Overview

Dataset statistics

Number of variables2
Number of observations3698
Missing cells0
Missing cells (%)0.0%
Duplicate rows230
Duplicate rows (%)6.2%
Total size in memory57.9 KiB
Average record size in memory16.0 B

Variable types

DateTime1
Text1

Dataset

Description부산시 영도구 2024년 불법주정차 단속 현황입니다단속수단은 고정형 무인단속 CCTV 및 이동형CCTV, 주민신고에 따른 단속입니다.
Author부산광역시 영도구
URLhttps://www.data.go.kr/data/15125353/fileData.do

Alerts

Dataset has 230 (6.2%) duplicate rowsDuplicates

Reproduction

Analysis started2024-03-23 04:56:11.829235
Analysis finished2024-03-23 04:56:12.587316
Duration0.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

단속일시
Date

Distinct3092
Distinct (%)83.6%
Missing0
Missing (%)0.0%
Memory size29.0 KiB
Minimum2024-01-01 00:41:00
Maximum2024-02-29 21:18:35
2024-03-23T04:56:12.845593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:56:13.536575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

단속장소
Text

Distinct748
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Memory size29.0 KiB
2024-03-23T04:56:14.337003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length23
Mean length11.636831
Min length6

Characters and Unicode

Total characters43033
Distinct characters166
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique397 ?
Unique (%)10.7%

Sample

1st row해안관광도로 감지 입구
2nd row태종대회전교차로주변-4
3rd row2 농협영도지점주변
4th row4 남항사거리부근
5th row2 농협영도지점주변
ValueCountFrequency (%)
동삼동 657
 
7.7%
인도 462
 
5.4%
청학동 363
 
4.3%
교차로모퉁이 303
 
3.6%
횡단보도 281
 
3.3%
봉래나루로 231
 
2.7%
봉래동3가 211
 
2.5%
주변 211
 
2.5%
동삼동농협 211
 
2.5%
보조카 211
 
2.5%
Other values (606) 5375
63.1%
2024-03-23T04:56:15.997713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4820
 
11.2%
동 4001
 
9.3%
1 2089
 
4.9%
2 1670
 
3.9%
로 1632
 
3.8%
가 1501
 
3.5%
- 1314
 
3.1%
3 1175
 
2.7%
영 1018
 
2.4%
삼 1016
 
2.4%
Other values (156) 22797
53.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29022
67.4%
Decimal Number 7786
 
18.1%
Space Separator 4820
 
11.2%
Dash Punctuation 1314
 
3.1%
Uppercase Letter 40
 
0.1%
Other Punctuation 31
 
0.1%
Lowercase Letter 20
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 4001
 
13.8%
로 1632
 
5.6%
가 1501
 
5.2%
영 1018
 
3.5%
삼 1016
 
3.5%
도 1010
 
3.5%
교 919
 
3.2%
봉 897
 
3.1%
래 857
 
3.0%
대 771
 
2.7%
Other values (139) 15400
53.1%
Decimal Number
ValueCountFrequency (%)
1 2089
26.8%
2 1670
21.4%
3 1175
15.1%
4 737
 
9.5%
5 483
 
6.2%
9 400
 
5.1%
6 373
 
4.8%
7 298
 
3.8%
8 287
 
3.7%
0 274
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
D 20
50.0%
K 10
25.0%
T 10
25.0%
Space Separator
ValueCountFrequency (%)
4820
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1314
100.0%
Other Punctuation
ValueCountFrequency (%)
, 31
100.0%
Lowercase Letter
ValueCountFrequency (%)
o 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29022
67.4%
Common 13951
32.4%
Latin 60
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 4001
 
13.8%
로 1632
 
5.6%
가 1501
 
5.2%
영 1018
 
3.5%
삼 1016
 
3.5%
도 1010
 
3.5%
교 919
 
3.2%
봉 897
 
3.1%
래 857
 
3.0%
대 771
 
2.7%
Other values (139) 15400
53.1%
Common
ValueCountFrequency (%)
4820
34.5%
1 2089
15.0%
2 1670
 
12.0%
- 1314
 
9.4%
3 1175
 
8.4%
4 737
 
5.3%
5 483
 
3.5%
9 400
 
2.9%
6 373
 
2.7%
7 298
 
2.1%
Other values (3) 592
 
4.2%
Latin
ValueCountFrequency (%)
o 20
33.3%
D 20
33.3%
K 10
16.7%
T 10
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29022
67.4%
ASCII 14011
32.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4820
34.4%
1 2089
14.9%
2 1670
 
11.9%
- 1314
 
9.4%
3 1175
 
8.4%
4 737
 
5.3%
5 483
 
3.4%
9 400
 
2.9%
6 373
 
2.7%
7 298
 
2.1%
Other values (7) 652
 
4.7%
Hangul
ValueCountFrequency (%)
동 4001
 
13.8%
로 1632
 
5.6%
가 1501
 
5.2%
영 1018
 
3.5%
삼 1016
 
3.5%
도 1010
 
3.5%
교 919
 
3.2%
봉 897
 
3.1%
래 857
 
3.0%
대 771
 
2.7%
Other values (139) 15400
53.1%

Missing values

2024-03-23T04:56:12.241652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T04:56:12.431115image/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

단속일시단속장소
02024-01-01 08:43:00해안관광도로 감지 입구
12024-01-01 08:49:00태종대회전교차로주변-4
22024-01-01 09:02:002 농협영도지점주변
32024-01-01 09:05:004 남항사거리부근
42024-01-01 09:43:002 농협영도지점주변
52024-01-01 10:49:00영선불고기-1
62024-01-01 11:07:003 농협영도지점주변
72024-01-01 11:14:00동삼동농협 주변 보조카
82024-01-01 11:56:002 영도 메디컬빌딩 부근
92024-01-01 14:18:00동삼동농협 주변 보조카
단속일시단속장소
36882024-02-26 13:49:35동삼동 산 32-8 횡단보도
36892024-02-26 14:58:00봉래동3가 절영로13번길
36902024-02-18 19:03:12대교초등학교 주변-2
36912024-02-14 06:17:12남항동1가 178-27 교차로모퉁이
36922024-02-16 16:34:45동삼동 322-142 인도
36932024-02-27 15:06:03청학동 청명길
36942024-02-16 14:15:49해안관광도로 감지 입구
36952024-02-17 17:02:102송도삼거리 주변-1
36962024-02-22 14:19:00동삼동농협 주변 보조카
36972024-02-26 05:18:01동삼동 207-96 횡단보도

Duplicate rows

Most frequently occurring

단속일시단속장소# duplicates
1072024-01-22 09:54:00대교동1가 봉래나루로9
2022024-02-03 10:49:00대교동1가 봉래나루로8
112024-01-04 09:57:00청학동 청명길7
202024-01-04 15:12:00대평동1가 대평북로7
682024-01-15 10:53:00청학동 청명길7
1432024-01-26 10:25:00대교동1가 봉래나루로7
1942024-02-02 10:02:00대교동1가 태종로7
2092024-02-04 13:49:00청학동 청학남로7
2152024-02-04 13:53:00청학동 하나길6
02024-01-02 10:22:00청학동 해양로5