Overview

Dataset statistics

Number of variables7
Number of observations33
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory63.0 B

Variable types

Numeric2
Categorical4
Text1

Dataset

Description상주시 관내의 주정차금지구역에 대한 데이터로 구분, 구간, 거리, 차로수, 방향, 데이터기준일에 대하여 자료를 제공합니다.
Author경상북도 상주시
URLhttps://www.data.go.kr/data/15005365/fileData.do

Alerts

방향 has constant value ""Constant
데이터기준일 has constant value ""Constant
연번 is highly overall correlated with 구분High correlation
구분 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique
구간 has unique valuesUnique

Reproduction

Analysis started2023-12-12 03:36:25.585818
Analysis finished2023-12-12 03:36:26.681717
Duration1.1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17
Minimum1
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T12:36:26.768936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.6
Q19
median17
Q325
95-th percentile31.4
Maximum33
Range32
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.6695398
Coefficient of variation (CV)0.56879646
Kurtosis-1.2
Mean17
Median Absolute Deviation (MAD)8
Skewness0
Sum561
Variance93.5
MonotonicityStrictly increasing
2023-12-12T12:36:27.346240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1 1
 
3.0%
26 1
 
3.0%
20 1
 
3.0%
21 1
 
3.0%
22 1
 
3.0%
23 1
 
3.0%
24 1
 
3.0%
25 1
 
3.0%
27 1
 
3.0%
2 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
1 1
3.0%
2 1
3.0%
3 1
3.0%
4 1
3.0%
5 1
3.0%
6 1
3.0%
7 1
3.0%
8 1
3.0%
9 1
3.0%
10 1
3.0%
ValueCountFrequency (%)
33 1
3.0%
32 1
3.0%
31 1
3.0%
30 1
3.0%
29 1
3.0%
28 1
3.0%
27 1
3.0%
26 1
3.0%
25 1
3.0%
24 1
3.0%

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size396.0 B
정차금지
22 
주차금지
11 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정차금지 22
66.7%
주차금지 11
33.3%

Length

2023-12-12T12:36:27.554584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:36:27.715410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정차금지 22
66.7%
주차금지 11
33.3%

구간
Text

UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size396.0 B
2023-12-12T12:36:27.934566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length23
Mean length17.757576
Min length9

Characters and Unicode

Total characters586
Distinct characters135
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

Unique33 ?
Unique (%)100.0%

Sample

1st row함창윤직삼거리 ~ 농지개량조합앞
2nd row동아아파트 앞 ~ 제일은행
3rd row상주예식장 앞 ~ 제일은행
4th row성동건널목 ~ 동문파출소 앞
5th row서문사거리 ~ 동아아파트 앞
ValueCountFrequency (%)
16
 
13.0%
앞 11
 
8.9%
사거리 6
 
4.9%
～ 5
 
4.1%
서문사거리 4
 
3.3%
무양동 3
 
2.4%
터미널 3
 
2.4%
제일은행 2
 
1.6%
상주예식장 2
 
1.6%
∼청기와가든 2
 
1.6%
Other values (63) 69
56.1%
2023-12-12T12:36:28.396080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
91
 
15.5%
리 30
 
5.1%
거 27
 
4.6%
사 26
 
4.4%
동 23
 
3.9%
~ 23
 
3.9%
앞 16
 
2.7%
양 12
 
2.0%
교 10
 
1.7%
문 10
 
1.7%
Other values (125) 318
54.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 432
73.7%
Space Separator 91
 
15.5%
Math Symbol 32
 
5.5%
Decimal Number 19
 
3.2%
Close Punctuation 3
 
0.5%
Dash Punctuation 3
 
0.5%
Open Punctuation 3
 
0.5%
Uppercase Letter 2
 
0.3%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 30
 
6.9%
거 27
 
6.2%
사 26
 
6.0%
동 23
 
5.3%
앞 16
 
3.7%
양 12
 
2.8%
교 10
 
2.3%
문 10
 
2.3%
주 10
 
2.3%
무 9
 
2.1%
Other values (107) 259
60.0%
Decimal Number
ValueCountFrequency (%)
1 5
26.3%
2 4
21.1%
6 3
15.8%
7 2
 
