0.8%
영등포구 453
 
0.8%
Other values (9497) 40625
71.3%
2024-05-04T04:28:18.032576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
67467
22.2%
1 13455
 
4.4%
서 12040
 
4.0%
동 11043
 
3.6%
구 10444
 
3.4%
시 9937
 
3.3%
울 9730
 
3.2%
별 9677
 
3.2%
특 9673
 
3.2%
2 8686
 
2.9%
Other values (612) 141910
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 166233
54.7%
Space Separator 67467
22.2%
Decimal Number 63164
 
20.8%
Dash Punctuation 5411
 
1.8%
Uppercase Letter 1172
 
0.4%
Other Punctuation 258
 
0.1%
Lowercase Letter 120
 
< 0.1%
Close Punctuation 102
 
< 0.1%
Open Punctuation 102
 
< 0.1%
Letter Number 27
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 12040
 
7.2%
동 11043
 
6.6%
구 10444
 
6.3%
시 9937
 
6.0%
울 9730
 
5.9%
별 9677
 
5.8%
특 9673
 
5.8%
호 8535
 
5.1%
지 8410
 
5.1%
번 7942
 
4.8%
Other values (538) 68802
41.4%
Uppercase Letter
ValueCountFrequency (%)
B 257
21.9%
A 224
19.1%
D 89
 
7.6%
S 77
 
6.6%
C 60
 
5.1%
T 52
 
4.4%
I 49
 
4.2%
K 44
 
3.8%
E 39
 
3.3%
L 36
 
3.1%
Other values (16) 245
20.9%
Lowercase Letter
ValueCountFrequency (%)
e 15
12.5%
i 14
11.7%
n 11
 
9.2%
l 9
 
7.5%
r 8
 
6.7%
a 8
 
6.7%
t 7
 
5.8%
o 6
 
5.0%
w 6
 
5.0%
s 6
 
5.0%
Other values (9) 30
25.0%
Decimal Number
ValueCountFrequency (%)
1 13455
21.3%
2 8686
13.8%
0 7964
12.6%
3 7046
11.2%
4 5809
9.2%
5 5023
 
8.0%
6 4482
 
7.1%
7 3959
 
6.3%
9 3411
 
5.4%
8 3329
 
5.3%
Other Punctuation
ValueCountFrequency (%)
/ 97
37.6%
, 86
33.3%
. 69
26.7%
@ 3
 
1.2%
; 1
 
0.4%
？ 1
 
0.4%
& 1
 
0.4%
Letter Number
ValueCountFrequency (%)
Ⅱ 21
77.8%
Ⅰ 4
 
14.8%
Ⅲ 2
 
7.4%
Math Symbol
ValueCountFrequency (%)
~ 4
66.7%
< 1
 
16.7%
> 1
 
16.7%
Close Punctuation
ValueCountFrequency (%)
) 101
99.0%
] 1
 
1.0%
Open Punctuation
ValueCountFrequency (%)
( 101
99.0%
[ 1
 
1.0%
Space Separator
ValueCountFrequency (%)
67467
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5411
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 166231
54.7%
Common 136510
44.9%
Latin 1319
 
0.4%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 12040
 
7.2%
동 11043
 
6.6%
구 10444
 
6.3%
시 9937
 
6.0%
울 9730
 
5.9%
별 9677
 
5.8%
특 9673
 
5.8%
호 8535
 
5.1%
지 8410
 
5.1%
번 7942
 
4.8%
Other values (536) 68800
41.4%
Latin
ValueCountFrequency (%)
B 257
19.5%
A 224
17.0%
D 89
 
6.7%
S 77
 
5.8%
C 60
 
4.5%
T 52
 
3.9%
I 49
 
3.7%
K 44
 
3.3%
E 39
 
3.0%
L 36
 
2.7%
Other values (38) 392
29.7%
Common
ValueCountFrequency (%)
67467
49.4%
1 13455
 
