Overview

Dataset statistics

Number of variables3
Number of observations207
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.0 KiB
Average record size in memory24.6 B

Variable types

Text2
Categorical1

Dataset

Description불법주정차단속시스템 모니터링 구역정보(모니터링시스템 별 단속구역 정보) 입니다.하남시 내 신규 택지개발로 신규 도로 개통이 다수 발생했고 등록 차량대수 의 증가로 고정형 CCTV 설치 민원 및 필요성이 증대되어 하남시 전역으로 설치를 확대하고 있으며 그로 인해 단속건수 또한 증가를 보이고 있습니다. 그러므로 시민들의 편의를 위해 주정차 모니터링 구역 정보를 제공함으로써 시민 불편 해소 및 주정차 질서 확립에 기여합니다.
URLhttps://www.data.go.kr/data/15050134/fileData.do

Alerts

모니터 이름 has unique valuesUnique
단속구역 이름 has unique valuesUnique

Reproduction

Analysis started2023-12-12 08:18:42.807580
Analysis finished2023-12-12 08:18:43.097455
Duration0.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

모니터 이름
Text

UNIQUE 

Distinct207
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-12T17:18:43.458021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.4782609
Min length3

Characters and Unicode

Total characters927
Distinct characters12
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

Unique207 ?
Unique (%)100.0%

Sample

1st row운영1
2nd row운영2
3rd row운영3
4th row운영4
5th row운영5
ValueCountFrequency (%)
운영1 1
 
0.5%
운영131 1
 
0.5%
운영133 1
 
0.5%
운영134 1
 
0.5%
운영135 1
 
0.5%
운영136 1
 
0.5%
운영137 1
 
0.5%
운영138 1
 
0.5%
운영139 1
 
0.5%
운영140 1
 
0.5%
Other values (197) 197
95.2%
2023-12-12T17:18:44.053782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
운 207
22.3%
영 207
22.3%
1 141
15.2%
2 49
 
5.3%
4 41
 
4.4%
3 41
 
4.4%
5 41
 
4.4%
6 41
 
4.4%
7 41
 
4.4%
8 40
 
4.3%
Other values (2) 78
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 513
55.3%
Other Letter 414
44.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 141
27.5%
2 49
 
9.6%
4 41
 
8.0%
3 41
 
8.0%
5 41
 
8.0%
6 41
 
8.0%
7 41
 
8.0%
8 40
 
7.8%
9 40
 
7.8%
0 38
 
7.4%
Other Letter
ValueCountFrequency (%)
운 207
50.0%
영 207
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 513
55.3%
Hangul 414
44.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 141
27.5%
2 49
 
9.6%
4 41
 
8.0%
3 41
 
8.0%
5 41
 
8.0%
6 41
 
8.0%
7 41
 
8.0%
8 40
 
7.8%
9 40
 
7.8%
0 38
 
7.4%
Hangul
ValueCountFrequency (%)
운 207
50.0%
영 207
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 513
55.3%
Hangul 414
44.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
운 207
50.0%
영 207
50.0%
ASCII
ValueCountFrequency (%)
1 141
27.5%
2 49
 
9.6%
4 41
 
8.0%
3 41
 
8.0%
5 41
 
8.0%
6 41
 
8.0%
7 41
 
8.0%
8 40
 
7.8%
9 40
 
7.8%
0 38
 
7.4%

단속구역 이름
Text

UNIQUE 

Distinct207
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-12T17:18:44.333171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length11.652174
Min length9

Characters and Unicode

Total characters2412
Distinct characters264
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique207 ?
Unique (%)100.0%

Sample

1st row가락공판장주변-미사3동
2nd row덕풍시장대로변-덕풍2동
3rd row두산아파트-신장2동
4th row현대아파트-신장2동
5th row국민은행주변-신장2동
ValueCountFrequency (%)
가락공판장주변-미사3동 1
 
0.5%
신평초교사거리-신장2동 1
 
0.5%
이성산성경관주변-춘궁동 1
 
0.5%
사래기교차로주변-초이동 1
 
0.5%
하남테니스클럽-신장2동 1
 
0.5%
미사역파라곤-미사1동 1
 
0.5%
롯데헤븐시티-미사1동 1
 
0.5%
동원로얄듀크뒤-미사3동 1
 
0.5%
은가람중학교-미사2동 1
 
0.5%
하남이마트주변-덕풍3동 1
 
0.5%
Other values (197) 197
95.2%
2023-12-12T17:18:44.770780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 219
 
9.1%
- 207
 
8.6%
사 120
 
5.0%
미 112
 
4.6%
주 95
 
3.9%
변 95
 
3.9%
2 74
 
3.1%
풍 63
 
2.6%
덕 57
 
2.4%
장 55
 
2.3%
Other values (254) 1315
54.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1983
82.2%
Dash Punctuation 207
 
