Overview

Dataset statistics

Number of variables5
Number of observations29
Missing cells6
Missing cells (%)4.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 KiB
Average record size in memory44.6 B

Variable types

Categorical1
Text4

Dataset

Description대전교통공사에서 운영하고 있는 임대사업 현황 입니다. 대상물건별 수량 계약기간 이 포함되어 있습니다. 2022년 8월 31일을 기준으로 작성되었습니다.
URLhttps://www.data.go.kr/data/15090184/fileData.do

Alerts

업종 has 3 (10.3%) missing valuesMissing
계약기간 has 3 (10.3%) missing valuesMissing

Reproduction

Analysis started2023-12-12 21:48:03.557702
Analysis finished2023-12-12 21:48:04.163549
Duration0.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct9
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
상가
21 
현금인출기
 
1
물품보관함
 
1
즉석사진기
 
1
공유배터리
 
1
Other values (4)
4 

Length

Max length6
Median length2
Mean length2.8965517
Min length2

Unique

Unique8 ?
Unique (%)27.6%

Sample

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

Common Values

ValueCountFrequency (%)
상가 21
72.4%
현금인출기 1
 
3.4%
물품보관함 1
 
3.4%
즉석사진기 1
 
3.4%
공유배터리 1
 
3.4%
환승주차장 1
 
3.4%
본사대강당 1
 
3.4%
무인민발급기 1
 
3.4%
음료수자판기 1
 
3.4%

Length

2023-12-13T06:48:04.271763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:48:04.430195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상가 21
72.4%
현금인출기 1
 
3.4%
물품보관함 1
 
3.4%
즉석사진기 1
 
3.4%
공유배터리 1
 
3.4%
환승주차장 1
 
3.4%
본사대강당 1
 
3.4%
무인민발급기 1
 
3.4%
음료수자판기 1
 
3.4%

위치
Text

Distinct25
Distinct (%)86.2%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-13T06:48:04.683019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length10
Mean length5.862069
Min length3

Characters and Unicode

Total characters170
Distinct characters53
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

Unique21 ?
Unique (%)72.4%

Sample

1st row판암역
2nd row대동역
3rd row대전역(1호점)
4th row대전역(2호점)
5th row대전역(3,4호점)
ValueCountFrequency (%)
7개역 2
 
5.1%
유성온천역 2
 
5.1%
시청역 2
 
5.1%
중앙로역 2
 
5.1%
7대 2
 
5.1%
판암역 2
 
5.1%
반석역 2
 
5.1%
인근 1
 
2.6%
6대 1
 
2.6%
22개역 1
 
2.6%
Other values (22) 22
56.4%
2023-12-13T06:48:05.068263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
역 29
17.1%
대 14
 
8.2%
10
 
5.9%
전 8
 
4.7%
) 7
 
4.1%
점 7
 
4.1%
호 7
 
4.1%
( 7
 
4.1%
7 5
 
2.9%
개 5
 
2.9%
Other values (43) 71
41.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 120
70.6%
Decimal Number 23
 
13.5%
Space Separator 10
 
5.9%
Close Punctuation 7
 
4.1%
Open Punctuation 7
 
4.1%
Other Punctuation 3
 
1.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 29
24.2%
대 14
 
11.7%
전 8
 
6.7%
점 7
 
5.8%
호 7
 
5.8%
개 5
 
4.2%
사 3
 
2.5%
청 3
 
2.5%
암 2
 
1.7%
석 2
 
1.7%
Other values (30) 40
33.3%
Decimal Number
ValueCountFrequency (%)
7 5
21.7%
2 4
17.4%
1 3
13.0%
6 3
13.0%
8 2
 
8.7%
4 2
 
8.7%
3 2
 
8.7%
9 1
 
4.3%
5 1
 
4.3%
Space Separator
ValueCountFrequency (%)
10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 120
70.6%
Common 50
29.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 29
24.2%
대 14
 
11.7%
전 8
 
6.7%
점 7
 
5.8%
호 7
 
5.8%
개 5
 
4.2%
사 3
 
2.5%
청 3
 
2.5%
암 2
 
1.7%
석 2
 
1.7%
Other values (30) 40
33.3%
Common
ValueCountFrequency (%)
10
20.0%
) 7
14.0%
( 7
14.0%
7 5
10.0%
2 4
 
