Overview

Dataset statistics

Number of variables3
Number of observations60
Missing cells3
Missing cells (%)1.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory26.2 B

Variable types

Text3

Dataset

Description인천광역시 부평구_실내건축공사업 현황 데이터는 부평구 내에 실내건축공사업체의 업체명, 도로명 주소, 전화번호 정보를 제공하고 있습니다.
URLhttps://www.data.go.kr/data/15117928/fileData.do

Alerts

전화번호 has 3 (5.0%) missing valuesMissing
업체명 has unique valuesUnique
도로명주소 has unique valuesUnique

Reproduction

Analysis started2023-12-12 03:54:22.989307
Analysis finished2023-12-12 03:54:23.506724
Duration0.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업체명
Text

UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size612.0 B
2023-12-12T12:54:23.769880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length9
Mean length7.4333333
Min length4

Characters and Unicode

Total characters446
Distinct characters129
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)100.0%

Sample

1st row(유)애드게이트
2nd row(주)결실건축디자인
3rd row(주)금문건설
4th row(주)네이처디자인
5th row(주)누리아트
ValueCountFrequency (%)
유)애드게이트 1
 
1.7%
주)결실건축디자인 1
 
1.7%
주)현광이앤디 1
 
1.7%
주)제우스디자인 1
 
1.7%
주)제이엔엠 1
 
1.7%
주)중동 1
 
1.7%
주)지주산업개발 1
 
1.7%
주)진영공영 1
 
1.7%
주)참스케치건설 1
 
1.7%
주)청운건축 1
 
1.7%
Other values (50) 50
83.3%
2023-12-12T12:54:24.343315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 51
 
11.4%
( 49
 
11.0%
) 49
 
11.0%
스 15
 
3.4%
건 15
 
3.4%
디 13
 
2.9%
이 12
 
2.7%
엔 11
 
2.5%
인 10
 
2.2%
자 8
 
1.8%
Other values (119) 213
47.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 346
77.6%
Open Punctuation 49
 
11.0%
Close Punctuation 49
 
11.0%
Other Symbol 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 51
 
14.7%
스 15
 
4.3%
건 15
 
4.3%
디 13
 
3.8%
이 12
 
3.5%
엔 11
 
3.2%
인 10
 
2.9%
자 8
 
2.3%
씨 8
 
2.3%
설 8
 
2.3%
Other values (116) 195
56.4%
Open Punctuation
ValueCountFrequency (%)
( 49
100.0%
Close Punctuation
ValueCountFrequency (%)
) 49
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 348
78.0%
Common 98
 
22.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 51
 
14.7%
스 15
 
4.3%
건 15
 
4.3%
디 13
 
3.7%
이 12
 
3.4%
엔 11
 
3.2%
인 10
 
2.9%
자 8
 
2.3%
씨 8
 
2.3%
설 8
 
2.3%
Other values (117) 197
56.6%
Common
ValueCountFrequency (%)
( 49
50.0%
) 49
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 346
77.6%
ASCII 98
 
22.0%
None 2
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 51
 
14.7%
스 15
 
4.3%
건 15
 
4.3%
디 13
 
3.8%
이 12
 
3.5%
엔 11
 
3.2%
인 10
 
2.9%
자 8
 
2.3%
씨 8
 
2.3%
설 8
 
2.3%
Other values (116) 195
56.4%
ASCII
ValueCountFrequency (%)
( 49
50.0%
) 49
50.0%
None
ValueCountFrequency (%)
㈜ 2
100.0%

도로명주소
Text

UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size612.0 B
2023-12-12T12:54:24.674593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length36.5
Mean length30.2
Min length21

Characters and Unicode

Total characters1812
Distinct characters98
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)100.0%

Sample

1st row인천광역시 부평구 충선로 102 3층 (부개동)
2nd row인천광역시 부평구 평천로 290 603호 (갈산동)
3rd row인천광역시 부평구 길주남로125번길 24， 301호 (부개동)
4th row인천광역시 부평구 충선로203번길 24 삼산프라자 606호 (삼산동)
5th row인천광역시 부평구 경인로1148번길 24 (일신동)
ValueCountFrequency (%)
인천광역시 60
 
