Overview

Dataset statistics

Number of variables3
Number of observations32
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory900.0 B
Average record size in memory28.1 B

Variable types

Text3

Dataset

Description인천광역시 부평구_철근 콘크리트공사업 현황 데이터는 부평구 내에 철근 콘크리트공사업체의 업체명, 도로명 주소, 전화번호 정보를 제공하고 있습니다.
URLhttps://www.data.go.kr/data/15117940/fileData.do

Alerts

업체명 has unique valuesUnique
도로명주소 has unique valuesUnique
전화번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 00:29:53.436906
Analysis finished2023-12-12 00:29:53.758743
Duration0.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업체명
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-12T09:29:53.905542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length7.4375
Min length5

Characters and Unicode

Total characters238
Distinct characters75
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

Unique32 ?
Unique (%)100.0%

Sample

1st row(주)국민건설
2nd row(주)대동건업
3rd row(주)대맥이엔씨
4th row(주)보은건설
5th row(주)선은종합통상
ValueCountFrequency (%)
주)국민건설 1
 
3.1%
주)대동건업 1
 
3.1%
태형건영(주 1
 
3.1%
주식회사유현건설 1
 
3.1%
조이공영건설(주 1
 
3.1%
젠코건설(주 1
 
3.1%
임선건설(주 1
 
3.1%
일흥건설(주 1
 
3.1%
유청건설(주 1
 
3.1%
우애건설(주 1
 
3.1%
Other values (22) 22
68.8%
2023-12-12T09:29:54.267843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 30
 
12.6%
( 28
 
11.8%
) 28
 
11.8%
건 24
 
10.1%
설 19
 
8.0%
이 5
 
2.1%
영 4
 
1.7%
토 4
 
1.7%
업 4
 
1.7%
스 3
 
1.3%
Other values (65) 89
37.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 180
75.6%
Open Punctuation 28
 
11.8%
Close Punctuation 28
 
11.8%
Other Symbol 2
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 30
 
16.7%
건 24
 
13.3%
설 19
 
10.6%
이 5
 
2.8%
영 4
 
2.2%
토 4
 
2.2%
업 4
 
2.2%
스 3
 
1.7%
산 3
 
1.7%
선 3
 
1.7%
Other values (62) 81
45.0%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 182
76.5%
Common 56
 
23.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 30
 
16.5%
건 24
 
13.2%
설 19
 
10.4%
이 5
 
2.7%
영 4
 
2.2%
토 4
 
2.2%
업 4
 
2.2%
스 3
 
1.6%
산 3
 
1.6%
선 3
 
1.6%
Other values (63) 83
45.6%
Common
ValueCountFrequency (%)
( 28
50.0%
) 28
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 180
75.6%
ASCII 56
 
23.5%
None 2
 
0.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 30
 
16.7%
건 24
 
13.3%
설 19
 
10.6%
이 5
 
2.8%
영 4
 
2.2%
토 4
 
2.2%
업 4
 
2.2%
스 3
 
1.7%
산 3
 
1.7%
선 3
 
1.7%
Other values (62) 81
45.0%
ASCII
ValueCountFrequency (%)
( 28
50.0%
) 28
50.0%
None
ValueCountFrequency (%)
㈜ 2
100.0%

도로명주소
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-12T09:29:54.845166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length38
Mean length32.125
Min length22

Characters and Unicode

Total characters1028
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

Unique32 ?
Unique (%)100.0%

Sample

1st row인천광역시 부평구 길주남로 70-1 2층 (부평동)
2nd row인천광역시 부평구 마장로204번길 4 (산곡동)
3rd row인천광역시 부평구 백범로577번길 20 관리동 405호(십정동, 경인센타) (십정동)
4th row인천광역시 부평구 일신로 85 (일신동)
5th row인천광역시 부평구 화랑북로4번길 2 2층 (산곡동)
ValueCountFrequency (%)
인천광역시 32
 