10.5%
0 2
 
10.5%
8 1
 
5.3%
5 1
 
5.3%
3 1
 
5.3%
Math Symbol
ValueCountFrequency (%)
~ 23
71.9%
～ 5
 
15.6%
∼ 4
 
12.5%
Uppercase Letter
ValueCountFrequency (%)
U 1
50.0%
C 1
50.0%
Space Separator
ValueCountFrequency (%)
91
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 432
73.7%
Common 152
 
25.9%
Latin 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 30
 
6.9%
거 27
 
6.2%
사 26
 
6.0%
동 23
 
5.3%
앞 16
 
3.7%
양 12
 
2.8%
교 10
 
2.3%
문 10
 
2.3%
주 10
 
2.3%
무 9
 
2.1%
Other values (107) 259
60.0%
Common
ValueCountFrequency (%)
91
59.9%
~ 23
 
15.1%
～ 5
 
3.3%
1 5
 
3.3%
2 4
 
2.6%
∼ 4
 
2.6%
) 3
 
2.0%
- 3
 
2.0%
( 3
 
2.0%
6 3
 
2.0%
Other values (6) 8
 
5.3%
Latin
ValueCountFrequency (%)
U 1
50.0%
C 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 432
73.7%
ASCII 145
 
24.7%
None 5
 
0.9%
Math Operators 4
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
91
62.8%
~ 23
 
15.9%
1 5
 
3.4%
2 4
 
2.8%
) 3
 
2.1%
- 3
 
2.1%
( 3
 
2.1%
6 3
 
2.1%
7 2
 
1.4%
0 2
 
1.4%
Other values (6) 6
 
4.1%
Hangul
ValueCountFrequency (%)
리 30
 
6.9%
거 27
 
6.2%
사 26
 
6.0%
동 23
 
5.3%
앞 16
 
3.7%
양 12
 
2.8%
교 10
 
2.3%
문 10
 
2.3%
주 10
 
2.3%
무 9
 
2.1%
Other values (107) 259
60.0%
None
ValueCountFrequency (%)
～ 5
100.0%
Math Operators
ValueCountFrequency (%)
∼ 4
100.0%

거리(km)
Real number (ℝ)

Distinct22
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.786
Minimum0.1
Maximum4.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T12:36:28.586668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.112
Q10.25
median0.45
Q30.85
95-th percentile3.06
Maximum4.4
Range4.3
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.98215923
Coefficient of variation (CV)1.2495664
Kurtosis6.7775296
Mean0.786
Median Absolute Deviation (MAD)0.3
Skewness2.5834107
Sum25.938
Variance0.96463675
MonotonicityNot monotonic
2023-12-12T12:36:28.742067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.7 3
 
9.1%
0.12 3
 
9.1%
0.3 3
 
9.1%
0.6 3
 
9.1%
0.25 2
 
6.1%
0.4 2
 
6.1%
0.1 2
 
6.1%
0.8 1
 
3.0%
4.4 1
 
3.0%
2.7 1
 
3.0%
Other values (12) 12
36.4%
ValueCountFrequency (%)
0.1 2
6.1%
0.12 3
9.1%
0.13 1
 
3.0%
0.15 1
 
3.0%
0.2 1
 
3.0%
0.25 2
6.1%
0.288 1
 
3.0%
0.3 3
9.1%
0.4 2
6.1%
0.45 1
 
3.0%
ValueCountFrequency (%)
4.4 1
3.0%
3.6 1
3.0%
2.7 1
3.0%
1.3 1
3.0%
1.26 1
3.0%
1.1 1
3.0%
1.05 1
3.0%
1.0 1
3.0%
0.85 1
3.0%
0.8 1
3.0%

