Overview

Dataset statistics

Number of variables4
Number of observations40
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 KiB
Average record size in memory36.3 B

Variable types

Text2
Numeric1
DateTime1

Dataset

Description충청남도 공주시 주정차금지구역정보에 대한 데이터로 (도 로 명, 단속구간, 데이터갱신일, 거리) 등의 항목을 제공합니다.
Author충청남도 공주시
URLhttps://www.data.go.kr/data/3084526/fileData.do

Alerts

데이터갱신일 has constant value ""Constant
단 속 구 간 has unique valuesUnique

Reproduction

Analysis started2023-12-12 20:53:05.933900
Analysis finished2023-12-12 20:53:06.792194
Duration0.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

도 로 명
Text

Distinct36
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-13T05:53:06.986799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length5.125
Min length3

Characters and Unicode

Total characters205
Distinct characters67
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)80.0%

Sample

1st row웅진로
2nd row봉황로
3rd row왕릉로
4th row왕릉로 분리구간도로
5th row느티나무길>금강공원길
ValueCountFrequency (%)
한적2길 2
 
4.9%
신금2길>흑수골길 2
 
4.9%
무령로 2
 
4.9%
신관동 2
 
4.9%
왕릉로 2
 
4.9%
공주대학로 1
 
2.4%
동학사1로 1
 
2.4%
동학사1·2로 1
 
2.4%
유구마곡사로 1
 
2.4%
중앙1길 1
 
2.4%
Other values (26) 26
63.4%
2023-12-13T05:53:07.392818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
길 24
 
11.7%
로 23
 
11.2%
1 11
 
5.4%
2 9
 
4.4%
신 8
 
3.9%
금 8
 
3.9%
> 8
 
3.9%
영 6
 
2.9%
한 5
 
2.4%
관 5
 
2.4%
Other values (57) 98
47.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 173
84.4%
Decimal Number 21
 
10.2%
Math Symbol 8
 
3.9%
Other Punctuation 2
 
1.0%
Space Separator 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
길 24
 
13.9%
로 23
 
13.3%
신 8
 
4.6%
금 8
 
4.6%
영 6
 
3.5%
한 5
 
2.9%
관 5
 
2.9%
동 5
 
2.9%
적 5
 
2.9%
학 5
 
2.9%
Other values (50) 79
45.7%
Decimal Number
ValueCountFrequency (%)
1 11
52.4%
2 9
42.9%
3 1
 
4.8%
Other Punctuation
ValueCountFrequency (%)
· 1
50.0%
/ 1
50.0%
Math Symbol
ValueCountFrequency (%)
> 8
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 173
84.4%
Common 32
 
15.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
길 24
 
13.9%
로 23
 
13.3%
신 8
 
4.6%
금 8
 
4.6%
영 6
 
3.5%
한 5
 
2.9%
관 5
 
2.9%
동 5
 
2.9%
적 5
 
2.9%
학 5
 
2.9%
Other values (50) 79
45.7%
Common
ValueCountFrequency (%)
1 11
34.4%
2 9
28.1%
> 8
25.0%
· 1
 
3.1%
1
 
3.1%
3 1
 
3.1%
/ 1
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 173
84.4%
ASCII 31
 
15.1%
None 1
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
길 24
 
13.9%
로 23
 
13.3%
신 8
 
4.6%
금 8
 
4.6%
영 6
 
3.5%
한 5
 
2.9%
관 5
 
2.9%
동 5
 
2.9%
적 5
 
2.9%
학 5
 
2.9%
Other values (50) 79
45.7%
ASCII
ValueCountFrequency (%)
1 11
35.5%
2 9
29.0%
> 8
25.8%
1
 
3.2%
3 1
 
3.2%
/ 1
 
3.2%
None
ValueCountFrequency (%)
· 1
100.0%

단 속 구 간
Text

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-13T05:53:07.618671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length25.5
Mean length21.125
Min length6

Characters and Unicode

Total characters845
Distinct characters186
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)100.0%