9.9%
2 8686
 
6.4%
0 7964
 
5.8%
3 7046
 
5.2%
4 5809
 
4.3%
- 5411
 
4.0%
5 5023
 
3.7%
6 4482
 
3.3%
7 3959
 
2.9%
Other values (16) 7208
 
5.3%
Han
ValueCountFrequency (%)
塘 1
50.0%
金 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 166231
54.7%
ASCII 137801
45.3%
Number Forms 27
 
< 0.1%
None 1
 
< 0.1%
CJK 1
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
67467
49.0%
1 13455
 
9.8%
2 8686
 
6.3%
0 7964
 
5.8%
3 7046
 
5.1%
4 5809
 
4.2%
- 5411
 
3.9%
5 5023
 
3.6%
6 4482
 
3.3%
7 3959
 
2.9%
Other values (60) 8499
 
6.2%
Hangul
ValueCountFrequency (%)
서 12040
 
7.2%
동 11043
 
6.6%
구 10444
 
6.3%
시 9937
 
6.0%
울 9730
 
5.9%
별 9677
 
5.8%
특 9673
 
5.8%
호 8535
 
5.1%
지 8410
 
5.1%
번 7942
 
4.8%
Other values (536) 68800
41.4%
Number Forms
ValueCountFrequency (%)
Ⅱ 21
77.8%
Ⅰ 4
 
14.8%
Ⅲ 2
 
7.4%
None
ValueCountFrequency (%)
？ 1
100.0%
CJK
ValueCountFrequency (%)
塘 1
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
金 1
100.0%

소재지(도로명)
Text

MISSING 

Distinct4737
Distinct (%)90.9%
Missing4789
Missing (%)47.9%
Memory size156.2 KiB
2024-05-04T04:28:18.937320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length67
Median length52
Mean length37.09384
Min length19

Characters and Unicode

Total characters193296
Distinct characters615
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

Unique4309 ?
Unique (%)82.7%

Sample

1st row서울특별시 서초구 강남대로 309, 817호 (서초동, 코리아비지니스센타)
2nd row서울특별시 영등포구 국회대로68길 11, 삼보호정빌딩 9층 (여의도동)
3rd row서울특별시 강남구 테헤란로 327, 1313호 (역삼동, 빅토리아오피스텔)
4th row서울특별시 도봉구 해등로 109, 창동1단지주공아파트 지하층 제1호중 제30호 (창동)
5th row서울특별시 중랑구 겸재로10가길 11, 제3층 제303호 (면목동)
ValueCountFrequency (%)
서울특별시 5210
 
14.1%
강남구 954
 
2.6%
서초구 590
 
1.6%
2층 452
 
1.2%
역삼동 417
 
1.1%
서초동 385
 
1.0%
3층 377
 
1.0%
영등포구 326
 
0.9%
4층 309
 
0.8%
송파구 294
 
0.8%
Other values (6548) 27540
74.7%
2024-05-04T04:28:21.013557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31653
 
16.4%
1 7406
 
3.8%
, 7087
 
3.7%
서 6920
 
3.6%
동 6778
 
3.5%
로 5795
 
3.0%
구 5731
 
3.0%
시 5426
 
2.8%
울 5268
 
2.7%
) 5257
 
2.7%
Other values (605) 105975
54.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 107643
55.7%
Decimal Number 34408
 
17.8%
Space Separator 31653
 
16.4%
Other Punctuation 7099
 
3.7%
Close Punctuation 5258
 
2.7%
Open Punctuation 5258
 
2.7%
Dash Punctuation 1000
 
0.5%
Uppercase Letter 832
 
0.4%
Lowercase Letter 109
 
0.1%
Letter Number 26
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 6920
 
6.4%
동 6778
 
6.3%
로 5795
 
5.4%
구 5731
 
5.3%
시 5426
 
5.0%
울 5268
 
4.9%
별 5216
 
4.8%
특 5211
 
4.8%
호 4215
 
3.9%
길 2722
 
2.5%
Other values (530) 54361
50.5%
Uppercase Letter
ValueCountFrequency (%)
B 154
18.5%
A 115
13.8%
S 83
10.0%
C 52
 