15.5%
부평구 32
 
15.5%
부평동 8
 
3.9%
삼산동 6
 
2.9%
십정동 5
 
2.4%
2층 4
 
1.9%
부개동 4
 
1.9%
상가동 2
 
1.0%
갈산동 2
 
1.0%
3동 2
 
1.0%
Other values (97) 109
52.9%
2023-12-12T09:29:55.335078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
174
 
16.9%
부 56
 
5.4%
평 48
 
4.7%
동 42
 
4.1%
인 36
 
3.5%
1 35
 
3.4%
( 35
 
3.4%
천 35
 
3.4%
) 35
 
3.4%
시 33
 
3.2%
Other values (88) 499
48.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 581
56.5%
Decimal Number 181
 
17.6%
Space Separator 174
 
16.9%
Open Punctuation 35
 
3.4%
Close Punctuation 35
 
3.4%
Other Punctuation 11
 
1.1%
Uppercase Letter 6
 
0.6%
Dash Punctuation 5
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 56
 
9.6%
평 48
 
8.3%
동 42
 
7.2%
인 36
 
6.2%
천 35
 
6.0%
시 33
 
5.7%
광 32
 
5.5%
역 32
 
5.5%
구 32
 
5.5%
로 32
 
5.5%
Other values (66) 203
34.9%
Decimal Number
ValueCountFrequency (%)
1 35
19.3%
0 27
14.9%
3 24
13.3%
2 23
12.7%
4 20
11.0%
5 15
8.3%
7 11
 
6.1%
8 11
 
6.1%
6 11
 
6.1%
9 4
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
Z 1
16.7%
O 1
16.7%
N 1
16.7%
E 1
16.7%
H 1
16.7%
I 1
16.7%
Other Punctuation
ValueCountFrequency (%)
, 9
81.8%
， 2
 
18.2%
Space Separator
ValueCountFrequency (%)
174
100.0%
Open Punctuation
ValueCountFrequency (%)
( 35
100.0%
Close Punctuation
ValueCountFrequency (%)
) 35
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 581
56.5%
Common 441
42.9%
Latin 6
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 56
 
9.6%
평 48
 
8.3%
동 42
 
7.2%
인 36
 
6.2%
천 35
 
6.0%
시 33
 
5.7%
광 32
 
5.5%
역 32
 
5.5%
구 32
 
5.5%
로 32
 
5.5%
Other values (66) 203
34.9%
Common
ValueCountFrequency (%)
174
39.5%
1 35
 
7.9%
( 35
 
7.9%
) 35
 
7.9%
0 27
 
6.1%
3 24
 
5.4%
2 23
 
5.2%
4 20
 
4.5%
5 15
 
3.4%
7 11
 
2.5%
Other values (6) 42
 
9.5%
Latin
ValueCountFrequency (%)
Z 1
16.7%
O 1
16.7%
N 1
16.7%
E 1
16.7%
H 1
16.7%
I 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 581
56.5%
ASCII 445
43.3%
None 2
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
174
39.1%
1 35
 
7.9%
( 35
 
7.9%
) 35
 
7.9%
0 27
 
6.1%
3 24
 
5.4%
2 23
 
5.2%
4 20
 
4.5%
5 15
 
3.4%
7 11
 
2.5%
Other values (11) 46
 
10.3%
Hangul
ValueCountFrequency (%)
부 56
 
9.6%
평 48
 
8.3%
동 42
 
7.2%
인 36
 
6.2%
천 35
 
6.0%
시 33
 
5.7%
광 32
 
5.5%
역 32
 
5.5%
구 32
 
5.5%
로 32
 
5.5%
Other values (66) 203
34.9%
None
ValueCountFrequency (%)
， 2
100.0%

전화번호
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-12T09:29:55.556054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.03125
Min length11

Characters and Unicode

Total characters385
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

Unique32 ?
Unique (%)100.0%

Sample

1st row032-505-4537
2nd row032-504-6265
3rd row031-981-2712
4th row032-504-0044
5th row032-715-6691
ValueCountFrequency (%)
032-505-4537 1
 