8.0%
, 3
 
6.0%
1 3
 
6.0%
6 3
 
6.0%
8 2
 
4.0%
4 2
 
4.0%
Other values (3) 4
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 120
70.6%
ASCII 50
29.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
역 29
24.2%
대 14
 
11.7%
전 8
 
6.7%
점 7
 
5.8%
호 7
 
5.8%
개 5
 
4.2%
사 3
 
2.5%
청 3
 
2.5%
암 2
 
1.7%
석 2
 
1.7%
Other values (30) 40
33.3%
ASCII
ValueCountFrequency (%)
10
20.0%
) 7
14.0%
( 7
14.0%
7 5
10.0%
2 4
 
8.0%
, 3
 
6.0%
1 3
 
6.0%
6 3
 
6.0%
8 2
 
4.0%
4 2
 
4.0%
Other values (3) 4
 
8.0%

면적
Text

Distinct28
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-13T06:48:05.277440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length8
Mean length5.5862069
Min length2

Characters and Unicode

Total characters162
Distinct characters20
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

Unique27 ?
Unique (%)93.1%

Sample

1st row33.38㎡
2nd row6.185㎡
3rd row24㎡
4th row24㎡
5th row48㎡
ValueCountFrequency (%)
대당 4
 
11.1%
24㎡ 2
 
5.6%
1㎡ 2
 
5.6%
24.28㎡ 1
 
2.8%
70.35㎡ 1
 
2.8%
입실 1
 
2.8%
100명 1
 
2.8%
7,229㎡ 1
 
2.8%
0.5㎡ 1
 
2.8%
2.5㎡ 1
 
2.8%
Other values (21) 21
58.3%
2023-12-13T06:48:05.630273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
㎡ 29
17.9%
. 20
12.3%
2 17
10.5%
1 15
9.3%
3 15
9.3%
4 8
 
4.9%
0 8
 
4.9%
8 8
 
4.9%
7
 
4.3%
5 6
 
3.7%
Other values (10) 29
17.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 91
56.2%
Other Symbol 29
 
17.9%
Other Punctuation 21
 
13.0%
Other Letter 13
 
8.0%
Space Separator 7
 
4.3%
Math Symbol 1
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 17
18.7%
1 15
16.5%
3 15
16.5%
4 8
8.8%
0 8
8.8%
8 8
8.8%
5 6
 
6.6%
9 5
 
5.5%
6 5
 
5.5%
7 4
 
4.4%
Other Letter
ValueCountFrequency (%)
대 5
38.5%
당 5
38.5%
명 1
 
7.7%
입 1
 
7.7%
실 1
 
7.7%
Other Punctuation
ValueCountFrequency (%)
. 20
95.2%
, 1
 
4.8%
Other Symbol
ValueCountFrequency (%)
㎡ 29
100.0%
Space Separator
ValueCountFrequency (%)
7
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 149
92.0%
Hangul 13
 
8.0%

Most frequent character per script

Common
ValueCountFrequency (%)
㎡ 29
19.5%
. 20
13.4%
2 17
11.4%
1 15
10.1%
3 15
10.1%
4 8
 
5.4%
0 8
 
5.4%
8 8
 
5.4%
7
 
4.7%
5 6
 
4.0%
Other values (5) 16
10.7%
Hangul
ValueCountFrequency (%)
대 5
38.5%
당 5
38.5%
명 1
 
7.7%
입 1
 
7.7%
실 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120
74.1%
CJK Compat 29
 
17.9%
Hangul 13
 
8.0%

Most frequent character per block

CJK Compat
ValueCountFrequency (%)
㎡ 29
100.0%
ASCII
ValueCountFrequency (%)
. 20
16.7%
2 17
14.2%
1 15
12.5%
3 15
12.5%
4 8
 
6.7%
0 8
 
6.7%
8 8
 
6.7%
7
 
5.8%
5 6
 
5.0%
9 5
 
4.2%
Other values (4) 11
9.2%
Hangul
ValueCountFrequency (%)
대 5
38.5%
당 5
38.5%
명 1
 
