Overview

Dataset statistics

Number of variables5
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory993.0 B
Average record size in memory47.3 B

Variable types

Categorical4
Text1

Dataset

Description대전광역시 버스전용차로(가로변) 단속카메라 현황에 대하여 구분(가로변), 구간명, 구간, 설치위치, 대수 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15081428/fileData.do

Alerts

구분 has constant value ""Constant
대수 has constant value ""Constant
구간명 is highly overall correlated with 구간High correlation
구간 is highly overall correlated with 구간명High correlation
설치위치 has unique valuesUnique

Reproduction

Analysis started2023-12-12 13:38:12.259577
Analysis finished2023-12-12 13:38:12.594108
Duration0.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

CONSTANT 

Distinct1
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size300.0 B
가로변
21 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가로변 21
100.0%

Length

2023-12-12T22:38:12.658190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:38:12.766181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가로변 21
100.0%

구간명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size300.0 B
계백로
6 
계룡로
3 
도산로
3 
동서대로
3 
한밭대로
2 
Other values (2)
4 

Length

Max length4
Median length3
Mean length3.3333333
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
계백로 6
28.6%
계룡로 3
14.3%
도산로 3
14.3%
동서대로 3
14.3%
한밭대로 2
 
9.5%
대덕대로 2
 
9.5%
계족로 2
 
9.5%

Length

2023-12-12T22:38:12.886057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:38:13.031260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
계백로 6
28.6%
계룡로 3
14.3%
도산로 3
14.3%
동서대로 3
14.3%
한밭대로 2
 
9.5%
대덕대로 2
 
9.5%
계족로 2
 
9.5%

구간
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)47.6%
Missing0
Missing (%)0.0%
Memory size300.0 B
서대전4~도마4
3 
서대전4~유성4
3 
용문4~도마4
3 
가수원4~진잠4
2 
대전 IC~용전4
2 
Other values (5)
8 

Length

Max length9
Median length8
Mean length8
Min length7

Unique

Unique2 ?
Unique (%)9.5%

Sample

1st row서대전4~도마4
2nd row서대전4~도마4
3rd row서대전4~도마4
4th row도마4~가수원4
5th row가수원4~진잠4

Common Values

ValueCountFrequency (%)
서대전4~도마4 3
14.3%
서대전4~유성4 3
14.3%
용문4~도마4 3
14.3%
가수원4~진잠4 2
9.5%
대전 IC~용전4 2
9.5%
재뜰4~충대정문5 2
9.5%
계룡4~연구단지4 2
9.5%
중리4~읍내3 2
9.5%
도마4~가수원4 1
 
4.8%
용전4~태평5 1
 
4.8%

Length

2023-12-12T22:38:13.211237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:38:13.377065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서대전4~도마4 3
13.0%
서대전4~유성4 3
13.0%
용문4~도마4 3
13.0%
가수원4~진잠4 2
8.7%
대전 2
8.7%
ic~용전4 2
8.7%
재뜰4~충대정문5 2
8.7%
계룡4~연구단지4 2
8.7%
중리4~읍내3 2
8.7%
도마4~가수원4 1
 
4.3%

설치위치
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T22:38:13.616254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length14.47619
Min length9

Characters and Unicode

Total characters304
Distinct characters71
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row버드내네거리 → 유등교 방향
2nd row버드내네거리 → 유천네거리
3rd row유천 네거리 → 버드내네거리
4th row도마네거리 → 유등교 방향
5th row가수원네거리 → 가수원교방향
ValueCountFrequency (%)
→ 18
26.9%
네거리 4
 
6.0%
방향 4
 
6.0%
버드내네거리 3
 
4.5%
만년네거리 2
 
3.0%
가수원네거리 2
 
3.0%
향우네거리 2
 
3.0%
유등교 2
 
3.0%
도마네거리 1
 
1.5%
대전 1
 
1.5%
Other values (28) 28
41.8%
2023-12-12T22:38:13.967181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
47
15.5%
리 27
 
8.9%
거 26
 
8.6%
네 24
 
7.9%
→ 18
 
5.9%
대 13
 
4.3%
향 12
 
3.9%
방 11
 
3.6%
교 8
 
2.6%
동 7
 
2.3%
Other values (61) 111
36.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 235
77.3%
Space Separator 47
 