6.2%
E 49
 
5.9%
T 48
 
5.8%
I 42
 
5.0%
K 32
 
3.8%
L 32
 
3.8%
R 23
 
2.8%
Other values (16) 202
24.3%
Lowercase Letter
ValueCountFrequency (%)
e 16
14.7%
i 12
11.0%
r 9
 
8.3%
w 9
 
8.3%
o 8
 
7.3%
b 7
 
6.4%
n 7
 
6.4%
l 7
 
6.4%
s 5
 
4.6%
t 5
 
4.6%
Other values (11) 24
22.0%
Decimal Number
ValueCountFrequency (%)
1 7406
21.5%
2 5214
15.2%
0 4409
12.8%
3 4197
12.2%
4 2970
8.6%
5 2715
 
7.9%
6 2218
 
6.4%
7 1927
 
5.6%
8 1841
 
5.4%
9 1511
 
4.4%
Other Punctuation
ValueCountFrequency (%)
, 7087
99.8%
. 6
 
0.1%
@ 3
 
< 0.1%
/ 1
 
< 0.1%
# 1
 
< 0.1%
& 1
 
< 0.1%
Letter Number
ValueCountFrequency (%)
Ⅱ 17
65.4%
Ⅰ 5
 
19.2%
Ⅲ 4
 
15.4%
Math Symbol
ValueCountFrequency (%)
~ 8
80.0%
> 1
 
10.0%
< 1
 
10.0%
Close Punctuation
ValueCountFrequency (%)
) 5257
> 99.9%
] 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 5257
> 99.9%
[ 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
31653
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 107642
55.7%
Common 84686
43.8%
Latin 967
 
0.5%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 6920
 
6.4%
동 6778
 
6.3%
로 5795
 
5.4%
구 5731
 
5.3%
시 5426
 
5.0%
울 5268
 
4.9%
별 5216
 
4.8%
특 5211
 
4.8%
호 4215
 
3.9%
길 2722
 
2.5%
Other values (529) 54360
50.5%
Latin
ValueCountFrequency (%)
B 154
15.9%
A 115
 
11.9%
S 83
 
8.6%
C 52
 
5.4%
E 49
 
5.1%
T 48
 
5.0%
I 42
 
4.3%
K 32
 
3.3%
L 32
 
3.3%
R 23
 
2.4%
Other values (40) 337
34.9%
Common
ValueCountFrequency (%)
31653
37.4%
1 7406
 
8.7%
, 7087
 
8.4%
) 5257
 
6.2%
( 5257
 
6.2%
2 5214
 
6.2%
0 4409
 
5.2%
3 4197
 
5.0%
4 2970
 
3.5%
5 2715
 
3.2%
Other values (15) 8521
 
10.1%
Han
ValueCountFrequency (%)
家 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 107642
55.7%
ASCII 85627
44.3%
Number Forms 26
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31653
37.0%
1 7406
 
8.6%
, 7087
 
8.3%
) 5257
 
6.1%
( 5257
 
6.1%
2 5214
 
6.1%
0 4409
 
5.1%
3 4197
 
4.9%
4 2970
 
3.5%
5 2715
 
3.2%
Other values (62) 9462
 
11.1%
Hangul
ValueCountFrequency (%)
서 6920
 
6.4%
동 6778
 
6.3%
로 5795
 
5.4%
구 5731
 
5.3%
시 5426
 
5.0%
울 5268
 
4.9%
별 5216
 
4.8%
특 5211
 
4.8%
호 4215
 
3.9%
길 2722
 
2.5%
Other values (529) 54360
50.5%
Number Forms
ValueCountFrequency (%)
Ⅱ 17
65.4%
Ⅰ 5
 
