Overview

Dataset statistics

Number of variables5
Number of observations183
Missing cells462
Missing cells (%)50.5%
Duplicate rows1
Duplicate rows (%)0.5%
Total size in memory7.3 KiB
Average record size in memory40.7 B

Variable types

Text3
DateTime1
Categorical1

Dataset

Description대전광역시 부동산개발업체(상호, 소재지, 등록일, 상태)로 22년 4월 기준 등록업체는 68개 입니다.
Author대전광역시
URLhttps://www.data.go.kr/data/15073579/fileData.do

Alerts

Dataset has 1 (0.5%) duplicate rowsDuplicates
등록번호 has 115 (62.8%) missing valuesMissing
상호 has 115 (62.8%) missing valuesMissing
영업소 소재지 has 117 (63.9%) missing valuesMissing
등록일 has 115 (62.8%) missing valuesMissing

Reproduction

Analysis started2023-12-12 05:46:28.361733
Analysis finished2023-12-12 05:46:29.295402
Duration0.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

등록번호
Text

MISSING 

Distinct68
Distinct (%)100.0%
Missing115
Missing (%)62.8%
Memory size1.6 KiB
2023-12-12T14:46:29.528156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters544
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)100.0%

Sample

1st row대전080001
2nd row대전080002
3rd row대전080015
4th row대전080016
5th row대전080021
ValueCountFrequency (%)
대전180005 1
 
1.5%
대전180009 1
 
1.5%
대전200005 1
 
1.5%
대전200004 1
 
1.5%
대전200003 1
 
1.5%
대전200002 1
 
1.5%
대전190007 1
 
1.5%
대전190004 1
 
1.5%
대전190002 1
 
1.5%
대전080015 1
 
1.5%
Other values (58) 58
85.3%
2023-12-12T14:46:30.017424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 209
38.4%
1 77
 
14.2%
대 68
 
12.5%
전 68
 
12.5%
2 38
 
7.0%
8 27
 
5.0%
6 13
 
2.4%
4 13
 
2.4%
9 9
 
1.7%
5 8
 
1.5%
Other values (2) 14
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 408
75.0%
Other Letter 136
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 209
51.2%
1 77
 
18.9%
2 38
 
9.3%
8 27
 
6.6%
6 13
 
3.2%
4 13
 
3.2%
9 9
 
2.2%
5 8
 
2.0%
7 8
 
2.0%
3 6
 
1.5%
Other Letter
ValueCountFrequency (%)
대 68
50.0%
전 68
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 408
75.0%
Hangul 136
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 209
51.2%
1 77
 
18.9%
2 38
 
9.3%
8 27
 
6.6%
6 13
 
3.2%
4 13
 
3.2%
9 9
 
2.2%
5 8
 
2.0%
7 8
 
2.0%
3 6
 
1.5%
Hangul
ValueCountFrequency (%)
대 68
50.0%
전 68
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 408
75.0%
Hangul 136
 
25.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 209
51.2%
1 77
 
18.9%
2 38
 
9.3%
8 27
 
6.6%
6 13
 
3.2%
4 13
 
3.2%
9 9
 
2.2%
5 8
 
2.0%
7 8
 
2.0%
3 6
 
1.5%
Hangul
ValueCountFrequency (%)
대 68
50.0%
전 68
50.0%

상호
Text

MISSING 

Distinct68
Distinct (%)100.0%
Missing115
Missing (%)62.8%
Memory size1.6 KiB
2023-12-12T14:46:30.303931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length8.7941176
Min length5

Characters and Unicode

Total characters598
Distinct characters126
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)100.0%

Sample

1st row인덕건설㈜
2nd row(주)지산종합건설
3rd row㈜케이티앤지
4th row㈜종합건축사사무소목성
5th row씨에치건설㈜
ValueCountFrequency (%)
주식회사 13
 
16.0%
주)대전신세계 1
 
1.2%
명두종합건설(주 1
 
1.2%
궁도건설(주 1
 
1.2%
청운종합건설 1
 
1.2%
토드건설산업 1
 
1.2%
재경건설(주 1
 
1.2%
새로운종합건설(주 1
 
1.2%
대청종합건설(주 1
 
1.2%
주)부원건설 1
 
1.2%
Other values (59) 59
72.8%
2023-12-12T14:46:30.746319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 60
 
