Overview

Dataset statistics

Number of variables4
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory924.0 B
Average record size in memory38.5 B

Variable types

Numeric1
Text3

Dataset

Description불법주정차단속 CCTV 위치 공공데이터
Author경상북도 칠곡군
URLhttps://www.data.go.kr/data/15040522/fileData.do

Alerts

순번 has unique valuesUnique
명칭 has unique valuesUnique
주소 has unique valuesUnique

Reproduction

Analysis started2023-12-12 08:40:18.218459
Analysis finished2023-12-12 08:40:18.712980
Duration0.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.5
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T17:40:18.795218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.15
Q16.75
median12.5
Q318.25
95-th percentile22.85
Maximum24
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation7.0710678
Coefficient of variation (CV)0.56568542
Kurtosis-1.2
Mean12.5
Median Absolute Deviation (MAD)6
Skewness0
Sum300
Variance50
MonotonicityStrictly increasing
2023-12-12T17:40:18.950095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 1
 
4.2%
14 1
 
4.2%
24 1
 
4.2%
23 1
 
4.2%
22 1
 
4.2%
21 1
 
4.2%
20 1
 
4.2%
19 1
 
4.2%
18 1
 
4.2%
17 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
1 1
4.2%
2 1
4.2%
3 1
4.2%
4 1
4.2%
5 1
4.2%
6 1
4.2%
7 1
4.2%
8 1
4.2%
9 1
4.2%
10 1
4.2%
ValueCountFrequency (%)
24 1
4.2%
23 1
4.2%
22 1
4.2%
21 1
4.2%
20 1
4.2%
19 1
4.2%
18 1
4.2%
17 1
4.2%
16 1
4.2%
15 1
4.2%

명칭
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-12T17:40:19.170289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length5.875
Min length3

Characters and Unicode

Total characters141
Distinct characters74
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

Unique24 ?
Unique (%)100.0%

Sample

1st row대구은행앞
2nd row축산농협앞
3rd row왜관역사거리
4th row국민은행앞
5th row신협앞
ValueCountFrequency (%)
광암천 3
 
10.0%
앞 3
 
10.0%
대구은행앞 1
 
3.3%
북부정류장 1
 
3.3%
성곡교 1
 
3.3%
중리교 1
 
3.3%
중리2교 1
 
3.3%
칠곡교육지원청 1
 
3.3%
다채움빌라옆육교 1
 
3.3%
이원리버빌 1
 
3.3%
Other values (16) 16
53.3%
2023-12-12T17:40:19.626842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
앞 8
 
5.7%
7
 
5.0%
리 7
 
5.0%
교 6
 
4.3%
협 4
 
2.8%
왜 4
 
2.8%
관 4
 
2.8%
사 4
 
2.8%
거 3
 
2.1%
부 3
 
2.1%
Other values (64) 91
64.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 124
87.9%
Space Separator 7
 
5.0%
Uppercase Letter 7
 
5.0%
Decimal Number 2
 
1.4%
Other Punctuation 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 8
 
6.5%
리 7
 
5.6%
교 6
 
4.8%
협 4
 
3.2%
왜 4
 
3.2%
관 4
 
3.2%
사 4
 
3.2%
거 3
 
2.4%
부 3
 
2.4%
광 3
 
2.4%
Other values (56) 78
62.9%
Uppercase Letter
ValueCountFrequency (%)
G 3
42.9%
S 1
 
14.3%
L 1
 
14.3%
K 1
 
14.3%
T 1
 
14.3%
Space Separator
ValueCountFrequency (%)
7
100.0%
Decimal Number
ValueCountFrequency (%)
2 2
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 124
87.9%
Common 10
 
7.1%
Latin 7
 
5.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 8
 
6.5%
리 7
 
5.6%
교 6
 
4.8%
협 4
 
3.2%
왜 4
 
3.2%
관 4
 
3.2%
사 4
 
3.2%
거 3
 
2.4%
부 3
 
2.4%
광 3
 
2.4%
Other values (56) 78
62.9%
Latin
ValueCountFrequency (%)
G 3
42.9%
S 1
 
14.3%
L 1
 
14.3%
K 1
 
14.3%
T 1
 
14.3%
Common
ValueCountFrequency (%)
7
70.0%
2 2
 
20.0%
& 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 124
87.9%
ASCII 17
 
12.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
앞 8
 
6.5%
리 7
 
5.6%
교 6
 
4.8%
협 4
 
3.2%
왜 4
 
3.2%
관 4
 
3.2%
사 4
 
3.2%
거 3
 
2.4%
부 3
 
2.4%
광 3
 
2.4%
Other values (56) 78
62.9%
ASCII
ValueCountFrequency (%)
7
41.2%
G 3
17.6%
2 2
 