19.2%
Ⅲ 4
 
15.4%
CJK
ValueCountFrequency (%)
家 1
100.0%

우편번호
Real number (ℝ)

MISSING 

Distinct1398
Distinct (%)31.8%
Missing5610
Missing (%)56.1%
Infinite0
Infinite (%)0.0%
Mean136130.24
Minimum3163
Maximum429842
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T04:28:21.663494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3163
5-th percentile100861
Q1132010
median136110
Q3143190
95-th percentile157030.55
Maximum429842
Range426679
Interquartile range (IQR)11180

Descriptive statistics

Standard deviation16173.747
Coefficient of variation (CV)0.11881083
Kurtosis56.143192
Mean136130.24
Median Absolute Deviation (MAD)5251
Skewness1.0102817
Sum5.9761176 × 108
Variance2.6159008 × 108
MonotonicityNot monotonic
2024-05-04T04:28:22.090096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135080 168
 
1.7%
137070 139
 
1.4%
157010 62
 
0.6%
135010 60
 
0.6%
151050 47
 
0.5%
152050 47
 
0.5%
151015 45
 
0.4%
158070 42
 
0.4%
139200 37
 
0.4%
137060 36
 
0.4%
Other values (1388) 3707
37.1%
(Missing) 5610
56.1%
ValueCountFrequency (%)
3163 1
 
< 0.1%
4526 1
 
< 0.1%
4534 1
 
< 0.1%
4536 1
 
< 0.1%
4538 2
 
< 0.1%
4550 1
 
< 0.1%
4554 1
 
< 0.1%
7326 1
 
< 0.1%
7327 1
 
< 0.1%
100011 6
0.1%
ValueCountFrequency (%)
429842 1
 
< 0.1%
423060 1
 
< 0.1%
158877 1
 
< 0.1%
158871 1
 
< 0.1%
158865 1
 
< 0.1%
158864 3
< 0.1%
158863 1
 
< 0.1%
158860 4
< 0.1%
158859 4
< 0.1%
158857 1
 
< 0.1%

등록일자
Real number (ℝ)

HIGH CORRELATION 

Distinct3519
Distinct (%)35.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20136769
Minimum20030519
Maximum20240502
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T04:28:22.553904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20030519
5-th percentile20070807
Q120091115
median20130311
Q320170822
95-th percentile20230208
Maximum20240502
Range209983
Interquartile range (IQR)79706.25

Descriptive statistics

Standard deviation49095.116
Coefficient of variation (CV)0.0024380831
Kurtosis-0.923401
Mean20136769
Median Absolute Deviation (MAD)39696
Skewness0.45082131
Sum2.0136769 × 1011
Variance2.4103304 × 109
MonotonicityNot monotonic
2024-05-04T04:28:22.977001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20080814 30
 
0.3%
20080818 22
 
0.2%
20080731 20
 
0.2%
20090611 18
 
0.2%
20080822 16
 
0.2%
20080926 16
 
0.2%
20090213 14
 
0.1%
20081210 14
 
0.1%
20110711 13
 
0.1%
20160720 13
 
0.1%
Other values (3509) 9824
98.2%
ValueCountFrequency (%)
20030519 1
 
< 0.1%
20051216 1
 
< 0.1%
20060308 1
 
< 0.1%
20060310 1
 
< 0.1%
20060320 4
< 0.1%
20060323 3
< 0.1%
20060324 1
 
< 0.1%
20060327 1
 
< 0.1%
20060329 2
< 0.1%
20060405 3
< 0.1%
ValueCountFrequency (%)
20240502 3
< 0.1%
20240430 3
< 0.1%
20240429 1
 