Sample

1st row시청~의료원삼거리~중동교차로~연문교차로~금강교
2nd row시청~사대부설고~소방서~교육지원청
3rd row연문교차로~교육지원청~경찰서~경일@
4th row공주중~무령왕릉매표소 입구
5th row왕릉교~행복한 웨딩홀~새이학가든~금강교
ValueCountFrequency (%)
앞 5
 
6.2%
부근 4
 
5.0%
입구 3
 
3.8%
초입(롯데리아 2
 
2.5%
교차로 2
 
2.5%
2015.4.1 2
 
2.5%
시청~의료원삼거리~중동교차로~연문교차로~금강교 1
 
1.2%
뒤 1
 
1.2%
입구)~법원·검찰청 1
 
1.2%
금흥교차로(주공3차 1
 
1.2%
Other values (58) 58
72.5%
2023-12-13T05:53:08.008954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
~ 74
 
8.8%
교 48
 
5.7%
40
 
4.7%
차 37
 
4.4%
로 32
 
3.8%
) 17
 
2.0%
( 17
 
2.0%
대 16
 
1.9%
주 15
 
1.8%
공 14
 
1.7%
Other values (176) 535
63.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 647
76.6%
Math Symbol 74
 
8.8%
Space Separator 40
 
4.7%
Other Punctuation 22
 
2.6%
Decimal Number 21
 
2.5%
Close Punctuation 17
 
2.0%
Open Punctuation 17
 
2.0%
Uppercase Letter 6
 
0.7%
Other Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 48
 
7.4%
차 37
 
5.7%
로 32
 
4.9%
대 16
 
2.5%
주 15
 
2.3%
공 14
 
2.2%
관 14
 
2.2%
구 12
 
1.9%
금 12
 
1.9%
신 12
 
1.9%
Other values (158) 435
67.2%
Decimal Number
ValueCountFrequency (%)
1 6
28.6%
4 4
19.0%
2 4
19.0%
3 3
14.3%
0 2
 
9.5%
5 2
 
9.5%
Other Punctuation
ValueCountFrequency (%)
@ 12
54.5%
. 6
27.3%
· 3
 
13.6%
/ 1
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
S 3
50.0%
G 2
33.3%
K 1
 
16.7%
Math Symbol
ValueCountFrequency (%)
~ 74
100.0%
Space Separator
ValueCountFrequency (%)
40
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Other Symbol
ValueCountFrequency (%)
ⓐ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 647
76.6%
Common 192
 
22.7%
Latin 6
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 48
 
7.4%
차 37
 
5.7%
로 32
 
4.9%
대 16
 
2.5%
주 15
 
2.3%
공 14
 
2.2%
관 14
 
2.2%
구 12
 
1.9%
금 12
 
1.9%
신 12
 
1.9%
Other values (158) 435
67.2%
Common
ValueCountFrequency (%)
~ 74
38.5%
40
20.8%
) 17
 
8.9%
( 17
 
8.9%
@ 12
 
6.2%
1 6
 
3.1%
. 6
 
3.1%
4 4
 
2.1%
2 4
 
2.1%
3 3
 
1.6%
Other values (5) 9
 
4.7%
Latin
ValueCountFrequency (%)
S 3
50.0%
G 2
33.3%
K 1
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 647
76.6%
ASCII 194
 
23.0%
None 3
 
0.4%
Enclosed Alphanum 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
~ 74
38.1%
40
20.6%
) 17
 
8.8%
( 17
 
8.8%
@ 12
 
6.2%
1 6
 
3.1%
. 6
 
3.1%
4 4
 
2.1%
2 4
 
2.1%
3 3
 
1.5%
Other values (6) 11
 
5.7%
Hangul
ValueCountFrequency (%)
교 48
 
7.4%
차 37
 
5.7%
로 32
 
4.9%
대 16
 
2.5%
주 15
 
2.3%
공 14
 
2.2%
관 14
 
2.2%
구 12
 
1.9%
금 12
 
1.9%
신 12
 
1.9%
Other values (158) 435
67.2%
None
ValueCountFrequency (%)
· 3
100.0%
Enclosed Alphanum
ValueCountFrequency (%)
ⓐ 1
100.0%