11.8%
S 1
 
5.9%
L 1
 
5.9%
K 1
 
5.9%
T 1
 
5.9%
& 1
 
5.9%

주소
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-12T17:40:19.871439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length17.708333
Min length14

Characters and Unicode

Total characters425
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)100.0%

Sample

1st row칠곡군 왜관읍 왜관리 1380-3
2nd row칠곡군 왜관읍 왜관리 222-5
3rd row칠곡군 왜관읍 왜관리 228-12
4th row칠곡군 왜관읍 왜관리 212-87
5th row칠곡군 왜관읍 왜관리 211-188
ValueCountFrequency (%)
칠곡군 24
24.7%
왜관리 15
15.5%
왜관읍 15
15.5%
석적읍 7
 
7.2%
중리 6
 
6.2%
북삼읍 2
 
2.1%
인평리 2
 
2.1%
255-45 1
 
1.0%
261-1 1
 
1.0%
743-1 1
 
1.0%
Other values (23) 23
23.7%
2023-12-12T17:40:20.387333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
79
18.6%
왜 30
 
7.1%
관 30
 
7.1%
1 25
 
5.9%
칠 24
 
5.6%
곡 24
 
5.6%
군 24
 
5.6%
읍 24
 
5.6%
리 24
 
5.6%
2 24
 
5.6%
Other values (20) 117
27.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 212
49.9%
Decimal Number 111
26.1%
Space Separator 79
 
18.6%
Dash Punctuation 23
 
5.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
왜 30
14.2%
관 30
14.2%
칠 24
11.3%
곡 24
11.3%
군 24
11.3%
읍 24
11.3%
리 24
11.3%
적 7
 
3.3%
석 7
 
3.3%
중 6
 
2.8%
Other values (8) 12
 
5.7%
Decimal Number
ValueCountFrequency (%)
1 25
22.5%
2 24
21.6%
8 16
14.4%
4 12
10.8%
7 7
 
6.3%
0 7
 
6.3%
3 7
 
6.3%
5 6
 
5.4%
9 4
 
3.6%
6 3
 
2.7%
Space Separator
ValueCountFrequency (%)
79
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 213
50.1%
Hangul 212
49.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
왜 30
14.2%
관 30
14.2%
칠 24
11.3%
곡 24
11.3%
군 24
11.3%
읍 24
11.3%
리 24
11.3%
적 7
 
3.3%
석 7
 
3.3%
중 6
 
2.8%
Other values (8) 12
 
5.7%
Common
ValueCountFrequency (%)
79
37.1%
1 25
 
11.7%
2 24
 
11.3%
- 23
 
10.8%
8 16
 
7.5%
4 12
 
5.6%
7 7
 
3.3%
0 7
 
3.3%
3 7
 
3.3%
5 6
 
2.8%
Other values (2) 7
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 213
50.1%
Hangul 212
49.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
79
37.1%
1 25
 
11.7%
2 24
 
11.3%
- 23
 
10.8%
8 16
 
7.5%
4 12
 
5.6%
7 7
 
3.3%
0 7
 
3.3%
3 7
 
3.3%
5 6
 
2.8%
Other values (2) 7
 
3.3%
Hangul
ValueCountFrequency (%)
왜 30
14.2%
관 30
14.2%
칠 24
11.3%
곡 24
11.3%
군 24
11.3%
읍 24
11.3%
리 24
11.3%
적 7
 
3.3%
석 7
 
3.3%
중 6
 
2.8%
Other values (8) 12
 
5.7%

인접시설
Text

Distinct23
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-12T17:40:20.642049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length8
Mean length5.875
Min length3

Characters and Unicode

Total characters141
Distinct characters80
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

Unique22 ?
Unique (%)91.7%

Sample

1st row대구은행 왜관지점
2nd row축산농협 왜관지점
3rd row왜관역
4th row국민은행 왜관지점
5th row왜관신협 북부지소
ValueCountFrequency (%)
왜관지점 3
 
10.3%
왜관역 2
 
6.9%
대구은행 1
 
3.4%
북부정류장 1
 
3.4%
왜관 1
 
3.4%
성곡교 1
 
3.4%
중리교 1
 
3.4%
중리2교 1
 
3.4%
칠곡교육지원청 1
 
3.4%
다채움빌라 1
 
3.4%
Other values (16) 16
55.2%
2023-12-12T17:40:21.023473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
왜 9
 