< 0.1%
20240425 5
0.1%
20240424 3
< 0.1%
20240422 4
< 0.1%
20240419 1
 
< 0.1%
20240418 1
 
< 0.1%
20240416 1
 
< 0.1%
20240411 3
< 0.1%

유효기간만료일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3303
Distinct (%)41.7%
Missing2075
Missing (%)20.8%
Infinite0
Infinite (%)0.0%
Mean20181572
Minimum20090310
Maximum20270502
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T04:28:23.470037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20090310
5-th percentile20120316
Q120141027
median20180109
Q320220110
95-th percentile20260508
Maximum20270502
Range180192
Interquartile range (IQR)79083

Descriptive statistics

Standard deviation44663.835
Coefficient of variation (CV)0.0022130999
Kurtosis-0.99521398
Mean20181572
Median Absolute Deviation (MAD)39088
Skewness0.30727996
Sum1.5993896 × 1011
Variance1.9948582 × 109
MonotonicityNot monotonic
2024-05-04T04:28:24.049013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20140831 14
 
0.1%
20110831 13
 
0.1%
20190720 13
 
0.1%
20140711 13
 
0.1%
20140713 12
 
0.1%
20190216 12
 
0.1%
20150531 11
 
0.1%
20140721 11
 
0.1%
20140811 11
 
0.1%
20170701 11
 
0.1%
Other values (3293) 7804
78.0%
(Missing) 2075
 
20.8%
ValueCountFrequency (%)
20090310 1
< 0.1%
20090514 1
< 0.1%
20090907 1
< 0.1%
20091116 2
< 0.1%
20100112 1
< 0.1%
20100117 1
< 0.1%
20100219 1
< 0.1%
20100308 1
< 0.1%
20100321 1
< 0.1%
20100323 1
< 0.1%
ValueCountFrequency (%)
20270502 2
 
< 0.1%
20270501 1
 
< 0.1%
20270430 3
< 0.1%
20270429 1
 
< 0.1%
20270425 5
0.1%
20270424 3
< 0.1%
20270422 2
 
< 0.1%
20270421 2
 
< 0.1%
20270419 1
 
< 0.1%
20270418 1
 
< 0.1%

폐쇄일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3113
Distinct (%)37.0%
Missing1579
Missing (%)15.8%
Infinite0
Infinite (%)0.0%
Mean20141816
Minimum20050517
Maximum20240503
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T04:28:24.585400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20050517
5-th percentile20090907
Q120110329
median20130724
Q320170413
95-th percentile20220928
Maximum20240503
Range189986
Interquartile range (IQR)60084

Descriptive statistics

Standard deviation40911.7
Coefficient of variation (CV)0.0020311823
Kurtosis-0.56078373
Mean20141816
Median Absolute Deviation (MAD)29797
Skewness0.68499146
Sum1.6961423 × 1011
Variance1.6737672 × 109
MonotonicityNot monotonic
2024-05-04T04:28:25.179998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20091116 225
 
2.2%
20100927 75
 
0.8%
20170124 21
 
0.2%
20101213 17
 
0.2%
20110425 15
 
0.1%
20120719 14
 
0.1%
20100723 14
 
0.1%
20110412 14
 
0.1%
20170125 13
 
0.1%
20110729 13
 
0.1%
Other values (3103) 8000
80.0%
(Missing) 1579
 
15.8%
ValueCountFrequency (%)
20050517 1
 
< 0.1%
20060920 1
 
< 0.1%
20071030 1
 
< 0.1%
20071115 1
 
< 0.1%
20081023 1
 
< 0.1%
20081217 1
 
< 0.1%
20090305 1
 
< 0.1%
20090306 2
 
< 0.1%
20090309 2
 
< 0.1%
20090311 6
0.1%
ValueCountFrequency (%)
20240503 4
< 0.1%
20240501 2
< 0.1%
20240430 1
 
