Overview

Dataset statistics

Number of variables3
Number of observations30
Missing cells1
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory882.0 B
Average record size in memory29.4 B

Variable types

Text2
Categorical1

Dataset

Description대전광역시 버스전용차로 EEB(버스장착형 단속시스템) 단속카메라 현황에 대한 데이터로 노선번호, 기점-종점, 대수를 제공합니다.
URLhttps://www.data.go.kr/data/15081426/fileData.do

Alerts

기점-종점 has 1 (3.3%) missing valuesMissing
노선번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 11:59:31.623938
Analysis finished2023-12-12 11:59:31.939800
Duration0.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

노선번호
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-12T20:59:32.129651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.0333333
Min length4

Characters and Unicode

Total characters121
Distinct characters16
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

Unique30 ?
Unique (%)100.0%

Sample

1st row급행2번
2nd row102번
3rd row103번
4th row104번
5th row105번
ValueCountFrequency (%)
급행2번 1
 
3.3%
102번 1
 
3.3%
802번 1
 
3.3%
711번 1
 
3.3%
705번 1
 
3.3%
703번 1
 
3.3%
619번 1
 
3.3%
617번 1
 
3.3%
613번 1
 
3.3%
612번 1
 
3.3%
Other values (20) 20
66.7%
2023-12-12T20:59:32.597577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
번 29
24.0%
1 27
22.3%
0 15
12.4%
6 12
9.9%
3 10
 
8.3%
2 8
 
6.6%
5 4
 
3.3%
7 4
 
3.3%
4 3
 
2.5%
9 3
 
2.5%
Other values (6) 6
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 87
71.9%
Other Letter 34
 
28.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 27
31.0%
0 15
17.2%
6 12
13.8%
3 10
 
11.5%
2 8
 
9.2%
5 4
 
4.6%
7 4
 
4.6%
4 3
 
3.4%
9 3
 
3.4%
8 1
 
1.1%
Other Letter
ValueCountFrequency (%)
번 29
85.3%
급 1
 
2.9%
행 1
 
2.9%
개 1
 
2.9%
노 1
 
2.9%
선 1
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 87
71.9%
Hangul 34
 
28.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 27
31.0%
0 15
17.2%
6 12
13.8%
3 10
 
11.5%
2 8
 
9.2%
5 4
 
4.6%
7 4
 
4.6%
4 3
 
3.4%
9 3
 
3.4%
8 1
 
1.1%
Hangul
ValueCountFrequency (%)
번 29
85.3%
급 1
 
2.9%
행 1
 
2.9%
개 1
 
2.9%
노 1
 
2.9%
선 1
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 87
71.9%
Hangul 34
 
28.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
번 29
85.3%
급 1
 
2.9%
행 1
 
2.9%
개 1
 
2.9%
노 1
 
2.9%
선 1
 
2.9%
ASCII
ValueCountFrequency (%)
1 27
31.0%
0 15
17.2%
6 12
13.8%
3 10
 
11.5%
2 8
 
9.2%
5 4
 
4.6%
7 4
 
4.6%
4 3
 
3.4%
9 3
 
3.4%
8 1
 
1.1%

기점-종점
Text

MISSING 

Distinct29
Distinct (%)100.0%
Missing1
Missing (%)3.3%
Memory size372.0 B
2023-12-12T20:59:32.805263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length10.724138
Min length8

Characters and Unicode

Total characters311
Distinct characters87
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)100.0%

Sample

1st row봉산동 ↔ 옥계동
2nd row수통골 ↔ 대전역
3rd row수통골 ↔ 동춘당
4th row수통골 ↔ 탄방역
5th row충대농대 ↔ 비래삼호A
ValueCountFrequency (%)
↔ 29
33.3%
비래동 5
 
5.7%
봉산동 3
 
3.4%
수통골 3
 
3.4%
신탄진 3
 
3.4%
목원대 3
 
3.4%
오월드(동물원 3
 
3.4%
갈마아파트 2
 
2.3%
대전대 2
 
2.3%
대전역 2
 
2.3%
Other values (29) 32
36.8%
2023-12-12T20:59:33.232950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
58
18.6%
↔ 29
 