6.4%
관 9
 
6.4%
지 8
 
5.7%
5
 
3.5%
교 4
 
2.8%
점 4
 
2.8%
성 4
 
2.8%
G 3
 
2.1%
리 3
 
2.1%
이 3
 
2.1%
Other values (70) 89
63.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 126
89.4%
Uppercase Letter 7
 
5.0%
Space Separator 5
 
3.5%
Decimal Number 2
 
1.4%
Other Punctuation 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
왜 9
 
7.1%
관 9
 
7.1%
지 8
 
6.3%
교 4
 
3.2%
점 4
 
3.2%
성 4
 
3.2%
리 3
 
2.4%
이 3
 
2.4%
부 3
 
2.4%
중 2
 
1.6%
Other values (62) 77
61.1%
Uppercase Letter
ValueCountFrequency (%)
G 3
42.9%
S 1
 
14.3%
K 1
 
14.3%
T 1
 
14.3%
L 1
 
14.3%
Space Separator
ValueCountFrequency (%)
5
100.0%
Decimal Number
ValueCountFrequency (%)
2 2
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 126
89.4%
Common 8
 
5.7%
Latin 7
 
5.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
왜 9
 
7.1%
관 9
 
7.1%
지 8
 
6.3%
교 4
 
3.2%
점 4
 
3.2%
성 4
 
3.2%
리 3
 
2.4%
이 3
 
2.4%
부 3
 
2.4%
중 2
 
1.6%
Other values (62) 77
61.1%
Latin
ValueCountFrequency (%)
G 3
42.9%
S 1
 
14.3%
K 1
 
14.3%
T 1
 
14.3%
L 1
 
14.3%
Common
ValueCountFrequency (%)
5
62.5%
2 2
 
25.0%
& 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 126
89.4%
ASCII 15
 
10.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
왜 9
 
7.1%
관 9
 
7.1%
지 8
 
6.3%
교 4
 
3.2%
점 4
 
3.2%
성 4
 
3.2%
리 3
 
2.4%
이 3
 
2.4%
부 3
 
2.4%
중 2
 
1.6%
Other values (62) 77
61.1%
ASCII
ValueCountFrequency (%)
5
33.3%
G 3
20.0%
2 2
 
13.3%
S 1
 
6.7%
K 1
 
6.7%
T 1
 
6.7%
& 1
 
6.7%
L 1
 
6.7%

Interactions

2023-12-12T17:40:18.426491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:40:21.139570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번명칭주소인접시설
순번1.0001.0001.0000.945
명칭1.0001.0001.0001.000
주소1.0001.0001.0001.000
인접시설0.9451.0001.0001.000

Missing values

2023-12-12T17:40:18.566085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:40:18.677663image/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

순번명칭주소인접시설
01대구은행앞칠곡군 왜관읍 왜관리 1380-3대구은행 왜관지점
12축산농협앞칠곡군 왜관읍 왜관리 222-5축산농협 왜관지점
23왜관역사거리칠곡군 왜관읍 왜관리 228-12왜관역
34국민은행앞칠곡군 왜관읍 왜관리 212-87국민은행 왜관지점
45신협앞칠곡군 왜관읍 왜관리 211-188왜관신협 북부지소
56북삼농협 앞칠곡군 북삼읍 인평리 1082-4거성소금구이
67부영사거리칠곡군 석적읍 중리 140-1베스킨라빈스
78중리농협 앞칠곡군 석적읍 중리 139-13봉순이굴국밥
89왜관역앞칠곡군 왜관읍 왜관리 229-4왜관역
910왜관성당 앞칠곡군 왜관읍 왜관리 281-50왜관성당
순번명칭주소인접시설
1415로얄맨션칠곡군 왜관읍 왜관리 298-4로얄맨션
1516GS슈퍼마켓칠곡군 왜관읍 왜관리 777-82GS슈퍼마켓
1617삼부쇼핑칠곡군 왜관읍 왜관리 789-126삼부쇼핑
1718이원리버빌칠곡군 왜관읍 왜관리 208-21이원리버빌
1819다채움빌라옆육교칠곡군 왜관읍 왜관리 255-45다채움빌라
1920칠곡교육지원청칠곡군 왜관읍 왜관리 261-1칠곡교육지원청
2021광암천 중리2교칠곡군 석적읍 중리 884-6중리2교
2122광암천 중리교칠곡군 석적읍 중리 311중리교
2223광암천 성곡교칠곡군 석적읍 중리 884-145성곡교
2324인평성당칠곡군 북삼읍 인평리 1014-4인평성당