< 0.1%
20240429 1
 
< 0.1%
20240425 1
 
< 0.1%
20240424 1
 
< 0.1%
20240423 3
< 0.1%
20240422 1
 
< 0.1%
20240419 2
< 0.1%
20240418 3
< 0.1%

지점설립일자
Text

MISSING 

Distinct3562
Distinct (%)40.7%
Missing1238
Missing (%)12.4%
Memory size156.2 KiB
2024-05-04T04:28:26.270923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters70096
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

Unique1329 ?
Unique (%)15.2%

Sample

1st row20150529
2nd row20080221
3rd row20090820
4th row20111111
5th row20090217
ValueCountFrequency (%)
20090611 22
 
0.3%
20090820 18
 
0.2%
20090520 17
 
0.2%
20090528 15
 
0.2%
20090507 14
 
0.2%
20090512 14
 
0.2%
20090511 13
 
0.1%
20090529 13
 
0.1%
20160412 13
 
0.1%
20160720 13
 
0.1%
Other values (3552) 8610
98.3%
2024-05-04T04:28:28.034193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 22664
32.3%
2 16023
22.9%
1 14072
20.1%
3 2867
 
4.1%
7 2611
 
3.7%
9 2599
 
3.7%
6 2426
 
3.5%
5 2350
 
3.4%
8 2267
 
3.2%
4 2211
 
3.2%
Other values (4) 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70090
> 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 22664
32.3%
2 16023
22.9%
1 14072
20.1%
3 2867
 
4.1%
7 2611
 
3.7%
9 2599
 
3.7%
6 2426
 
3.5%
5 2350
 
3.4%
8 2267
 
3.2%
4 2211
 
3.2%
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 70093
> 99.9%
Latin 3
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22664
32.3%
2 16023
22.9%
1 14072
20.1%
3 2867
 
4.1%
7 2611
 
3.7%
9 2599
 
3.7%
6 2426
 
3.5%
5 2350
 
3.4%
8 2267
 
3.2%
4 2211
 
3.2%
Latin
ValueCountFrequency (%)
M 1
33.3%
a 1
33.3%
y 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22664
32.3%
2 16023
22.9%
1 14072
20.1%
3 2867
 
4.1%
7 2611
 
3.7%
9 2599
 
3.7%
6 2426
 
3.5%
5 2350
 
3.4%
8 2267
 
3.2%
4 2211
 
3.2%
Other values (4) 6
 
< 0.1%

본점여부
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
본점
9926 
지점
 
74

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 9926
99.3%
지점 74
 
0.7%

Length

2024-05-04T04:28:28.497832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T04:28:28.830469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
본점 9926
99.3%
지점 74
 
0.7%

최근수정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct3166
Distinct (%)31.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20152885
Minimum20090519
Maximum20240503
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T04:28:29.225439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20090519
5-th percentile20091117
Q120111005
median20140808
Q320190424
95-th percentile20231013
Maximum20240503
Range149984
Interquartile range (IQR)79419

Descriptive statistics

Standard deviation46019.433
Coefficient of variation (CV)0.0022835159
Kurtosis-1.0671046
Mean20152885
Median Absolute Deviation (MAD)30501
Skewness0.44594656
Sum2.0152885 × 1011
Variance2.1177882 × 109
MonotonicityNot monotonic
2024-05-04T04:28:29.811141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20091117 82
 