7.7%
입 1
 
7.7%
실 1
 
7.7%

업종
Text

MISSING 

Distinct19
Distinct (%)73.1%
Missing3
Missing (%)10.3%
Memory size364.0 B
2023-12-13T06:48:05.816747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.7692308
Min length2

Characters and Unicode

Total characters98
Distinct characters60
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

Unique17 ?
Unique (%)65.4%

Sample

1st row커피점
2nd row무인샌드위치
3rd row무인카페
4th row공예체험장
5th row휴대폰 등
ValueCountFrequency (%)
편의점 6
22.2%
의류점 3
 
11.1%
카페 1
 
3.7%
민원발급기 1
 
3.7%
대강당 1
 
3.7%
주차장 1
 
3.7%
공유배터리 1
 
3.7%
즉석사진기 1
 
3.7%
물품보관함 1
 
3.7%
현금인출기 1
 
3.7%
Other values (10) 10
37.0%
2023-12-13T06:48:06.137204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 12
 
12.2%
의 9
 
9.2%
편 6
 
6.1%
기 4
 
4.1%
인 3
 
3.1%
류 3
 
3.1%
공 3
 
3.1%
페 2
 
2.0%
무 2
 
2.0%
대 2
 
2.0%
Other values (50) 52
53.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 97
99.0%
Space Separator 1
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 12
 
12.4%
의 9
 
9.3%
편 6
 
6.2%
기 4
 
4.1%
인 3
 
3.1%
류 3
 
3.1%
공 3
 
3.1%
페 2
 
2.1%
무 2
 
2.1%
대 2
 
2.1%
Other values (49) 51
52.6%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 97
99.0%
Common 1
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 12
 
12.4%
의 9
 
9.3%
편 6
 
6.2%
기 4
 
4.1%
인 3
 
3.1%
류 3
 
3.1%
공 3
 
3.1%
페 2
 
2.1%
무 2
 
2.1%
대 2
 
2.1%
Other values (49) 51
52.6%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 97
99.0%
ASCII 1
 
1.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 12
 
12.4%
의 9
 
9.3%
편 6
 
6.2%
기 4
 
4.1%
인 3
 
3.1%
류 3
 
3.1%
공 3
 
3.1%
페 2
 
2.1%
무 2
 
2.1%
대 2
 
2.1%
Other values (49) 51
52.6%
ASCII
ValueCountFrequency (%)
1
100.0%

계약기간
Text

MISSING 

Distinct24
Distinct (%)92.3%
Missing3
Missing (%)10.3%
Memory size364.0 B
2023-12-13T06:48:06.301711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length26
Mean length24.038462
Min length2

Characters and Unicode

Total characters625
Distinct characters20
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

Unique22 ?
Unique (%)84.6%

Sample

1st row2020-01-01~2024-12-31 (5년)
2nd row2021-04-05~2026-04-04 (5년)
3rd row2020-05-06~2025-05-05 (5년)
4th row2019-05-01~2024-04-30 (5년)
5th row2020-05-10~2025-05-09 (5년)
ValueCountFrequency (%)
5년 12
26.7%
3년 5
 
11.1%
2020-01-01~2024-12-31 2
 
4.4%
2023-03-30~2023-09-29 2
 
4.4%
6개월 2
 
4.4%
2021-10-27~2026-10-26 1
 
2.2%
수시 1
 
2.2%
2021-07-27~2024-07-26 1
 
2.2%
2021-06-21~2024-06-20 1
 
2.2%
2019-08-09~2024-08-08 1
 
2.2%
Other values (17) 17
37.8%
2023-12-13T06:48:06.580390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 124
19.8%
2 117
18.7%
- 96
15.4%
1 41
 
6.6%
3 33
 
5.3%
5 25
 
4.0%
~ 24
 
3.8%
) 24
 
3.8%
( 24
 
3.8%
년 23
 
3.7%
Other values (10) 94
15.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 409
65.4%
Dash Punctuation 96
 
