Overview

Dataset statistics

Number of variables4
Number of observations207
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.8 KiB
Average record size in memory33.6 B

Variable types

Categorical2
Text1
Numeric1

Dataset

Description불법주정차무인단속시스템 단속건수정보(단속기간, 지역별 단속건수)입니다.경기도 하남시 관내 불법 주정차 무인 단속 시스템 단속 건 수 정보(단속기간, 지역별 단속건수)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.
URLhttps://www.data.go.kr/data/15050135/fileData.do

Alerts

단속기간 has constant value ""Constant
단속구분 has constant value ""Constant
단속장소 has unique valuesUnique
단속건수 has 9 (4.3%) zerosZeros

Reproduction

Analysis started2023-12-12 08:53:56.973576
Analysis finished2023-12-12 08:53:57.436222
Duration0.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

단속기간
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-01-01~2023-08-10
207 

Length

Max length21
Median length21
Mean length21
Min length21

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-01-01~2023-08-10
2nd row2023-01-01~2023-08-10
3rd row2023-01-01~2023-08-10
4th row2023-01-01~2023-08-10
5th row2023-01-01~2023-08-10

Common Values

ValueCountFrequency (%)
2023-01-01~2023-08-10 207
100.0%

Length

2023-12-12T17:53:57.511303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:53:57.614707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-01-01~2023-08-10 207
100.0%

단속구분
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
고정형CCTV단속
207 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
고정형CCTV단속 207
100.0%

Length

2023-12-12T17:53:57.717477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:53:57.868501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고정형cctv단속 207
100.0%

단속장소
Text

UNIQUE 

Distinct207
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-12T17:53:58.111409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length11.719807
Min length9

Characters and Unicode

Total characters2426
Distinct characters264
Distinct categories6 ?
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 (%)
주변-초이동 2
 
0.9%
가락공판장주변-미사3동 1
 
0.5%
현대지식2차대로-덕풍3동 1
 
0.5%
은가람중학교-미사2동 1
 
0.5%
하남이마트주변-덕풍3동 1
 
0.5%
미사고등학교-미사2동 1
 
0.5%
디지털도서관-미사1동 1
 
0.5%
신평초교사거리-신장2동 1
 
0.5%
미사7~8단지주변-미사2동 1
 
0.5%
오벨리스크주변-미사1동 1
 
0.5%
Other values (207) 207
95.0%
2023-12-12T17:53:58.524418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 219
 
9.0%
- 207
 
8.5%
사 121
 
5.0%
미 114
 
4.7%
주 95
 
3.9%
변 95
 
3.9%
2 74
 
3.1%
풍 62
 
2.6%
덕 57
 
2.3%
3 56
 
2.3%
Other values (254) 1326
54.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1987
81.9%
Dash Punctuation 207
 
8.5%
Decimal Number 199
 
8.2%
Uppercase Letter 21
 
0.9%
Space Separator 11
 
0.5%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 219
 
11.0%
사 121
 
6.1%
미 114
 
5.7%
주 95
 
4.8%
변 95
 
4.8%
풍 62
 
3.1%
덕 57
 
2.9%
장 55
 
2.8%
신 45
 
2.3%
리 44
 
2.2%
Other values (234) 1080
54.4%
Decimal Number
ValueCountFrequency (%)
2 74
37.2%
3 56
28.1%
1 54
27.1%
4 9
 
4.5%
5 2
 
1.0%
9 1
 
0.5%
7 1
 
0.5%
6 1
 
0.5%
8 1
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
A 6
28.6%
S 3
14.3%
K 3
14.3%
V 2
 
9.5%
U 2
 
9.5%
P 2
 
9.5%
C 2
 
9.5%
T 1
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 207
100.0%
Space Separator
ValueCountFrequency (%)
11
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1987
81.9%
Common 418
 
17.2%
Latin 21
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 219
 
11.0%
사 121
 
6.1%
미 114
 
5.7%
주 95
 
4.8%
변 95
 
4.8%
풍 62
 
3.1%
덕 57
 
2.9%
장 55
 
2.8%
신 45
 
2.3%
리 44
 
2.2%
Other values (234) 1080
54.4%
Common
ValueCountFrequency (%)
- 207
49.5%
2 74
 
17.7%
3 56
 
13.4%
1 54
 
12.9%
11
 
2.6%
4 9
 
2.2%
5 2
 
0.5%
9 1
 
0.2%
7 1
 
0.2%
6 1
 
0.2%
Other values (2) 2
 
0.5%
Latin
ValueCountFrequency (%)
A 6
28.6%
S 3
14.3%
K 3
14.3%
V 2
 
9.5%
U 2
 
9.5%
P 2
 
9.5%
C 2
 
9.5%
T 1
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1987
81.9%
ASCII 439
 