0.8%
20090609 64
 
0.6%
20091118 53
 
0.5%
20091116 53
 
0.5%
20100927 51
 
0.5%
20100330 44
 
0.4%
20091119 42
 
0.4%
20090622 40
 
0.4%
20130621 40
 
0.4%
20100517 32
 
0.3%
Other values (3156) 9499
95.0%
ValueCountFrequency (%)
20090519 1
 
< 0.1%
20090521 5
 
0.1%
20090601 4
 
< 0.1%
20090602 3
 
< 0.1%
20090603 12
 
0.1%
20090604 14
 
0.1%
20090605 7
 
0.1%
20090608 4
 
< 0.1%
20090609 64
0.6%
20090610 21
 
0.2%
ValueCountFrequency (%)
20240503 14
0.1%
20240502 4
 
< 0.1%
20240501 3
 
< 0.1%
20240430 6
0.1%
20240429 5
 
0.1%
20240426 1
 
< 0.1%
20240425 9
0.1%
20240424 4
 
< 0.1%
20240423 6
0.1%
20240422 6
0.1%

Interactions

2024-05-04T04:28:02.373989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T04:27:56.000611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T04:27:57.696783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T04:27:59.252250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T04:28:00.710313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T04:28:02.720336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T04:27:56.370348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T04:27:57.962854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T04:27:59.529533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T04:28:00.972407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T04:28:03.077785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T04:27:56.746209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T04:27:58.220394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T04:27:59.814270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T04:28:01.290535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T04:28:03.502733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T04:27:57.133155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T04:27:58.631884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T04:28:00.092970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T04:28:01.722409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T04:28:03.895044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T04:27:57.411403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T04:27:58.866999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T04:28:00.375709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T04:28:02.042129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T04:28:30.189798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록신청사업영업구분법인여부우편번호등록일자유효기간만료일자폐쇄일자본점여부최근수정일자
등록신청사업1.0000.0640.0220.0250.2300.1640.2130.0000.173
영업구분0.0641.0000.1840.0550.5640.5960.1910.0400.482
법인여부0.0220.1841.0000.0940.3280.2560.2520.2040.332
우편번호0.0250.0550.0941.0000.2570.2960.3840.0000.283
등록일자0.2300.5640.3280.2571.0000.9600.9470.0880.936
유효기간만료일자0.1640.5960.2560.2960.9601.0000.8480.0790.925
폐쇄일자0.2130.1910.2520.3840.9470.8481.0000.0550.985
본점여부0.0000.0400.2040.0000.0880.0790.0551.0000.099
최근수정일자0.1730.4820.3320.2830.9360.9250.9850.0991.000
2024-05-04T04:28:30.619431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영업구분법인여부본점여부등록신청사업
영업구분1.0000.1970.0430.069
법인여부0.1971.0000.1310.014
본점여부0.0430.1311.0000.000
등록신청사업0.0690.0140.0001.000
2024-05-04T04:28:30.973601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호등록일자유효기간만료일자폐쇄일자최근수정일자등록신청사업영업구분법인여부본점여부
우편번호1.0000.0120.0330.0330.0190.0190.0420.0570.000
등록일자0.0121.0000.9960.9620.9650.1770.3410.2520.067
유효기간만료일자0.0330.9961.0000.9640.9660.1260.3540.1970.060
폐쇄일자0.0330.9620.9641.0000.9900.1640.1110.1930.042
최근수정일자0.0190.9650.9660.9901.0000.1330.2790.2550.076
등록신청사업0.0190.1770.1260.1640.1331.0000.0690.0140.000
영업구분0.0420.3410.3540.1110.2790.0691.0000.1970.043
법인여부0.0570.2520.1970.1930.2550.0140.1971.0000.131
본점여부0.0000.0670.0600.0420.0760.0000.0430.1311.000