3.1%
032-504-6265 1
 
3.1%
032-326-0409 1
 
3.1%
032-330-1640 1
 
3.1%
032-513-2002 1
 
3.1%
032-506-9231 1
 
3.1%
032-513-1625 1
 
3.1%
032-529-8100 1
 
3.1%
032-327-9572 1
 
3.1%
070-4082-0404 1
 
3.1%
Other values (22) 22
68.8%
2023-12-12T09:29:56.003565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 65
16.9%
- 64
16.6%
2 60
15.6%
3 50
13.0%
5 31
8.1%
4 27
7.0%
1 23
 
6.0%
6 22
 
5.7%
9 15
 
3.9%
8 15
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 321
83.4%
Dash Punctuation 64
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 65
20.2%
2 60
18.7%
3 50
15.6%
5 31
9.7%
4 27
8.4%
1 23
 
7.2%
6 22
 
6.9%
9 15
 
4.7%
8 15
 
4.7%
7 13
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 385
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 65
16.9%
- 64
16.6%
2 60
15.6%
3 50
13.0%
5 31
8.1%
4 27
7.0%
1 23
 
6.0%
6 22
 
5.7%
9 15
 
3.9%
8 15
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 385
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 65
16.9%
- 64
16.6%
2 60
15.6%
3 50
13.0%
5 31
8.1%
4 27
7.0%
1 23
 
6.0%
6 22
 
5.7%
9 15
 
3.9%
8 15
 
3.9%

Correlations

2023-12-12T09:29:56.098522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업체명도로명주소전화번호
업체명1.0001.0001.000
도로명주소1.0001.0001.000
전화번호1.0001.0001.000

Missing values

2023-12-12T09:29:53.657814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:29:53.730269image/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(주)국민건설인천광역시 부평구 길주남로 70-1 2층 (부평동)032-505-4537
1(주)대동건업인천광역시 부평구 마장로204번길 4 (산곡동)032-504-6265
2(주)대맥이엔씨인천광역시 부평구 백범로577번길 20 관리동 405호(십정동, 경인센타) (십정동)031-981-2712
3(주)보은건설인천광역시 부평구 일신로 85 (일신동)032-504-0044
4(주)선은종합통상인천광역시 부평구 화랑북로4번길 2 2층 (산곡동)032-715-6691
5(주)성희건설인천광역시 부평구 영성로 16 1동 201호 (삼산동)032-505-1008
6(주)승혜건설인천광역시 부평구 경인로 707 403호 (십정동)032-424-9846
7(주)에이원이앤지인천광역시 부평구 청안로 8 상가동 202호 (청천동)032-503-8847
8(주)엘에스폼웍인천광역시 부평구 경인로1046번길 7 동아빌딩 2층 (부개동)032-508-6750
9(주)인정산업개발인천광역시 부평구 일신로 85 2층 (일신동)070-4466-6997
업체명도로명주소전화번호
22신정건설(주)인천광역시 부평구 백범로 533 진영빌딩 302호 (십정동)032-813-2285
23우애건설(주)인천광역시 부평구 부평대로 153 101동 18층 1816호 (부평동)070-4082-0404
24유청건설(주)인천광역시 부평구 체육관로 32 하이존(HI-ZONE) 403호 (삼산동)032-327-9572
25일흥건설(주)인천광역시 부평구 부흥로365번길 3, 701호(부평동, 상인빌딩)032-529-8100
26임선건설(주)인천광역시 부평구 대정로 66 4층 406호 (부평동)032-513-1625
27젠코건설(주)인천광역시 부평구 수변로 95 301호 (부개동)032-506-9231
28조이공영건설(주)인천광역시 부평구 부흥로 344, 302호 (부평동, 부평펜타곤빌딩)032-513-2002
29주식회사유현건설인천광역시 부평구 주부토로81번길 50 3동 214호 (부평동)032-330-1640
30태형건영(주)인천광역시 부평구 부평문화로 196 (주)케이티 남부평사옥 본관3층 (부개동)032-326-0409
31화평토건주식회사인천광역시 부평구 장제로 45 3동 622호 (부평동)02-525-2341