18.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 219
 
11.0%
사 121
 
6.1%
미 114
 
5.7%
주 95
 
4.8%
변 95
 
4.8%
풍 62
 
3.1%
덕 57
 
2.9%
장 55
 
2.8%
신 45
 
2.3%
리 44
 
2.2%
Other values (234) 1080
54.4%
ASCII
ValueCountFrequency (%)
- 207
47.2%
2 74
 
16.9%
3 56
 
12.8%
1 54
 
12.3%
11
 
2.5%
4 9
 
2.1%
A 6
 
1.4%
S 3
 
0.7%
K 3
 
0.7%
V 2
 
0.5%
Other values (10) 14
 
3.2%

단속건수
Real number (ℝ)

ZEROS 

Distinct109
Distinct (%)52.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.202899
Minimum0
Maximum921
Zeros9
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T17:53:58.719757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q114.5
median36
Q379
95-th percentile267.2
Maximum921
Range921
Interquartile range (IQR)64.5

Descriptive statistics

Standard deviation117.76819
Coefficient of variation (CV)1.6087914
Kurtosis21.445699
Mean73.202899
Median Absolute Deviation (MAD)27
Skewness4.104594
Sum15153
Variance13869.347
MonotonicityNot monotonic
2023-12-12T17:53:58.892416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9
 
4.3%
1 7
 
3.4%
35 6
 
2.9%
40 5
 
2.4%
11 5
 
2.4%
75 5
 
2.4%
32 4
 
1.9%
16 4
 
1.9%
17 4
 
1.9%
18 4
 
1.9%
Other values (99) 154
74.4%
ValueCountFrequency (%)
0 9
4.3%
1 7
3.4%
2 3
 
1.4%
3 3
 
1.4%
4 3
 
1.4%
5 3
 
1.4%
6 2
 
1.0%
7 3
 
1.4%
8 2
 
1.0%
9 2
 
1.0%
ValueCountFrequency (%)
921 1
0.5%
753 1
0.5%
681 1
0.5%
488 1
0.5%
432 1
0.5%
375 1
0.5%
333 1
0.5%
331 1
0.5%
298 1
0.5%
290 1
0.5%

Interactions

2023-12-12T17:53:57.163085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2023-12-12T17:53:57.285915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:53:57.393308image/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

단속기간단속구분단속장소단속건수
02023-01-01~2023-08-10고정형CCTV단속가락공판장주변-미사3동2
12023-01-01~2023-08-10고정형CCTV단속덕풍시장대로변-덕풍2동40
22023-01-01~2023-08-10고정형CCTV단속두산아파트-신장2동40
32023-01-01~2023-08-10고정형CCTV단속현대아파트-신장2동35
42023-01-01~2023-08-10고정형CCTV단속국민은행주변-신장2동7
52023-01-01~2023-08-10고정형CCTV단속유진사우나-신장1동28
62023-01-01~2023-08-10고정형CCTV단속성원아파트-신장1동88
72023-01-01~2023-08-10고정형CCTV단속우체국주변-덕풍3동33
82023-01-01~2023-08-10고정형CCTV단속검단로주변-천현동1
92023-01-01~2023-08-10고정형CCTV단속벽산아파트주변-덕풍1동58
단속기간단속구분단속장소단속건수
1972023-01-01~2023-08-10고정형CCTV단속퀸즈파크 미사 1차-미사1동200
1982023-01-01~2023-08-10고정형CCTV단속덕풍쌍용아파트-덕풍1동2
1992023-01-01~2023-08-10고정형CCTV단속벤츠 하남AS센터-초이동11
2002023-01-01~2023-08-10고정형CCTV단속태화 내장건업 주변-초이동1
2012023-01-01~2023-08-10고정형CCTV단속현대 오일뱅크직영-신장2동208
2022023-01-01~2023-08-10고정형CCTV단속유테크밸리후문-미사3동432
2032023-01-01~2023-08-10고정형CCTV단속감일제일풍경채-감일동47
2042023-01-01~2023-08-10고정형CCTV단속위례제일풍경채-위례동0
2052023-01-01~2023-08-10고정형CCTV단속위례숲우미린정문주변-위례동0
2062023-01-01~2023-08-10고정형CCTV단속위례호반써밋주변-위례동0