8.6%
Decimal Number 197
 
8.2%
Uppercase Letter 24
 
1.0%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 219
 
11.0%
사 120
 
6.1%
미 112
 
5.6%
주 95
 
4.8%
변 95
 
4.8%
풍 63
 
3.2%
덕 57
 
2.9%
장 55
 
2.8%
신 45
 
2.3%
리 43
 
2.2%
Other values (233) 1079
54.4%
Uppercase Letter
ValueCountFrequency (%)
A 7
29.2%
S 3
12.5%
K 3
12.5%
U 2
 
8.3%
C 2
 
8.3%
P 2
 
8.3%
V 2
 
8.3%
H 1
 
4.2%
T 1
 
4.2%
D 1
 
4.2%
Decimal Number
ValueCountFrequency (%)
2 74
37.6%
3 55
27.9%
1 54
27.4%
4 8
 
4.1%
5 2
 
1.0%
8 1
 
0.5%
9 1
 
0.5%
7 1
 
0.5%
6 1
 
0.5%
Dash Punctuation
ValueCountFrequency (%)
- 207
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1983
82.2%
Common 405
 
16.8%
Latin 24
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 219
 
11.0%
사 120
 
6.1%
미 112
 
5.6%
주 95
 
4.8%
변 95
 
4.8%
풍 63
 
3.2%
덕 57
 
2.9%
장 55
 
2.8%
신 45
 
2.3%
리 43
 
2.2%
Other values (233) 1079
54.4%
Common
ValueCountFrequency (%)
- 207
51.1%
2 74
 
18.3%
3 55
 
13.6%
1 54
 
13.3%
4 8
 
2.0%
5 2
 
0.5%
8 1
 
0.2%
9 1
 
0.2%
~ 1
 
0.2%
7 1
 
0.2%
Latin
ValueCountFrequency (%)
A 7
29.2%
S 3
12.5%
K 3
12.5%
U 2
 
8.3%
C 2
 
8.3%
P 2
 
8.3%
V 2
 
8.3%
H 1
 
4.2%
T 1
 
4.2%
D 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1983
82.2%
ASCII 429
 
17.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 219
 
11.0%
사 120
 
6.1%
미 112
 
5.6%
주 95
 
4.8%
변 95
 
4.8%
풍 63
 
3.2%
덕 57
 
2.9%
장 55
 
2.8%
신 45
 
2.3%
리 43
 
2.2%
Other values (233) 1079
54.4%
ASCII
ValueCountFrequency (%)
- 207
48.3%
2 74
 
17.2%
3 55
 
12.8%
1 54
 
12.6%
4 8
 
1.9%
A 7
 
1.6%
S 3
 
0.7%
K 3
 
0.7%
U 2
 
0.5%
C 2
 
0.5%
Other values (11) 14
 
3.3%

단속구역 주소
Categorical

Distinct14
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
미사1동
33 
신장2동
29 
미사3동
27 
미사2동
26 
덕풍3동
24 
Other values (9)
68 

Length

Max length4
Median length4
Mean length3.7922705
Min length3

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row미사3동
2nd row덕풍2동
3rd row신장2동
4th row신장2동
5th row신장2동

Common Values

ValueCountFrequency (%)
미사1동 33
15.9%
신장2동 29
14.0%
미사3동 27
13.0%
미사2동 26
12.6%
덕풍3동 24
11.6%
위례동 13
 
6.3%
덕풍2동 12
 
5.8%
천현동 12
 
5.8%
초이동 12
 
5.8%
덕풍1동 7
 
3.4%
Other values (4) 12
 
5.8%

Length

2023-12-12T17:18:44.936913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미사1동 33
15.9%
신장2동 29
14.0%
미사3동 27
13.0%
미사2동 26
12.6%
덕풍3동 24
11.6%
위례동 13
 
6.3%
덕풍2동 12
 
5.8%
천현동 12
 
5.8%
초이동 12
 
5.8%
덕풍1동 7
 
3.4%
Other values (4) 12
 
5.8%

Missing values

2023-12-12T17:18:42.979113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:18:43.064752image/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

모니터 이름단속구역 이름단속구역 주소
0운영1가락공판장주변-미사3동미사3동
1운영2덕풍시장대로변-덕풍2동덕풍2동
2운영3두산아파트-신장2동신장2동
3운영4현대아파트-신장2동신장2동
4운영5국민은행주변-신장2동신장2동
5운영6유진사우나-신장1동신장1동
6운영7성원아파트-신장1동신장1동
7운영8우체국주변-덕풍3동덕풍3동
8운영9검단로주변-천현동천현동
9운영10벽산아파트주변-덕풍1동덕풍1동
모니터 이름단속구역 이름단속구역 주소
197운영198퀸즈파크미사1차-미사1동미사1동
198운영199덕풍쌍용아파트-덕풍1동덕풍1동
199운영200벤츠하남AS센터-초이동초이동
200운영201태화내장건업주변-초이동초이동
201운영202HD오일뱅크직영-신장2동신장2동
202운영203유테크밸리후문-풍산동미사3동
203운영204감일제일풍경채-감일동감일동
204운영205위례제일풍경채-위례동위례동
205운영206위례송하유치원-위례동위례동
206운영207위례호반써밋주변-위례동위례동