16.0%
부평구 60
 
16.0%
부평동 12
 
3.2%
갈산동 10
 
2.7%
2층 9
 
2.4%
산곡동 9
 
2.4%
청천동 8
 
2.1%
부개동 8
 
2.1%
삼산동 7
 
1.9%
1층 7
 
1.9%
Other values (130) 186
49.5%
2023-12-12T12:54:25.287222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
316
 
17.4%
부 93
 
5.1%
평 84
 
4.6%
천 73
 
4.0%
동 69
 
3.8%
인 65
 
3.6%
1 64
 
3.5%
2 61
 
3.4%
시 60
 
3.3%
역 60
 
3.3%
Other values (88) 867
47.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1017
56.1%
Decimal Number 338
 
18.7%
Space Separator 316
 
17.4%
Open Punctuation 60
 
3.3%
Close Punctuation 60
 
3.3%
Dash Punctuation 9
 
0.5%
Other Punctuation 8
 
0.4%
Uppercase Letter 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 93
 
9.1%
평 84
 
8.3%
천 73
 
7.2%
동 69
 
6.8%
인 65
 
6.4%
시 60
 
5.9%
역 60
 
5.9%
구 60
 
5.9%
로 60
 
5.9%
광 60
 
5.9%
Other values (71) 333
32.7%
Decimal Number
ValueCountFrequency (%)
1 64
18.9%
2 61
18.0%
0 45
13.3%
3 39
11.5%
4 32
9.5%
6 25
 
7.4%
9 20
 
5.9%
5 20
 
5.9%
8 19
 
5.6%
7 13
 
3.8%
Other Punctuation
ValueCountFrequency (%)
, 6
75.0%
， 2
 
25.0%
Space Separator
ValueCountFrequency (%)
316
100.0%
Open Punctuation
ValueCountFrequency (%)
( 60
100.0%
Close Punctuation
ValueCountFrequency (%)
) 60
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1017
56.1%
Common 791
43.7%
Latin 4
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 93
 
9.1%
평 84
 
8.3%
천 73
 
7.2%
동 69
 
6.8%
인 65
 
6.4%
시 60
 
5.9%
역 60
 
5.9%
구 60
 
5.9%
로 60
 
5.9%
광 60
 
5.9%
Other values (71) 333
32.7%
Common
ValueCountFrequency (%)
316
39.9%
1 64
 
8.1%
2 61
 
7.7%
( 60
 
7.6%
) 60
 
7.6%
0 45
 
5.7%
3 39
 
4.9%
4 32
 
4.0%
6 25
 
3.2%
9 20
 
2.5%
Other values (6) 69
 
8.7%
Latin
ValueCountFrequency (%)
B 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1017
56.1%
ASCII 793
43.8%
None 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
316
39.8%
1 64
 
8.1%
2 61
 
7.7%
( 60
 
7.6%
) 60
 
7.6%
0 45
 
5.7%
3 39
 
4.9%
4 32
 
4.0%
6 25
 
3.2%
9 20
 
2.5%
Other values (6) 71
 
9.0%
Hangul
ValueCountFrequency (%)
부 93
 
9.1%
평 84
 
8.3%
천 73
 
7.2%
동 69
 
6.8%
인 65
 
6.4%
시 60
 
5.9%
역 60
 
5.9%
구 60
 
5.9%
로 60
 
5.9%
광 60
 
5.9%
Other values (71) 333
32.7%
None
ValueCountFrequency (%)
， 2
100.0%

전화번호
Text

MISSING 

Distinct56
Distinct (%)98.2%
Missing3
Missing (%)5.0%
Memory size612.0 B
2023-12-12T12:54:25.631481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12
Min length9

Characters and Unicode

Total characters684
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)96.5%

Sample

1st row032-522-3960
2nd row02-565-2070
3rd row032-514-0100
4th row070-7596-5567
5th row032-327-0485
ValueCountFrequency (%)
032-466-9995 2
 