차로수(편도)
Categorical

Distinct3
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size396.0 B
1
23 
2
7 
<NA>
3 

Length

Max length4
Median length1
Mean length1.2727273
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 23
69.7%
2 7
 
21.2%
<NA> 3
 
9.1%

Length

2023-12-12T12:36:28.930621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:36:29.089728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 23
69.7%
2 7
 
21.2%
na 3
 
9.1%

방향
Categorical

CONSTANT 

Distinct1
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size396.0 B
양방향
33 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
양방향 33
100.0%

Length

2023-12-12T12:36:29.226629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:36:29.334325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
양방향 33
100.0%

데이터기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size396.0 B
2021-10-26
33 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-10-26
2nd row2021-10-26
3rd row2021-10-26
4th row2021-10-26
5th row2021-10-26

Common Values

ValueCountFrequency (%)
2021-10-26 33
100.0%

Length

2023-12-12T12:36:29.441892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:36:29.546689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-10-26 33
100.0%

Interactions

2023-12-12T12:36:26.150091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:36:25.914165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:36:26.285845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:36:26.040002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:36:29.609605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구분구간거리(km)차로수(편도)
연번1.0000.9951.0000.5060.691
구분0.9951.0001.0000.7020.000
구간1.0001.0001.0001.0001.000
거리(km)0.5060.7021.0001.0000.111
차로수(편도)0.6910.0001.0000.1111.000
2023-12-12T12:36:29.711345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분차로수(편도)
구분1.0000.000
차로수(편도)0.0001.000
2023-12-12T12:36:29.812040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번거리(km)구분차로수(편도)
연번1.000-0.2130.8050.368
거리(km)-0.2131.0000.4790.174
구분0.8050.4791.0000.000
차로수(편도)0.3680.1740.0001.000

Missing values

2023-12-12T12:36:26.464904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:36:26.623275image/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

연번구분구간거리(km)차로수(편도)방향데이터기준일
01주차금지함창윤직삼거리 ~ 농지개량조합앞1.01양방향2021-10-26
12주차금지동아아파트 앞 ~ 제일은행1.11양방향2021-10-26
23주차금지상주예식장 앞 ~ 제일은행0.851양방향2021-10-26
34주차금지성동건널목 ~ 동문파출소 앞1.051양방향2021-10-26
45주차금지서문사거리 ~ 동아아파트 앞1.32양방향2021-10-26
56주차금지서문사거리 ~ 후천교사거리1.262양방향2021-10-26
67주차금지냉림동 ~ 상산교 사거리0.451양방향2021-10-26
78주차금지낙양5주공아파트 ~ 잠업검사소0.31양방향2021-10-26
89주차금지알리안츠생명사거리 ~ 천일약국 사거리0.251양방향2021-10-26
910주차금지시외버스터미널사거리 ~ 북천교사거리0.61양방향2021-10-26
연번구분구간거리(km)차로수(편도)방향데이터기준일
2324정차금지무양동 162-7 ∼ 무양동 상주예식장0.31양방향2021-10-26
2425정차금지무양터미널남측삼거리~서문동 축협사거리0.61양방향2021-10-26
2526정차금지서문동 축협사거리 ∼ 복룡 지하도0.61양방향2021-10-26
2627정차금지통계사무소 ~ 상주임업사0.251양방향2021-10-26
2728정차금지북천교 남단사거리~ 화개동 화개삼거리(화개교)3.6<NA>양방향2021-10-26
2829정차금지낙양 시의회 사거리 ~ 터미널 사거리2.7<NA>양방향2021-10-26
2930정차금지북천교 남단 ~ 이마트 입구 횡단보도4.4<NA>양방향2021-10-26
3031정차금지8주공CU~동보106동앞,동수나무사거리~상주초등후문0.41양방향2021-10-26
3132정차금지현대아파트~영어마을사이0.11양방향2021-10-26
3233정차금지상주시청 앞~낙양동 대흥자재마트 앞0.72양방향2021-10-26