Missing values

2024-05-04T04:28:04.491899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T04:28:05.428358image/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.
2024-05-04T04:28:06.067283image/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
9908대부업타시군구이관2015-서울서초-0065(대부업)천사펀딩대부개인070-4262-1763서울특별시 서초구 서초동 1338번지 21호 현대렉시온-1117서울특별시 서초구 강남대로 309, 817호 (서초동, 코리아비지니스센타)<NA>2015052920180529<NA>20150529본점20171113
29271대부업<NA>2008-서울특별시-01256(대부업)대원개인024763368서울특별시 강동구 천호동 221-39<NA><NA>20080229<NA>2009122820080221본점20091229
28973대부중개업<NA>2009-서울특별시-02006(대부중개성일금융대부개인027717870서울특별시 중구 무교동 25번지 1호 원창빌딩 304호<NA><NA>20090820<NA>2010021020090820본점20100217
16795대부중개업폐업2011-서울동작-00043(대부중개업)MS credit 대부개인<NA>서울특별시 동작구 상도동 285번지 9호 401<NA>15684620111111201411112014012920111111본점20140129
4960대부업유효기간만료2016-서울영등포-0894(대부업)나이스제7차대부(유)법인02-761-6875서울특별시 영등포구 여의도동 14번지 24호 삼보호정빌딩서울특별시 영등포구 국회대로68길 11, 삼보호정빌딩 9층 (여의도동)<NA>2018120520211203<NA><NA>본점20211206
26313대부업<NA>2009-서울특별시-00389(대부업)경도개인<NA>서울특별시 강서구 화곡동 452-32<NA><NA>20090217<NA>2010112620090217본점20101126
9005대부중개업폐업2017-서울강남-0239(대부중개업)현대부동산투자개인02-553-7772서울특별시 강남구 역삼동 705번지 1호 빅토리아오피스텔-1313서울특별시 강남구 테헤란로 327, 1313호 (역삼동, 빅토리아오피스텔)<NA>20170913202009132018050820170913본점20180508
4438대부업영업중2022-서울도봉-0007(대부)진대부개인02-906-8780서울특별시 도봉구 창동 307번지 2호 창동1단지주공아파트서울특별시 도봉구 해등로 109, 창동1단지주공아파트 지하층 제1호중 제30호 (창동)<NA>2021110220241102<NA>20211102본점20220419
7327대부업유효기간만료2018-서울중랑-0014(대부업)THE 아이티대부개인<NA>서울특별시 중랑구 면목동 174번지 67호 제3층-제303서울특별시 중랑구 겸재로10가길 11, 제3층 제303호 (면목동)<NA>20160901201909022019090320160831본점20190904
11376대부중개업유효기간만료2014-서울서초-0107(대부중개업)(주)제이앤비에셋대부법인02-3487-9456서울특별시 서초구 서초동 1426번지 2호 삼원빌딩-402서울특별시 서초구 남부순환로 2449, 402호 (서초동, 삼원빌딩)13786420130620201606202016062220130620본점20170117
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30835<NA><NA>2008-서울특별시-02291굿머니개인024937030서울특별시 중랑구 면목7동 527-300 청산빌라 3가동 101호<NA>13120720080902<NA>20090514<NA>본점20090622
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28375대부업<NA><NA>한울금융대부개인029451755서울특별시 성북구 길음동 1070번지 19호 길빌딩 203호<NA><NA>20091019<NA>2010042620091019본점20100426
25356대부중개업<NA>2010-서울중구-0150(대부중개업)경민상사대부중개개인027755216서울특별시 중구 명동1가 7번지 태흥빌딩 1505호<NA><NA>20101108201311072011031120071107본점20110311
7856대부업폐업2013-서울영등포-0479(대부업)영찬대부개인<NA><NA>서울특별시 영등포구 국회대로29길 13, 3동 405호 (당산동4가, 유원제일아파트)<NA>20160504201905042019041620071109본점20190416
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1739대부업유효기간만료2014-서울양천-00051(대부업)달빛대부개인<NA>서울특별시 양천구 신정동 942번지 14호 경도오피스텔-701서울특별시 양천구 중앙로 320, 경도오피스텔 701호 (신정동)<NA>2020081420230814<NA>20140923본점20230914
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0대부업타시군구이관2020-서울마포-0027(대부업)브릿지오토론대부개인1544-5919서울특별시 마포구 공덕동 462번지 마포공덕파크팰리스 Ⅱ서울특별시 마포구 마포대로 143, 마포공덕파크팰리스 Ⅱ 804호 (공덕동)<NA>20200602202306022022012820200602본점202201282