9.3%
동 26
 
8.4%
대 18
 
5.8%
원 9
 
2.9%
신 8
 
2.6%
전 7
 
2.3%
고 6
 
1.9%
래 6
 
1.9%
비 6
 
1.9%
Other values (77) 138
44.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 213
68.5%
Space Separator 58
 
18.6%
Math Symbol 29
 
9.3%
Close Punctuation 3
 
1.0%
Open Punctuation 3
 
1.0%
Uppercase Letter 3
 
1.0%
Decimal Number 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 26
 
12.2%
대 18
 
8.5%
원 9
 
4.2%
신 8
 
3.8%
전 7
 
3.3%
고 6
 
2.8%
래 6
 
2.8%
비 6
 
2.8%
역 5
 
2.3%
산 5
 
2.3%
Other values (68) 117
54.9%
Uppercase Letter
ValueCountFrequency (%)
I 1
33.3%
C 1
33.3%
A 1
33.3%
Decimal Number
ValueCountFrequency (%)
5 1
50.0%
2 1
50.0%
Space Separator
ValueCountFrequency (%)
58
100.0%
Math Symbol
ValueCountFrequency (%)
↔ 29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 213
68.5%
Common 95
30.5%
Latin 3
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 26
 
12.2%
대 18
 
8.5%
원 9
 
4.2%
신 8
 
3.8%
전 7
 
3.3%
고 6
 
2.8%
래 6
 
2.8%
비 6
 
2.8%
역 5
 
2.3%
산 5
 
2.3%
Other values (68) 117
54.9%
Common
ValueCountFrequency (%)
58
61.1%
↔ 29
30.5%
) 3
 
3.2%
( 3
 
3.2%
5 1
 
1.1%
2 1
 
1.1%
Latin
ValueCountFrequency (%)
I 1
33.3%
C 1
33.3%
A 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 213
68.5%
ASCII 69
 
22.2%
Arrows 29
 
9.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
58
84.1%
) 3
 
4.3%
( 3
 
4.3%
5 1
 
1.4%
I 1
 
1.4%
C 1
 
1.4%
A 1
 
1.4%
2 1
 
1.4%
Arrows
ValueCountFrequency (%)
↔ 29
100.0%
Hangul
ValueCountFrequency (%)
동 26
 
12.2%
대 18
 
8.5%
원 9
 
4.2%
신 8
 
3.8%
전 7
 
3.3%
고 6
 
2.8%
래 6
 
2.8%
비 6
 
2.8%
역 5
 
2.3%
산 5
 
2.3%
Other values (68) 117
54.9%

대수
Categorical

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
4
19 
3
8 
5
2 
110
 
1

Length

Max length3
Median length1
Mean length1.0666667
Min length1

Unique

Unique1 ?
Unique (%)3.3%

Sample

1st row5
2nd row4
3rd row5
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 19
63.3%
3 8
26.7%
5 2
 
6.7%
110 1
 
3.3%

Length

2023-12-12T20:59:33.470400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:59:33.649259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 19
63.3%
3 8
26.7%
5 2
 
6.7%
110 1
 
3.3%

Correlations

2023-12-12T20:59:33.758803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선번호기점-종점대수
노선번호1.0001.0001.000
기점-종점1.0001.0001.000
대수1.0001.0001.000

Missing values

2023-12-12T20:59:31.798302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:59:31.901789image/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

노선번호기점-종점대수
0급행2번봉산동 ↔ 옥계동5
1102번수통골 ↔ 대전역4
2103번수통골 ↔ 동춘당5
3104번수통골 ↔ 탄방역4
4105번충대농대 ↔ 비래삼호A4
5106번목원대 ↔ 비래동3
6113번서남부터미널 ↔ 학하동4
7119번안산동 ↔ 효동4
8201번원내차고지 ↔ 대전IC4
9301번봉산동 ↔ 오월드(동물원)4
노선번호기점-종점대수
20611번신대동 ↔ 세천공원3
21612번동신과학고 ↔ 배재대4
22613번비래동 ↔ 갈마아파트4
23617번비래동 ↔ 변동53
24619번동신과학고 ↔ 서대전여고4
25703번신탄진 ↔ 정림동4
26705번신탄진 ↔ 대전역동광장4
27711번신탄진 ↔ 대전역4
28802번봉산동 ↔ 보문산3
2929개노선<NA>110