거리(미터)
Real number (ℝ)

Distinct30
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean628.25
Minimum50
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-13T05:53:08.142840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile109
Q1225
median415
Q3787.5
95-th percentile1905
Maximum2400
Range2350
Interquartile range (IQR)562.5

Descriptive statistics

Standard deviation589.65391
Coefficient of variation (CV)0.93856571
Kurtosis1.6585962
Mean628.25
Median Absolute Deviation (MAD)210
Skewness1.5619791
Sum25130
Variance347691.73
MonotonicityNot monotonic
2023-12-13T05:53:08.273192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
400 3
 
7.5%
500 3
 
7.5%
1000 3
 
7.5%
340 2
 
5.0%
600 2
 
5.0%
350 2
 
5.0%
180 2
 
5.0%
2400 1
 
2.5%
240 1
 
2.5%
130 1
 
2.5%
Other values (20) 20
50.0%
ValueCountFrequency (%)
50 1
2.5%
90 1
2.5%
110 1
2.5%
120 1
2.5%
130 1
2.5%
150 1
2.5%
180 2
5.0%
200 1
2.5%
210 1
2.5%
230 1
2.5%
ValueCountFrequency (%)
2400 1
 
2.5%
2000 1
 
2.5%
1900 1
 
2.5%
1750 1
 
2.5%
1600 1
 
2.5%
1500 1
 
2.5%
1000 3
7.5%
900 1
 
2.5%
750 1
 
2.5%
600 2
5.0%

데이터갱신일
Date

CONSTANT 

Distinct1
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
Minimum2017-10-31 00:00:00
Maximum2017-10-31 00:00:00
2023-12-13T05:53:08.369595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:53:08.449939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T05:53:06.201198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:53:08.509254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도 로 명단 속 구 간거리(미터)
도 로 명1.0001.0000.969
단 속 구 간1.0001.0001.000
거리(미터)0.9691.0001.000

Missing values

2023-12-13T05:53:06.638447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:53:06.750984image/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

도 로 명단 속 구 간거리(미터)데이터갱신일
0웅진로시청~의료원삼거리~중동교차로~연문교차로~금강교24002017-10-31
1봉황로시청~사대부설고~소방서~교육지원청19002017-10-31
2왕릉로연문교차로~교육지원청~경찰서~경일@16002017-10-31
3왕릉로 분리구간도로공주중~무령왕릉매표소 입구4002017-10-31
4느티나무길>금강공원길왕릉교~행복한 웨딩홀~새이학가든~금강교5002017-10-31
5용당길중앙약국~산성교~교동초~왕릉로(황새바위)9002017-10-31
6무령로중동교차로~세무서 앞 교차로~핫썬치킨(교동천주교회 부근)4002017-10-31
7감영길의료원삼거리(국민도서)~대통교~사대부설고2002017-10-31
8우체국길>국고개길공주문화원~반죽교~충남역사박물관~버드나무길교차로7502017-10-31
9금성길시내버스정류장(시민교통)~금성교~공주여중후문 초입2302017-10-31
도 로 명단 속 구 간거리(미터)데이터갱신일
30한적2길초대교회~한적교차로~금흥교차로4902017-10-31
31한적2길우남퍼스트빌 앞2402017-10-31
32전막1길전막교차로~(구)신관지구대 교차로1802017-10-31
33신관동덕성그린시티빌 앞 (2015.4.1.)902017-10-31
34신관동메가박스 앞1802017-10-31
35중앙1길수촌교 삼거리~읍사무소~석남삼거리~마이스터고10002017-10-31
36유구마곡사로석남삼거리~시외버스터미널1102017-10-31
37동학사1·2로동학사주차장 입구~무풍교~S마트(나들가게)5202017-10-31
38동학사1로무풍교~동학사 매표소2102017-10-31
39동학사학봉3거리 ~ 삐까삐까 (2015.4.1.)1202017-10-31