15.5%
Math Symbol 18
 
5.9%
Uppercase Letter 2
 
0.7%
Open Punctuation 1
 
0.3%
Close Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 27
 
11.5%
거 26
 
11.1%
네 24
 
10.2%
대 13
 
5.5%
향 12
 
5.1%
방 11
 
4.7%
교 8
 
3.4%
동 7
 
3.0%
유 5
 
2.1%
전 4
 
1.7%
Other values (55) 98
41.7%
Uppercase Letter
ValueCountFrequency (%)
C 1
50.0%
I 1
50.0%
Space Separator
ValueCountFrequency (%)
47
100.0%
Math Symbol
ValueCountFrequency (%)
→ 18
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 235
77.3%
Common 67
 
22.0%
Latin 2
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 27
 
11.5%
거 26
 
11.1%
네 24
 
10.2%
대 13
 
5.5%
향 12
 
5.1%
방 11
 
4.7%
교 8
 
3.4%
동 7
 
3.0%
유 5
 
2.1%
전 4
 
1.7%
Other values (55) 98
41.7%
Common
ValueCountFrequency (%)
47
70.1%
→ 18
 
26.9%
( 1
 
1.5%
) 1
 
1.5%
Latin
ValueCountFrequency (%)
C 1
50.0%
I 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 235
77.3%
ASCII 51
 
16.8%
Arrows 18
 
5.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
47
92.2%
C 1
 
2.0%
I 1
 
2.0%
( 1
 
2.0%
) 1
 
2.0%
Hangul
ValueCountFrequency (%)
리 27
 
11.5%
거 26
 
11.1%
네 24
 
10.2%
대 13
 
5.5%
향 12
 
5.1%
방 11
 
4.7%
교 8
 
3.4%
동 7
 
3.0%
유 5
 
2.1%
전 4
 
1.7%
Other values (55) 98
41.7%
Arrows
ValueCountFrequency (%)
→ 18
100.0%

대수
Categorical

CONSTANT 

Distinct1
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size300.0 B
1
21 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 21
100.0%

Length

2023-12-12T22:38:14.115043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:38:14.229776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 21
100.0%

Correlations

2023-12-12T22:38:14.325460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구간명구간설치위치
구간명1.0001.0001.000
구간1.0001.0001.000
설치위치1.0001.0001.000
2023-12-12T22:38:14.431810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구간구간명
구간1.0000.886
구간명0.8861.000
2023-12-12T22:38:14.527230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구간명구간
구간명1.0000.886
구간0.8861.000

Missing values

2023-12-12T22:38:12.425613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:38:12.533535image/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

구분구간명구간설치위치대수
0가로변계백로서대전4~도마4버드내네거리 → 유등교 방향1
1가로변계백로서대전4~도마4버드내네거리 → 유천네거리1
2가로변계백로서대전4~도마4유천 네거리 → 버드내네거리1
3가로변계백로도마4~가수원4도마네거리 → 유등교 방향1
4가로변계백로가수원4~진잠4가수원네거리 → 가수원교방향1
5가로변계백로가수원4~진잠4가수원네거리 → 건양대 방향1
6가로변계룡로서대전4~유성4큰마을네거리 → 갈마 네거리1
7가로변계룡로서대전4~유성4동서로네거리 → 서대전 네거리1
8가로변계룡로서대전4~유성4유성네거리 → 만년교 방향1
9가로변도산로용문4~도마4가장동 래미안아파트건너편1
구분구간명구간설치위치대수
11가로변도산로용문4~도마4향우네거리 → (구)농도원네거리1
12가로변동서대로대전 IC~용전4대전복합터미널건너편1
13가로변동서대로대전 IC~용전4대전 IC앞네거리 → 동부네거리방향1
14가로변동서대로용전4~태평5목동충남여고건너편1
15가로변한밭대로재뜰4~충대정문5갑천대교 → 충대정문오거리방향1
16가로변한밭대로재뜰4~충대정문5충대정문오거리 → 갑천대교방향1
17가로변대덕대로계룡4~연구단지4대덕대교 → 만년네거리1
18가로변대덕대로계룡4~연구단지4만년네거리 → 대덕대교방향1
19가로변계족로중리4~읍내3중리네거리 → 법동 네거리1
20가로변계족로중리4~읍내3대전지방국세청 → 법동네거리1