10.0%
건 49
 
8.2%
( 44
 
7.4%
) 44
 
7.4%
설 43
 
7.2%
종 22
 
3.7%
합 22
 
3.7%
사 18
 
3.0%
회 14
 
2.3%
식 14
 
2.3%
Other values (116) 268
44.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 487
81.4%
Open Punctuation 44
 
7.4%
Close Punctuation 44
 
7.4%
Space Separator 13
 
2.2%
Other Symbol 9
 
1.5%
Decimal Number 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 60
 
12.3%
건 49
 
10.1%
설 43
 
8.8%
종 22
 
4.5%
합 22
 
4.5%
사 18
 
3.7%
회 14
 
2.9%
식 14
 
2.9%
산 11
 
2.3%
업 10
 
2.1%
Other values (111) 224
46.0%
Open Punctuation
ValueCountFrequency (%)
( 44
100.0%
Close Punctuation
ValueCountFrequency (%)
) 44
100.0%
Space Separator
ValueCountFrequency (%)
13
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 9
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 496
82.9%
Common 102
 
17.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 60
 
12.1%
건 49
 
9.9%
설 43
 
8.7%
종 22
 
4.4%
합 22
 
4.4%
사 18
 
3.6%
회 14
 
2.8%
식 14
 
2.8%
산 11
 
2.2%
업 10
 
2.0%
Other values (112) 233
47.0%
Common
ValueCountFrequency (%)
( 44
43.1%
) 44
43.1%
13
 
12.7%
1 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 487
81.4%
ASCII 102
 
17.1%
None 9
 
1.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 60
 
12.3%
건 49
 
10.1%
설 43
 
8.8%
종 22
 
4.5%
합 22
 
4.5%
사 18
 
3.7%
회 14
 
2.9%
식 14
 
2.9%
산 11
 
2.3%
업 10
 
2.1%
Other values (111) 224
46.0%
ASCII
ValueCountFrequency (%)
( 44
43.1%
) 44
43.1%
13
 
12.7%
1 1
 
1.0%
None
ValueCountFrequency (%)
㈜ 9
100.0%

영업소 소재지
Text

MISSING 

Distinct63
Distinct (%)95.5%
Missing117
Missing (%)63.9%
Memory size1.6 KiB
2023-12-12T14:46:31.172592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length37
Mean length29.787879
Min length16

Characters and Unicode

Total characters1966
Distinct characters149
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)90.9%

Sample

1st row대전광역시 유성구 대학로 53, 201호(봉명동, 솔리안)
2nd row대전광역시 대덕구 벚꽃길 71(평촌동)
3rd row대전광역시 서구 계룡로509번길 41(탄방동)
4th row대전광역시 중구 중앙로130번길 43, 4층(대흥동,대종빌딩)
5th row대전광역시 대덕구 계족로598번길 16, 201호
ValueCountFrequency (%)
대전광역시 65
 
18.8%
서구 27
 
7.8%
유성구 20
 
5.8%
중구 9
 
2.6%
대덕구 7
 
2.0%
계룡로 5
 
1.4%
대학로 4
 
1.2%
201호 3
 
0.9%
3층 3
 
0.9%
43 2
 
0.6%
Other values (182) 201
58.1%
2023-12-12T14:46:31.732651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
280
 
14.2%
대 89
 
4.5%
, 72
 
3.7%
구 71
 
3.6%
시 70
 
3.6%
동 67
 
3.4%
역 66
 
3.4%
광 65
 
3.3%
전 65
 
3.3%
로 65
 
3.3%
Other values (139) 1056
53.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1146
58.3%
Decimal Number 348
 
17.7%
Space Separator 280
 
14.2%
Other Punctuation 72
 
3.7%
Open Punctuation 55
 
2.8%
Close Punctuation 55
 
2.8%
Dash Punctuation 10
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 89
 
7.8%
구 71
 
6.2%
시 70
 
6.1%
동 67
 
5.8%
역 66
 
5.8%
광 65
 
5.7%
전 65
 
5.7%
로 65
 
5.7%
서 36
 
3.1%
길 33
 
2.9%
Other values (124) 519
45.3%
Decimal Number
ValueCountFrequency (%)
1 52
14.9%
2 48
13.8%
3 46
13.2%
0 43
12.4%
5 37
10.6%
4 35
10.1%
7 29
8.3%
8 26
7.5%
9 19
 
5.5%
6 13
 
3.7%
Space Separator
ValueCountFrequency (%)
280
100.0%
Other Punctuation
ValueCountFrequency (%)
, 72
100.0%
Open Punctuation
ValueCountFrequency (%)
( 55
100.0%
Close Punctuation
ValueCountFrequency (%)
) 55
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1146
58.3%
Common 820
41.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 89
 