15.4%
Other Letter 29
 
4.6%
Math Symbol 24
 
3.8%
Close Punctuation 24
 
3.8%
Open Punctuation 24
 
3.8%
Space Separator 19
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 124
30.3%
2 117
28.6%
1 41
 
10.0%
3 33
 
8.1%
5 25
 
6.1%
4 19
 
4.6%
6 19
 
4.6%
9 15
 
3.7%
7 9
 
2.2%
8 7
 
1.7%
Other Letter
ValueCountFrequency (%)
년 23
79.3%
개 2
 
6.9%
월 2
 
6.9%
수 1
 
3.4%
시 1
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
- 96
100.0%
Math Symbol
ValueCountFrequency (%)
~ 24
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%
Space Separator
ValueCountFrequency (%)
19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 596
95.4%
Hangul 29
 
4.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 124
20.8%
2 117
19.6%
- 96
16.1%
1 41
 
6.9%
3 33
 
5.5%
5 25
 
4.2%
~ 24
 
4.0%
) 24
 
4.0%
( 24
 
4.0%
19
 
3.2%
Other values (5) 69
11.6%
Hangul
ValueCountFrequency (%)
년 23
79.3%
개 2
 
6.9%
월 2
 
6.9%
수 1
 
3.4%
시 1
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 596
95.4%
Hangul 29
 
4.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 124
20.8%
2 117
19.6%
- 96
16.1%
1 41
 
6.9%
3 33
 
5.5%
5 25
 
4.2%
~ 24
 
4.0%
) 24
 
4.0%
( 24
 
4.0%
19
 
3.2%
Other values (5) 69
11.6%
Hangul
ValueCountFrequency (%)
년 23
79.3%
개 2
 
6.9%
월 2
 
6.9%
수 1
 
3.4%
시 1
 
3.4%

Correlations

2023-12-13T06:48:06.671700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분위치면적업종계약기간
구분1.0000.0001.0001.0000.912
위치0.0001.0000.9580.8650.986
면적1.0000.9581.0000.9090.962
업종1.0000.8650.9091.0000.889
계약기간0.9120.9860.9620.8891.000

Missing values

2023-12-13T06:48:03.878713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:48:03.997472image/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.
2023-12-13T06:48:04.112857image/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

구분위치면적업종계약기간
0상가판암역33.38㎡<NA><NA>
1상가대동역6.185㎡커피점2020-01-01~2024-12-31 (5년)
2상가대전역(1호점)24㎡무인샌드위치2021-04-05~2026-04-04 (5년)
3상가대전역(2호점)24㎡무인카페2020-05-06~2025-05-05 (5년)
4상가대전역(3,4호점)48㎡공예체험장2019-05-01~2024-04-30 (5년)
5상가대전역(5호점)41.2㎡휴대폰 등2020-05-10~2025-05-09 (5년)
6상가대전역(6호점)22.3㎡의류점2021-11-01~2023-10-31 (2년)
7상가대전역(7호점)39.27㎡의류점2023-08-24~2026-08-23(3년)
8상가대전역(8,9호점)631㎡청년활동공간2023-04-10~2026-08-23(3년)
9상가중앙로역13.83㎡편의점2023-03-30~2023-09-29 (6개월)
구분위치면적업종계약기간
19상가지족역19.25㎡<NA><NA>
20상가반석역19㎡카페2021-05-15~2024-05-14 (3년)
21현금인출기6개역 6대대당 2.0㎡현금인출기2021-06-10~2024-06-09 (3년)
22물품보관함7개역 7대대당 0.75㎡ ~ 2.5㎡물품보관함2023-01-10~2026-01-09(3년)
23즉석사진기7개역 7대대당 1㎡즉석사진기2019-08-09~2024-08-08 (5년)
24공유배터리22개역 24대대당 0.5㎡공유배터리2021-06-21~2024-06-20 (3년)
25환승주차장반석역 인근7,229㎡주차장2021-07-27~2024-07-26 (3년)
26본사대강당공사 내100명 입실대강당수시
27무인민발급기유성온천역1㎡민원발급기2020-01-01~2024-12-31 (5년)
28음료수자판기판암역 등 18개 역사, 31대대당1.331㎡자판기5년