3.5%
032-507-7704 1
 
1.8%
032-504-3081 1
 
1.8%
032-505-9788 1
 
1.8%
032-519-2204 1
 
1.8%
032-504-1163 1
 
1.8%
032-575-4021 1
 
1.8%
070-8784-1949 1
 
1.8%
032-516-6863 1
 
1.8%
032-330-5222 1
 
1.8%
Other values (46) 46
80.7%
2023-12-12T12:54:26.073185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 116
17.0%
- 113
16.5%
2 98
14.3%
3 95
13.9%
5 56
8.2%
7 41
 
6.0%
1 41
 
6.0%
6 37
 
5.4%
4 35
 
5.1%
8 27
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 571
83.5%
Dash Punctuation 113
 
16.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 116
20.3%
2 98
17.2%
3 95
16.6%
5 56
9.8%
7 41
 
7.2%
1 41
 
7.2%
6 37
 
6.5%
4 35
 
6.1%
8 27
 
4.7%
9 25
 
4.4%
Dash Punctuation
ValueCountFrequency (%)
- 113
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 684
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 116
17.0%
- 113
16.5%
2 98
14.3%
3 95
13.9%
5 56
8.2%
7 41
 
6.0%
1 41
 
6.0%
6 37
 
5.4%
4 35
 
5.1%
8 27
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 116
17.0%
- 113
16.5%
2 98
14.3%
3 95
13.9%
5 56
8.2%
7 41
 
6.0%
1 41
 
6.0%
6 37
 
5.4%
4 35
 
5.1%
8 27
 
3.9%

Correlations

2023-12-12T12:54:26.201417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업체명도로명주소전화번호
업체명1.0001.0001.000
도로명주소1.0001.0001.000
전화번호1.0001.0001.000

Missing values

2023-12-12T12:54:23.364491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:54:23.462319image/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.

Sample

업체명도로명주소전화번호
0(유)애드게이트인천광역시 부평구 충선로 102 3층 (부개동)032-522-3960
1(주)결실건축디자인인천광역시 부평구 평천로 290 603호 (갈산동)02-565-2070
2(주)금문건설인천광역시 부평구 길주남로125번길 24， 301호 (부개동)032-514-0100
3(주)네이처디자인인천광역시 부평구 충선로203번길 24 삼산프라자 606호 (삼산동)070-7596-5567
4(주)누리아트인천광역시 부평구 경인로1148번길 24 (일신동)032-327-0485
5(주)도원인천광역시 부평구 길주로547번길 5, 4층 401호032-264-2409
6(주)들마건설인천광역시 부평구 동수로128번길 14 2층 (부개동)032-515-0093
7(주)디자인브로인천광역시 부평구 주부토로 236 B동 9층 905호 (갈산동)032-277-1220
8(주)리더스하우시스인천광역시 부평구 장제로159번길 4 201호 (부평동)032-361-1033
9(주)명품건설인천광역시 부평구 안남로 8 (부평동)032-566-4040
업체명도로명주소전화번호
50공간크라징인천광역시 부평구 일신로 69-1 (일신동)032-613-9842
51넥스트홈인천광역시 부평구 굴포로 83 101호 (갈산동)032-511-5551
52대한안전건설(주)인천광역시 부평구 마장로324번길 23 4층 (산곡동)032-223-1482
53샤론디자인인천광역시 부평구 마장로 141 1층 (산곡동)032-513-5533
54씨티이엔지(주)인천광역시 부평구 화랑로 134-5 (산곡동)070-4131-6404
55인테리어하경인천광역시 부평구 주부토로 261 107호 (갈산동, 신협빌딩) (갈산동)032-321-1672
56주식회사필앤미인천광역시 부평구 주부토로280번길 4 2층 (갈산동)1688-2725
57케스켐(주)인천광역시 부평구 부평대로 283 씨동 6층 611-2호 (청천동)032-505-0520
58피다건축인천광역시 부평구 평천로 290 6층 603호 (갈산동)<NA>
59휴스토리인천광역시 부평구 체육관로 18 202호 (삼산동)032-330-3033