7.8%
구 71
 
6.2%
시 70
 
6.1%
동 67
 
5.8%
역 66
 
5.8%
광 65
 
5.7%
전 65
 
5.7%
로 65
 
5.7%
서 36
 
3.1%
길 33
 
2.9%
Other values (124) 519
45.3%
Common
ValueCountFrequency (%)
280
34.1%
, 72
 
8.8%
( 55
 
6.7%
) 55
 
6.7%
1 52
 
6.3%
2 48
 
5.9%
3 46
 
5.6%
0 43
 
5.2%
5 37
 
4.5%
4 35
 
4.3%
Other values (5) 97
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1146
58.3%
ASCII 820
41.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
280
34.1%
, 72
 
8.8%
( 55
 
6.7%
) 55
 
6.7%
1 52
 
6.3%
2 48
 
5.9%
3 46
 
5.6%
0 43
 
5.2%
5 37
 
4.5%
4 35
 
4.3%
Other values (5) 97
 
11.8%
Hangul
ValueCountFrequency (%)
대 89
 
7.8%
구 71
 
6.2%
시 70
 
6.1%
동 67
 
5.8%
역 66
 
5.8%
광 65
 
5.7%
전 65
 
5.7%
로 65
 
5.7%
서 36
 
3.1%
길 33
 
2.9%
Other values (124) 519
45.3%

등록일
Date

MISSING 

Distinct63
Distinct (%)92.6%
Missing115
Missing (%)62.8%
Memory size1.6 KiB
Minimum2008-01-10 00:00:00
Maximum2022-03-08 00:00:00
2023-12-12T14:46:31.913935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:46:32.088045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

상태
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
<NA>
115 
등록완료
68 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row등록완료
2nd row등록완료
3rd row등록완료
4th row등록완료
5th row등록완료

Common Values

ValueCountFrequency (%)
<NA> 115
62.8%
등록완료 68
37.2%

Length

2023-12-12T14:46:32.208793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:46:32.307532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 115
62.8%
등록완료 68
37.2%

Correlations

2023-12-12T14:46:32.375814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록번호상호영업소 소재지등록일
등록번호1.0001.0001.0001.000
상호1.0001.0001.0001.000
영업소 소재지1.0001.0001.0000.977
등록일1.0001.0000.9771.000

Missing values

2023-12-12T14:46:28.956585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:46:29.093972image/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.
2023-12-12T14:46:29.222626image/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

등록번호상호영업소 소재지등록일상태
0대전080001인덕건설㈜대전광역시 유성구 대학로 53, 201호(봉명동, 솔리안)2008-01-10등록완료
1대전080002(주)지산종합건설<NA>2008-01-10등록완료
2대전080015㈜케이티앤지대전광역시 대덕구 벚꽃길 71(평촌동)2008-04-30등록완료
3대전080016㈜종합건축사사무소목성대전광역시 서구 계룡로509번길 41(탄방동)2008-05-02등록완료
4대전080021씨에치건설㈜대전광역시 중구 중앙로130번길 43, 4층(대흥동,대종빌딩)2008-05-16등록완료
5대전080022㈜원평종합건설대전광역시 대덕구 계족로598번길 16, 201호2008-05-16등록완료
6대전080030㈜금성백조주택대전광역시 서구 계룡로583번길 9(탄방동)2008-06-05등록완료
7대전080040동휘건설㈜대전광역시 동구 동구청로 89-22(가오동)2008-07-09등록완료
8대전080047계룡건설산업㈜대전광역시 서구 문정로48번길 48(탄방동)2008-12-30등록완료
9대전100004나성산업개발(주)대전광역시 중구 계백로 1605, 3층 302호(유천동)2010-04-23등록완료
등록번호상호영업소 소재지등록일상태
173<NA><NA><NA><NA><NA>
174<NA><NA><NA><NA><NA>
175<NA><NA><NA><NA><NA>
176<NA><NA><NA><NA><NA>
177<NA><NA><NA><NA><NA>
178<NA><NA><NA><NA><NA>
179<NA><NA><NA><NA><NA>
180<NA><NA><NA><NA><NA>
181<NA><NA><NA><NA><NA>
182<NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

등록번호상호영업소 소재지등록일상태# duplicates
0<NA><NA><NA><NA><NA>115